From 32444d2336bf5dfc0f7c984385047ec904b83899 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Mon, 24 May 2021 18:18:21 +0200
Subject: [PATCH 001/118] Adding the missing installation step for the
 Autodownloader

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 CMake/Autodownload.cmake | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CMake/Autodownload.cmake b/CMake/Autodownload.cmake
index b82ab001b9..9339dcc9bf 100644
--- a/CMake/Autodownload.cmake
+++ b/CMake/Autodownload.cmake
@@ -45,9 +45,11 @@ macro(get_deps LINK DEPS_NAME PACKAGE)
   if (DIRECTORIES_LEN GREATER 0)
     list(GET DIRECTORIES 0 DEPENDENCY_DIR)
     set(GENERIC_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/include")
-    # Clean this line when boost is removed.
+    install(DIRECTORY "${GENERIC_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
+    # Clean these lines when boost is removed.
     if (${DEPS_NAME} MATCHES "boost")
       set(Boost_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/")
+      install(DIRECTORY "${Boost_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     endif()
   else ()
     message(FATAL_ERROR

From 5ceb8daf1611b94f47a809ce8626d42e28cb01a8 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Thu, 27 May 2021 16:15:06 +0200
Subject: [PATCH 002/118] Check of the deps name is not equal boost

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 CMake/Autodownload.cmake | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CMake/Autodownload.cmake b/CMake/Autodownload.cmake
index 9339dcc9bf..ce23e32909 100644
--- a/CMake/Autodownload.cmake
+++ b/CMake/Autodownload.cmake
@@ -44,12 +44,13 @@ macro(get_deps LINK DEPS_NAME PACKAGE)
   list(LENGTH DIRECTORIES DIRECTORIES_LEN)
   if (DIRECTORIES_LEN GREATER 0)
     list(GET DIRECTORIES 0 DEPENDENCY_DIR)
-    set(GENERIC_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/include")
-    install(DIRECTORY "${GENERIC_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     # Clean these lines when boost is removed.
     if (${DEPS_NAME} MATCHES "boost")
       set(Boost_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/")
       install(DIRECTORY "${Boost_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
+    else()
+      set(GENERIC_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/include")
+      install(DIRECTORY "${GENERIC_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     endif()
   else ()
     message(FATAL_ERROR

From 1325745fecb2d4e84ba7468be25143c23787cd02 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Thu, 10 Jun 2021 17:11:24 +0200
Subject: [PATCH 003/118] Try to fix the installation dir, do not push

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 CMake/Autodownload.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMake/Autodownload.cmake b/CMake/Autodownload.cmake
index ce23e32909..9d93704801 100644
--- a/CMake/Autodownload.cmake
+++ b/CMake/Autodownload.cmake
@@ -47,7 +47,7 @@ macro(get_deps LINK DEPS_NAME PACKAGE)
     # Clean these lines when boost is removed.
     if (${DEPS_NAME} MATCHES "boost")
       set(Boost_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/")
-      install(DIRECTORY "${Boost_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
+      install(DIRECTORY "${Boost_INCLUDE_DIR}/boost" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     else()
       set(GENERIC_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/include")
       install(DIRECTORY "${GENERIC_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")

From 3740a1ac7cd9ac79e09d42a99a1bbc8e05e670d7 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sat, 26 Jun 2021 17:05:57 +0200
Subject: [PATCH 004/118] First successful test

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/cli/add_to_cli11.hpp | 76 ++++++++++++------------
 1 file changed, 38 insertions(+), 38 deletions(-)

diff --git a/src/mlpack/bindings/cli/add_to_cli11.hpp b/src/mlpack/bindings/cli/add_to_cli11.hpp
index ceb03c64e2..cf117067d3 100644
--- a/src/mlpack/bindings/cli/add_to_cli11.hpp
+++ b/src/mlpack/bindings/cli/add_to_cli11.hpp
@@ -33,15 +33,15 @@ template<typename T>
 void AddToCLI11(const std::string& cliName,
                 util::ParamData& param,
                 CLI::App& app,
-                const typename boost::disable_if<std::is_same<T,
-                    bool>>::type* = 0,
-                const typename boost::disable_if<
-                    arma::is_arma_type<T>>::type* = 0,
-                const typename boost::disable_if<
-                    data::HasSerialize<T>>::type* = 0,
-                const typename boost::enable_if<std::is_same<T,
+                const typename std::enable_if<!std::is_same<T,
+                    bool>::value>::type* = 0,
+                const typename std::enable_if<!
+                    arma::is_arma_type<T>::value>::type* = 0,
+                const typename std::enable_if<!
+                    data::HasSerialize<T>::value>::type* = 0,
+                const typename std::enable_if<std::is_same<T,
                     std::tuple<mlpack::data::DatasetInfo,
-                    arma::mat>>>::type* = 0)
+                    arma::mat>>::value>::type* = 0)
 {
   app.add_option_function<std::string>(cliName.c_str(),
       [&param](const std::string& value)
@@ -65,15 +65,15 @@ template<typename T>
 void AddToCLI11(const std::string& cliName,
                 util::ParamData& param,
                 CLI::App& app,
-                const typename boost::disable_if<std::is_same<T,
-                    bool>>::type* = 0,
-                const typename boost::disable_if<
-                    arma::is_arma_type<T>>::type* = 0,
-                const typename boost::enable_if<
-                    data::HasSerialize<T>>::type* = 0,
-                const typename boost::disable_if<std::is_same<T,
+                const typename std::enable_if<!std::is_same<T,
+                    bool>::value>::type* = 0,
+                const typename std::enable_if<!
+                    arma::is_arma_type<T>::value>::type* = 0,
+                const typename std::enable_if<
+                    data::HasSerialize<T>::value>::type* = 0,
+                const typename std::enable_if<!std::is_same<T,
                     std::tuple<mlpack::data::DatasetInfo,
-                    arma::mat>>>::type* = 0)
+                    arma::mat>>::value>::type* = 0)
 {
   app.add_option_function<std::string>(cliName.c_str(),
       [&param](const std::string& value)
@@ -97,13 +97,13 @@ template<typename T>
 void AddToCLI11(const std::string& cliName,
                 util::ParamData& param,
                 CLI::App& app,
-                const typename boost::disable_if<
-                    std::is_same<T, bool>>::type* = 0,
-                const typename boost::enable_if<
-                    arma::is_arma_type<T>>::type* = 0,
-                const typename boost::disable_if<std::is_same<T,
+                const typename std::enable_if<!
+                    std::is_same<T, bool>::value>::type* = 0,
+                const typename std::enable_if<
+                    arma::is_arma_type<T>::value>::type* = 0,
+                const typename std::enable_if<!std::is_same<T,
                   std::tuple<mlpack::data::DatasetInfo,
-                    arma::mat>>>::type* = 0)
+                    arma::mat>>::value>::type* = 0)
 {
   app.add_option_function<std::string>(cliName.c_str(),
       [&param](const std::string& value)
@@ -127,15 +127,15 @@ template<typename T>
 void AddToCLI11(const std::string& cliName,
                 util::ParamData& param,
                 CLI::App& app,
-                const typename boost::disable_if<
-                    std::is_same<T, bool>>::type* = 0,
-                const typename boost::disable_if<
-                    arma::is_arma_type<T>>::type* = 0,
-                const typename boost::disable_if<
-                    data::HasSerialize<T>>::type* = 0,
-                const typename boost::disable_if<std::is_same<T,
+                const typename std::enable_if<!
+                    std::is_same<T, bool>::value>::type* = 0,
+                const typename std::enable_if<!
+                    arma::is_arma_type<T>::value>::type* = 0,
+                const typename std::enable_if<!
+                    data::HasSerialize<T>::value>::type* = 0,
+                const typename std::enable_if<!std::is_same<T,
                     std::tuple<mlpack::data::DatasetInfo,
-                    arma::mat>>>::type* = 0)
+                    arma::mat>>::value>::type* = 0)
 {
   app.add_option_function<T>(cliName.c_str(),
       [&param](const T& value)
@@ -157,15 +157,15 @@ template<typename T>
 void AddToCLI11(const std::string& cliName,
                 util::ParamData& param,
                 CLI::App& app,
-                const typename boost::enable_if<
-                    std::is_same<T, bool>>::type* = 0,
-                const typename boost::disable_if<
-                    arma::is_arma_type<T>>::type* = 0,
-                const typename boost::disable_if<
-                    data::HasSerialize<T>>::type* = 0,
-                const typename boost::disable_if<std::is_same<T,
+                const typename std::enable_if<
+                    std::is_same<T, bool>::value>::type* = 0,
+                const typename std::enable_if<!
+                    arma::is_arma_type<T>::value>::type* = 0,
+                const typename std::enable_if<!
+                    data::HasSerialize<T>::value>::type* = 0,
+                const typename std::enable_if<!std::is_same<T,
                     std::tuple<mlpack::data::DatasetInfo,
-                    arma::mat>>>::type* = 0)
+                    arma::mat>>::value>::type* = 0)
 {
   app.add_flag_function(cliName.c_str(),
       [&param](const T& value)

From 93de3217f20a471c2d1e5b1611c5b64107584e7c Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sat, 26 Jun 2021 18:04:37 +0200
Subject: [PATCH 005/118] Remove half of boost in this directory

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/cli/default_param.hpp     | 20 ++++++++---------
 .../bindings/cli/default_param_impl.hpp       | 20 ++++++++---------
 .../bindings/cli/get_printable_param.hpp      | 14 ++++++------
 .../bindings/cli/get_printable_param_name.hpp | 18 +++++++--------
 .../cli/get_printable_param_name_impl.hpp     | 18 +++++++--------
 .../bindings/cli/get_printable_type_impl.hpp  | 14 ++++++------
 src/mlpack/bindings/cli/output_param.hpp      | 22 +++++++++----------
 src/mlpack/bindings/cli/output_param_impl.hpp | 22 +++++++++----------
 src/mlpack/bindings/cli/print_type_doc.hpp    | 14 ++++++------
 .../bindings/cli/print_type_doc_impl.hpp      | 14 ++++++------
 10 files changed, 88 insertions(+), 88 deletions(-)

diff --git a/src/mlpack/bindings/cli/default_param.hpp b/src/mlpack/bindings/cli/default_param.hpp
index 1eecd76eec..f8a7ea894c 100644
--- a/src/mlpack/bindings/cli/default_param.hpp
+++ b/src/mlpack/bindings/cli/default_param.hpp
@@ -26,12 +26,12 @@ namespace cli {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a vector option.
@@ -39,7 +39,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Return the default value of a string option.
@@ -47,7 +47,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type* = 0);
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type* = 0);
 
 /**
  * Return the default value of a matrix option, a tuple option, a
@@ -69,8 +69,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Return the default value of an option.  This is the function that will be
diff --git a/src/mlpack/bindings/cli/default_param_impl.hpp b/src/mlpack/bindings/cli/default_param_impl.hpp
index 26e9d7e6cd..db95caf008 100644
--- a/src/mlpack/bindings/cli/default_param_impl.hpp
+++ b/src/mlpack/bindings/cli/default_param_impl.hpp
@@ -24,12 +24,12 @@ namespace cli {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::ostringstream oss;
   if (!std::is_same<T, bool>::value)
@@ -44,7 +44,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   // Print each element in an array delimited by square brackets.
   std::ostringstream oss;
@@ -88,7 +88,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type*)
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type*)
 {
   const std::string& s = *boost::any_cast<std::string>(&data.value);
   return "'" + s + "'";
@@ -115,8 +115,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   return "''";
 }
diff --git a/src/mlpack/bindings/cli/get_printable_param.hpp b/src/mlpack/bindings/cli/get_printable_param.hpp
index ca13c679e9..2cd2221101 100644
--- a/src/mlpack/bindings/cli/get_printable_param.hpp
+++ b/src/mlpack/bindings/cli/get_printable_param.hpp
@@ -27,11 +27,11 @@ namespace cli {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Print a vector option, with spaces between it.
@@ -57,8 +57,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print an option into a std::string.  This should print a short, one-line
diff --git a/src/mlpack/bindings/cli/get_printable_param_name.hpp b/src/mlpack/bindings/cli/get_printable_param_name.hpp
index 38d3c64164..b875d2f72d 100644
--- a/src/mlpack/bindings/cli/get_printable_param_name.hpp
+++ b/src/mlpack/bindings/cli/get_printable_param_name.hpp
@@ -26,10 +26,10 @@ namespace cli {
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter name for a matrix type (where the user has to pass the file
@@ -38,7 +38,7 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a serializable model type (where the user has to
@@ -47,8 +47,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a mapped matrix type (where the user has to pass
@@ -57,8 +57,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter's name as seen by the user.
diff --git a/src/mlpack/bindings/cli/get_printable_param_name_impl.hpp b/src/mlpack/bindings/cli/get_printable_param_name_impl.hpp
index 4bb0da75e0..7c355c3dbc 100644
--- a/src/mlpack/bindings/cli/get_printable_param_name_impl.hpp
+++ b/src/mlpack/bindings/cli/get_printable_param_name_impl.hpp
@@ -26,10 +26,10 @@ namespace cli {
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "--" + data.name;
 }
@@ -41,7 +41,7 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*)
 {
   return "--" + data.name + "_file";
 }
@@ -53,8 +53,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return "--" + data.name + "_file";
 }
@@ -66,8 +66,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "--" + data.name + "_file";
 }
diff --git a/src/mlpack/bindings/cli/get_printable_type_impl.hpp b/src/mlpack/bindings/cli/get_printable_type_impl.hpp
index 7e7da8b038..d02dae49fc 100644
--- a/src/mlpack/bindings/cli/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/cli/get_printable_type_impl.hpp
@@ -25,11 +25,11 @@ namespace cli {
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   if (std::is_same<T, bool>::value)
     return "flag";
@@ -101,8 +101,8 @@ std::string GetPrintableType(
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return data.cppType + " file";
 }
diff --git a/src/mlpack/bindings/cli/output_param.hpp b/src/mlpack/bindings/cli/output_param.hpp
index 898f2d1a39..bef23ed43e 100644
--- a/src/mlpack/bindings/cli/output_param.hpp
+++ b/src/mlpack/bindings/cli/output_param.hpp
@@ -26,11 +26,11 @@ namespace cli {
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Output a vector option (print to stdout).
@@ -38,7 +38,7 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Output a matrix option (this saves it to the given file).
@@ -46,7 +46,7 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
 
 /**
  * Output a serializable class option (this saves it to the given file).
@@ -54,8 +54,8 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Output a mapped dataset.
@@ -63,8 +63,8 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Output an option.  This is the function that will be called by the IO
diff --git a/src/mlpack/bindings/cli/output_param_impl.hpp b/src/mlpack/bindings/cli/output_param_impl.hpp
index ab2f1e8822..d34b55fd74 100644
--- a/src/mlpack/bindings/cli/output_param_impl.hpp
+++ b/src/mlpack/bindings/cli/output_param_impl.hpp
@@ -24,11 +24,11 @@ namespace cli {
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::cout << data.name << ": " << *boost::any_cast<T>(&data.value)
       << std::endl;
@@ -38,7 +38,7 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   std::cout << data.name << ": ";
   const T& t = *boost::any_cast<T>(&data.value);
@@ -51,7 +51,7 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* /* junk */)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* /* junk */)
 {
   typedef std::tuple<T, std::tuple<std::string, size_t, size_t>> TupleType;
   const T& output = std::get<0>(*boost::any_cast<TupleType>(&data.value));
@@ -71,8 +71,8 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   // The const cast is necessary here because Serialize() can't ever be marked
   // const.  In this case we can assume it though, since we will be saving and
@@ -91,8 +91,8 @@ void OutputParamImpl(
 template<typename T>
 void OutputParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   // Output the matrix with the mappings.
   typedef std::tuple<T, std::tuple<std::string, size_t, size_t>> TupleType;
diff --git a/src/mlpack/bindings/cli/print_type_doc.hpp b/src/mlpack/bindings/cli/print_type_doc.hpp
index 873d5465f2..10acab4f5b 100644
--- a/src/mlpack/bindings/cli/print_type_doc.hpp
+++ b/src/mlpack/bindings/cli/print_type_doc.hpp
@@ -25,11 +25,11 @@ namespace cli {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a vector.
@@ -62,8 +62,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print the command-line type of an option into a string.
diff --git a/src/mlpack/bindings/cli/print_type_doc_impl.hpp b/src/mlpack/bindings/cli/print_type_doc_impl.hpp
index 51e3106de5..1836732bf8 100644
--- a/src/mlpack/bindings/cli/print_type_doc_impl.hpp
+++ b/src/mlpack/bindings/cli/print_type_doc_impl.hpp
@@ -24,11 +24,11 @@ namespace cli {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   // A flag type.
   if (std::is_same<T, bool>::value)
@@ -165,8 +165,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return "A filename containing an mlpack model.  These can have one of three "
       "formats: binary (.bin), text (.txt), and XML (.xml).  The XML format "

From 632b43b46717096b91eae46a008c31711bfe7db3 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sat, 26 Jun 2021 18:54:19 +0200
Subject: [PATCH 006/118] Compiling, remove boost_enable_if entirely from CLI
 binding

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/cli/default_param.hpp      |  2 +-
 src/mlpack/bindings/cli/default_param_impl.hpp |  2 +-
 .../bindings/cli/delete_allocated_memory.hpp   | 10 +++++-----
 .../bindings/cli/get_allocated_memory.hpp      | 10 +++++-----
 src/mlpack/bindings/cli/get_param.hpp          | 18 +++++++++---------
 .../bindings/cli/get_printable_param_impl.hpp  | 14 +++++++-------
 .../bindings/cli/get_printable_param_value.hpp | 18 +++++++++---------
 .../cli/get_printable_param_value_impl.hpp     | 18 +++++++++---------
 src/mlpack/bindings/cli/get_printable_type.hpp | 14 +++++++-------
 src/mlpack/bindings/cli/get_raw_param.hpp      | 14 +++++++-------
 src/mlpack/bindings/cli/map_parameter_name.hpp | 10 +++++-----
 src/mlpack/bindings/cli/set_param.hpp          | 18 +++++++++---------
 src/mlpack/bindings/cli/string_type_param.hpp  |  8 ++++----
 .../bindings/cli/string_type_param_impl.hpp    |  8 ++++----
 14 files changed, 82 insertions(+), 82 deletions(-)

diff --git a/src/mlpack/bindings/cli/default_param.hpp b/src/mlpack/bindings/cli/default_param.hpp
index f8a7ea894c..093a03c567 100644
--- a/src/mlpack/bindings/cli/default_param.hpp
+++ b/src/mlpack/bindings/cli/default_param.hpp
@@ -57,7 +57,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* /* junk */ = 0);
diff --git a/src/mlpack/bindings/cli/default_param_impl.hpp b/src/mlpack/bindings/cli/default_param_impl.hpp
index db95caf008..b002e94611 100644
--- a/src/mlpack/bindings/cli/default_param_impl.hpp
+++ b/src/mlpack/bindings/cli/default_param_impl.hpp
@@ -100,7 +100,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* /* junk */)
diff --git a/src/mlpack/bindings/cli/delete_allocated_memory.hpp b/src/mlpack/bindings/cli/delete_allocated_memory.hpp
index 35d305bb99..f3123c52e8 100644
--- a/src/mlpack/bindings/cli/delete_allocated_memory.hpp
+++ b/src/mlpack/bindings/cli/delete_allocated_memory.hpp
@@ -21,8 +21,8 @@ namespace cli {
 template<typename T>
 void DeleteAllocatedMemoryImpl(
     util::ParamData& /* d */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0)
 {
   // Do nothing.
 }
@@ -30,7 +30,7 @@ void DeleteAllocatedMemoryImpl(
 template<typename T>
 void DeleteAllocatedMemoryImpl(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Do nothing.
 }
@@ -38,8 +38,8 @@ void DeleteAllocatedMemoryImpl(
 template<typename T>
 void DeleteAllocatedMemoryImpl(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Delete the allocated memory (hopefully we actually own it).
   typedef std::tuple<T*, std::string> TupleType;
diff --git a/src/mlpack/bindings/cli/get_allocated_memory.hpp b/src/mlpack/bindings/cli/get_allocated_memory.hpp
index 0f70fe0adf..08e97e1e39 100644
--- a/src/mlpack/bindings/cli/get_allocated_memory.hpp
+++ b/src/mlpack/bindings/cli/get_allocated_memory.hpp
@@ -22,8 +22,8 @@ namespace cli {
 template<typename T>
 void* GetAllocatedMemory(
     util::ParamData& /* d */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0)
 {
   return NULL;
 }
@@ -31,7 +31,7 @@ void* GetAllocatedMemory(
 template<typename T>
 void* GetAllocatedMemory(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   return NULL;
 }
@@ -39,8 +39,8 @@ void* GetAllocatedMemory(
 template<typename T>
 void* GetAllocatedMemory(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Here we have a model, which is a tuple, and we need the address of the
   // memory.
diff --git a/src/mlpack/bindings/cli/get_param.hpp b/src/mlpack/bindings/cli/get_param.hpp
index d401e0e554..34463e939c 100644
--- a/src/mlpack/bindings/cli/get_param.hpp
+++ b/src/mlpack/bindings/cli/get_param.hpp
@@ -28,10 +28,10 @@ namespace cli {
 template<typename T>
 T& GetParam(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // No mapping is needed, so just cast it directly.
   return *boost::any_cast<T>(&d.value);
@@ -45,7 +45,7 @@ T& GetParam(
 template<typename T>
 T& GetParam(
     util::ParamData& d,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // If the matrix is an input matrix, we have to load the matrix.  'value'
   // contains the filename.  It's possible we could load empty matrices many
@@ -80,8 +80,8 @@ T& GetParam(
 template<typename T>
 T& GetParam(
     util::ParamData& d,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // If this is an input parameter, we need to load both the matrix and the
   // dataset info.
@@ -110,8 +110,8 @@ T& GetParam(
 template<typename T>
 T*& GetParam(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // If the model is an input model, we have to load it from file.  'value'
   // contains the filename.
diff --git a/src/mlpack/bindings/cli/get_printable_param_impl.hpp b/src/mlpack/bindings/cli/get_printable_param_impl.hpp
index 9898c49ee6..89f3c066f0 100644
--- a/src/mlpack/bindings/cli/get_printable_param_impl.hpp
+++ b/src/mlpack/bindings/cli/get_printable_param_impl.hpp
@@ -23,11 +23,11 @@ namespace cli {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::ostringstream oss;
   oss << boost::any_cast<T>(data.value);
@@ -103,8 +103,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   // Extract the string from the tuple that's being held.
   typedef std::tuple<T*, typename ParameterType<T>::type> TupleType;
diff --git a/src/mlpack/bindings/cli/get_printable_param_value.hpp b/src/mlpack/bindings/cli/get_printable_param_value.hpp
index 208afdb87b..621640b3c1 100644
--- a/src/mlpack/bindings/cli/get_printable_param_value.hpp
+++ b/src/mlpack/bindings/cli/get_printable_param_value.hpp
@@ -27,10 +27,10 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter name for a matrix type (where the user has to pass the file
@@ -40,7 +40,7 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a serializable model type (where the user has to
@@ -50,8 +50,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a mapped matrix type (where the user has to pass
@@ -61,8 +61,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter's name as seen by the user.
diff --git a/src/mlpack/bindings/cli/get_printable_param_value_impl.hpp b/src/mlpack/bindings/cli/get_printable_param_value_impl.hpp
index 09389614a1..3bb42b01b1 100644
--- a/src/mlpack/bindings/cli/get_printable_param_value_impl.hpp
+++ b/src/mlpack/bindings/cli/get_printable_param_value_impl.hpp
@@ -28,10 +28,10 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return input;
 }
@@ -44,7 +44,7 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*)
 {
   return input + ".csv";
 }
@@ -57,8 +57,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return input + ".bin";
 }
@@ -71,8 +71,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return input + ".arff";
 }
diff --git a/src/mlpack/bindings/cli/get_printable_type.hpp b/src/mlpack/bindings/cli/get_printable_type.hpp
index 9c29711240..07fd10609a 100644
--- a/src/mlpack/bindings/cli/get_printable_type.hpp
+++ b/src/mlpack/bindings/cli/get_printable_type.hpp
@@ -23,11 +23,11 @@ namespace cli {
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a vector.
@@ -60,8 +60,8 @@ std::string GetPrintableType(
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print the command-line type of an option into a string.
diff --git a/src/mlpack/bindings/cli/get_raw_param.hpp b/src/mlpack/bindings/cli/get_raw_param.hpp
index 46c3956a1f..38b340387e 100644
--- a/src/mlpack/bindings/cli/get_raw_param.hpp
+++ b/src/mlpack/bindings/cli/get_raw_param.hpp
@@ -27,10 +27,10 @@ namespace cli {
 template<typename T>
 T& GetRawParam(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // No mapping is needed, so just cast it directly.
   return *boost::any_cast<T>(&d.value);
@@ -42,7 +42,7 @@ T& GetRawParam(
 template<typename T>
 T& GetRawParam(
     util::ParamData& d,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* = 0)
@@ -59,8 +59,8 @@ T& GetRawParam(
 template<typename T>
 T*& GetRawParam(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Don't load the model.
   typedef std::tuple<T*, std::string> TupleType;
diff --git a/src/mlpack/bindings/cli/map_parameter_name.hpp b/src/mlpack/bindings/cli/map_parameter_name.hpp
index 1835bd5602..74f20a6431 100644
--- a/src/mlpack/bindings/cli/map_parameter_name.hpp
+++ b/src/mlpack/bindings/cli/map_parameter_name.hpp
@@ -27,10 +27,10 @@ namespace cli {
 template<typename T>
 std::string MapParameterName(
     const std::string& identifier,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return identifier;
 }
@@ -43,7 +43,7 @@ std::string MapParameterName(
 template<typename T>
 std::string MapParameterName(
     const std::string& identifier,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value ||
diff --git a/src/mlpack/bindings/cli/set_param.hpp b/src/mlpack/bindings/cli/set_param.hpp
index f800fe0553..f76f058d2e 100644
--- a/src/mlpack/bindings/cli/set_param.hpp
+++ b/src/mlpack/bindings/cli/set_param.hpp
@@ -27,11 +27,11 @@ template<typename T>
 void SetParam(
     util::ParamData& d,
     const boost::any& value,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T, bool>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T, bool>::value>::type* = 0)
 {
   // No mapping is needed.
   d.value = value;
@@ -44,7 +44,7 @@ template<typename T>
 void SetParam(
     util::ParamData& d,
     const boost::any& /* value */,
-    const typename boost::enable_if<std::is_same<T, bool>>::type* = 0)
+    const typename std::enable_if<std::is_same<T, bool>::value>::type* = 0)
 {
   // Force set to the value of whether or not this was passed.
   d.value = d.wasPassed;
@@ -60,7 +60,7 @@ void SetParam(
     const boost::any& value,
     const typename std::enable_if<arma::is_arma_type<T>::value ||
                                   std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
+        std::tuple<data::DatasetInfo, arma::mat>>::value::value>::type* = 0)
 {
   // We're setting the string filename.
   typedef std::tuple<T, typename ParameterType<T>::type> TupleType;
@@ -76,8 +76,8 @@ template<typename T>
 void SetParam(
     util::ParamData& d,
     const boost::any& value,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // We're setting the string filename.
   typedef std::tuple<T*, typename ParameterType<T>::type> TupleType;
diff --git a/src/mlpack/bindings/cli/string_type_param.hpp b/src/mlpack/bindings/cli/string_type_param.hpp
index eb4550e3ca..decf9e794d 100644
--- a/src/mlpack/bindings/cli/string_type_param.hpp
+++ b/src/mlpack/bindings/cli/string_type_param.hpp
@@ -26,22 +26,22 @@ namespace cli {
  */
 template<typename T>
 std::string StringTypeParamImpl(
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Return a string containing the type of the parameter, for vector options.
  */
 template<typename T>
 std::string StringTypeParamImpl(
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Return a string containing the type of the parameter,
  */
 template<typename T>
 std::string StringTypeParamImpl(
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Return a string containing the type of a parameter.  This overload is used if
diff --git a/src/mlpack/bindings/cli/string_type_param_impl.hpp b/src/mlpack/bindings/cli/string_type_param_impl.hpp
index 7bf1df6516..195d1b5a3b 100644
--- a/src/mlpack/bindings/cli/string_type_param_impl.hpp
+++ b/src/mlpack/bindings/cli/string_type_param_impl.hpp
@@ -23,8 +23,8 @@ namespace cli {
  */
 template<typename T>
 std::string StringTypeParamImpl(
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */)
 {
   // Don't know what type this is.
   return "unknown";
@@ -35,7 +35,7 @@ std::string StringTypeParamImpl(
  */
 template<typename T>
 std::string StringTypeParamImpl(
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   return "vector";
 }
@@ -45,7 +45,7 @@ std::string StringTypeParamImpl(
  */
 template<typename T>
 std::string StringTypeParamImpl(
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   return "string";
 }

From 88ddd5baf470428aba2213c3a3edef72416f96b0 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sat, 26 Jun 2021 19:38:24 +0200
Subject: [PATCH 007/118] Yeah, markdown have changed now

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 .../bindings/cli/get_printable_type_impl.hpp  |  2 +-
 .../bindings/markdown/get_printable_param.hpp | 22 +++++++++----------
 .../markdown/get_printable_param_name.hpp     | 18 +++++++--------
 .../get_printable_param_name_impl.hpp         | 18 +++++++--------
 .../markdown/get_printable_param_value.hpp    | 18 +++++++--------
 .../get_printable_param_value_impl.hpp        | 18 +++++++--------
 .../bindings/markdown/is_serializable.hpp     |  6 ++---
 7 files changed, 51 insertions(+), 51 deletions(-)

diff --git a/src/mlpack/bindings/cli/get_printable_type_impl.hpp b/src/mlpack/bindings/cli/get_printable_type_impl.hpp
index d02dae49fc..14a259b8b6 100644
--- a/src/mlpack/bindings/cli/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/cli/get_printable_type_impl.hpp
@@ -101,7 +101,7 @@ std::string GetPrintableType(
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename std::enable_if<arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
     const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return data.cppType + " file";
diff --git a/src/mlpack/bindings/markdown/get_printable_param.hpp b/src/mlpack/bindings/markdown/get_printable_param.hpp
index 91df84d61f..e94e9a7ca9 100644
--- a/src/mlpack/bindings/markdown/get_printable_param.hpp
+++ b/src/mlpack/bindings/markdown/get_printable_param.hpp
@@ -25,11 +25,11 @@ namespace markdown {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << boost::any_cast<T>(data.value);
@@ -42,7 +42,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   const T& t = boost::any_cast<T>(data.value);
 
@@ -58,7 +58,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Get the matrix.
   const T& matrix = boost::any_cast<T>(data.value);
@@ -74,8 +74,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << data.cppType << " model at " << boost::any_cast<T*>(data.value);
@@ -88,8 +88,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // Get the matrix.
   const T& tuple = boost::any_cast<T>(data.value);
diff --git a/src/mlpack/bindings/markdown/get_printable_param_name.hpp b/src/mlpack/bindings/markdown/get_printable_param_name.hpp
index 9d19c05f6d..c222ea10fd 100644
--- a/src/mlpack/bindings/markdown/get_printable_param_name.hpp
+++ b/src/mlpack/bindings/markdown/get_printable_param_name.hpp
@@ -26,10 +26,10 @@ namespace markdown {
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter name for a matrix type (where the user has to pass the file
@@ -38,7 +38,7 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a serializable model type (where the user has to
@@ -47,8 +47,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a mapped matrix type (where the user has to pass
@@ -57,8 +57,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter's name as seen by the user.
diff --git a/src/mlpack/bindings/markdown/get_printable_param_name_impl.hpp b/src/mlpack/bindings/markdown/get_printable_param_name_impl.hpp
index 5a7a63a001..b7e9f91fac 100644
--- a/src/mlpack/bindings/markdown/get_printable_param_name_impl.hpp
+++ b/src/mlpack/bindings/markdown/get_printable_param_name_impl.hpp
@@ -26,10 +26,10 @@ namespace markdown {
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "--" + data.name;
 }
@@ -41,7 +41,7 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*)
 {
   return "--" + data.name + "_file";
 }
@@ -53,8 +53,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return "--" + data.name + "_file";
 }
@@ -66,8 +66,8 @@ std::string GetPrintableParamName(
 template<typename T>
 std::string GetPrintableParamName(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "--" + data.name + "_file";
 }
diff --git a/src/mlpack/bindings/markdown/get_printable_param_value.hpp b/src/mlpack/bindings/markdown/get_printable_param_value.hpp
index c7e44ebd07..a0708fc2af 100644
--- a/src/mlpack/bindings/markdown/get_printable_param_value.hpp
+++ b/src/mlpack/bindings/markdown/get_printable_param_value.hpp
@@ -27,10 +27,10 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter name for a matrix type (where the user has to pass the file
@@ -40,7 +40,7 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a serializable model type (where the user has to
@@ -50,8 +50,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Get the parameter name for a mapped matrix type (where the user has to pass
@@ -61,8 +61,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& data,
     const std::string& value,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Get the parameter's name as seen by the user.
diff --git a/src/mlpack/bindings/markdown/get_printable_param_value_impl.hpp b/src/mlpack/bindings/markdown/get_printable_param_value_impl.hpp
index 22da560948..0753a7b03e 100644
--- a/src/mlpack/bindings/markdown/get_printable_param_value_impl.hpp
+++ b/src/mlpack/bindings/markdown/get_printable_param_value_impl.hpp
@@ -28,10 +28,10 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return input;
 }
@@ -44,7 +44,7 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*)
 {
   return input + ".csv";
 }
@@ -57,8 +57,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return input + ".bin";
 }
@@ -71,8 +71,8 @@ template<typename T>
 std::string GetPrintableParamValue(
     util::ParamData& /* data */,
     const std::string& input,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return input + ".arff";
 }
diff --git a/src/mlpack/bindings/markdown/is_serializable.hpp b/src/mlpack/bindings/markdown/is_serializable.hpp
index daeda8e9cb..fa66d53f08 100644
--- a/src/mlpack/bindings/markdown/is_serializable.hpp
+++ b/src/mlpack/bindings/markdown/is_serializable.hpp
@@ -25,7 +25,7 @@ namespace markdown {
  */
 template<typename T>
 bool IsSerializable(
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0)
 {
   return false;
 }
@@ -35,8 +35,8 @@ bool IsSerializable(
  */
 template<typename T>
 bool IsSerializable(
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0)
 {
   return true;
 }

From 20e114a8248732fe5718c2028e4a671c4ed6da83 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sat, 26 Jun 2021 20:31:18 +0200
Subject: [PATCH 008/118] Yeah, Julia bindings too.

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/julia/default_param.hpp   | 24 +++++++++----------
 .../bindings/julia/default_param_impl.hpp     | 22 ++++++++---------
 .../bindings/julia/get_printable_param.hpp    | 22 ++++++++---------
 .../bindings/julia/get_printable_type.hpp     | 14 +++++------
 .../julia/get_printable_type_impl.hpp         | 14 +++++------
 .../bindings/julia/print_type_doc_impl.hpp    | 14 +++++------
 6 files changed, 55 insertions(+), 55 deletions(-)

diff --git a/src/mlpack/bindings/julia/default_param.hpp b/src/mlpack/bindings/julia/default_param.hpp
index 4c61cec7e6..fca7d4a488 100644
--- a/src/mlpack/bindings/julia/default_param.hpp
+++ b/src/mlpack/bindings/julia/default_param.hpp
@@ -26,12 +26,12 @@ namespace julia {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a vector option.
@@ -39,7 +39,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Return the default value of a string option.
@@ -47,7 +47,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type* = 0);
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type* = 0);
 
 /**
  * Return the default value of a matrix option, a tuple option, a
@@ -57,10 +57,10 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
-                                   arma::mat>>::value>::type* /* junk */ = 0);
+                                   arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a model option (this returns the default
@@ -69,8 +69,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Return the default value of an option.  This is the function that will be
diff --git a/src/mlpack/bindings/julia/default_param_impl.hpp b/src/mlpack/bindings/julia/default_param_impl.hpp
index 9b975989e1..f1a71e7cf2 100644
--- a/src/mlpack/bindings/julia/default_param_impl.hpp
+++ b/src/mlpack/bindings/julia/default_param_impl.hpp
@@ -24,12 +24,12 @@ namespace julia {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::ostringstream oss;
   if (std::is_same<T, bool>::value)
@@ -46,7 +46,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   // Print each element in an array delimited by square brackets.
   std::ostringstream oss;
@@ -89,7 +89,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type*)
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type*)
 {
   const std::string& s = *boost::any_cast<std::string>(&data.value);
   return "\"" + s + "\"";
@@ -102,7 +102,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* /* junk */)
@@ -134,8 +134,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   return "nothing";
 }
diff --git a/src/mlpack/bindings/julia/get_printable_param.hpp b/src/mlpack/bindings/julia/get_printable_param.hpp
index a7c241857e..f6e7e442bc 100644
--- a/src/mlpack/bindings/julia/get_printable_param.hpp
+++ b/src/mlpack/bindings/julia/get_printable_param.hpp
@@ -25,11 +25,11 @@ namespace julia {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << boost::any_cast<T>(data.value);
@@ -42,7 +42,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   const T& t = boost::any_cast<T>(data.value);
 
@@ -58,7 +58,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Get the matrix.
   const T& matrix = boost::any_cast<T>(data.value);
@@ -74,8 +74,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << data.cppType << " model at " << boost::any_cast<T*>(data.value);
@@ -88,8 +88,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // Get the matrix.
   const T& tuple = boost::any_cast<T>(data.value);
diff --git a/src/mlpack/bindings/julia/get_printable_type.hpp b/src/mlpack/bindings/julia/get_printable_type.hpp
index 5d4a24c3ee..9fa2a03fec 100644
--- a/src/mlpack/bindings/julia/get_printable_type.hpp
+++ b/src/mlpack/bindings/julia/get_printable_type.hpp
@@ -23,11 +23,11 @@ namespace julia {
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a vector.
@@ -60,8 +60,8 @@ std::string GetPrintableType(
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print the command-line type of an option into a string.
diff --git a/src/mlpack/bindings/julia/get_printable_type_impl.hpp b/src/mlpack/bindings/julia/get_printable_type_impl.hpp
index f11c92dd8c..bbf32869b2 100644
--- a/src/mlpack/bindings/julia/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/julia/get_printable_type_impl.hpp
@@ -26,11 +26,11 @@ namespace julia {
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   if (std::is_same<T, bool>::value)
     return "Bool";
@@ -102,8 +102,8 @@ std::string GetPrintableType(
 template<typename T>
 std::string GetPrintableType(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   std::string type = util::StripType(data.cppType);
   if (type == "mlpackModel")
diff --git a/src/mlpack/bindings/julia/print_type_doc_impl.hpp b/src/mlpack/bindings/julia/print_type_doc_impl.hpp
index 7e6da1f606..6b72612f69 100644
--- a/src/mlpack/bindings/julia/print_type_doc_impl.hpp
+++ b/src/mlpack/bindings/julia/print_type_doc_impl.hpp
@@ -24,11 +24,11 @@ namespace julia {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   // A flag type.
   if (std::is_same<T, bool>::value)
@@ -153,8 +153,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return "An mlpack model pointer.  `<Model>` refers to the type of model that "
       "is being stored, so, e.g., for `CF()`, the type will be `CFModel`. "

From 7e6dea54968857e68625708f7a73353f5ee28d83 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sat, 26 Jun 2021 20:37:52 +0200
Subject: [PATCH 009/118] Ooops, a missing file

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/julia/print_type_doc.hpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/mlpack/bindings/julia/print_type_doc.hpp b/src/mlpack/bindings/julia/print_type_doc.hpp
index 407fa3ee1d..eabda5a067 100644
--- a/src/mlpack/bindings/julia/print_type_doc.hpp
+++ b/src/mlpack/bindings/julia/print_type_doc.hpp
@@ -25,11 +25,11 @@ namespace julia {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a vector.
@@ -62,8 +62,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print the command-line type of an option into a string.

From 863c5672e5a7f7c8272d15d752d8088a48293508 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sun, 27 Jun 2021 16:29:36 +0200
Subject: [PATCH 010/118] Add the R bindings

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/default_param.hpp       | 22 ++---
 src/mlpack/bindings/R/default_param_impl.hpp  | 22 ++---
 src/mlpack/bindings/R/get_printable_param.hpp | 22 ++---
 src/mlpack/bindings/R/get_printable_type.hpp  | 84 +++++++++----------
 .../bindings/R/get_printable_type_impl.hpp    | 84 +++++++++----------
 src/mlpack/bindings/R/get_r_type.hpp          | 76 ++++++++---------
 src/mlpack/bindings/R/get_type.hpp            | 80 +++++++++---------
 .../bindings/R/print_input_processing.hpp     | 18 ++--
 .../bindings/R/print_output_processing.hpp    | 18 ++--
 src/mlpack/bindings/R/print_type_doc.hpp      | 20 ++---
 src/mlpack/bindings/R/print_type_doc_impl.hpp | 20 ++---
 11 files changed, 233 insertions(+), 233 deletions(-)

diff --git a/src/mlpack/bindings/R/default_param.hpp b/src/mlpack/bindings/R/default_param.hpp
index 4d50eb16f5..8fdf41dfbf 100644
--- a/src/mlpack/bindings/R/default_param.hpp
+++ b/src/mlpack/bindings/R/default_param.hpp
@@ -26,12 +26,12 @@ namespace r {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a vector option.
@@ -39,7 +39,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Return the default value of a string option.
@@ -47,7 +47,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type* = 0);
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type* = 0);
 
 /**
  * Return the default value of a matrix option, a tuple option, a
@@ -57,7 +57,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* /* junk */ = 0);
@@ -69,8 +69,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Return the default value of an option.  This is the function that will be
diff --git a/src/mlpack/bindings/R/default_param_impl.hpp b/src/mlpack/bindings/R/default_param_impl.hpp
index de529d011a..751834baca 100644
--- a/src/mlpack/bindings/R/default_param_impl.hpp
+++ b/src/mlpack/bindings/R/default_param_impl.hpp
@@ -24,12 +24,12 @@ namespace r {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::ostringstream oss;
   if (std::is_same<T, bool>::value)
@@ -46,7 +46,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   // Print each element in an array delimited by square brackets.
   std::ostringstream oss;
@@ -89,7 +89,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type*)
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type*)
 {
   const std::string& s = *boost::any_cast<std::string>(&data.value);
   return "\"" + s + "\"";
@@ -102,7 +102,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* /* junk */)
@@ -132,8 +132,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   return "NA";
 }
diff --git a/src/mlpack/bindings/R/get_printable_param.hpp b/src/mlpack/bindings/R/get_printable_param.hpp
index b76d62efa8..261d363642 100644
--- a/src/mlpack/bindings/R/get_printable_param.hpp
+++ b/src/mlpack/bindings/R/get_printable_param.hpp
@@ -25,11 +25,11 @@ namespace r {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << boost::any_cast<T>(data.value);
@@ -42,7 +42,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   const T& t = boost::any_cast<T>(data.value);
 
@@ -58,7 +58,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Get the matrix.
   const T& matrix = boost::any_cast<T>(data.value);
@@ -74,8 +74,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << data.cppType << " model at " << boost::any_cast<T*>(data.value);
@@ -88,8 +88,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // Get the matrix.
   const T& tuple = boost::any_cast<T>(data.value);
diff --git a/src/mlpack/bindings/R/get_printable_type.hpp b/src/mlpack/bindings/R/get_printable_type.hpp
index 730422cade..4b19c21cdf 100644
--- a/src/mlpack/bindings/R/get_printable_type.hpp
+++ b/src/mlpack/bindings/R/get_printable_type.hpp
@@ -23,84 +23,84 @@ namespace r {
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<>
 inline std::string GetPrintableType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<size_t>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<size_t>>::type*,
-    const typename boost::disable_if<data::HasSerialize<size_t>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<size_t>>::type*,
-    const typename boost::disable_if<std::is_same<size_t,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<size_t>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<size_t>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<size_t>::value>::type*,
+    const typename std::enable_if<!std::is_same<size_t,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 void GetPrintableType(util::ParamData& d,
diff --git a/src/mlpack/bindings/R/get_printable_type_impl.hpp b/src/mlpack/bindings/R/get_printable_type_impl.hpp
index cedecdf2ab..a163a615f5 100644
--- a/src/mlpack/bindings/R/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/R/get_printable_type_impl.hpp
@@ -22,11 +22,11 @@ namespace r {
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "unknown";
 }
@@ -34,11 +34,11 @@ inline std::string GetPrintableType(
 template<>
 inline std::string GetPrintableType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "integer";
 }
@@ -46,11 +46,11 @@ inline std::string GetPrintableType<int>(
 template<>
 inline std::string GetPrintableType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "numeric";
 }
@@ -58,11 +58,11 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "character";
 }
@@ -70,11 +70,11 @@ inline std::string GetPrintableType<std::string>(
 template<>
 inline std::string GetPrintableType<size_t>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<size_t>>::type*,
-    const typename boost::disable_if<data::HasSerialize<size_t>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<size_t>>::type*,
-    const typename boost::disable_if<std::is_same<size_t,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<size_t>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<size_t>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<size_t>::value>::type*,
+    const typename std::enable_if<!std::is_same<size_t,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "integer";
 }
@@ -82,11 +82,11 @@ inline std::string GetPrintableType<size_t>(
 template<>
 inline std::string GetPrintableType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "logical";
 }
@@ -94,9 +94,9 @@ inline std::string GetPrintableType<bool>(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "vector of " + GetPrintableType<typename T::value_type>(d) + "s";
 }
@@ -104,9 +104,9 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   std::string type = "numeric matrix";
   if (std::is_same<typename T::elem_type, double>::value)
@@ -127,8 +127,8 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "categorical matrix/data.frame";
 }
@@ -136,10 +136,10 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   std::string type = util::StripType(d.cppType);
   if (type == "mlpackModel")
diff --git a/src/mlpack/bindings/R/get_r_type.hpp b/src/mlpack/bindings/R/get_r_type.hpp
index a32995dbca..d01559ea66 100644
--- a/src/mlpack/bindings/R/get_r_type.hpp
+++ b/src/mlpack/bindings/R/get_r_type.hpp
@@ -23,11 +23,11 @@ namespace r {
 template<typename T>
 inline std::string GetRType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return "unknown";
 }
@@ -35,11 +35,11 @@ inline std::string GetRType(
 template<>
 inline std::string GetRType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "logical";
 }
@@ -47,11 +47,11 @@ inline std::string GetRType<bool>(
 template<>
 inline std::string GetRType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "integer";
 }
@@ -59,11 +59,11 @@ inline std::string GetRType<int>(
 template<>
 inline std::string GetRType<size_t>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<size_t>>::type*,
-    const typename boost::disable_if<data::HasSerialize<size_t>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<size_t>>::type*,
-    const typename boost::disable_if<std::is_same<size_t,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<size_t>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<size_t>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<size_t>::value>::type*,
+    const typename std::enable_if<!std::is_same<size_t,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "integer";
 }
@@ -71,11 +71,11 @@ inline std::string GetRType<size_t>(
 template<>
 inline std::string GetRType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "numeric";
 }
@@ -83,11 +83,11 @@ inline std::string GetRType<double>(
 template<>
 inline std::string GetRType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "character";
 }
@@ -95,7 +95,7 @@ inline std::string GetRType<std::string>(
 template<typename T>
 inline std::string GetRType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   return GetRType<typename T::value_type>(d) + " vector";
 }
@@ -103,9 +103,9 @@ inline std::string GetRType(
 template<typename T>
 inline std::string GetRType(
     util::ParamData& d,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0,
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   std::string elemType = GetRType<typename T::elem_type>(d);
   std::string type = "matrix";
@@ -120,8 +120,8 @@ inline std::string GetRType(
 template<typename T>
 inline std::string GetRType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return "numeric matrix/data.frame with info";
 }
@@ -129,8 +129,8 @@ inline std::string GetRType(
 template<typename T>
 inline std::string GetRType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   return util::StripType(d.cppType);
 }
diff --git a/src/mlpack/bindings/R/get_type.hpp b/src/mlpack/bindings/R/get_type.hpp
index 0437fefab0..574ec6674c 100644
--- a/src/mlpack/bindings/R/get_type.hpp
+++ b/src/mlpack/bindings/R/get_type.hpp
@@ -24,11 +24,11 @@ namespace r {
 template<typename T>
 inline std::string GetType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return "unknown";
 }
@@ -36,11 +36,11 @@ inline std::string GetType(
 template<>
 inline std::string GetType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "Int";
 }
@@ -48,11 +48,11 @@ inline std::string GetType<int>(
 template<>
 inline std::string GetType<float>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<float>>::type*,
-    const typename boost::disable_if<data::HasSerialize<float>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<float>>::type*,
-    const typename boost::disable_if<std::is_same<float,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<float>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<float>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<float>::value>::type*,
+    const typename std::enable_if<!std::is_same<float,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "Float";
 }
@@ -60,11 +60,11 @@ inline std::string GetType<float>(
 template<>
 inline std::string GetType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "Double";
 }
@@ -72,11 +72,11 @@ inline std::string GetType<double>(
 template<>
 inline std::string GetType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "String";
 }
@@ -84,11 +84,11 @@ inline std::string GetType<std::string>(
 template<>
 inline std::string GetType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "Bool";
 }
@@ -96,9 +96,9 @@ inline std::string GetType<bool>(
 template<typename T>
 inline std::string GetType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return "Vec" + GetType<typename T::value_type>(d);
 }
@@ -106,9 +106,9 @@ inline std::string GetType(
 template<typename T>
 inline std::string GetType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   std::string type = "";
   if (std::is_same<typename T::elem_type, double>::value)
@@ -136,8 +136,8 @@ inline std::string GetType(
 template<typename T>
 inline std::string GetType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return "MatWithInfo";
 }
@@ -145,8 +145,8 @@ inline std::string GetType(
 template<typename T>
 inline std::string GetType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   return d.cppType;
 }
diff --git a/src/mlpack/bindings/R/print_input_processing.hpp b/src/mlpack/bindings/R/print_input_processing.hpp
index 4565a6d503..34d10d5b1a 100644
--- a/src/mlpack/bindings/R/print_input_processing.hpp
+++ b/src/mlpack/bindings/R/print_input_processing.hpp
@@ -26,10 +26,10 @@ namespace r {
 template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   if (!d.required)
   {
@@ -72,7 +72,7 @@ void PrintInputProcessing(
 template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   if (!d.required)
   {
@@ -108,8 +108,8 @@ void PrintInputProcessing(
 template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   if (!d.required)
   {
@@ -155,8 +155,8 @@ void PrintInputProcessing(
 template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   if (!d.required)
   {
diff --git a/src/mlpack/bindings/R/print_output_processing.hpp b/src/mlpack/bindings/R/print_output_processing.hpp
index 3345f6e437..77c6e4451c 100644
--- a/src/mlpack/bindings/R/print_output_processing.hpp
+++ b/src/mlpack/bindings/R/print_output_processing.hpp
@@ -26,10 +26,10 @@ namespace r {
 template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   /**
    * This gives us code like:
@@ -48,7 +48,7 @@ void PrintOutputProcessing(
 template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0,
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
@@ -69,8 +69,8 @@ void PrintOutputProcessing(
 template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   /**
    * This gives us code like:
@@ -89,8 +89,8 @@ void PrintOutputProcessing(
 template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   /**
    * This gives us code like:
diff --git a/src/mlpack/bindings/R/print_type_doc.hpp b/src/mlpack/bindings/R/print_type_doc.hpp
index c6b37ce1e4..92bbd07dbe 100644
--- a/src/mlpack/bindings/R/print_type_doc.hpp
+++ b/src/mlpack/bindings/R/print_type_doc.hpp
@@ -25,11 +25,11 @@ namespace r {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a vector.
@@ -37,7 +37,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a matrix option.
@@ -45,7 +45,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a matrix tuple option.
@@ -54,7 +54,7 @@ template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
     const typename std::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
+        std::tuple<data::DatasetInfo, arma::mat>>::value::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a model.
@@ -62,8 +62,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print the command-line type of an option into a string.
diff --git a/src/mlpack/bindings/R/print_type_doc_impl.hpp b/src/mlpack/bindings/R/print_type_doc_impl.hpp
index b2b87bdbdd..79c03bed48 100644
--- a/src/mlpack/bindings/R/print_type_doc_impl.hpp
+++ b/src/mlpack/bindings/R/print_type_doc_impl.hpp
@@ -24,11 +24,11 @@ namespace r {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   // A flag type.
   if (std::is_same<T, bool>::value)
@@ -64,7 +64,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<util::IsStdVector<T>::value>::type*)
+    const typename std::enable_if<util::IsStdVector<T>::value::value>::type*)
 {
   if (std::is_same<T, std::vector<int>>::value)
   {
@@ -86,7 +86,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<arma::is_arma_type<T>::value>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value::value>::type*)
 {
   if (std::is_same<typename T::elem_type, double>::value)
   {
@@ -129,7 +129,7 @@ template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& /* data */,
     const typename std::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
+        std::tuple<data::DatasetInfo, arma::mat>>::value::value>::type*)
 {
   return "A 2-d array containing `numeric` data.  Like the regular 2-d matrices"
       ", this can be a `matrix`, or a `data.frame`. However, this type can also"
@@ -146,8 +146,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return "An mlpack model pointer.  `<Model>` refers to the type of model that "
       "is being stored, so, e.g., for `cf()`, the type will be `CFModel`. "

From fe7bfbbb789b9fe14be35822ea674fe875eb0c2c Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sun, 27 Jun 2021 16:57:27 +0200
Subject: [PATCH 011/118] Python Binding now...

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/python/default_param.hpp  | 24 +++---
 .../bindings/python/default_param_impl.hpp    | 22 ++---
 .../bindings/python/get_cython_type.hpp       | 44 +++++-----
 .../bindings/python/get_printable_param.hpp   | 22 ++---
 .../bindings/python/get_printable_type.hpp    | 84 +++++++++----------
 .../python/get_printable_type_impl.hpp        | 84 +++++++++----------
 src/mlpack/bindings/python/import_decl.hpp    | 10 +--
 .../bindings/python/print_class_defn.hpp      | 10 +--
 .../python/print_input_processing.hpp         | 36 ++++----
 .../python/print_output_processing.hpp        | 18 ++--
 src/mlpack/bindings/python/print_type_doc.hpp | 14 ++--
 .../bindings/python/print_type_doc_impl.hpp   | 14 ++--
 12 files changed, 191 insertions(+), 191 deletions(-)

diff --git a/src/mlpack/bindings/python/default_param.hpp b/src/mlpack/bindings/python/default_param.hpp
index 15504a74b4..36a6b19c32 100644
--- a/src/mlpack/bindings/python/default_param.hpp
+++ b/src/mlpack/bindings/python/default_param.hpp
@@ -26,12 +26,12 @@ namespace python {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a vector option.
@@ -39,7 +39,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Return the default value of a string option.
@@ -47,7 +47,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type* = 0);
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type* = 0);
 
 /**
  * Return the default value of a matrix option, a tuple option, a
@@ -57,10 +57,10 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
-                                   arma::mat>>::value>::type* /* junk */ = 0);
+                                   arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a model option (this returns the default
@@ -69,8 +69,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Return the default value of an option.  This is the function that will be
diff --git a/src/mlpack/bindings/python/default_param_impl.hpp b/src/mlpack/bindings/python/default_param_impl.hpp
index 15c7f344ae..d953c2efb2 100644
--- a/src/mlpack/bindings/python/default_param_impl.hpp
+++ b/src/mlpack/bindings/python/default_param_impl.hpp
@@ -24,12 +24,12 @@ namespace python {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::ostringstream oss;
   if (std::is_same<T, bool>::value)
@@ -46,7 +46,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   // Print each element in an array delimited by square brackets.
   std::ostringstream oss;
@@ -89,7 +89,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type*)
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type*)
 {
   const std::string& s = *boost::any_cast<std::string>(&data.value);
   return "'" + s + "'";
@@ -102,7 +102,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* /* junk */)
@@ -134,8 +134,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   return "None";
 }
diff --git a/src/mlpack/bindings/python/get_cython_type.hpp b/src/mlpack/bindings/python/get_cython_type.hpp
index bb01da538e..705b785758 100644
--- a/src/mlpack/bindings/python/get_cython_type.hpp
+++ b/src/mlpack/bindings/python/get_cython_type.hpp
@@ -23,9 +23,9 @@ namespace python {
 template<typename T>
 inline std::string GetCythonType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0)
 {
   return "unknown";
 }
@@ -33,9 +33,9 @@ inline std::string GetCythonType(
 template<>
 inline std::string GetCythonType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*)
 {
   return "int";
 }
@@ -43,9 +43,9 @@ inline std::string GetCythonType<int>(
 template<>
 inline std::string GetCythonType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*)
 {
   return "double";
 }
@@ -53,9 +53,9 @@ inline std::string GetCythonType<double>(
 template<>
 inline std::string GetCythonType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
 {
   return "string";
 }
@@ -63,9 +63,9 @@ inline std::string GetCythonType<std::string>(
 template<>
 inline std::string GetCythonType<size_t>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<size_t>>::type*,
-    const typename boost::disable_if<data::HasSerialize<size_t>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<size_t>>::type*)
+    const typename std::enable_if<!util::IsStdVector<size_t>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<size_t>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<size_t>::value>::type*)
 {
   return "size_t";
 }
@@ -73,9 +73,9 @@ inline std::string GetCythonType<size_t>(
 template<>
 inline std::string GetCythonType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*)
 {
   return "cbool";
 }
@@ -83,7 +83,7 @@ inline std::string GetCythonType<bool>(
 template<typename T>
 inline std::string GetCythonType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   return "vector[" + GetCythonType<typename T::value_type>(d) + "]";
 }
@@ -91,7 +91,7 @@ inline std::string GetCythonType(
 template<typename T>
 inline std::string GetCythonType(
     util::ParamData& d,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   std::string type = "Mat";
   if (T::is_row)
@@ -105,8 +105,8 @@ inline std::string GetCythonType(
 template<typename T>
 inline std::string GetCythonType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   return d.cppType + "*";
 }
diff --git a/src/mlpack/bindings/python/get_printable_param.hpp b/src/mlpack/bindings/python/get_printable_param.hpp
index effdf4f07b..44ab825687 100644
--- a/src/mlpack/bindings/python/get_printable_param.hpp
+++ b/src/mlpack/bindings/python/get_printable_param.hpp
@@ -25,11 +25,11 @@ namespace python {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << boost::any_cast<T>(data.value);
@@ -42,7 +42,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   const T& t = boost::any_cast<T>(data.value);
 
@@ -58,7 +58,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Get the matrix.
   const T& matrix = boost::any_cast<T>(data.value);
@@ -74,8 +74,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << data.cppType << " model at " << boost::any_cast<T*>(data.value);
@@ -88,8 +88,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // Get the matrix.
   const T& tuple = boost::any_cast<T>(data.value);
diff --git a/src/mlpack/bindings/python/get_printable_type.hpp b/src/mlpack/bindings/python/get_printable_type.hpp
index 2b7cfb40ed..41593e4681 100644
--- a/src/mlpack/bindings/python/get_printable_type.hpp
+++ b/src/mlpack/bindings/python/get_printable_type.hpp
@@ -23,84 +23,84 @@ namespace python {
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<>
 inline std::string GetPrintableType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<size_t>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<size_t>>::type*,
-    const typename boost::disable_if<data::HasSerialize<size_t>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<size_t>>::type*,
-    const typename boost::disable_if<std::is_same<size_t,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<size_t>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<size_t>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<size_t>::value>::type*,
+    const typename std::enable_if<!std::is_same<size_t,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 void GetPrintableType(util::ParamData& d,
diff --git a/src/mlpack/bindings/python/get_printable_type_impl.hpp b/src/mlpack/bindings/python/get_printable_type_impl.hpp
index 0181079aa1..9494065b4b 100644
--- a/src/mlpack/bindings/python/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/python/get_printable_type_impl.hpp
@@ -22,11 +22,11 @@ namespace python {
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "unknown";
 }
@@ -34,11 +34,11 @@ inline std::string GetPrintableType(
 template<>
 inline std::string GetPrintableType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "int";
 }
@@ -46,11 +46,11 @@ inline std::string GetPrintableType<int>(
 template<>
 inline std::string GetPrintableType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "float";
 }
@@ -58,11 +58,11 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "str";
 }
@@ -70,11 +70,11 @@ inline std::string GetPrintableType<std::string>(
 template<>
 inline std::string GetPrintableType<size_t>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<size_t>>::type*,
-    const typename boost::disable_if<data::HasSerialize<size_t>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<size_t>>::type*,
-    const typename boost::disable_if<std::is_same<size_t,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<size_t>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<size_t>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<size_t>::value>::type*,
+    const typename std::enable_if<!std::is_same<size_t,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "int";
 }
@@ -82,11 +82,11 @@ inline std::string GetPrintableType<size_t>(
 template<>
 inline std::string GetPrintableType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "bool";
 }
@@ -94,9 +94,9 @@ inline std::string GetPrintableType<bool>(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "list of " + GetPrintableType<typename T::value_type>(d) + "s";
 }
@@ -104,9 +104,9 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   std::string type = "matrix";
   if (std::is_same<typename T::elem_type, double>::value)
@@ -127,8 +127,8 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "categorical matrix";
 }
@@ -136,10 +136,10 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return d.cppType + "Type";
 }
diff --git a/src/mlpack/bindings/python/import_decl.hpp b/src/mlpack/bindings/python/import_decl.hpp
index b060a66dfe..e6518ce461 100644
--- a/src/mlpack/bindings/python/import_decl.hpp
+++ b/src/mlpack/bindings/python/import_decl.hpp
@@ -26,8 +26,8 @@ template<typename T>
 void ImportDecl(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // First, we have to parse the type.  If we have something like, e.g.,
   // 'LogisticRegression<>', we must convert this to 'LogisticRegression[T=*].'
@@ -53,8 +53,8 @@ template<typename T>
 void ImportDecl(
     util::ParamData& /* d */,
     const size_t /* indent */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0)
 {
   // Print nothing.
 }
@@ -66,7 +66,7 @@ template<typename T>
 void ImportDecl(
     util::ParamData& /* d */,
     const size_t /* indent */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Print nothing.
 }
diff --git a/src/mlpack/bindings/python/print_class_defn.hpp b/src/mlpack/bindings/python/print_class_defn.hpp
index ff1de4b26e..53d07d11e7 100644
--- a/src/mlpack/bindings/python/print_class_defn.hpp
+++ b/src/mlpack/bindings/python/print_class_defn.hpp
@@ -25,8 +25,8 @@ namespace python {
 template<typename T>
 void PrintClassDefn(
     util::ParamData& /* d */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0)
 {
   // Do nothing.
 }
@@ -37,7 +37,7 @@ void PrintClassDefn(
 template<typename T>
 void PrintClassDefn(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Do nothing.
 }
@@ -48,8 +48,8 @@ void PrintClassDefn(
 template<typename T>
 void PrintClassDefn(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // First, we have to parse the type.  If we have something like, e.g.,
   // 'LogisticRegression<>', we must convert this to 'LogisticRegression[].'
diff --git a/src/mlpack/bindings/python/print_input_processing.hpp b/src/mlpack/bindings/python/print_input_processing.hpp
index c4a8ed5006..5137db40e9 100644
--- a/src/mlpack/bindings/python/print_input_processing.hpp
+++ b/src/mlpack/bindings/python/print_input_processing.hpp
@@ -31,11 +31,11 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // The copy_all_inputs parameter must be handled first, and therefore is
   // outside the scope of this code.
@@ -164,11 +164,11 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0,
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -251,8 +251,8 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -372,9 +372,9 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // First, get the correct class name if needed.
   std::string strippedType, printedType, defaultsType;
@@ -445,9 +445,9 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // The user should pass in a matrix type of some sort.
   const std::string prefix(indent, ' ');
diff --git a/src/mlpack/bindings/python/print_output_processing.hpp b/src/mlpack/bindings/python/print_output_processing.hpp
index af1ef95a93..e9c89200b3 100644
--- a/src/mlpack/bindings/python/print_output_processing.hpp
+++ b/src/mlpack/bindings/python/print_output_processing.hpp
@@ -30,10 +30,10 @@ void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
     const bool onlyOutput,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -86,7 +86,7 @@ void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
     const bool onlyOutput,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -128,8 +128,8 @@ void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
     const bool onlyOutput,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -170,8 +170,8 @@ void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
     const bool onlyOutput,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Get the type names we need to use.
   std::string strippedType, printedType, defaultsType;
diff --git a/src/mlpack/bindings/python/print_type_doc.hpp b/src/mlpack/bindings/python/print_type_doc.hpp
index 88186faafb..aad90ee4b7 100644
--- a/src/mlpack/bindings/python/print_type_doc.hpp
+++ b/src/mlpack/bindings/python/print_type_doc.hpp
@@ -25,11 +25,11 @@ namespace python {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a vector.
@@ -62,8 +62,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print the command-line type of an option into a string.
diff --git a/src/mlpack/bindings/python/print_type_doc_impl.hpp b/src/mlpack/bindings/python/print_type_doc_impl.hpp
index 8ab5986721..64b6ca3e22 100644
--- a/src/mlpack/bindings/python/print_type_doc_impl.hpp
+++ b/src/mlpack/bindings/python/print_type_doc_impl.hpp
@@ -24,11 +24,11 @@ namespace python {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   // A flag type.
   if (std::is_same<T, bool>::value)
@@ -150,8 +150,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return "An mlpack model pointer.  This type can be pickled to or from disk, "
       "and internally holds a pointer to C++ memory containing the mlpack "

From c6e76d5919d84b3dc50ccc68367dd00f6a9eb409 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sun, 27 Jun 2021 17:20:27 +0200
Subject: [PATCH 012/118] GO also, could not check it locally, hard to install
 gonum

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/go/default_param.hpp      | 24 +++---
 src/mlpack/bindings/go/default_param_impl.hpp | 22 +++---
 src/mlpack/bindings/go/get_go_type.hpp        | 76 +++++++++----------
 .../bindings/go/get_printable_param.hpp       | 22 +++---
 src/mlpack/bindings/go/get_printable_type.hpp | 74 +++++++++---------
 .../bindings/go/get_printable_type_impl.hpp   | 74 +++++++++---------
 src/mlpack/bindings/go/get_type.hpp           | 44 +++++------
 src/mlpack/bindings/go/print_defn_input.hpp   | 18 ++---
 src/mlpack/bindings/go/print_defn_output.hpp  | 18 ++---
 .../bindings/go/print_input_processing.hpp    | 18 ++---
 .../bindings/go/print_method_config.hpp       | 18 ++---
 src/mlpack/bindings/go/print_method_init.hpp  | 18 ++---
 .../bindings/go/print_output_processing.hpp   | 18 ++---
 src/mlpack/bindings/go/print_type_doc.hpp     | 14 ++--
 .../bindings/go/print_type_doc_impl.hpp       | 14 ++--
 15 files changed, 236 insertions(+), 236 deletions(-)

diff --git a/src/mlpack/bindings/go/default_param.hpp b/src/mlpack/bindings/go/default_param.hpp
index 7469b30ac3..4b967b270e 100644
--- a/src/mlpack/bindings/go/default_param.hpp
+++ b/src/mlpack/bindings/go/default_param.hpp
@@ -26,12 +26,12 @@ namespace go {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a vector option.
@@ -39,7 +39,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Return the default value of a string option.
@@ -47,7 +47,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type* = 0);
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type* = 0);
 
 /**
  * Return the default value of a matrix option, a tuple option, a
@@ -57,10 +57,10 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
-                                   arma::mat>>::value>::type* /* junk */ = 0);
+                                   arma::mat>>::value>::type* = 0);
 
 /**
  * Return the default value of a model option (this returns the default
@@ -69,8 +69,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Return the default value of an option.  This is the function that will be
diff --git a/src/mlpack/bindings/go/default_param_impl.hpp b/src/mlpack/bindings/go/default_param_impl.hpp
index e3a9a07103..4d3c0feca1 100644
--- a/src/mlpack/bindings/go/default_param_impl.hpp
+++ b/src/mlpack/bindings/go/default_param_impl.hpp
@@ -24,12 +24,12 @@ namespace go {
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T, std::string>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::ostringstream oss;
   if (std::is_same<T, bool>::value)
@@ -46,7 +46,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   // Print each element in an array delimited by square brackets.
   std::ostringstream oss;
@@ -90,7 +90,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T, std::string>>::type*)
+    const typename std::enable_if<std::is_same<T, std::string>::value>::type*)
 {
   const std::string& s = *boost::any_cast<std::string>(&data.value);
   return "\"" + s + "\"";
@@ -102,7 +102,7 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::enable_if_c<
+    const typename std::enable_if<
         arma::is_arma_type<T>::value ||
         std::is_same<T, std::tuple<mlpack::data::DatasetInfo,
                                    arma::mat>>::value>::type* /* junk */)
@@ -134,8 +134,8 @@ std::string DefaultParamImpl(
 template<typename T>
 std::string DefaultParamImpl(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   return "nil";
 }
diff --git a/src/mlpack/bindings/go/get_go_type.hpp b/src/mlpack/bindings/go/get_go_type.hpp
index 77c851cb99..55bafb2ddb 100644
--- a/src/mlpack/bindings/go/get_go_type.hpp
+++ b/src/mlpack/bindings/go/get_go_type.hpp
@@ -25,11 +25,11 @@ namespace go {
 template<typename T>
 inline std::string GetGoType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return "unknown";
 }
@@ -37,11 +37,11 @@ inline std::string GetGoType(
 template<>
 inline std::string GetGoType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "int";
 }
@@ -49,11 +49,11 @@ inline std::string GetGoType<int>(
 template<>
 inline std::string GetGoType<float>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<float>>::type*,
-    const typename boost::disable_if<data::HasSerialize<float>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<float>>::type*,
-    const typename boost::disable_if<std::is_same<float,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<float>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<float>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<float>::value>::type*,
+    const typename std::enable_if<!std::is_same<float,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "float32";
 }
@@ -61,11 +61,11 @@ inline std::string GetGoType<float>(
 template<>
 inline std::string GetGoType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "float64";
 }
@@ -73,11 +73,11 @@ inline std::string GetGoType<double>(
 template<>
 inline std::string GetGoType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "string";
 }
@@ -85,11 +85,11 @@ inline std::string GetGoType<std::string>(
 template<>
 inline std::string GetGoType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "bool";
 }
@@ -97,7 +97,7 @@ inline std::string GetGoType<bool>(
 template<typename T>
 inline std::string GetGoType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   return "[]" + GetGoType<typename T::value_type>(d);
 }
@@ -105,9 +105,9 @@ inline std::string GetGoType(
 template<typename T>
 inline std::string GetGoType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0,
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   return "mat.Dense";
 }
@@ -115,8 +115,8 @@ inline std::string GetGoType(
 template<typename T>
 inline std::string GetGoType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   return "matrixWithInfo";
 }
@@ -124,8 +124,8 @@ inline std::string GetGoType(
 template<typename T>
 inline std::string GetGoType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   std::string goStrippedType, strippedType, printedType, defaultsType;
   StripType(d.cppType, goStrippedType, strippedType, printedType, defaultsType);
diff --git a/src/mlpack/bindings/go/get_printable_param.hpp b/src/mlpack/bindings/go/get_printable_param.hpp
index 90e5f74590..091bfcba80 100644
--- a/src/mlpack/bindings/go/get_printable_param.hpp
+++ b/src/mlpack/bindings/go/get_printable_param.hpp
@@ -25,11 +25,11 @@ namespace go {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << boost::any_cast<T>(data.value);
@@ -42,7 +42,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   const T& t = boost::any_cast<T>(data.value);
 
@@ -58,7 +58,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Get the matrix.
   const T& matrix = boost::any_cast<T>(data.value);
@@ -74,8 +74,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   std::ostringstream oss;
   oss << data.cppType << " model at " << boost::any_cast<T*>(data.value);
@@ -88,8 +88,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // Get the matrix.
   const T& tuple = boost::any_cast<T>(data.value);
diff --git a/src/mlpack/bindings/go/get_printable_type.hpp b/src/mlpack/bindings/go/get_printable_type.hpp
index 014fcad297..b16533da18 100644
--- a/src/mlpack/bindings/go/get_printable_type.hpp
+++ b/src/mlpack/bindings/go/get_printable_type.hpp
@@ -23,75 +23,75 @@ namespace go {
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<>
 inline std::string GetPrintableType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
 inline std::string GetPrintableType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*);
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 template<typename T>
 void GetPrintableType(util::ParamData& d,
diff --git a/src/mlpack/bindings/go/get_printable_type_impl.hpp b/src/mlpack/bindings/go/get_printable_type_impl.hpp
index da4df3bec9..921f5f53a1 100644
--- a/src/mlpack/bindings/go/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/go/get_printable_type_impl.hpp
@@ -23,11 +23,11 @@ namespace go {
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "unknown";
 }
@@ -35,11 +35,11 @@ inline std::string GetPrintableType(
 template<>
 inline std::string GetPrintableType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*,
-    const typename boost::disable_if<std::is_same<int,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*,
+    const typename std::enable_if<!std::is_same<int,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "int";
 }
@@ -47,11 +47,11 @@ inline std::string GetPrintableType<int>(
 template<>
 inline std::string GetPrintableType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*,
-    const typename boost::disable_if<std::is_same<double,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*,
+    const typename std::enable_if<!std::is_same<double,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "float64";
 }
@@ -59,11 +59,11 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*,
-    const typename boost::disable_if<std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<std::string,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "string";
 }
@@ -71,11 +71,11 @@ inline std::string GetPrintableType<std::string>(
 template<>
 inline std::string GetPrintableType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*,
-    const typename boost::disable_if<std::is_same<bool,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*,
+    const typename std::enable_if<!std::is_same<bool,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "bool";
 }
@@ -83,9 +83,9 @@ inline std::string GetPrintableType<bool>(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "array of " + GetPrintableType<typename T::value_type>(d) + "s";
 }
@@ -93,9 +93,9 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   std::string type = "*mat.Dense";
   if (T::is_row || T::is_col)
@@ -107,8 +107,8 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "matrixWithInfo";
 }
@@ -116,10 +116,10 @@ inline std::string GetPrintableType(
 template<typename T>
 inline std::string GetPrintableType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   std::string goStrippedType, strippedType, printedType, defaultsType;
   StripType(d.cppType, goStrippedType, strippedType, printedType, defaultsType);
diff --git a/src/mlpack/bindings/go/get_type.hpp b/src/mlpack/bindings/go/get_type.hpp
index c46c87651d..0a1417ccab 100644
--- a/src/mlpack/bindings/go/get_type.hpp
+++ b/src/mlpack/bindings/go/get_type.hpp
@@ -24,9 +24,9 @@ namespace go {
 template<typename T>
 inline std::string GetType(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0)
 {
   return "unknown";
 }
@@ -34,9 +34,9 @@ inline std::string GetType(
 template<>
 inline std::string GetType<int>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<int>>::type*,
-    const typename boost::disable_if<data::HasSerialize<int>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<int>>::type*)
+    const typename std::enable_if<!util::IsStdVector<int>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<int>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<int>::value>::type*)
 {
   return "Int";
 }
@@ -44,9 +44,9 @@ inline std::string GetType<int>(
 template<>
 inline std::string GetType<float>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<float>>::type*,
-    const typename boost::disable_if<data::HasSerialize<float>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<float>>::type*)
+    const typename std::enable_if<!util::IsStdVector<float>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<float>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<float>::value>::type*)
 {
   return "Float";
 }
@@ -54,9 +54,9 @@ inline std::string GetType<float>(
 template<>
 inline std::string GetType<double>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<double>>::type*,
-    const typename boost::disable_if<data::HasSerialize<double>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<double>>::type*)
+    const typename std::enable_if<!util::IsStdVector<double>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<double>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<double>::value>::type*)
 {
   return "Double";
 }
@@ -64,9 +64,9 @@ inline std::string GetType<double>(
 template<>
 inline std::string GetType<std::string>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<std::string>>::type*,
-    const typename boost::disable_if<data::HasSerialize<std::string>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<std::string>>::type*)
+    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
 {
   return "String";
 }
@@ -74,9 +74,9 @@ inline std::string GetType<std::string>(
 template<>
 inline std::string GetType<bool>(
     util::ParamData& /* d */,
-    const typename boost::disable_if<util::IsStdVector<bool>>::type*,
-    const typename boost::disable_if<data::HasSerialize<bool>>::type*,
-    const typename boost::disable_if<arma::is_arma_type<bool>>::type*)
+    const typename std::enable_if<!util::IsStdVector<bool>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<bool>::value>::type*,
+    const typename std::enable_if<!arma::is_arma_type<bool>::value>::type*)
 {
   return "Bool";
 }
@@ -84,7 +84,7 @@ inline std::string GetType<bool>(
 template<typename T>
 inline std::string GetType(
     util::ParamData& d,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0)
 {
   return "Vec" + GetType<typename T::value_type>(d);
 }
@@ -92,7 +92,7 @@ inline std::string GetType(
 template<typename T>
 inline std::string GetType(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   std::string type = "";
   if (std::is_same<typename T::elem_type, double>::value)
@@ -120,8 +120,8 @@ inline std::string GetType(
 template<typename T>
 inline std::string GetType(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   return d.cppType + "*";
 }
diff --git a/src/mlpack/bindings/go/print_defn_input.hpp b/src/mlpack/bindings/go/print_defn_input.hpp
index 5ae6662a97..c51dfa110b 100644
--- a/src/mlpack/bindings/go/print_defn_input.hpp
+++ b/src/mlpack/bindings/go/print_defn_input.hpp
@@ -28,10 +28,10 @@ namespace go {
 template<typename T>
 void PrintDefnInput(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   if (d.required)
   {
@@ -46,7 +46,7 @@ void PrintDefnInput(
 template<typename T>
 void PrintDefnInput(
     util::ParamData& d,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // param_name *mat.Dense
   if (d.required)
@@ -62,8 +62,8 @@ void PrintDefnInput(
 template<typename T>
 void PrintDefnInput(
     util::ParamData& d,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // param_name *DataWithInfo
   if (d.required)
@@ -79,8 +79,8 @@ void PrintDefnInput(
 template<typename T>
 void PrintDefnInput(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Get the type names we need to use.
   std::string goStrippedType, strippedType, printedType, defaultsType;
diff --git a/src/mlpack/bindings/go/print_defn_output.hpp b/src/mlpack/bindings/go/print_defn_output.hpp
index fa70517f10..b18233d1d0 100644
--- a/src/mlpack/bindings/go/print_defn_output.hpp
+++ b/src/mlpack/bindings/go/print_defn_output.hpp
@@ -27,10 +27,10 @@ namespace go {
 template<typename T>
 void PrintDefnOutput(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   std::cout << GetGoType<T>(d);
 }
@@ -41,7 +41,7 @@ void PrintDefnOutput(
 template<typename T>
 void PrintDefnOutput(
     util::ParamData& d,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // *mat.Dense
   std::cout << "*" << GetGoType<T>(d);
@@ -53,8 +53,8 @@ void PrintDefnOutput(
 template<typename T>
 void PrintDefnOutput(
     util::ParamData& d,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // *mat.Dense
   std::cout << "*" << GetGoType<T>(d);
@@ -66,8 +66,8 @@ void PrintDefnOutput(
 template<typename T>
 void PrintDefnOutput(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Get the type names we need to use.
   std::string goStrippedType, strippedType, printedType, defaultsType;
diff --git a/src/mlpack/bindings/go/print_input_processing.hpp b/src/mlpack/bindings/go/print_input_processing.hpp
index 99b0f9a9ed..8b6fa77eea 100644
--- a/src/mlpack/bindings/go/print_input_processing.hpp
+++ b/src/mlpack/bindings/go/print_input_processing.hpp
@@ -29,10 +29,10 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -129,7 +129,7 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -189,8 +189,8 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -250,8 +250,8 @@ template<typename T>
 void PrintInputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // First, get the correct classparamName if needed.
   std::string goStrippedType, strippedType, printedType, defaultsType;
diff --git a/src/mlpack/bindings/go/print_method_config.hpp b/src/mlpack/bindings/go/print_method_config.hpp
index b4be743d1a..6a51d7205d 100644
--- a/src/mlpack/bindings/go/print_method_config.hpp
+++ b/src/mlpack/bindings/go/print_method_config.hpp
@@ -29,10 +29,10 @@ template<typename T>
 void PrintMethodConfig(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -64,7 +64,7 @@ template<typename T>
 void PrintMethodConfig(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -96,8 +96,8 @@ template<typename T>
 void PrintMethodConfig(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -129,8 +129,8 @@ template<typename T>
 void PrintMethodConfig(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
diff --git a/src/mlpack/bindings/go/print_method_init.hpp b/src/mlpack/bindings/go/print_method_init.hpp
index f1877f48ff..1a9a363c48 100644
--- a/src/mlpack/bindings/go/print_method_init.hpp
+++ b/src/mlpack/bindings/go/print_method_init.hpp
@@ -29,10 +29,10 @@ template<typename T>
 void PrintMethodInit(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -86,7 +86,7 @@ template<typename T>
 void PrintMethodInit(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -118,8 +118,8 @@ template<typename T>
 void PrintMethodInit(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -151,8 +151,8 @@ template<typename T>
 void PrintMethodInit(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
diff --git a/src/mlpack/bindings/go/print_output_processing.hpp b/src/mlpack/bindings/go/print_output_processing.hpp
index 5a5c77fa74..46cec80184 100644
--- a/src/mlpack/bindings/go/print_output_processing.hpp
+++ b/src/mlpack/bindings/go/print_output_processing.hpp
@@ -29,10 +29,10 @@ template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -56,7 +56,7 @@ template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0,
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
@@ -83,8 +83,8 @@ template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   const std::string prefix(indent, ' ');
 
@@ -109,8 +109,8 @@ template<typename T>
 void PrintOutputProcessing(
     util::ParamData& d,
     const size_t indent,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Get the type names we need to use.
   std::string goStrippedType, strippedType, printedType, defaultsType;
diff --git a/src/mlpack/bindings/go/print_type_doc.hpp b/src/mlpack/bindings/go/print_type_doc.hpp
index 2da51fbb67..b5dde90fa0 100644
--- a/src/mlpack/bindings/go/print_type_doc.hpp
+++ b/src/mlpack/bindings/go/print_type_doc.hpp
@@ -25,11 +25,11 @@ namespace go {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a vector.
@@ -62,8 +62,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print the command-line type of an option into a string.
diff --git a/src/mlpack/bindings/go/print_type_doc_impl.hpp b/src/mlpack/bindings/go/print_type_doc_impl.hpp
index d0a5fef659..568ef9b059 100644
--- a/src/mlpack/bindings/go/print_type_doc_impl.hpp
+++ b/src/mlpack/bindings/go/print_type_doc_impl.hpp
@@ -24,11 +24,11 @@ namespace go {
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::disable_if<util::IsStdVector<T>>::type*,
-    const typename boost::disable_if<data::HasSerialize<T>>::type*,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type*,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   // A flag type.
   if (std::is_same<T, bool>::value)
@@ -122,8 +122,8 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& /* data */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type*,
-    const typename boost::enable_if<data::HasSerialize<T>>::type*)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type*,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type*)
 {
   return "An mlpack model pointer.  This type holds a pointer to C++ memory "
       "containing the mlpack model.  Note that this means the mlpack model "

From 3c7d044227c5bf455853c33fd9ecb3bca988206e Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sun, 27 Jun 2021 17:22:17 +0200
Subject: [PATCH 013/118] Fix value::value, even if it has passed the tests

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/print_type_doc.hpp      | 6 +++---
 src/mlpack/bindings/R/print_type_doc_impl.hpp | 6 +++---
 src/mlpack/bindings/cli/set_param.hpp         | 2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/mlpack/bindings/R/print_type_doc.hpp b/src/mlpack/bindings/R/print_type_doc.hpp
index 92bbd07dbe..5f0253578b 100644
--- a/src/mlpack/bindings/R/print_type_doc.hpp
+++ b/src/mlpack/bindings/R/print_type_doc.hpp
@@ -37,7 +37,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<util::IsStdVector<T>::value::value>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a matrix option.
@@ -45,7 +45,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<arma::is_arma_type<T>::value::value>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a matrix tuple option.
@@ -54,7 +54,7 @@ template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
     const typename std::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>::value::value>::type* = 0);
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Return a string representing the command-line type of a model.
diff --git a/src/mlpack/bindings/R/print_type_doc_impl.hpp b/src/mlpack/bindings/R/print_type_doc_impl.hpp
index 79c03bed48..bc8ba85f25 100644
--- a/src/mlpack/bindings/R/print_type_doc_impl.hpp
+++ b/src/mlpack/bindings/R/print_type_doc_impl.hpp
@@ -64,7 +64,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<util::IsStdVector<T>::value::value>::type*)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type*)
 {
   if (std::is_same<T, std::vector<int>>::value)
   {
@@ -86,7 +86,7 @@ std::string PrintTypeDoc(
 template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& data,
-    const typename std::enable_if<arma::is_arma_type<T>::value::value>::type*)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type*)
 {
   if (std::is_same<typename T::elem_type, double>::value)
   {
@@ -129,7 +129,7 @@ template<typename T>
 std::string PrintTypeDoc(
     util::ParamData& /* data */,
     const typename std::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>::value::value>::type*)
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "A 2-d array containing `numeric` data.  Like the regular 2-d matrices"
       ", this can be a `matrix`, or a `data.frame`. However, this type can also"
diff --git a/src/mlpack/bindings/cli/set_param.hpp b/src/mlpack/bindings/cli/set_param.hpp
index f76f058d2e..8fab3e0fb0 100644
--- a/src/mlpack/bindings/cli/set_param.hpp
+++ b/src/mlpack/bindings/cli/set_param.hpp
@@ -60,7 +60,7 @@ void SetParam(
     const boost::any& value,
     const typename std::enable_if<arma::is_arma_type<T>::value ||
                                   std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>::value::value>::type* = 0)
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // We're setting the string filename.
   typedef std::tuple<T, typename ParameterType<T>::type> TupleType;

From dc28faa8b44397653976e8e384f5bf3d99562040 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sun, 27 Jun 2021 17:25:23 +0200
Subject: [PATCH 014/118] Finish with tests

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 .../tests/delete_allocated_memory.hpp         | 10 ++++-----
 .../bindings/tests/get_allocated_memory.hpp   | 10 ++++-----
 .../bindings/tests/get_printable_param.hpp    | 22 +++++++++----------
 .../tests/get_printable_param_impl.hpp        | 22 +++++++++----------
 4 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/src/mlpack/bindings/tests/delete_allocated_memory.hpp b/src/mlpack/bindings/tests/delete_allocated_memory.hpp
index 08f59d74e8..5dd60fa294 100644
--- a/src/mlpack/bindings/tests/delete_allocated_memory.hpp
+++ b/src/mlpack/bindings/tests/delete_allocated_memory.hpp
@@ -21,8 +21,8 @@ namespace tests {
 template<typename T>
 void DeleteAllocatedMemoryImpl(
     util::ParamData& /* d */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0)
 {
   // Do nothing.
 }
@@ -30,7 +30,7 @@ void DeleteAllocatedMemoryImpl(
 template<typename T>
 void DeleteAllocatedMemoryImpl(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   // Do nothing.
 }
@@ -38,8 +38,8 @@ void DeleteAllocatedMemoryImpl(
 template<typename T>
 void DeleteAllocatedMemoryImpl(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Delete the allocated memory (hopefully we actually own it).
   delete *boost::any_cast<T*>(&d.value);
diff --git a/src/mlpack/bindings/tests/get_allocated_memory.hpp b/src/mlpack/bindings/tests/get_allocated_memory.hpp
index 579dbfdc8e..fb4a903b80 100644
--- a/src/mlpack/bindings/tests/get_allocated_memory.hpp
+++ b/src/mlpack/bindings/tests/get_allocated_memory.hpp
@@ -22,8 +22,8 @@ namespace tests {
 template<typename T>
 void* GetAllocatedMemory(
     util::ParamData& /* d */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0)
 {
   return NULL;
 }
@@ -31,7 +31,7 @@ void* GetAllocatedMemory(
 template<typename T>
 void* GetAllocatedMemory(
     util::ParamData& /* d */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0)
 {
   return NULL;
 }
@@ -39,8 +39,8 @@ void* GetAllocatedMemory(
 template<typename T>
 void* GetAllocatedMemory(
     util::ParamData& d,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0)
 {
   // Here we have a model; return its memory location.
   return *boost::any_cast<T*>(&d.value);
diff --git a/src/mlpack/bindings/tests/get_printable_param.hpp b/src/mlpack/bindings/tests/get_printable_param.hpp
index 556c28bd2f..0bf5e2ff24 100644
--- a/src/mlpack/bindings/tests/get_printable_param.hpp
+++ b/src/mlpack/bindings/tests/get_printable_param.hpp
@@ -27,11 +27,11 @@ namespace tests {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Print a vector option, with spaces between it.
@@ -39,7 +39,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* = 0);
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* = 0);
 
 /**
  * Print a matrix option (this just prints the filename).
@@ -47,7 +47,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* = 0);
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* = 0);
 
 /**
  * Print a serializable class option (this just prints the filename).
@@ -55,8 +55,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* = 0);
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* = 0);
 
 /**
  * Print a mapped matrix option (this just prints the filename).
@@ -64,8 +64,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* = 0);
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
 /**
  * Print an option into a std::string.  This should print a short, one-line
diff --git a/src/mlpack/bindings/tests/get_printable_param_impl.hpp b/src/mlpack/bindings/tests/get_printable_param_impl.hpp
index c14d8a4bed..3a6c604b69 100644
--- a/src/mlpack/bindings/tests/get_printable_param_impl.hpp
+++ b/src/mlpack/bindings/tests/get_printable_param_impl.hpp
@@ -22,11 +22,11 @@ namespace tests {
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::disable_if<util::IsStdVector<T>>::type* /* junk */,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* /* junk */,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   std::ostringstream oss;
   oss << boost::any_cast<T>(data.value);
@@ -37,7 +37,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::enable_if<util::IsStdVector<T>>::type* /* junk */)
+    const typename std::enable_if<util::IsStdVector<T>::value>::type* /* junk */)
 {
   const T& t = boost::any_cast<T>(data.value);
 
@@ -51,7 +51,7 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& /* data */,
-    const typename boost::enable_if<arma::is_arma_type<T>>::type* /* junk */)
+    const typename std::enable_if<arma::is_arma_type<T>::value>::type* /* junk */)
 {
   return "matrix type";
 }
@@ -60,8 +60,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& data,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* /* junk */,
-    const typename boost::enable_if<data::HasSerialize<T>>::type* /* junk */)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
+    const typename std::enable_if<data::HasSerialize<T>::value>::type* /* junk */)
 {
   // Extract the string from the tuple that's being held.
   std::ostringstream oss;
@@ -73,8 +73,8 @@ std::string GetPrintableParam(
 template<typename T>
 std::string GetPrintableParam(
     util::ParamData& /* data */,
-    const typename boost::enable_if<std::is_same<T,
-        std::tuple<data::DatasetInfo, arma::mat>>>::type* /* junk */)
+    const typename std::enable_if<std::is_same<T,
+        std::tuple<data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
   return "matrix/DatatsetInfo tuple";
 }

From 2e0b7980e5acc94f3fb8da3715e5c9b573dedf5a Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Sun, 27 Jun 2021 17:31:25 +0200
Subject: [PATCH 015/118] Forgetten one..

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/cli/in_place_copy.hpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/mlpack/bindings/cli/in_place_copy.hpp b/src/mlpack/bindings/cli/in_place_copy.hpp
index d3ed1c9521..f266267bb6 100644
--- a/src/mlpack/bindings/cli/in_place_copy.hpp
+++ b/src/mlpack/bindings/cli/in_place_copy.hpp
@@ -31,10 +31,10 @@ template<typename T>
 void InPlaceCopyInternal(
     util::ParamData& /* d */,
     util::ParamData& /* input */,
-    const typename boost::disable_if<arma::is_arma_type<T>>::type* = 0,
-    const typename boost::disable_if<data::HasSerialize<T>>::type* = 0,
-    const typename boost::disable_if<std::is_same<T,
-        std::tuple<mlpack::data::DatasetInfo, arma::mat>>>::type* = 0)
+    const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
+    const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0)
 {
   // Nothing to do.
 }

From e6ead307efd91c8784af7b187f4e696fb7926d7e Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Tue, 29 Jun 2021 12:03:01 +0200
Subject: [PATCH 016/118] Let us test the asterix solution *

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 CMake/Autodownload.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMake/Autodownload.cmake b/CMake/Autodownload.cmake
index 9d93704801..8302e4733c 100644
--- a/CMake/Autodownload.cmake
+++ b/CMake/Autodownload.cmake
@@ -50,7 +50,7 @@ macro(get_deps LINK DEPS_NAME PACKAGE)
       install(DIRECTORY "${Boost_INCLUDE_DIR}/boost" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     else()
       set(GENERIC_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/include")
-      install(DIRECTORY "${GENERIC_INCLUDE_DIR}" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
+      install(DIRECTORY "${GENERIC_INCLUDE_DIR}/*" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     endif()
   else ()
     message(FATAL_ERROR

From 4fea37fc2ae43563dd1611df0f2d3df5a7582c99 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Tue, 29 Jun 2021 12:27:25 +0200
Subject: [PATCH 017/118] No need for Asterix

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 CMake/Autodownload.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMake/Autodownload.cmake b/CMake/Autodownload.cmake
index 8302e4733c..4864e5004c 100644
--- a/CMake/Autodownload.cmake
+++ b/CMake/Autodownload.cmake
@@ -50,7 +50,7 @@ macro(get_deps LINK DEPS_NAME PACKAGE)
       install(DIRECTORY "${Boost_INCLUDE_DIR}/boost" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     else()
       set(GENERIC_INCLUDE_DIR "${CMAKE_BINARY_DIR}/deps/${DEPENDENCY_DIR}/include")
-      install(DIRECTORY "${GENERIC_INCLUDE_DIR}/*" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
+      install(DIRECTORY "${GENERIC_INCLUDE_DIR}/" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}")
     endif()
   else ()
     message(FATAL_ERROR

From 68bb344cc13be667f4813efe0a1ecafb2d6ad298 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Thu, 1 Jul 2021 23:32:42 +0200
Subject: [PATCH 018/118] Fix some of them manually, but great solution

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/get_printable_type.hpp      | 12 ++++++++----
 src/mlpack/bindings/R/get_printable_type_impl.hpp | 14 +++++++++-----
 src/mlpack/bindings/R/get_r_type.hpp              | 12 ++++++++----
 3 files changed, 25 insertions(+), 13 deletions(-)

diff --git a/src/mlpack/bindings/R/get_printable_type.hpp b/src/mlpack/bindings/R/get_printable_type.hpp
index 4b19c21cdf..6ca4932fe1 100644
--- a/src/mlpack/bindings/R/get_printable_type.hpp
+++ b/src/mlpack/bindings/R/get_printable_type.hpp
@@ -50,10 +50,14 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
-    const typename std::enable_if<!std::is_same<std::string,
+    const typename std::enable_if<
+        !util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
 template<>
diff --git a/src/mlpack/bindings/R/get_printable_type_impl.hpp b/src/mlpack/bindings/R/get_printable_type_impl.hpp
index a163a615f5..88aa270199 100644
--- a/src/mlpack/bindings/R/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/R/get_printable_type_impl.hpp
@@ -58,11 +58,15 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
-    const typename std::enable_if<!std::is_same<std::string,
-        std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
+    const typename std::enable_if<
+        !util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !std::is_same<std::string,
+         std::tuple<data::DatasetInfo,arma::mat>>::value>::type*)
 {
   return "character";
 }
diff --git a/src/mlpack/bindings/R/get_r_type.hpp b/src/mlpack/bindings/R/get_r_type.hpp
index d01559ea66..3e92bb717d 100644
--- a/src/mlpack/bindings/R/get_r_type.hpp
+++ b/src/mlpack/bindings/R/get_r_type.hpp
@@ -83,10 +83,14 @@ inline std::string GetRType<double>(
 template<>
 inline std::string GetRType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
-    const typename std::enable_if<!std::is_same<std::string,
+    const typename std::enable_if<
+        !util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
   return "character";

From 3f0a40fa107780dda5b4a68ce8f5e9f320677cda Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Thu, 1 Jul 2021 23:47:50 +0200
Subject: [PATCH 019/118] Do the first line with regexp

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/get_type.hpp                     | 3 ++-
 src/mlpack/bindings/go/get_go_type.hpp                 | 3 ++-
 src/mlpack/bindings/go/get_printable_type.hpp          | 3 ++-
 src/mlpack/bindings/go/get_printable_type_impl.hpp     | 3 ++-
 src/mlpack/bindings/go/get_type.hpp                    | 3 ++-
 src/mlpack/bindings/python/get_cython_type.hpp         | 3 ++-
 src/mlpack/bindings/python/get_printable_type.hpp      | 3 ++-
 src/mlpack/bindings/python/get_printable_type_impl.hpp | 3 ++-
 8 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/mlpack/bindings/R/get_type.hpp b/src/mlpack/bindings/R/get_type.hpp
index 574ec6674c..81d57b99c5 100644
--- a/src/mlpack/bindings/R/get_type.hpp
+++ b/src/mlpack/bindings/R/get_type.hpp
@@ -72,7 +72,8 @@ inline std::string GetType<double>(
 template<>
 inline std::string GetType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
diff --git a/src/mlpack/bindings/go/get_go_type.hpp b/src/mlpack/bindings/go/get_go_type.hpp
index 55bafb2ddb..9dbaf67bb7 100644
--- a/src/mlpack/bindings/go/get_go_type.hpp
+++ b/src/mlpack/bindings/go/get_go_type.hpp
@@ -73,7 +73,8 @@ inline std::string GetGoType<double>(
 template<>
 inline std::string GetGoType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
diff --git a/src/mlpack/bindings/go/get_printable_type.hpp b/src/mlpack/bindings/go/get_printable_type.hpp
index b16533da18..4d4bb18ff7 100644
--- a/src/mlpack/bindings/go/get_printable_type.hpp
+++ b/src/mlpack/bindings/go/get_printable_type.hpp
@@ -50,7 +50,8 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
diff --git a/src/mlpack/bindings/go/get_printable_type_impl.hpp b/src/mlpack/bindings/go/get_printable_type_impl.hpp
index 921f5f53a1..229c14cf8b 100644
--- a/src/mlpack/bindings/go/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/go/get_printable_type_impl.hpp
@@ -59,7 +59,8 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
diff --git a/src/mlpack/bindings/go/get_type.hpp b/src/mlpack/bindings/go/get_type.hpp
index 0a1417ccab..1d86f84879 100644
--- a/src/mlpack/bindings/go/get_type.hpp
+++ b/src/mlpack/bindings/go/get_type.hpp
@@ -64,7 +64,8 @@ inline std::string GetType<double>(
 template<>
 inline std::string GetType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
 {
diff --git a/src/mlpack/bindings/python/get_cython_type.hpp b/src/mlpack/bindings/python/get_cython_type.hpp
index 705b785758..ab3d09e633 100644
--- a/src/mlpack/bindings/python/get_cython_type.hpp
+++ b/src/mlpack/bindings/python/get_cython_type.hpp
@@ -53,7 +53,8 @@ inline std::string GetCythonType<double>(
 template<>
 inline std::string GetCythonType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
 {
diff --git a/src/mlpack/bindings/python/get_printable_type.hpp b/src/mlpack/bindings/python/get_printable_type.hpp
index 41593e4681..f904fd09c6 100644
--- a/src/mlpack/bindings/python/get_printable_type.hpp
+++ b/src/mlpack/bindings/python/get_printable_type.hpp
@@ -50,7 +50,8 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
diff --git a/src/mlpack/bindings/python/get_printable_type_impl.hpp b/src/mlpack/bindings/python/get_printable_type_impl.hpp
index 9494065b4b..4f364e2d3c 100644
--- a/src/mlpack/bindings/python/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/python/get_printable_type_impl.hpp
@@ -58,7 +58,8 @@ inline std::string GetPrintableType<double>(
 template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
-    const typename std::enable_if<!util::IsStdVector<std::string>::value>::type*,
+    const typename std::enable_if<
+        !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,

From 433d27bcb1b89af8fb4d65305970fcae5461d227 Mon Sep 17 00:00:00 2001
From: Aakash kaushik <kaushikaakash7539@gmail.com>
Date: Fri, 2 Jul 2021 09:32:02 +0530
Subject: [PATCH 020/118] Move Sequential layers to LayerTypes (#3004)

* Move layers to LayerTypes

* remove extra line
---
 src/mlpack/methods/ann/layer/layer_types.hpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/mlpack/methods/ann/layer/layer_types.hpp b/src/mlpack/methods/ann/layer/layer_types.hpp
index d72cd1fa33..e3c97f0d08 100644
--- a/src/mlpack/methods/ann/layer/layer_types.hpp
+++ b/src/mlpack/methods/ann/layer/layer_types.hpp
@@ -222,6 +222,7 @@ template <typename InputDataType,
 class AdaptiveMeanPooling;
 
 using MoreTypes = boost::variant<
+        FlexibleReLU<arma::mat, arma::mat>*,
         Linear3D<arma::mat, arma::mat, NoRegularizer>*,
         LpPooling<arma::mat, arma::mat>*,
         PixelShuffle<arma::mat, arma::mat>*,
@@ -234,8 +235,7 @@ using MoreTypes = boost::variant<
         ReinforceNormal<arma::mat, arma::mat>*,
         Reparametrization<arma::mat, arma::mat>*,
         Select<arma::mat, arma::mat>*,
-        Sequential<arma::mat, arma::mat, false>*,
-        Sequential<arma::mat, arma::mat, true>*,
+        SpatialDropout<arma::mat, arma::mat>*,
         Subview<arma::mat, arma::mat>*,
         VRClassReward<arma::mat, arma::mat>*,
         VirtualBatchNorm<arma::mat, arma::mat>*,
@@ -277,7 +277,6 @@ using LayerTypes = boost::variant<
     Dropout<arma::mat, arma::mat>*,
     ELU<arma::mat, arma::mat>*,
     FastLSTM<arma::mat, arma::mat>*,
-    FlexibleReLU<arma::mat, arma::mat>*,
     GRU<arma::mat, arma::mat>*,
     HardTanH<arma::mat, arma::mat>*,
     Join<arma::mat, arma::mat>*,
@@ -297,8 +296,9 @@ using LayerTypes = boost::variant<
     NoisyLinear<arma::mat, arma::mat>*,
     Padding<arma::mat, arma::mat>*,
     PReLU<arma::mat, arma::mat>*,
+    Sequential<arma::mat, arma::mat, false>*,
+    Sequential<arma::mat, arma::mat, true>*,
     Softmax<arma::mat, arma::mat>*,
-    SpatialDropout<arma::mat, arma::mat>*,
     TransposedConvolution<NaiveConvolution<ValidConvolution>,
             NaiveConvolution<ValidConvolution>,
             NaiveConvolution<ValidConvolution>, arma::mat, arma::mat>*,

From e664bb022b1a95f490fa11b63b2cf91aa0f3eece Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 14:49:10 +0200
Subject: [PATCH 021/118] Remove this boost pointer vector header.

Why it is here in the first place?

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/methods/ann/layer/concat.hpp              | 2 --
 src/mlpack/methods/ann/layer/concat_performance.hpp  | 2 --
 src/mlpack/methods/ann/layer/highway.hpp             | 2 --
 src/mlpack/methods/ann/layer/recurrent_attention.hpp | 1 -
 src/mlpack/methods/ann/layer/sequential.hpp          | 2 --
 5 files changed, 9 deletions(-)

diff --git a/src/mlpack/methods/ann/layer/concat.hpp b/src/mlpack/methods/ann/layer/concat.hpp
index 13d29458cb..e693234f3e 100644
--- a/src/mlpack/methods/ann/layer/concat.hpp
+++ b/src/mlpack/methods/ann/layer/concat.hpp
@@ -19,8 +19,6 @@
 #include "../visitor/delta_visitor.hpp"
 #include "../visitor/output_parameter_visitor.hpp"
 
-#include <boost/ptr_container/ptr_vector.hpp>
-
 #include "layer_types.hpp"
 
 namespace mlpack {
diff --git a/src/mlpack/methods/ann/layer/concat_performance.hpp b/src/mlpack/methods/ann/layer/concat_performance.hpp
index b576430479..b7ddbe1625 100644
--- a/src/mlpack/methods/ann/layer/concat_performance.hpp
+++ b/src/mlpack/methods/ann/layer/concat_performance.hpp
@@ -14,8 +14,6 @@
 
 #include <mlpack/prereqs.hpp>
 
-#include <boost/ptr_container/ptr_vector.hpp>
-
 #include "layer_types.hpp"
 
 namespace mlpack {
diff --git a/src/mlpack/methods/ann/layer/highway.hpp b/src/mlpack/methods/ann/layer/highway.hpp
index aa539a4972..526a434d23 100644
--- a/src/mlpack/methods/ann/layer/highway.hpp
+++ b/src/mlpack/methods/ann/layer/highway.hpp
@@ -15,8 +15,6 @@
 
 #include <mlpack/prereqs.hpp>
 
-#include <boost/ptr_container/ptr_vector.hpp>
-
 #include "../visitor/delete_visitor.hpp"
 #include "../visitor/delta_visitor.hpp"
 #include "../visitor/output_height_visitor.hpp"
diff --git a/src/mlpack/methods/ann/layer/recurrent_attention.hpp b/src/mlpack/methods/ann/layer/recurrent_attention.hpp
index b8b4a55a89..63838dc479 100644
--- a/src/mlpack/methods/ann/layer/recurrent_attention.hpp
+++ b/src/mlpack/methods/ann/layer/recurrent_attention.hpp
@@ -13,7 +13,6 @@
 #define MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_HPP
 
 #include <mlpack/prereqs.hpp>
-#include <boost/ptr_container/ptr_vector.hpp>
 
 #include "../visitor/delta_visitor.hpp"
 #include "../visitor/output_parameter_visitor.hpp"
diff --git a/src/mlpack/methods/ann/layer/sequential.hpp b/src/mlpack/methods/ann/layer/sequential.hpp
index ebf3c6fd6a..bd1857b1ba 100644
--- a/src/mlpack/methods/ann/layer/sequential.hpp
+++ b/src/mlpack/methods/ann/layer/sequential.hpp
@@ -15,8 +15,6 @@
 
 #include <mlpack/prereqs.hpp>
 
-#include <boost/ptr_container/ptr_vector.hpp>
-
 #include "../visitor/delete_visitor.hpp"
 #include "../visitor/copy_visitor.hpp"
 #include "../visitor/delta_visitor.hpp"

From 71bc9f6dad373e53c076f7cbd2d515ef2a9c8394 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 14:58:46 +0200
Subject: [PATCH 022/118] This one is dangerous..

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/tests/lrsdp_test.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/mlpack/tests/lrsdp_test.cpp b/src/mlpack/tests/lrsdp_test.cpp
index b2ac089997..0a2b79eb01 100644
--- a/src/mlpack/tests/lrsdp_test.cpp
+++ b/src/mlpack/tests/lrsdp_test.cpp
@@ -12,7 +12,6 @@
 #include <mlpack/core.hpp>
 #include <mlpack/core/optimizers/sdp/lrsdp.hpp>
 
-#include <boost/test/unit_test.hpp>
 #include "test_tools.hpp"
 
 using namespace mlpack;

From 32806792738d55845491188f2fb666da0c836f50 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 15:45:30 +0200
Subject: [PATCH 023/118] Remove another headers

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/methods/dbscan/dbscan.hpp         | 1 -
 src/mlpack/methods/mean_shift/mean_shift.hpp | 1 -
 2 files changed, 2 deletions(-)

diff --git a/src/mlpack/methods/dbscan/dbscan.hpp b/src/mlpack/methods/dbscan/dbscan.hpp
index 73c887027e..6658b7a99d 100644
--- a/src/mlpack/methods/dbscan/dbscan.hpp
+++ b/src/mlpack/methods/dbscan/dbscan.hpp
@@ -18,7 +18,6 @@
 #include <mlpack/methods/emst/union_find.hpp>
 #include "random_point_selection.hpp"
 #include "ordered_point_selection.hpp"
-#include <boost/dynamic_bitset.hpp>
 
 namespace mlpack {
 namespace dbscan {
diff --git a/src/mlpack/methods/mean_shift/mean_shift.hpp b/src/mlpack/methods/mean_shift/mean_shift.hpp
index 7f382e4434..dab1f45c25 100644
--- a/src/mlpack/methods/mean_shift/mean_shift.hpp
+++ b/src/mlpack/methods/mean_shift/mean_shift.hpp
@@ -17,7 +17,6 @@
 #include <mlpack/core/kernels/gaussian_kernel.hpp>
 #include <mlpack/core/kernels/kernel_traits.hpp>
 #include <mlpack/core/metrics/lmetric.hpp>
-#include <boost/utility.hpp>
 
 namespace mlpack {
 namespace meanshift /** Mean shift clustering. */ {

From af6254d49b4b3b889b6e315797a3179afdc7c43f Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 16:00:37 +0200
Subject: [PATCH 024/118] Remove another boost version header

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/tests/test_catch_tools.hpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/mlpack/tests/test_catch_tools.hpp b/src/mlpack/tests/test_catch_tools.hpp
index 879cac8b49..7e2a0b4afe 100644
--- a/src/mlpack/tests/test_catch_tools.hpp
+++ b/src/mlpack/tests/test_catch_tools.hpp
@@ -13,7 +13,6 @@
 #define MLPACK_TESTS_TEST_CATCH_TOOLS_HPP
 
 #include <mlpack/core.hpp>
-#include <boost/version.hpp>
 
 #include "catch.hpp"
 

From 5cbf8f81d7c7a6d1307e957e51398b7745c404e8 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 16:58:01 +0200
Subject: [PATCH 025/118] Fix std::is_same<T, std::string> style issue

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/default_param.hpp           | 3 ++-
 src/mlpack/bindings/R/default_param_impl.hpp      | 3 ++-
 src/mlpack/bindings/cli/default_param.hpp         | 3 ++-
 src/mlpack/bindings/cli/default_param_impl.hpp    | 3 ++-
 src/mlpack/bindings/go/default_param.hpp          | 3 ++-
 src/mlpack/bindings/go/default_param_impl.hpp     | 3 ++-
 src/mlpack/bindings/julia/default_param.hpp       | 3 ++-
 src/mlpack/bindings/julia/default_param_impl.hpp  | 3 ++-
 src/mlpack/bindings/python/default_param.hpp      | 3 ++-
 src/mlpack/bindings/python/default_param_impl.hpp | 3 ++-
 10 files changed, 20 insertions(+), 10 deletions(-)

diff --git a/src/mlpack/bindings/R/default_param.hpp b/src/mlpack/bindings/R/default_param.hpp
index 8fdf41dfbf..651d976b9b 100644
--- a/src/mlpack/bindings/R/default_param.hpp
+++ b/src/mlpack/bindings/R/default_param.hpp
@@ -29,7 +29,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/R/default_param_impl.hpp b/src/mlpack/bindings/R/default_param_impl.hpp
index 751834baca..8cf4110696 100644
--- a/src/mlpack/bindings/R/default_param_impl.hpp
+++ b/src/mlpack/bindings/R/default_param_impl.hpp
@@ -27,7 +27,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/cli/default_param.hpp b/src/mlpack/bindings/cli/default_param.hpp
index 093a03c567..9b5edc9229 100644
--- a/src/mlpack/bindings/cli/default_param.hpp
+++ b/src/mlpack/bindings/cli/default_param.hpp
@@ -29,7 +29,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/cli/default_param_impl.hpp b/src/mlpack/bindings/cli/default_param_impl.hpp
index b002e94611..33a9524e3f 100644
--- a/src/mlpack/bindings/cli/default_param_impl.hpp
+++ b/src/mlpack/bindings/cli/default_param_impl.hpp
@@ -27,7 +27,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/go/default_param.hpp b/src/mlpack/bindings/go/default_param.hpp
index 4b967b270e..e2efe85d0a 100644
--- a/src/mlpack/bindings/go/default_param.hpp
+++ b/src/mlpack/bindings/go/default_param.hpp
@@ -29,7 +29,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/go/default_param_impl.hpp b/src/mlpack/bindings/go/default_param_impl.hpp
index 4d3c0feca1..f33c6cccb3 100644
--- a/src/mlpack/bindings/go/default_param_impl.hpp
+++ b/src/mlpack/bindings/go/default_param_impl.hpp
@@ -27,7 +27,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/julia/default_param.hpp b/src/mlpack/bindings/julia/default_param.hpp
index fca7d4a488..db714ccc5d 100644
--- a/src/mlpack/bindings/julia/default_param.hpp
+++ b/src/mlpack/bindings/julia/default_param.hpp
@@ -29,7 +29,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/julia/default_param_impl.hpp b/src/mlpack/bindings/julia/default_param_impl.hpp
index f1a71e7cf2..216758df4f 100644
--- a/src/mlpack/bindings/julia/default_param_impl.hpp
+++ b/src/mlpack/bindings/julia/default_param_impl.hpp
@@ -27,7 +27,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/python/default_param.hpp b/src/mlpack/bindings/python/default_param.hpp
index 36a6b19c32..cde42983bb 100644
--- a/src/mlpack/bindings/python/default_param.hpp
+++ b/src/mlpack/bindings/python/default_param.hpp
@@ -29,7 +29,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* = 0,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type* = 0,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/python/default_param_impl.hpp b/src/mlpack/bindings/python/default_param_impl.hpp
index d953c2efb2..0fe60d8ee0 100644
--- a/src/mlpack/bindings/python/default_param_impl.hpp
+++ b/src/mlpack/bindings/python/default_param_impl.hpp
@@ -27,7 +27,8 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!arma::is_arma_type<T>::value>::type* /* junk */,
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
-    const typename std::enable_if<!std::is_same<T, std::string>::value>::type*,
+    const typename std::enable_if<!std::is_same<T,
+        !std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {

From 1beac191adbe216a151ef983c9a7d35a71bc15e7 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 17:11:37 +0200
Subject: [PATCH 026/118] Break line on HasSerialize

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/get_type.hpp                     | 3 ++-
 src/mlpack/bindings/go/get_go_type.hpp                 | 3 ++-
 src/mlpack/bindings/go/get_printable_type.hpp          | 3 ++-
 src/mlpack/bindings/go/get_printable_type_impl.hpp     | 3 ++-
 src/mlpack/bindings/go/get_type.hpp                    | 3 ++-
 src/mlpack/bindings/python/get_cython_type.hpp         | 3 ++-
 src/mlpack/bindings/python/get_printable_type.hpp      | 3 ++-
 src/mlpack/bindings/python/get_printable_type_impl.hpp | 3 ++-
 8 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/mlpack/bindings/R/get_type.hpp b/src/mlpack/bindings/R/get_type.hpp
index 81d57b99c5..a39ffc46cb 100644
--- a/src/mlpack/bindings/R/get_type.hpp
+++ b/src/mlpack/bindings/R/get_type.hpp
@@ -74,7 +74,8 @@ inline std::string GetType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
diff --git a/src/mlpack/bindings/go/get_go_type.hpp b/src/mlpack/bindings/go/get_go_type.hpp
index 9dbaf67bb7..9fac01f417 100644
--- a/src/mlpack/bindings/go/get_go_type.hpp
+++ b/src/mlpack/bindings/go/get_go_type.hpp
@@ -75,7 +75,8 @@ inline std::string GetGoType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
diff --git a/src/mlpack/bindings/go/get_printable_type.hpp b/src/mlpack/bindings/go/get_printable_type.hpp
index 4d4bb18ff7..4ec81417ce 100644
--- a/src/mlpack/bindings/go/get_printable_type.hpp
+++ b/src/mlpack/bindings/go/get_printable_type.hpp
@@ -52,7 +52,8 @@ inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
diff --git a/src/mlpack/bindings/go/get_printable_type_impl.hpp b/src/mlpack/bindings/go/get_printable_type_impl.hpp
index 229c14cf8b..33e85f7c06 100644
--- a/src/mlpack/bindings/go/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/go/get_printable_type_impl.hpp
@@ -61,7 +61,8 @@ inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
diff --git a/src/mlpack/bindings/go/get_type.hpp b/src/mlpack/bindings/go/get_type.hpp
index 1d86f84879..5d7f736301 100644
--- a/src/mlpack/bindings/go/get_type.hpp
+++ b/src/mlpack/bindings/go/get_type.hpp
@@ -66,7 +66,8 @@ inline std::string GetType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
 {
   return "String";
diff --git a/src/mlpack/bindings/python/get_cython_type.hpp b/src/mlpack/bindings/python/get_cython_type.hpp
index ab3d09e633..c9a21f1ed0 100644
--- a/src/mlpack/bindings/python/get_cython_type.hpp
+++ b/src/mlpack/bindings/python/get_cython_type.hpp
@@ -55,7 +55,8 @@ inline std::string GetCythonType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
 {
   return "string";
diff --git a/src/mlpack/bindings/python/get_printable_type.hpp b/src/mlpack/bindings/python/get_printable_type.hpp
index f904fd09c6..eccd123b1a 100644
--- a/src/mlpack/bindings/python/get_printable_type.hpp
+++ b/src/mlpack/bindings/python/get_printable_type.hpp
@@ -52,7 +52,8 @@ inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
diff --git a/src/mlpack/bindings/python/get_printable_type_impl.hpp b/src/mlpack/bindings/python/get_printable_type_impl.hpp
index 4f364e2d3c..836aa9fa1d 100644
--- a/src/mlpack/bindings/python/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/python/get_printable_type_impl.hpp
@@ -60,7 +60,8 @@ inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
         !util::isStdVector<std::string>::value>::type*,
-    const typename std::enable_if<!data::HasSerialize<std::string>::value>::type*,
+    const typename std::enable_if<
+        !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)

From 0c7b38f17ebdeb007117db330bdbb094161bdcaf Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 17:17:05 +0200
Subject: [PATCH 027/118] Fix is_arma_type

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/get_type.hpp                     | 3 ++-
 src/mlpack/bindings/go/get_go_type.hpp                 | 3 ++-
 src/mlpack/bindings/go/get_printable_type.hpp          | 3 ++-
 src/mlpack/bindings/go/get_printable_type_impl.hpp     | 3 ++-
 src/mlpack/bindings/go/get_type.hpp                    | 3 ++-
 src/mlpack/bindings/python/get_cython_type.hpp         | 3 ++-
 src/mlpack/bindings/python/get_printable_type.hpp      | 3 ++-
 src/mlpack/bindings/python/get_printable_type_impl.hpp | 3 ++-
 8 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/mlpack/bindings/R/get_type.hpp b/src/mlpack/bindings/R/get_type.hpp
index a39ffc46cb..b9c6e93c80 100644
--- a/src/mlpack/bindings/R/get_type.hpp
+++ b/src/mlpack/bindings/R/get_type.hpp
@@ -76,7 +76,8 @@ inline std::string GetType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
diff --git a/src/mlpack/bindings/go/get_go_type.hpp b/src/mlpack/bindings/go/get_go_type.hpp
index 9fac01f417..6d00610c47 100644
--- a/src/mlpack/bindings/go/get_go_type.hpp
+++ b/src/mlpack/bindings/go/get_go_type.hpp
@@ -77,7 +77,8 @@ inline std::string GetGoType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
diff --git a/src/mlpack/bindings/go/get_printable_type.hpp b/src/mlpack/bindings/go/get_printable_type.hpp
index 4ec81417ce..0a9593b8cd 100644
--- a/src/mlpack/bindings/go/get_printable_type.hpp
+++ b/src/mlpack/bindings/go/get_printable_type.hpp
@@ -54,7 +54,8 @@ inline std::string GetPrintableType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
diff --git a/src/mlpack/bindings/go/get_printable_type_impl.hpp b/src/mlpack/bindings/go/get_printable_type_impl.hpp
index 33e85f7c06..3ef255dd8a 100644
--- a/src/mlpack/bindings/go/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/go/get_printable_type_impl.hpp
@@ -63,7 +63,8 @@ inline std::string GetPrintableType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {
diff --git a/src/mlpack/bindings/go/get_type.hpp b/src/mlpack/bindings/go/get_type.hpp
index 5d7f736301..d6b6d6af88 100644
--- a/src/mlpack/bindings/go/get_type.hpp
+++ b/src/mlpack/bindings/go/get_type.hpp
@@ -68,7 +68,8 @@ inline std::string GetType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*)
 {
   return "String";
 }
diff --git a/src/mlpack/bindings/python/get_cython_type.hpp b/src/mlpack/bindings/python/get_cython_type.hpp
index c9a21f1ed0..087b97341e 100644
--- a/src/mlpack/bindings/python/get_cython_type.hpp
+++ b/src/mlpack/bindings/python/get_cython_type.hpp
@@ -57,7 +57,8 @@ inline std::string GetCythonType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*)
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*)
 {
   return "string";
 }
diff --git a/src/mlpack/bindings/python/get_printable_type.hpp b/src/mlpack/bindings/python/get_printable_type.hpp
index eccd123b1a..12e8783235 100644
--- a/src/mlpack/bindings/python/get_printable_type.hpp
+++ b/src/mlpack/bindings/python/get_printable_type.hpp
@@ -54,7 +54,8 @@ inline std::string GetPrintableType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*);
 
diff --git a/src/mlpack/bindings/python/get_printable_type_impl.hpp b/src/mlpack/bindings/python/get_printable_type_impl.hpp
index 836aa9fa1d..d6517c8139 100644
--- a/src/mlpack/bindings/python/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/python/get_printable_type_impl.hpp
@@ -62,7 +62,8 @@ inline std::string GetPrintableType<std::string>(
         !util::isStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
-    const typename std::enable_if<!arma::is_arma_type<std::string>::value>::type*,
+    const typename std::enable_if<
+        !arma::is_arma_type<std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<std::string,
         std::tuple<data::DatasetInfo, arma::mat>>::value>::type*)
 {

From a27700b0724b7ff5dc991e878533ee345b4f827a Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 17:31:05 +0200
Subject: [PATCH 028/118] Fix regexp error

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/default_param.hpp           | 2 +-
 src/mlpack/bindings/R/default_param_impl.hpp      | 2 +-
 src/mlpack/bindings/cli/default_param.hpp         | 2 +-
 src/mlpack/bindings/cli/default_param_impl.hpp    | 2 +-
 src/mlpack/bindings/go/default_param.hpp          | 2 +-
 src/mlpack/bindings/go/default_param_impl.hpp     | 2 +-
 src/mlpack/bindings/julia/default_param.hpp       | 2 +-
 src/mlpack/bindings/julia/default_param_impl.hpp  | 2 +-
 src/mlpack/bindings/python/default_param.hpp      | 2 +-
 src/mlpack/bindings/python/default_param_impl.hpp | 2 +-
 10 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/mlpack/bindings/R/default_param.hpp b/src/mlpack/bindings/R/default_param.hpp
index 651d976b9b..2006e2eb23 100644
--- a/src/mlpack/bindings/R/default_param.hpp
+++ b/src/mlpack/bindings/R/default_param.hpp
@@ -30,7 +30,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type* = 0,
+        std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/R/default_param_impl.hpp b/src/mlpack/bindings/R/default_param_impl.hpp
index 8cf4110696..5447d8738a 100644
--- a/src/mlpack/bindings/R/default_param_impl.hpp
+++ b/src/mlpack/bindings/R/default_param_impl.hpp
@@ -28,7 +28,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type*,
+        std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/cli/default_param.hpp b/src/mlpack/bindings/cli/default_param.hpp
index 9b5edc9229..9be4b4c73d 100644
--- a/src/mlpack/bindings/cli/default_param.hpp
+++ b/src/mlpack/bindings/cli/default_param.hpp
@@ -30,7 +30,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type* = 0,
+        std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/cli/default_param_impl.hpp b/src/mlpack/bindings/cli/default_param_impl.hpp
index 33a9524e3f..b9defc6374 100644
--- a/src/mlpack/bindings/cli/default_param_impl.hpp
+++ b/src/mlpack/bindings/cli/default_param_impl.hpp
@@ -28,7 +28,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type*,
+        std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/go/default_param.hpp b/src/mlpack/bindings/go/default_param.hpp
index e2efe85d0a..3ebdaae7b3 100644
--- a/src/mlpack/bindings/go/default_param.hpp
+++ b/src/mlpack/bindings/go/default_param.hpp
@@ -30,7 +30,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type* = 0,
+        std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/go/default_param_impl.hpp b/src/mlpack/bindings/go/default_param_impl.hpp
index f33c6cccb3..2458012966 100644
--- a/src/mlpack/bindings/go/default_param_impl.hpp
+++ b/src/mlpack/bindings/go/default_param_impl.hpp
@@ -28,7 +28,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type*,
+        std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/julia/default_param.hpp b/src/mlpack/bindings/julia/default_param.hpp
index db714ccc5d..0d2b368381 100644
--- a/src/mlpack/bindings/julia/default_param.hpp
+++ b/src/mlpack/bindings/julia/default_param.hpp
@@ -30,7 +30,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type* = 0,
+        std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/julia/default_param_impl.hpp b/src/mlpack/bindings/julia/default_param_impl.hpp
index 216758df4f..667ecae5f4 100644
--- a/src/mlpack/bindings/julia/default_param_impl.hpp
+++ b/src/mlpack/bindings/julia/default_param_impl.hpp
@@ -28,7 +28,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type*,
+        std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {
diff --git a/src/mlpack/bindings/python/default_param.hpp b/src/mlpack/bindings/python/default_param.hpp
index cde42983bb..5f8bc32ea4 100644
--- a/src/mlpack/bindings/python/default_param.hpp
+++ b/src/mlpack/bindings/python/default_param.hpp
@@ -30,7 +30,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* = 0,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type* = 0,
+        std::string>::value>::type* = 0,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* = 0);
 
diff --git a/src/mlpack/bindings/python/default_param_impl.hpp b/src/mlpack/bindings/python/default_param_impl.hpp
index 0fe60d8ee0..543253c18b 100644
--- a/src/mlpack/bindings/python/default_param_impl.hpp
+++ b/src/mlpack/bindings/python/default_param_impl.hpp
@@ -28,7 +28,7 @@ std::string DefaultParamImpl(
     const typename std::enable_if<!util::IsStdVector<T>::value>::type* /* junk */,
     const typename std::enable_if<!data::HasSerialize<T>::value>::type* /* junk */,
     const typename std::enable_if<!std::is_same<T,
-        !std::string>::value>::type*,
+        std::string>::value>::type*,
     const typename std::enable_if<!std::is_same<T,
         std::tuple<mlpack::data::DatasetInfo, arma::mat>>::value>::type* /* junk */)
 {

From bbb8b4b640aaa7ed37f3afd1e7e3981edf111d32 Mon Sep 17 00:00:00 2001
From: Omar Shrit <omar@shrit.me>
Date: Fri, 2 Jul 2021 17:42:34 +0200
Subject: [PATCH 029/118] Fix IsStdVector

Signed-off-by: Omar Shrit <omar@shrit.me>
---
 src/mlpack/bindings/R/get_type.hpp                     | 2 +-
 src/mlpack/bindings/go/get_go_type.hpp                 | 2 +-
 src/mlpack/bindings/go/get_printable_type.hpp          | 2 +-
 src/mlpack/bindings/go/get_printable_type_impl.hpp     | 2 +-
 src/mlpack/bindings/go/get_type.hpp                    | 2 +-
 src/mlpack/bindings/python/get_cython_type.hpp         | 2 +-
 src/mlpack/bindings/python/get_printable_type.hpp      | 2 +-
 src/mlpack/bindings/python/get_printable_type_impl.hpp | 2 +-
 8 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/mlpack/bindings/R/get_type.hpp b/src/mlpack/bindings/R/get_type.hpp
index b9c6e93c80..55264eedb5 100644
--- a/src/mlpack/bindings/R/get_type.hpp
+++ b/src/mlpack/bindings/R/get_type.hpp
@@ -73,7 +73,7 @@ template<>
 inline std::string GetType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<
diff --git a/src/mlpack/bindings/go/get_go_type.hpp b/src/mlpack/bindings/go/get_go_type.hpp
index 6d00610c47..4fc800ecbf 100644
--- a/src/mlpack/bindings/go/get_go_type.hpp
+++ b/src/mlpack/bindings/go/get_go_type.hpp
@@ -74,7 +74,7 @@ template<>
 inline std::string GetGoType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<
diff --git a/src/mlpack/bindings/go/get_printable_type.hpp b/src/mlpack/bindings/go/get_printable_type.hpp
index 0a9593b8cd..5b2cadece3 100644
--- a/src/mlpack/bindings/go/get_printable_type.hpp
+++ b/src/mlpack/bindings/go/get_printable_type.hpp
@@ -51,7 +51,7 @@ template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<
diff --git a/src/mlpack/bindings/go/get_printable_type_impl.hpp b/src/mlpack/bindings/go/get_printable_type_impl.hpp
index 3ef255dd8a..cdbfe9feb5 100644
--- a/src/mlpack/bindings/go/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/go/get_printable_type_impl.hpp
@@ -60,7 +60,7 @@ template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<
diff --git a/src/mlpack/bindings/go/get_type.hpp b/src/mlpack/bindings/go/get_type.hpp
index d6b6d6af88..828085d375 100644
--- a/src/mlpack/bindings/go/get_type.hpp
+++ b/src/mlpack/bindings/go/get_type.hpp
@@ -65,7 +65,7 @@ template<>
 inline std::string GetType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<
diff --git a/src/mlpack/bindings/python/get_cython_type.hpp b/src/mlpack/bindings/python/get_cython_type.hpp
index 087b97341e..9809e1224e 100644
--- a/src/mlpack/bindings/python/get_cython_type.hpp
+++ b/src/mlpack/bindings/python/get_cython_type.hpp
@@ -54,7 +54,7 @@ template<>
 inline std::string GetCythonType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<
diff --git a/src/mlpack/bindings/python/get_printable_type.hpp b/src/mlpack/bindings/python/get_printable_type.hpp
index 12e8783235..1d454b172a 100644
--- a/src/mlpack/bindings/python/get_printable_type.hpp
+++ b/src/mlpack/bindings/python/get_printable_type.hpp
@@ -51,7 +51,7 @@ template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<
diff --git a/src/mlpack/bindings/python/get_printable_type_impl.hpp b/src/mlpack/bindings/python/get_printable_type_impl.hpp
index d6517c8139..5a903af575 100644
--- a/src/mlpack/bindings/python/get_printable_type_impl.hpp
+++ b/src/mlpack/bindings/python/get_printable_type_impl.hpp
@@ -59,7 +59,7 @@ template<>
 inline std::string GetPrintableType<std::string>(
     util::ParamData& /* d */,
     const typename std::enable_if<
-        !util::isStdVector<std::string>::value>::type*,
+        !util::IsStdVector<std::string>::value>::type*,
     const typename std::enable_if<
         !data::HasSerialize<std::string>::value>::type*,
     const typename std::enable_if<

From 42071e92be63221da73a620de5beac0a5c537a9c Mon Sep 17 00:00:00 2001
From: Aakash kaushik <kaushikaakash7539@gmail.com>
Date: Mon, 12 Jul 2021 09:36:46 +0530
Subject: [PATCH 030/118] Addition of ReLU6 (#3009)

* relu6 files added

* add into layer's cmake

* added tests

* applied suggestions

* comment about torch values in tests
---
 src/mlpack/methods/ann/layer/CMakeLists.txt   |   2 +
 src/mlpack/methods/ann/layer/layer.hpp        |   1 +
 src/mlpack/methods/ann/layer/layer_types.hpp  |   3 +
 src/mlpack/methods/ann/layer/relu6.hpp        | 103 ++++++++++++++++++
 src/mlpack/methods/ann/layer/relu6_impl.hpp   |  77 +++++++++++++
 .../tests/activation_functions_test.cpp       |  47 ++++++++
 6 files changed, 233 insertions(+)
 create mode 100644 src/mlpack/methods/ann/layer/relu6.hpp
 create mode 100644 src/mlpack/methods/ann/layer/relu6_impl.hpp

diff --git a/src/mlpack/methods/ann/layer/CMakeLists.txt b/src/mlpack/methods/ann/layer/CMakeLists.txt
index d1eb91ff55..e5d21d215a 100644
--- a/src/mlpack/methods/ann/layer/CMakeLists.txt
+++ b/src/mlpack/methods/ann/layer/CMakeLists.txt
@@ -99,6 +99,8 @@ set(SOURCES
   recurrent_attention_impl.hpp
   reinforce_normal.hpp
   reinforce_normal_impl.hpp
+  relu6.hpp
+  relu6_impl.hpp
   reparametrization.hpp
   reparametrization_impl.hpp
   radial_basis_function.hpp
diff --git a/src/mlpack/methods/ann/layer/layer.hpp b/src/mlpack/methods/ann/layer/layer.hpp
index b2f598b985..abc246f7f4 100644
--- a/src/mlpack/methods/ann/layer/layer.hpp
+++ b/src/mlpack/methods/ann/layer/layer.hpp
@@ -64,6 +64,7 @@
 #include "recurrent_attention.hpp"
 #include "recurrent.hpp"
 #include "reinforce_normal.hpp"
+#include "relu6.hpp"
 #include "reparametrization.hpp"
 #include "select.hpp"
 #include "sequential.hpp"
diff --git a/src/mlpack/methods/ann/layer/layer_types.hpp b/src/mlpack/methods/ann/layer/layer_types.hpp
index e3c97f0d08..4a5a202e67 100644
--- a/src/mlpack/methods/ann/layer/layer_types.hpp
+++ b/src/mlpack/methods/ann/layer/layer_types.hpp
@@ -47,6 +47,7 @@
 #include <mlpack/methods/ann/layer/pixel_shuffle.hpp>
 #include <mlpack/methods/ann/layer/positional_encoding.hpp>
 #include <mlpack/methods/ann/layer/reinforce_normal.hpp>
+#include <mlpack/methods/ann/layer/relu6.hpp>
 #include <mlpack/methods/ann/layer/reparametrization.hpp>
 #include <mlpack/methods/ann/layer/select.hpp>
 #include <mlpack/methods/ann/layer/softmax.hpp>
@@ -83,6 +84,7 @@ template<typename InputDataType, typename OutputDataType> class FastLSTM;
 template<typename InputDataType, typename OutputDataType> class VRClassReward;
 template<typename InputDataType, typename OutputDataType> class Concatenate;
 template<typename InputDataType, typename OutputDataType> class Padding;
+template<typename InputDataType, typename OutputDataType> class ReLU6;
 
 template<typename InputDataType,
          typename OutputDataType,
@@ -233,6 +235,7 @@ using MoreTypes = boost::variant<
         Recurrent<arma::mat, arma::mat>*,
         RecurrentAttention<arma::mat, arma::mat>*,
         ReinforceNormal<arma::mat, arma::mat>*,
+        ReLU6<arma::mat, arma::mat>*,
         Reparametrization<arma::mat, arma::mat>*,
         Select<arma::mat, arma::mat>*,
         SpatialDropout<arma::mat, arma::mat>*,
diff --git a/src/mlpack/methods/ann/layer/relu6.hpp b/src/mlpack/methods/ann/layer/relu6.hpp
new file mode 100644
index 0000000000..1a9c0a2ff7
--- /dev/null
+++ b/src/mlpack/methods/ann/layer/relu6.hpp
@@ -0,0 +1,103 @@
+/**
+ * @file methods/ann/layer/relu6.hpp
+ * @author Aakash kaushik
+ *
+ * For more information, kindly refer to the following paper.
+ *
+ * @code
+ * @article{Andrew G2017,
+ *  author = {Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko,
+ *      Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam},
+ *  title = {MobileNets: Efficient Convolutional Neural Networks for Mobile
+ *      Vision Applications},
+ *  year = {2017},
+ *  url = {https://arxiv.org/pdf/1704.04861}
+ * }
+ * @endcode
+ * 
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_METHODS_ANN_LAYER_RELU6_HPP
+#define MLPACK_METHODS_ANN_LAYER_RELU6_HPP
+
+#include <mlpack/prereqs.hpp>
+
+namespace mlpack {
+namespace ann /** Artificial Neural Network. */ {
+
+/**
+ * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
+ *         arma::sp_mat or arma::cube).
+ * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
+ *         arma::sp_mat or arma::cube).
+ */
+template<typename InputDataType = arma::mat,
+         typename OutputDataType = arma::mat>
+class ReLU6
+{
+ public:
+
+ /**
+  * Create the ReLU6 object.
+  */
+  ReLU6();
+
+  /**
+   * Ordinary feed forward pass of a neural network, evaluating the function
+   * f(x) by propagating the activity forward through f.
+   *
+   * @param input Input data used for evaluating the specified function.
+   * @param output Resulting output activation.
+   */
+  template<typename InputType, typename OutputType>
+  void Forward(const InputType& input, OutputType& output);
+
+  /**
+   * Ordinary feed backward pass of a neural network, calculating the function
+   * f(x) by propagating x backwards through f. Using the results from the feed
+   * forward pass.
+   *
+   * @param input The propagated input activation.
+   * @param gy The backpropagated error.
+   * @param g The calculated gradient.
+   */
+  template<typename DataType>
+  void Backward(const DataType& input, const DataType& gy, DataType& g);
+
+  //! Get the output parameter.
+  OutputDataType const& OutputParameter() const { return outputParameter; }
+  //! Modify the output parameter.
+  OutputDataType& OutputParameter() { return outputParameter; }
+
+  //! Get the delta.
+  OutputDataType const& Delta() const { return delta; }
+  //! Modify the delta.
+  OutputDataType& Delta() { return delta; }
+
+  //! Get size of weights.
+  size_t WeightSize() const { return 0; }
+
+  /**
+   * Serialize the layer.
+   */
+  template<typename Archive>
+  void serialize(Archive& ar, const uint32_t /* version */);
+
+ private:
+  //! Locally-stored output parameter object.
+  OutputDataType outputParameter;
+
+  //! Locally-stored delta object.
+  OutputDataType delta;
+}; // class ReLU6
+
+} // namespace ann
+} // namespace mlpack
+
+// Include implementation.
+#include "relu6_impl.hpp"
+
+#endif
diff --git a/src/mlpack/methods/ann/layer/relu6_impl.hpp b/src/mlpack/methods/ann/layer/relu6_impl.hpp
new file mode 100644
index 0000000000..d65dfd8998
--- /dev/null
+++ b/src/mlpack/methods/ann/layer/relu6_impl.hpp
@@ -0,0 +1,77 @@
+/**
+ * @file methods/ann/layer/relu6_impl.hpp
+ * @author Aakash kaushik
+ *
+ * For more information, kindly refer to the following paper.
+ *
+ * @code
+ * @article{Andrew G2017,
+ *  author = {Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko,
+ *      Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam},
+ *  title = {MobileNets: Efficient Convolutional Neural Networks for Mobile
+ *      Vision Applications},
+ *  year = {2017},
+ *  url = {https://arxiv.org/pdf/1704.04861}
+ * }
+ * @endcode
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_METHODS_ANN_LAYER_RELU6_IMPL_HPP
+#define MLPACK_METHODS_ANN_LAYER_RELU6_IMPL_HPP
+
+// In case it hasn't yet been included.
+#include "relu6.hpp"
+
+namespace mlpack {
+namespace ann /** Artificial Neural Network. */ {
+
+template<typename InputDataType, typename OutputDataType>
+ReLU6<InputDataType, OutputDataType>::ReLU6()
+{
+  // Nothing to do here.
+}
+
+template<typename InputDataType, typename OutputDataType>
+template<typename InputType, typename OutputType>
+void ReLU6<InputDataType, OutputDataType>::Forward(
+    const InputType& input, OutputType& output)
+{ 
+  OutputType outputTemp(arma::size(input));
+  outputTemp.fill(6.0);
+  output = arma::zeros<OutputType>(arma::size(input));
+  output = arma::min(arma::max(output, input), outputTemp);
+}
+
+template<typename InputDataType, typename OutputDataType>
+template<typename DataType>
+void ReLU6<InputDataType, OutputDataType>::Backward(
+    const DataType& input, const DataType& gy, DataType& g)
+{
+  DataType derivative(arma::size(gy));
+  derivative.fill(0.0);
+  for (size_t i = 0; i < input.n_elem; ++i)
+  {
+    if (input(i) < 6 && input(i) > 0)
+      derivative(i) = 1.0;
+  }
+
+  g = gy % derivative;
+}
+
+template<typename InputDataType, typename OutputDataType>
+template<typename Archive>
+void ReLU6<InputDataType, OutputDataType>::serialize(
+    Archive& ar,
+    const uint32_t /* version */)
+{
+  // Nothing to do here.
+}
+
+} // namespace ann
+} // namespace mlpack
+
+#endif
diff --git a/src/mlpack/tests/activation_functions_test.cpp b/src/mlpack/tests/activation_functions_test.cpp
index b60855d9b4..1c4458c4f9 100644
--- a/src/mlpack/tests/activation_functions_test.cpp
+++ b/src/mlpack/tests/activation_functions_test.cpp
@@ -704,6 +704,53 @@ void CheckFlattenTSwishDerivateCorrect(const arma::colvec input,
   }
 }
 
+/**
+ * Implementation of the ReLU6 activation function derivative test. The function
+ * is implemented as ReLU6 layer in the file relu6.hpp.
+ *
+ * @param input Input data used for evaluating the ReLU6 activation function.
+ * @param target Target data used to evaluate the ReLU6 activation.
+ */
+void CheckReLU6Correct(const arma::colvec input,
+                       const arma::colvec ActivationTarget,
+                       const arma::colvec DerivativeTarget)
+{
+  // Initialize ReLU6 object.
+  ReLU6<> relu6;
+
+  // Test the calculation of the derivatives using the entire vector as input.
+  arma::colvec derivatives, activations;
+
+  // This error vector will be set to 1 to get the derivatives.
+  arma::colvec error = arma::ones<arma::colvec>(input.n_elem);
+  relu6.Forward(input, activations);
+  for (size_t i = 0; i < activations.n_elem; ++i)
+  {
+    REQUIRE(activations.at(i) == Approx(ActivationTarget.at(i)).epsilon(1e-5));
+  }
+  relu6.Backward(activations, error, derivatives);
+  for (size_t i = 0; i < derivatives.n_elem; ++i)
+  {
+    REQUIRE(derivatives.at(i) == Approx(DerivativeTarget.at(i)).epsilon(1e-5));
+  }
+}
+
+/**
+ * Basic test of the ReLU6 function.
+ */
+TEST_CASE("ReLU6FunctionTest", "[ActivationFunctionsTest]")
+{
+  const arma::colvec activationData("-2.0 3.0 0.0 6.0 24.0");
+
+  // desiredActivations taken from PyTorch.
+  const arma::colvec desiredActivations("0.0 3.0 0.0 6.0 6.0");
+
+  // desiredDerivatives taken from PyTorch.
+  const arma::colvec desiredDerivatives("0.0 1.0 0.0 0.0 0.0");
+
+  CheckReLU6Correct(activationData, desiredActivations, desiredDerivatives);
+}
+
 /**
  * Basic test of the tanh function.
  */

From 80a094e5453ada62170f3c5683c29e23b2923948 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 6 Apr 2021 00:22:14 +0530
Subject: [PATCH 031/118] Added mad gain implementation and tests

---
 src/mlpack/methods/decision_tree/mad_gain.hpp | 187 ++++++++++++++++++
 src/mlpack/tests/decision_tree_test.cpp       |  48 +++++
 2 files changed, 235 insertions(+)
 create mode 100644 src/mlpack/methods/decision_tree/mad_gain.hpp

diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
new file mode 100644
index 0000000000..065de37dcb
--- /dev/null
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -0,0 +1,187 @@
+/**
+ * @file methods/decision_tree/mse_gain.hpp
+ * @author Rishabh Garg
+ *
+ * The mean absolute deviation gain class, a fitness funtion for regression
+ * based decision trees.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more informatio
+n.
+ */
+#ifndef MLPACK_METHODS_DECISION_TREE_MAD_GAIN_HPP
+#define MLPACK_METHODS_DECISION_TREE_MAD_GAIN_HPP
+
+#include <mlpack/prereqs.hpp>
+
+namespace mlpack {
+namespace tree {
+
+/**
+ * The MAD (Mean absolute deviation) gain, is a measure of set purity based on
+ * the deviation of dependent values present in the node. This is same thing as
+ * negation of deviation of dependent variable from the mean in the node as we
+ * will try to maximize this quantity to maximize gain (and thus reduce
+ * absolute deviation of a set).
+*/
+class MADGain
+{
+ public:
+  /**
+   * Evaluate the mean absolute deviation gain from begin to end index. Note
+   * that gain can be slightly greater than 0 due to floating-point
+   * representation issues. Thus if you are checking for perfect fit, be sure
+   * to use 'gain >= 0.0'. Not 'gain == 0.0'. The labels should always be of
+   * type arma::Row<double> or arma::rowvec.
+   *
+   * @param labels Set of labels to evaluate MAD gain on.
+   * @param weights Weight of labels.
+   * @param begin Start index.
+   * @param end End index.
+   */
+  template<bool UseWeights, typename WeightVecType>
+  static double Evaluate(const arma::rowvec& labels,
+                         const WeightVecType& weights,
+                         const size_t begin,
+                         const size_t end)
+  {
+    double mad = 0.0;
+
+    if (UseWeights)
+    {
+      double accWeights[4] = { 0.0, 0.0, 0.0, 0.0 };
+      double weightedMean[4] = { 0.0, 0.0, 0.0, 0.0 };
+
+      // SIMD loop: sums four elements simultaneously (if the compiler manages
+      // to vectorize the loop).
+      for (size_t i = begin + 3; i < end; i += 4)
+      {
+        const double weight1 = weights[i - 3];
+        const double weight2 = weights[i - 2];
+        const double weight3 = weights[i - 1];
+        const double weight4 = weights[i];
+
+        weightedMean[0] += weight1 * labels[i - 3];
+        weightedMean[1] += weight2 * labels[i - 2];
+        weightedMean[2] += weight3 * labels[i - 1];
+        weightedMean[3] += weight4 * labels[i];
+
+        accWeights[0] += weight1;
+        accWeights[1] += weight2;
+        accWeights[2] += weight3;
+        accWeights[3] += weight4;
+      }
+
+      // Handle leftovers.
+      if ((end - begin) % 4 == 1)
+      {
+        const double weight1 = weights[end - 1];
+        weightedMean[0] += weight1 * labels[end - 1];
+        accWeights[0] += weight1;
+      }
+      else if ((end - begin) % 4 == 2)
+      {
+        const double weight1 = weights[end - 2];
+        const double weight2 = weights[end - 1];
+
+        weightedMean[0] += weight1 * labels[end - 2];
+        weightedMean[1] += weight2 * labels[end - 1];
+
+        accWeights[0] += weight1;
+        accWeights[1] += weight2;
+      }
+      else if ((end - begin) % 4 == 3)
+      {
+        const double weight1 = weights[end - 3];
+        const double weight2 = weights[end - 2];
+        const double weight3 = weights[end - 1];
+
+        weightedMean[0] += weight1 * labels[end - 3];
+        weightedMean[1] += weight2 * labels[end - 2];
+        weightedMean[2] += weight1 * labels[end - 1];
+
+        accWeights[0] += weight1;
+        accWeights[1] += weight2;
+        accWeights[2] += weight3;
+      }
+
+      accWeights[0] += accWeights[1] + accWeights[2] + accWeights[3];
+      weightedMean[0] += weightedMean[1] + weightedMean[2] + weightedMean[3];
+
+      // Catch edge case: if there are no weights, the impurity is zero.
+      if (accWeights[0] == 0.0)
+        return 0.0;
+
+      for (size_t i = begin; i < end; ++i)
+      {
+        const double f = weights[i] * (std::abs(labels[i] - weightedMean[0]));
+        mad += f / accWeights[0];
+      }
+    }
+    else
+    {
+      double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
+
+      // SIMD loop: add counts for four elements simultaneously (if the compiler
+      // manages to vectorize the loop).
+      for (size_t i = begin + 3; i < end; i += 4)
+      {
+        mean[0] += labels[i - 3];
+        mean[1] += labels[i - 2];
+        mean[2] += labels[i - 1];
+        mean[3] += labels[i];
+      }
+
+      // Handle leftovers.
+      if (labels.n_elem % 4 == 1)
+      {
+        mean[0] += labels[end - 1];
+      }
+      else if (labels.n_elem % 4 == 2)
+      {
+        mean[0] += labels[end - 2];
+        mean[1] += labels[end - 1];
+      }
+      else if (labels.n_elem % 4 == 3)
+      {
+        mean[0] += labels[end - 3];
+        mean[1] += labels[end - 2];
+        mean[2] += labels[end - 1];
+      }
+
+      mean[0] += mean[1] + mean[2] + mean[3];
+
+      for (size_t i = begin; i < end; ++i)
+        mad += std::abs(labels[i] - mean[0]);
+
+      mad /= (double) (end - begin);
+    }
+
+    return -mad;
+  }
+
+  /**
+   * Evaluate the MAD gain on the complete vector.
+   *
+   * @param labels Set of labels to evaluate MAD gain on.
+   * @param weights Weights associated to each label.
+   */
+  template<bool UseWeights, typename WeightVecType>
+  static double Evaluate(const arma::rowvec& labels,
+                         const WeightVecType& weights)
+  {
+    // Corner case: if there are no elements, the impurity is zero.
+    if (labels.n_elem == 0)
+      return 0.0;
+
+    return Evaluate<UseWeights>(labels, weights, 0, labels.n_elem);
+  }
+
+};
+
+} // namespace tree
+} // namespace mlpack
+
+#endif
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index d0bc624ca3..d14bc5fc3a 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -24,6 +24,54 @@ using namespace mlpack;
 using namespace mlpack::tree;
 using namespace mlpack::distribution;
 
+/**
+ * Make sure the MSE gain is zero when the labels are perfect.
+ */
+TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights(10, arma::fill::ones);
+  arma::rowvec labels;
+  labels.ones(10);
+
+  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+}
+
+/**
+ * Make sure that for a normal distribution of labels,
+ * MAD_gain = mean of absolute values of the distribution.
+ */
+TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
+{
+  arma::rowvec weights(10, arma::fill::ones);
+  arma::rowvec labels(10, arma::fill::randn); // Mean = 0.
+
+  // Theoretical gain.
+  const double theoreticalGain = 0.0;
+  for (size_t i = 0; i < labels.n_elem; ++i)
+    theoreticalGain += std::abs(labels[i]);
+  theoreticalGain /= (double) labels.n_elem;
+
+  // Calculated gain.
+  const double calculatedGain = MADGain::Evaluate<false>(labels, weights);
+
+  REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-5));
+}
+
+/**
+ * The MAD gain of an empty vector is 0.
+ */
+TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights = arma::ones<arma::rowvec>(10);
+  arma::rowvec predictors;
+  REQUIRE(MADGain::Evaluate<false>(predictors, weights) ==
+          Approx(0.0).margin(1e-5));
+
+  REQUIRE(MADGain::Evaluate<true>(predictors, weights) ==
+          Approx(0.0).margin(1e-5));
+}
+
 /**
  * Make sure the Gini gain is zero when the labels are perfect.
  */

From 8d3a7d7799db1ed040e668f91c17be73aabaab7d Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 6 Apr 2021 00:51:45 +0530
Subject: [PATCH 032/118] Added forgotten import and fixed test

---
 src/mlpack/tests/decision_tree_test.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index d14bc5fc3a..8aaa7194e2 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -13,6 +13,7 @@
 #include <mlpack/methods/decision_tree/decision_tree.hpp>
 #include <mlpack/methods/decision_tree/information_gain.hpp>
 #include <mlpack/methods/decision_tree/gini_gain.hpp>
+#include <mlpack/methods/decision_tree/mad_gain.hpp>
 #include <mlpack/methods/decision_tree/random_dimension_select.hpp>
 #include <mlpack/methods/decision_tree/multiple_random_dimension_select.hpp>
 
@@ -47,9 +48,9 @@ TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
   arma::rowvec labels(10, arma::fill::randn); // Mean = 0.
 
   // Theoretical gain.
-  const double theoreticalGain = 0.0;
+  double theoreticalGain = 0.0;
   for (size_t i = 0; i < labels.n_elem; ++i)
-    theoreticalGain += std::abs(labels[i]);
+    theoreticalGain -= std::abs(labels[i]);
   theoreticalGain /= (double) labels.n_elem;
 
   // Calculated gain.

From 0c265ccffe2b56b4a39330144fcc0cbd6926d8be Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 6 Apr 2021 11:09:41 +0530
Subject: [PATCH 033/118] Fixed implementation bug and normal distribution test

---
 src/mlpack/methods/decision_tree/mad_gain.hpp | 4 ++++
 src/mlpack/tests/decision_tree_test.cpp       | 6 +++---
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index 065de37dcb..1f3e460a77 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -109,6 +109,8 @@ class MADGain
 
       accWeights[0] += accWeights[1] + accWeights[2] + accWeights[3];
       weightedMean[0] += weightedMean[1] + weightedMean[2] + weightedMean[3];
+      weightedMean[0] /= (double) (end - begin);
+      std::cout << "WeightedMean: " << weightedMean[0] << std::endl;
 
       // Catch edge case: if there are no weights, the impurity is zero.
       if (accWeights[0] == 0.0)
@@ -152,6 +154,8 @@ class MADGain
       }
 
       mean[0] += mean[1] + mean[2] + mean[3];
+      mean[0] /= (double) (end - begin);
+      std::cout << "Mean: " << mean[0] << std::endl;
 
       for (size_t i = begin; i < end; ++i)
         mad += std::abs(labels[i] - mean[0]);
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 8aaa7194e2..ab8aa094ae 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -39,13 +39,13 @@ TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressionTest]")
 }
 
 /**
- * Make sure that for a normal distribution of labels,
- * MAD_gain = mean of absolute values of the distribution.
+ * Make sure that when mean of labels is zero, MAD_gain = mean of
+ * absolute values of the distribution.
  */
 TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
 {
   arma::rowvec weights(10, arma::fill::ones);
-  arma::rowvec labels(10, arma::fill::randn); // Mean = 0.
+  arma::rowvec labels = { 1, 2, 3, 4, 5, -1, -2, -3, -4, -5 }; // Mean = 0.
 
   // Theoretical gain.
   double theoreticalGain = 0.0;

From d8634ca0836f65cc3040c538874e7e81110337a1 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 8 Apr 2021 19:37:59 +0530
Subject: [PATCH 034/118] Fixed logic error in calculating weighted mean

---
 src/mlpack/methods/decision_tree/mad_gain.hpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index 1f3e460a77..0643e4d01d 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -109,13 +109,13 @@ class MADGain
 
       accWeights[0] += accWeights[1] + accWeights[2] + accWeights[3];
       weightedMean[0] += weightedMean[1] + weightedMean[2] + weightedMean[3];
-      weightedMean[0] /= (double) (end - begin);
-      std::cout << "WeightedMean: " << weightedMean[0] << std::endl;
 
       // Catch edge case: if there are no weights, the impurity is zero.
       if (accWeights[0] == 0.0)
         return 0.0;
 
+      weightedMean[0] /= accWeights[0];
+
       for (size_t i = begin; i < end; ++i)
       {
         const double f = weights[i] * (std::abs(labels[i] - weightedMean[0]));
@@ -155,7 +155,6 @@ class MADGain
 
       mean[0] += mean[1] + mean[2] + mean[3];
       mean[0] /= (double) (end - begin);
-      std::cout << "Mean: " << mean[0] << std::endl;
 
       for (size_t i = begin; i < end; ++i)
         mad += std::abs(labels[i] - mean[0]);

From 24cd615fc6c6b584b70ca7462130768655a47f7f Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 8 Apr 2021 22:08:11 +0530
Subject: [PATCH 035/118] Implemented SIMD sum in utils.hpp

---
 src/mlpack/methods/decision_tree/utils.hpp | 130 +++++++++++++++++++++
 1 file changed, 130 insertions(+)
 create mode 100644 src/mlpack/methods/decision_tree/utils.hpp

diff --git a/src/mlpack/methods/decision_tree/utils.hpp b/src/mlpack/methods/decision_tree/utils.hpp
new file mode 100644
index 0000000000..d7da7cbd16
--- /dev/null
+++ b/src/mlpack/methods/decision_tree/utils.hpp
@@ -0,0 +1,130 @@
+/**
+ * @file methods/decision_tree/utils.hpp
+ * @author Rishabh Garg
+ *
+ * Various utility functions used in decision tree implementation.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_METHODS_DECISION_TREE_UTILS_HPP
+#define MLPACK_METHODS_DECISION_TREE_UTILS_HPP
+
+/**
+ * Calculates the weighted sum and total weight of labels.
+ */
+void WeightedSum(const arma::rowvec& labels,
+                 const arma::rowvec& weights,
+                 const size_t begin,
+                 const size_t end,
+                 double& accWeights,
+                 double& weightedMean)
+{
+  double totalWeights[4] = { 0.0, 0.0, 0.0, 0.0 };
+  double weightedSum[4] = { 0.0, 0.0, 0.0, 0.0 };
+
+  // SIMD loop: sums four elements simultaneously (if the compiler manages
+  // to vectorize the loop).
+  for (size_t i = begin + 3; i < end; i += 4)
+  {
+    const double weight1 = weights[i - 3];
+    const double weight2 = weights[i - 2];
+    const double weight3 = weights[i - 1];
+    const double weight4 = weights[i];
+
+    weightedSum[0] += weight1 * labels[i - 3];
+    weightedSum[1] += weight2 * labels[i - 2];
+    weightedSum[2] += weight3 * labels[i - 1];
+    weightedSum[3] += weight4 * labels[i];
+
+    totalWeights[0] += weight1;
+    totalWeights[1] += weight2;
+    totalWeights[2] += weight3;
+    totalWeights[3] += weight4;
+  }
+
+  // Handle leftovers.
+  if ((end - begin) % 4 == 1)
+  {
+    const double weight1 = weights[end - 1];
+    weightedSum[0] += weight1 * labels[end - 1];
+    totalWeights[0] += weight1;
+  }
+  else if ((end - begin) % 4 == 2)
+  {
+    const double weight1 = weights[end - 2];
+    const double weight2 = weights[end - 1];
+
+    weightedSum[0] += weight1 * labels[end - 2];
+    weightedSum[1] += weight2 * labels[end - 1];
+
+    totalWeights[0] += weight1;
+    totalWeights[1] += weight2;
+  }
+  else if ((end - begin) % 4 == 3)
+  {
+    const double weight1 = weights[end - 3];
+    const double weight2 = weights[end - 2];
+    const double weight3 = weights[end - 1];
+
+    weightedSum[0] += weight1 * labels[end - 3];
+    weightedSum[1] += weight2 * labels[end - 2];
+    weightedSum[2] += weight1 * labels[end - 1];
+
+    totalWeights[0] += weight1;
+    totalWeights[1] += weight2;
+    totalWeights[2] += weight3;
+  }
+
+  totalWeights[0] += totalWeights[1] + totalWeights[2] + totalWeights[3];
+  weightedSum[0] += weightedSum[1] + weightedSum[2] + weightedSum[3];
+
+  accWeights = totalWeights[0];
+  weightedMean = weightedSum[0];
+}
+
+/**
+ * Sums up the labels vector.
+ */
+void Sum(const arma::rowvec& labels,
+        const size_t begin,
+        const size_t end,
+        double& mean)
+{
+  double total[4] = { 0.0, 0.0, 0.0, 0.0 };
+
+  // SIMD loop: add counts for four elements simultaneously (if the compiler
+  // manages to vectorize the loop).
+  for (size_t i = begin + 3; i < end; i += 4)
+  {
+    total[0] += labels[i - 3];
+    total[1] += labels[i - 2];
+    total[2] += labels[i - 1];
+    total[3] += labels[i];
+  }
+
+  // Handle leftovers.
+  if (labels.n_elem % 4 == 1)
+  {
+    total[0] += labels[end - 1];
+  }
+  else if (labels.n_elem % 4 == 2)
+  {
+    total[0] += labels[end - 2];
+    total[1] += labels[end - 1];
+  }
+  else if (labels.n_elem % 4 == 3)
+  {
+    total[0] += labels[end - 3];
+    total[1] += labels[end - 2];
+    total[2] += labels[end - 1];
+  }
+
+  total[0] += total[1] + total[2] + total[3];
+
+  mean = total[0];
+}
+
+#endif

From e47962f18c165e67397765ba66ea6db3f71382bd Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 8 Apr 2021 22:10:18 +0530
Subject: [PATCH 036/118] Refactored mad_gain to use utils.hpp

---
 src/mlpack/methods/decision_tree/mad_gain.hpp | 107 +++---------------
 1 file changed, 13 insertions(+), 94 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index 0643e4d01d..32102a5de3 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -15,6 +15,7 @@ n.
 #define MLPACK_METHODS_DECISION_TREE_MAD_GAIN_HPP
 
 #include <mlpack/prereqs.hpp>
+#include "utils.hpp"
 
 namespace mlpack {
 namespace tree {
@@ -51,113 +52,31 @@ class MADGain
 
     if (UseWeights)
     {
-      double accWeights[4] = { 0.0, 0.0, 0.0, 0.0 };
-      double weightedMean[4] = { 0.0, 0.0, 0.0, 0.0 };
+      double accWeights = 0.0;
+      double weightedMean = 0.0;
 
-      // SIMD loop: sums four elements simultaneously (if the compiler manages
-      // to vectorize the loop).
-      for (size_t i = begin + 3; i < end; i += 4)
-      {
-        const double weight1 = weights[i - 3];
-        const double weight2 = weights[i - 2];
-        const double weight3 = weights[i - 1];
-        const double weight4 = weights[i];
-
-        weightedMean[0] += weight1 * labels[i - 3];
-        weightedMean[1] += weight2 * labels[i - 2];
-        weightedMean[2] += weight3 * labels[i - 1];
-        weightedMean[3] += weight4 * labels[i];
-
-        accWeights[0] += weight1;
-        accWeights[1] += weight2;
-        accWeights[2] += weight3;
-        accWeights[3] += weight4;
-      }
-
-      // Handle leftovers.
-      if ((end - begin) % 4 == 1)
-      {
-        const double weight1 = weights[end - 1];
-        weightedMean[0] += weight1 * labels[end - 1];
-        accWeights[0] += weight1;
-      }
-      else if ((end - begin) % 4 == 2)
-      {
-        const double weight1 = weights[end - 2];
-        const double weight2 = weights[end - 1];
-
-        weightedMean[0] += weight1 * labels[end - 2];
-        weightedMean[1] += weight2 * labels[end - 1];
-
-        accWeights[0] += weight1;
-        accWeights[1] += weight2;
-      }
-      else if ((end - begin) % 4 == 3)
-      {
-        const double weight1 = weights[end - 3];
-        const double weight2 = weights[end - 2];
-        const double weight3 = weights[end - 1];
-
-        weightedMean[0] += weight1 * labels[end - 3];
-        weightedMean[1] += weight2 * labels[end - 2];
-        weightedMean[2] += weight1 * labels[end - 1];
-
-        accWeights[0] += weight1;
-        accWeights[1] += weight2;
-        accWeights[2] += weight3;
-      }
-
-      accWeights[0] += accWeights[1] + accWeights[2] + accWeights[3];
-      weightedMean[0] += weightedMean[1] + weightedMean[2] + weightedMean[3];
+      WeightedSum(labels, weights, begin, end, accWeights, weightedMean);
 
       // Catch edge case: if there are no weights, the impurity is zero.
-      if (accWeights[0] == 0.0)
+      if (accWeights == 0.0)
         return 0.0;
 
-      weightedMean[0] /= accWeights[0];
+      weightedMean /= accWeights;
 
       for (size_t i = begin; i < end; ++i)
       {
-        const double f = weights[i] * (std::abs(labels[i] - weightedMean[0]));
-        mad += f / accWeights[0];
+        mad += weights[i] * (std::abs(labels[i] - weightedMean));
+      }
+      mad /= accWeights;
       }
-    }
     else
     {
-      double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
-
-      // SIMD loop: add counts for four elements simultaneously (if the compiler
-      // manages to vectorize the loop).
-      for (size_t i = begin + 3; i < end; i += 4)
-      {
-        mean[0] += labels[i - 3];
-        mean[1] += labels[i - 2];
-        mean[2] += labels[i - 1];
-        mean[3] += labels[i];
-      }
-
-      // Handle leftovers.
-      if (labels.n_elem % 4 == 1)
-      {
-        mean[0] += labels[end - 1];
-      }
-      else if (labels.n_elem % 4 == 2)
-      {
-        mean[0] += labels[end - 2];
-        mean[1] += labels[end - 1];
-      }
-      else if (labels.n_elem % 4 == 3)
-      {
-        mean[0] += labels[end - 3];
-        mean[1] += labels[end - 2];
-        mean[2] += labels[end - 1];
-      }
-
-      mean[0] += mean[1] + mean[2] + mean[3];
-      mean[0] /= (double) (end - begin);
+      double mean = 0.0;
+      Sum(labels, begin, end, mean);
+      mean /= (double) (end - begin);
 
       for (size_t i = begin; i < end; ++i)
-        mad += std::abs(labels[i] - mean[0]);
+        mad += std::abs(labels[i] - mean);
 
       mad /= (double) (end - begin);
     }

From be9ff3437e684d1f7857a5ba3db498785aa9c0b5 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 8 Apr 2021 22:11:28 +0530
Subject: [PATCH 037/118] Implemented MSE gain

---
 src/mlpack/methods/decision_tree/mse_gain.hpp | 104 ++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 src/mlpack/methods/decision_tree/mse_gain.hpp

diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
new file mode 100644
index 0000000000..7d2b3fa488
--- /dev/null
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -0,0 +1,104 @@
+/**
+ * @file methods/decision_tree/mse_gain.hpp
+ * @author Rishabh Garg
+ *
+ * The mean squared error gain class, which is a fitness funtion for
+ * regression based decision trees.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_METHODS_DECISION_TREE_MSE_GAIN_HPP
+#define MLPACK_METHODS_DECISION_TREE_MSE_GAIN_HPP
+
+#include <mlpack/prereqs.hpp>
+#include "utils.hpp"
+
+namespace mlpack {
+namespace tree {
+
+/**
+ * The MSE (Mean squared error) gain, is a measure of set purity based on the
+ * variance of response values present in the node. This is same thing as
+ * negation of variance of dependent variable in the node as we will try to
+ * maximize this quantity to maximize gain (and thus reduce variance of a set).
+ */
+class MSEGain
+{
+ public:
+  /**
+   * Evaluate the mean squared error gain of labls from begin to end index.
+   * Note that gain can be slightly greater than 0 due to floating-point
+   * representation issues. Thus if you are checking for perfect fit, be
+   * sure to use 'gain >= 0.0' and not 'gain == 0.0'. The labels vector should
+   * always be of type arma::Row<double> or arma::rowvec.
+   *
+   * @param labels Set of labels to evaluate MAD gain on.
+   * @param weights Weight of labels.
+   * @param begin Start index.
+   * @param end End index.
+   */
+  template<bool UseWeights, typename WeightVecType>
+  static double Evaluate(const arma::rowvec& labels,
+                         const WeightVecType& weights,
+                         const size_t begin,
+                         const size_t end)
+  {
+    double mse = 0.0;
+
+    if (UseWeights)
+    {
+      double accWeights = 0.0;
+      double weightedMean = 0.0;
+      WeightedSum(labels, weights, begin, end, accWeights, weightedMean);
+
+      // Catch edge case: if there are no weights, the impurity is zero.
+      if (accWeights == 0.0)
+        return 0.0;
+
+      weightedMean /= accWeights;
+
+      for (size_t i = begin; i < end; ++i)
+        mse += weights[i] * std::pow(labels[i] - weightedMean, 2);
+
+      mse /= accWeights;
+    }
+    else
+    {
+      double mean = 0.0;
+      Sum(labels, begin, end, mean);
+      mean /= (double) (end - begin);
+
+      for (size_t i = begin; i < end; ++i)
+        mse += std::pow(labels[i] - mean, 2);
+
+      mse /= (double) (end - begin);
+    }
+
+    return -mse;
+  }
+
+  /**
+   * Evaluate the MSE gain on the complete vector.
+   *
+   * @param labels Set of labels to evaluate MAD gain on.
+   * @param weights Weights associated to each label.
+   */
+  template<bool UseWeights, typename WeightVecType>
+  static double Evaluate(const arma::rowvec& labels,
+                         const WeightVecType& weights)
+  {
+    // Corner case: if there are no elements, the impurity is zero.
+    if (labels.n_elem == 0)
+      return 0.0;
+
+    return Evaluate<UseWeights>(labels, weights, 0, labels.n_elem);
+  }
+};
+
+} // namespace tree
+} // namespace mlpack
+
+#endif

From b7d4c25c329aeca9bfda1e4ed5c383e16c4915c6 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 8 Apr 2021 22:11:47 +0530
Subject: [PATCH 038/118] Implemented tests for MSE gain

---
 src/mlpack/tests/decision_tree_test.cpp | 52 +++++++++++++++++++++++--
 1 file changed, 49 insertions(+), 3 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index ab8aa094ae..20689256e0 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -14,6 +14,7 @@
 #include <mlpack/methods/decision_tree/information_gain.hpp>
 #include <mlpack/methods/decision_tree/gini_gain.hpp>
 #include <mlpack/methods/decision_tree/mad_gain.hpp>
+#include <mlpack/methods/decision_tree/mse_gain.hpp>
 #include <mlpack/methods/decision_tree/random_dimension_select.hpp>
 #include <mlpack/methods/decision_tree/multiple_random_dimension_select.hpp>
 
@@ -28,6 +29,51 @@ using namespace mlpack::distribution;
 /**
  * Make sure the MSE gain is zero when the labels are perfect.
  */
+TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights(10, arma::fill::ones);
+  arma::rowvec labels;
+  labels.ones(10);
+
+  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+}
+
+/**
+ * Make sure that the MSE gain is equal to negative of variance.
+ */
+TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights(100, arma::fill::ones);
+  arma::rowvec labels(100, arma::fill::randn);
+
+  // Theoretical gain.
+  double theoreticalGain = - arma::var(labels) * 99.0 / 100.0;
+
+  // Calculated gain.
+  const double calculatedGain = MSEGain::Evaluate<false>(labels, weights);
+
+  REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-9));
+  std::cout << "MSEGain\n";
+}
+
+/**
+ * The MSE gain of an empty vector is 0.
+ */
+TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights = arma::ones<arma::rowvec>(10);
+  arma::rowvec labels;
+  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+
+  REQUIRE(MSEGain::Evaluate<true>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+}
+
+/**
+ * Make sure the MAD gain is zero when the labels are perfect.
+ */
 TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressionTest]")
 {
   arma::rowvec weights(10, arma::fill::ones);
@@ -65,11 +111,11 @@ TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
 TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressionTest]")
 {
   arma::rowvec weights = arma::ones<arma::rowvec>(10);
-  arma::rowvec predictors;
-  REQUIRE(MADGain::Evaluate<false>(predictors, weights) ==
+  arma::rowvec labels;
+  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
           Approx(0.0).margin(1e-5));
 
-  REQUIRE(MADGain::Evaluate<true>(predictors, weights) ==
+  REQUIRE(MADGain::Evaluate<true>(labels, weights) ==
           Approx(0.0).margin(1e-5));
 }
 

From 415ac24c50d1fc0c842e6448c402661d4c698004 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 8 Apr 2021 22:13:59 +0530
Subject: [PATCH 039/118] Removed print statement

---
 src/mlpack/tests/decision_tree_test.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 20689256e0..67c926ef04 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -54,7 +54,6 @@ TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressionTest]")
   const double calculatedGain = MSEGain::Evaluate<false>(labels, weights);
 
   REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-9));
-  std::cout << "MSEGain\n";
 }
 
 /**

From 2690c2c489cc031a5a476dd98e77562ac053027f Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 9 Apr 2021 13:08:03 +0530
Subject: [PATCH 040/118] Removed documentation of ElemType

---
 src/mlpack/methods/decision_tree/decision_tree.hpp | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/decision_tree.hpp b/src/mlpack/methods/decision_tree/decision_tree.hpp
index df81dc61ad..f73720d6db 100644
--- a/src/mlpack/methods/decision_tree/decision_tree.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree.hpp
@@ -31,11 +31,6 @@ namespace tree {
  *
  * The class inherits from the auxiliary split information in order to prevent
  * an empty auxiliary split information struct from taking any extra size.
- *
- * Note that `ElemType` is a template parameter controlling the type that is
- * used to store split information.  In general, you would want to set this to
- * be the same as the type of the data that you will be using, but it's not
- * required to do that.
  */
 template<typename FitnessFunction = GiniGain,
          template<typename> class NumericSplitType = BestBinaryNumericSplit,

From 13311bb25c09fde4d4a109e96c8fce474ec3a40b Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 9 Apr 2021 13:33:37 +0530
Subject: [PATCH 041/118] Added constructors

---
 src/mlpack/methods/decision_tree/decision_tree_regressor.hpp | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 src/mlpack/methods/decision_tree/decision_tree_regressor.hpp

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
new file mode 100644
index 0000000000..e69de29bb2

From a030a556d50bb045e9e3735d844148475f21dcb6 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 9 Apr 2021 13:34:19 +0530
Subject: [PATCH 042/118] Added constructors

---
 .../decision_tree/decision_tree_regressor.hpp | 235 ++++++++++++++++++
 1 file changed, 235 insertions(+)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index e69de29bb2..1134e293a1 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -0,0 +1,235 @@
+/**
+ * @file methods/decision_tree/decision_tree_regressor.hpp
+ * @author Rishabh Garg
+ *
+ * The decision tree regressor class. Its behavior can be controlled via the
+ * template arguments.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_HPP
+#define MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_HPP
+
+#include <mlpack/prereqs.hpp>
+#include "mad_gain.hpp"
+#include "mse_gain.hpp"
+#include "best_binary_numeric_split.hpp"
+#include "all_categorical_split.hpp"
+#include "all_dimension_select.hpp"
+#include <type_traits>
+
+
+namespace mlpack {
+namespace tree {
+
+/**
+ * This class implements a generic decision tree learner.  Its behavior can be
+ * controlled via its template arguments.
+ *
+ * The class inherits from the auxiliary split information in order to prevent
+ * an empty auxiliary split information struct from taking any extra size.
+ */
+template<typename FitnessFunction = MSEGain,
+         template<typename> class NumericSplitType = BestBinaryNumericSplit,
+         template<typename> class CategoricalSplitType = AllCategoricalSplit,
+         typename DimensionSelectionType = AllDimensionSelect,
+         bool NoRecursion = false>
+class DecisionTreeRegressor :
+    public NumericSplitType<FitnessFunction>::AuxiliarySplitInfo,
+    public CategoricalSplitType<FitnessFunction>::AuxiliarySplitInfo
+{
+ public:
+  //! Allow access to the numeric split type.
+  typedef NumericSplitType<FitnessFunction> NumericSplit;
+  //! Allow access to the categorical split type.
+  typedef CategoricalSplitType<FitnessFunction> CategoricalSplit;
+  //! Allow access to the dimension selection type.
+  typedef DimensionSelectionType DimensionSelection;
+
+  /**
+   * Construct a decision tree without training it.  It will be a leaf node.
+   */
+  DecisionTreeRegressor();
+
+  /**
+   * Construct the decision tree on the given data and labels, where the data
+   * can be both numeric and categorical. Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
+   * @param data Dataset to train on.
+   * @param datasetInfo Type information for each dimension of the dataset.
+   * @param labels Labels for each training point.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   */
+  template<typename MatType, typename LabelsType>
+  DecisionTreeRegressor(MatType data,
+                        const data::DatasetInfo& datasetInfo,
+                        LabelsType labels,
+                        const size_t minimumLeafSize = 10,
+                        const double minimumGainSplit = 1e-7,
+                        const size_t maximumDepth = 0,
+                        DimensionSelectionType dimensionSelector =
+                            DimensionSelectionType());
+
+  /**
+   * Construct the decision tree on the given data and labels, assuming that
+   * the data is all of the numeric type.  Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
+   * @param data Dataset to train on.
+   * @param labels Labels for each training point.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   */
+  template<typename MatType, typename LabelsType>
+  DecisionTreeRegressor(MatType data,
+                        LabelsType labels,
+                        const size_t minimumLeafSize = 10,
+                        const double minimumGainSplit = 1e-7,
+                        const size_t maximumDepth = 0,
+                        DimensionSelectionType dimensionSelector =
+                            DimensionSelectionType());
+
+  /**
+   * Construct the decision tree on the given data and labels with weights,
+   * where the data can be both numeric and categorical. Setting minimumLeafSize
+   * and minimumGainSplit too small may cause the tree to overfit, but setting
+   * them too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
+   * @param data Dataset to train on.
+   * @param datasetInfo Type information for each dimension of the dataset.
+   * @param labels Labels for each training point.
+   * @param weights The weight list of given label.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  DecisionTreeRegressor(
+      MatType data,
+      const data::DatasetInfo& datasetInfo,
+      LabelsType labels,
+      WeightsType weights,
+      const size_t minimumLeafSize = 10,
+      const double minimumGainSplit = 1e-7,
+      const size_t maximumDepth = 0,
+      DimensionSelectionType dimensionSelector = DimensionSelectionType(),
+      const std::enable_if_t<arma::is_arma_type<
+          typename std::remove_reference<WeightsType>::type>::value>* = 0);
+
+  /**
+   * Construct the decision tree on the given data and labels with weights,
+   * assuming that the data is all of the numeric type. Setting minimumLeafSize
+   * and minimumGainSplit too small may cause the tree to overfit, but setting
+   * them too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
+   * @param data Dataset to train on.
+   * @param labels Labels for each training point.
+   * @param weights The Weight list of given labels.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  DecisionTreeRegressor(
+      MatType data,
+      LabelsType labels,
+      WeightsType weights,
+      const size_t minimumLeafSize = 10,
+      const double minimumGainSplit = 1e-7,
+      const size_t maximumDepth = 0,
+      DimensionSelectionType dimensionSelector = DimensionSelectionType(),
+      const std::enable_if_t<arma::is_arma_type<
+          typename std::remove_reference<WeightsType>::type>::value>* = 0);
+
+  /**
+   * Take ownership of another decision tree and train on the given data and
+   * labels with weights, where the data can be both numeric and categorical.
+   * Setting minimumLeafSize and minimumGainSplit too small may cause the
+   * tree to overfit, but setting them too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
+   * @param other Tree to take ownership of.
+   * @param data Dataset to train on.
+   * @param datasetInfo Type information for each dimension of the dataset.
+   * @param labels Labels for each training point.
+   * @param weights The weight list of given label.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  DecisionTreeRegressor(
+      const DecisionTreeRegressor& other,
+      MatType data,
+      const data::DatasetInfo& datasetInfo,
+      LabelsType labels,
+      WeightsType weights,
+      const size_t minimumLeafSize = 10,
+      const double minimumGainSplit = 1e-7,
+      const std::enable_if_t<arma::is_arma_type<
+          typename std::remove_reference<WeightsType>::type>::value>* = 0);
+
+  /**
+   * Take ownership of another decision tree and train on the given data and labels
+   * with weights, assuming that the data is all of the numeric type. Setting
+   * minimumLeafSize and minimumGainSplit too small may cause the tree to
+   * overfit, but setting them too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   * @param other Tree to take ownership of.
+   * @param data Dataset to train on.
+   * @param labels Labels for each training point.
+   * @param weights The Weight list of given labels.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  DecisionTreeRegressor(
+      const DecisionTreeRegressor& other,
+      MatType data,
+      LabelsType labels,
+      WeightsType weights,
+      const size_t minimumLeafSize = 10,
+      const double minimumGainSplit = 1e-7,
+      const size_t maximumDepth = 0,
+      DimensionSelectionType dimensionSelector = DimensionSelectionType(),
+      const std::enable_if_t<arma::is_arma_type<
+          typename std::remove_reference<WeightsType>::type>::value>* = 0);
+
+};
+
+
+} // namespace tree
+} // namespace mlpack
+
+// Include implementation.
+#include "decision_tree_regressor_impl.hpp"
+
+#endif

From 03a3f378d27ff692a78f9ec5a9a66e03b6f1ad23 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 9 Apr 2021 13:36:16 +0530
Subject: [PATCH 043/118] Added copy and move ctors and dtor

---
 .../decision_tree/decision_tree_regressor.hpp | 34 +++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index 1134e293a1..8911e8c79f 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -223,6 +223,40 @@ class DecisionTreeRegressor :
       const std::enable_if_t<arma::is_arma_type<
           typename std::remove_reference<WeightsType>::type>::value>* = 0);
 
+  /**
+   * Copy another tree.  This may use a lot of memory---be sure that it's what
+   * you want to do.
+   *
+   * @param other Tree to copy.
+   */
+  DecisionTreeRegressor(const DecisionTreeRegressor& other);
+
+  /**
+   * Take ownership of another tree.
+   *
+   * @param other Tree to take ownership of.
+   */
+  DecisionTreeRegressor(DecisionTreeRegressor&& other);
+
+  /**
+   * Copy another tree.  This may use a lot of memory---be sure that it's what
+   * you want to do.
+   *
+   * @param other Tree to copy.
+   */
+  DecisionTreeRegressor& operator=(const DecisionTreeRegressor& other);
+
+  /**
+   * Take ownership of another tree.
+   *
+   * @param other Tree to take ownership of.
+   */
+  DecisionTreeRegressor& operator=(DecisionTreeRegressor&& other);
+
+  /**
+   * Clean up memory.
+   */
+  ~DecisionTreeRegressor();
 };
 
 

From 0dd0715b5f06da2085d76e58ff834e26bb9149fb Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 9 Apr 2021 15:09:25 +0530
Subject: [PATCH 044/118] Implemented ctors and dtor

---
 .../decision_tree_regressor_impl.hpp          | 427 ++++++++++++++++++
 1 file changed, 427 insertions(+)
 create mode 100644 src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
new file mode 100644
index 0000000000..9ba0623341
--- /dev/null
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -0,0 +1,427 @@
+/**
+ * @file methods/decision_tree/decision_tree_regressor_impl.hpp
+ * @author Rishabh Garg
+ *
+ * Implementation of decision tree regressor class.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_IMPL_HPP
+#define MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_IMPL_HPP
+
+#include "decision_tree_regressor.hpp"
+
+namespace mlpack {
+namespace tree {
+
+//! Construct, don't train.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor() :
+    splitDimension(0),
+    dimensionTypeOrMajorityClass(0),
+    classProbabilities(numClasses)
+{
+  // Initialize utility vector.
+  classProbabilities.fill(1.0 / (double) numClasses);
+}
+
+//! Construct and train without weight.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+      weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Construct and train without weight on numeric data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, weights,
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+}
+
+//! Construct and train with weights.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<arma::is_arma_type<
+        typename std::remove_reference<WeightsType>::type>::value>*)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+      tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Construct and train on numeric data with weights.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<
+        arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+}
+
+//! Take ownership of another tree and train with weights.
+template<typename FitnessFunction,
+        template<typename> class NumericSplitType,
+        template<typename> class CategoricalSplitType,
+        typename DimensionSelectionType,
+        bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    const DecisionTreeRegressor& other,
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    const size_t numClasses,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const std::enable_if_t<arma::is_arma_type<
+        typename std::remove_reference<WeightsType>::type>::value>*):
+        NumericAuxiliarySplitInfo(other),
+        CategoricalAuxiliarySplitInfo(other)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+              tmpWeights, minimumLeafSize, minimumGainSplit);
+}
+
+//! Take ownership of another tree and train with weights.
+template<typename FitnessFunction,
+        template<typename> class NumericSplitType,
+        template<typename> class CategoricalSplitType,
+        typename DimensionSelectionType,
+        bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    const DecisionTreeRegressor& other,
+    MatType data,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*):
+        NumericAuxiliarySplitInfo(other),
+        CategoricalAuxiliarySplitInfo(other)  // other info does need to copy
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+}
+
+//! Copy another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+             NumericSplitType,
+             CategoricalSplitType,
+             DimensionSelectionType,
+             NoRecursion
+>::DecisionTreeRegressor(
+    const DecisionTreeRegressor& other) :
+    NumericAuxiliarySplitInfo(other),
+    CategoricalAuxiliarySplitInfo(other),
+    splitDimension(other.splitDimension),
+    dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
+    classProbabilities(other.classProbabilities)
+{
+  // Copy each child.
+  for (size_t i = 0; i < other.children.size(); ++i)
+    children.push_back(new DecisionTreeRegressor(*other.children[i]));
+}
+
+//! Take ownership of another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+             NumericSplitType,
+             CategoricalSplitType,
+             DimensionSelectionType,
+             NoRecursion
+>::DecisionTreeRegressor(
+    DecisionTreeRegressor&& other) :
+    NumericAuxiliarySplitInfo(std::move(other)),
+    CategoricalAuxiliarySplitInfo(std::move(other)),
+    children(std::move(other.children)),
+    splitDimension(other.splitDimension),
+    dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
+    classProbabilities(std::move(other.classProbabilities))
+{
+  // Reset the other object.
+  other.classProbabilities.ones(1); // One class, P(1) = 1.
+}
+
+//! Copy another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>&
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion
+>::operator=(const DecisionTreeRegressor& other)
+{
+  if (this == &other)
+    return *this; // Nothing to copy.
+
+  // Clean memory if needed.
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+  children.clear();
+
+  // Copy everything from the other tree.
+  splitDimension = other.splitDimension;
+  dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
+  classProbabilities = other.classProbabilities;
+
+  // Copy the children.
+  for (size_t i = 0; i < other.children.size(); ++i)
+    children.push_back(new DecisionTree(*other.children[i]));
+
+  // Copy the auxiliary info.
+  NumericAuxiliarySplitInfo::operator=(other);
+  CategoricalAuxiliarySplitInfo::operator=(other);
+
+  return *this;
+}
+
+//! Take ownership of another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>&
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion
+>::operator=(DecisionTreeRegressor&& other)
+{
+  if (this == &other)
+    return *this; // Nothing to move.
+
+  // Clean memory if needed.
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+  children.clear();
+
+  // Take ownership of the other tree's components.
+  children = std::move(other.children);
+  splitDimension = other.splitDimension;
+  dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
+  classProbabilities = std::move(other.classProbabilities);
+
+  // Reset the class probabilities of the other object.
+  other.classProbabilities.ones(1); // One class, P(1) = 1.
+
+  // Take ownership of the auxiliary info.
+  NumericAuxiliarySplitInfo::operator=(std::move(other));
+  CategoricalAuxiliarySplitInfo::operator=(std::move(other));
+
+  return *this;
+}
+
+//! Clean up memory.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+             NumericSplitType,
+             CategoricalSplitType,
+             DimensionSelectionType,
+             NoRecursion>::~DecisionTreeRegressor()
+{
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+}
+
+
+} // namespace tree
+} // namespace mlpack
+
+#endif

From a2c9cd0c2d177e0150b7830b8514972efcb6ef0d Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 9 Apr 2021 15:52:37 +0530
Subject: [PATCH 045/118] Added Train overloads

---
 .../decision_tree/decision_tree_regressor.hpp | 116 ++++++++++++
 .../decision_tree_regressor_impl.hpp          | 170 ++++++++++++++++++
 2 files changed, 286 insertions(+)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index 8911e8c79f..17358d07a6 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -257,6 +257,122 @@ class DecisionTreeRegressor :
    * Clean up memory.
    */
   ~DecisionTreeRegressor();
+
+  /**
+   * Train the decision tree on the given data.  This will overwrite the
+   * existing model.  The data may have numeric and categorical types, specified
+   * by the datasetInfo parameter.  Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
+   * @param data Dataset to train on.
+   * @param datasetInfo Type information for each dimension.
+   * @param labels Labels for each training point.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType>
+  double Train(MatType data,
+               const data::DatasetInfo& datasetInfo,
+               LabelsType labels,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType());
+
+  /**
+   * Train the decision tree on the given data, assuming that all dimensions are
+   * numeric.  This will overwrite the given model. Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
+   * @param data Dataset to train on.
+   * @param labels Labels for each training point.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType>
+  double Train(MatType data,
+               LabelsType labels,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType());
+
+  /**
+   * Train the decision tree on the given weighted data.  This will overwrite
+   * the existing model.  The data may have numeric and categorical types,
+   * specified by the datasetInfo parameter.  Setting minimumLeafSize and
+   * minimumGainSplit too small may cause the tree to overfit, but setting them
+   * too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
+   * @param data Dataset to train on.
+   * @param datasetInfo Type information for each dimension.
+   * @param labels Labels for each training point.
+   * @param weights Weights of all the labels
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  double Train(MatType data,
+               const data::DatasetInfo& datasetInfo,
+               LabelsType labels,
+               WeightsType weights,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType(),
+               const std::enable_if_t<arma::is_arma_type<typename
+                   std::remove_reference<WeightsType>::type>::value>* = 0);
+
+  /**
+   * Train the decision tree on the given weighted data, assuming that all
+   * dimensions are numeric.  This will overwrite the given model. Setting
+   * minimumLeafSize and minimumGainSplit too small may cause the tree to
+   * overfit, but setting them too large may cause it to underfit.
+   *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
+   * @param data Dataset to train on.
+   * @param labels Labels for each training point.
+   * @param weights Weights of all the labels
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @param dimensionSelector Instantiated dimension selection policy.
+   * @return The final entropy of decision tree.
+   */
+  template<typename MatType, typename LabelsType, typename WeightsType>
+  double Train(MatType data,
+               LabelsType labels,
+               WeightsType weights,
+               const size_t minimumLeafSize = 10,
+               const double minimumGainSplit = 1e-7,
+               const size_t maximumDepth = 0,
+               DimensionSelectionType dimensionSelector =
+                   DimensionSelectionType(),
+               const std::enable_if_t<arma::is_arma_type<typename
+                   std::remove_reference<WeightsType>::type>::value>* = 0);
 };
 
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 9ba0623341..1145b110e0 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -420,6 +420,176 @@ DecisionTreeRegressor<FitnessFunction,
     delete children[i];
 }
 
+//! Train on the given data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
+      numClasses, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Train on the given data, assuming all dimensions are numeric.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  return Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses,
+      weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Train on the given weighted data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<
+        arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
+      numClasses, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Train on the given weighted all numeric data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType data,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<
+        arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*)
+{
+  // Sanity check on data.
+  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  return Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses,
+      tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
 
 } // namespace tree
 } // namespace mlpack

From 37559821bf21993c3bfbeadbb6f7decd655dee6f Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 9 Apr 2021 23:48:36 +0530
Subject: [PATCH 046/118] Refactored AllCategoricalSplit

---
 .../decision_tree/all_categorical_split.hpp   | 10 ++++-----
 .../all_categorical_split_impl.hpp            | 18 +++++++--------
 .../decision_tree/decision_tree_impl.hpp      |  6 +++--
 src/mlpack/tests/decision_tree_test.cpp       | 22 +++++++++----------
 4 files changed, 28 insertions(+), 28 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split.hpp b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
index faa8f16c6b..09f717c155 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
@@ -47,23 +47,23 @@ class AllCategoricalSplit
    * @param weights Weights associated with labels.
    * @param minimumLeafSize Minimum number of points in a leaf node for
    *      splitting.
-   * @param classProbabilities Class probabilities vector, which may be filled
-   *      with split information a successful split.
+   * @param splitInfo Stores split information on a successful split.
    * @param minimumGainSplit Minimum  gain split.
    * @param aux Auxiliary split information, which may be modified on a
    *      successful split.
    */
-  template<bool UseWeights, typename VecType, typename WeightVecType>
+  template<bool UseWeights, typename VecType, typename ElemType, typename WeightVecType>
   static double SplitIfBetter(
       const double bestGain,
       const VecType& data,
       const size_t numCategories,
-      const arma::Row<size_t>& labels,
+      const arma::Row<ElemType>& labels,
+      const size_t begin,
       const size_t numClasses,
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
-      arma::vec& classProbabilities,
+      double& splitInfo,
       AuxiliarySplitInfo& aux);
 
   /**
diff --git a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
index 00135625ab..87da7b3d22 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
@@ -16,17 +16,18 @@ namespace mlpack {
 namespace tree {
 
 template<typename FitnessFunction>
-template<bool UseWeights, typename VecType, typename WeightVecType>
+template<bool UseWeights, typename VecType, typename ElemType, typename WeightVecType>
 double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
     const size_t numCategories,
-    const arma::Row<size_t>& labels,
+    const arma::Row<ElemType>& labels,
+    const size_t begin,
     const size_t numClasses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
-    arma::vec& classProbabilities,
+    double& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
   // Count the number of elements in each potential child.
@@ -58,7 +59,7 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
   // Calculate the gain of the split.  First we have to calculate the labels
   // that would be assigned to each child.
   arma::uvec childPositions(numCategories, arma::fill::zeros);
-  std::vector<arma::Row<size_t>> childLabels(numCategories);
+  std::vector<arma::Row<ElemType>> childLabels(numCategories);
   std::vector<arma::Row<double>> childWeights(numCategories);
   for (size_t i = 0; i < numCategories; ++i)
   {
@@ -75,12 +76,12 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
 
     if (UseWeights)
     {
-      childLabels[category][childPositions[category]] = labels[i];
+      childLabels[category][childPositions[category]] = labels[begin + i];
       childWeights[category][childPositions[category]++] = weights[i];
     }
     else
     {
-      childLabels[category][childPositions[category]++] = labels[i];
+      childLabels[category][childPositions[category]++] = labels[begin + i];
     }
   }
 
@@ -99,9 +100,8 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
 
   if (overallGain > bestGain + minimumGainSplit + epsilon)
   {
-    // This is better, so set up the class probabilities vector and return.
-    classProbabilities.set_size(1);
-    classProbabilities[0] = numCategories;
+    // This is better, so store it in splitInfo and return.
+    splitInfo = numCategories;
     return overallGain;
   }
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
index e4cd77851b..8f04e9f57c 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
@@ -642,15 +642,17 @@ double DecisionTree<FitnessFunction,
       double dimGain = -DBL_MAX;
       if (datasetInfo.Type(i) == data::Datatype::categorical)
       {
+        classProbabilities.set_size(1);
         dimGain = CategoricalSplit::template SplitIfBetter<UseWeights>(bestGain,
             data.cols(begin, begin + count - 1).row(i),
             datasetInfo.NumMappings(i),
-            labels.subvec(begin, begin + count - 1),
+            labels,
+            begin,
             numClasses,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
             minimumGainSplit,
-            classProbabilities,
+            classProbabilities[0],
             *this);
       }
       else if (datasetInfo.Type(i) == data::Datatype::numeric)
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 67c926ef04..45eb350523 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -583,17 +583,17 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest", "[DecisionTreeTest]")
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   AllCategoricalSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, 4, labels, 3, weights, 3, 1e-7, classProbabilities,
+      bestGain, values, 4, labels, 0, 3, weights, 3, 1e-7, classProbabilities[0],
       aux);
   const double weightedGain =
       AllCategoricalSplit<GiniGain>::SplitIfBetter<true>(bestGain, values, 4,
-      labels, 3, weights, 3, 1e-7, classProbabilities, aux);
+      labels, 0, 3, weights, 3, 1e-7, classProbabilities[0], aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -619,18 +619,17 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest", "[DecisionTreeTest]")
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   AllCategoricalSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, 4, labels, 3, weights, 4, 1e-7, classProbabilities,
-      aux);
+      bestGain, values, 4, labels, 0, 3, weights, 4, 1e-7,
+      classProbabilities[0], aux);
 
   // Make sure it's not split.
   REQUIRE(gain == DBL_MAX);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -652,22 +651,21 @@ TEST_CASE("AllCategoricalSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 2] = 2;
   }
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   AllCategoricalSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, 10, labels, 3, weights, 10, 1e-7,
-      classProbabilities, aux);
+      bestGain, values, 10, labels, 0, 3, weights, 10, 1e-7,
+      classProbabilities[0], aux);
   const double weightedGain =
       AllCategoricalSplit<GiniGain>::SplitIfBetter<true>(bestGain, values, 10,
-      labels, 3, weights, 10, 1e-7, classProbabilities, aux);
+      labels, 0, 3, weights, 10, 1e-7, classProbabilities[0], aux);
 
   // Make sure that there was no split.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**

From e3579871afb47311fa7f36b1f12f8801864cd215 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Apr 2021 00:11:47 +0530
Subject: [PATCH 047/118] Refactored AllCategoricalSplit patch 2

---
 .../decision_tree/all_categorical_split.hpp        | 14 +++++++-------
 .../decision_tree/all_categorical_split_impl.hpp   |  6 +++---
 .../methods/decision_tree/decision_tree_impl.hpp   |  6 +++---
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split.hpp b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
index 09f717c155..25b8ffcefd 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
@@ -34,9 +34,9 @@ class AllCategoricalSplit
   /**
    * Check if we can split a node.  If we can split a node in a way that
    * improves on 'bestGain', then we return the improved gain.  Otherwise we
-   * return the value 'bestGain'.  If a split is made, then classProbabilities
-   * and aux may be modified.  For this particular split type, aux will be empty
-   * and classProbabilities will hold one element---the number of children.
+   * return the value 'bestGain'.  If a split is made, then splitInfo and
+   * aux may be modified.  For this particular split type, aux will be empty
+   * and splitInfo will store the number of children of the node.
    *
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
@@ -69,23 +69,23 @@ class AllCategoricalSplit
   /**
    * Return the number of children in the split.
    *
-   * @param classProbabilities Auxiliary information for the split.
+   * @param splitInfo Auxiliary information for the split.
    * @param * (aux) Auxiliary information for the split (Unused).
    */
-  static size_t NumChildren(const arma::vec& classProbabilities,
+  static size_t NumChildren(const double& splitInfo,
                             const AuxiliarySplitInfo& /* aux */);
 
   /**
    * Calculate the direction a point should percolate to.
    *
    * @param point the Point to use.
-   * @param classProbabilities Column Vector of class probabilities.
+   * @param splitInfo Auxiliary information for the split.
    * @param * (aux) Auxiliary information for the split (Unused).
    */
   template<typename ElemType>
   static size_t CalculateDirection(
       const ElemType& point,
-      const arma::vec& classProbabilities,
+      const double& splitInfo,
       const AuxiliarySplitInfo& /* aux */);
 };
 
diff --git a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
index 87da7b3d22..f7191a035b 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
@@ -111,17 +111,17 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
 
 template<typename FitnessFunction>
 size_t AllCategoricalSplit<FitnessFunction>::NumChildren(
-    const arma::vec& classProbabilities,
+    const double& splitInfo,
     const AuxiliarySplitInfo& /* aux */)
 {
-  return size_t(classProbabilities[0]);
+  return (size_t) splitInfo;
 }
 
 template<typename FitnessFunction>
 template<typename ElemType>
 size_t AllCategoricalSplit<FitnessFunction>::CalculateDirection(
     const ElemType& point,
-    const arma::vec& /* classProbabilities */,
+    const double& /* splitInfo */,
     const AuxiliarySplitInfo& /* aux */)
 {
   return (size_t) point;
diff --git a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
index 8f04e9f57c..e6d5b63fb5 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
@@ -692,7 +692,7 @@ double DecisionTree<FitnessFunction,
     // Get the number of children we will have.
     size_t numChildren = 0;
     if (datasetInfo.Type(bestDim) == data::Datatype::categorical)
-      numChildren = CategoricalSplit::NumChildren(classProbabilities, *this);
+      numChildren = CategoricalSplit::NumChildren(classProbabilities[0], *this);
     else
       numChildren = NumericSplit::NumChildren(classProbabilities, *this);
 
@@ -702,7 +702,7 @@ double DecisionTree<FitnessFunction,
     {
       for (size_t j = begin; j < begin + count; ++j)
         childAssignments[j - begin] = CategoricalSplit::CalculateDirection(
-            data(bestDim, j), classProbabilities, *this);
+            data(bestDim, j), classProbabilities[0], *this);
     }
     else
     {
@@ -1091,7 +1091,7 @@ size_t DecisionTree<FitnessFunction,
   if ((data::Datatype) dimensionTypeOrMajorityClass ==
       data::Datatype::categorical)
     return CategoricalSplit::CalculateDirection(point[splitDimension],
-        classProbabilities, *this);
+        classProbabilities[0], *this);
   else
     return NumericSplit::CalculateDirection(point[splitDimension],
         classProbabilities, *this);

From 6e6d60f2b33aa11dae7ef533aa9ee08a85882b1d Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Apr 2021 00:25:56 +0530
Subject: [PATCH 048/118] Improved documentation

---
 src/mlpack/methods/decision_tree/all_categorical_split.hpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split.hpp b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
index 25b8ffcefd..ec7924bb76 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
@@ -20,7 +20,9 @@ namespace tree {
 
 /**
  * The AllCategoricalSplit is a splitting function that will split categorical
- * features into many children: one child for each category.
+ * features into many children: one child for each category. This is a generic
+ * splitting strategy and can be used for both regression and classification
+ * trees.
  *
  * @tparam FitnessFunction Fitness function to evaluate gain with.
  */
@@ -43,6 +45,7 @@ class AllCategoricalSplit
    * @param data The dimension of data points to check for a split in.
    * @param numCategories Number of categories in the categorical data.
    * @param labels Labels for each point.
+   * @param begin Start index of labels.
    * @param numClasses Number of classes in the dataset.
    * @param weights Weights associated with labels.
    * @param minimumLeafSize Minimum number of points in a leaf node for

From ff85e20b96425a57a40131d27cedc146ee932c85 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Apr 2021 22:49:07 +0530
Subject: [PATCH 049/118] Shifted regression tree tests to new file

---
 src/mlpack/tests/CMakeLists.txt               |   1 +
 .../tests/decision_tree_regressor_test.cpp    | 117 ++++++++++++++++++
 src/mlpack/tests/decision_tree_test.cpp       |  94 --------------
 3 files changed, 118 insertions(+), 94 deletions(-)
 create mode 100644 src/mlpack/tests/decision_tree_regressor_test.cpp

diff --git a/src/mlpack/tests/CMakeLists.txt b/src/mlpack/tests/CMakeLists.txt
index 2119879ab7..33b10aa735 100644
--- a/src/mlpack/tests/CMakeLists.txt
+++ b/src/mlpack/tests/CMakeLists.txt
@@ -29,6 +29,7 @@ add_executable(mlpack_test
   cv_test.cpp
   dbscan_test.cpp
   dcgan_test.cpp
+  decision_tree_regressor_test.cpp
   decision_tree_test.cpp
   det_test.cpp
   distribution_test.cpp
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
new file mode 100644
index 0000000000..d7ccb7e3b9
--- /dev/null
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -0,0 +1,117 @@
+/**
+ * @file tests/decision_tree_regressor_test.cpp
+ * @author Rishabh Garg
+ *
+ * Tests for the DecisionTreeRegressor class and related classes.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#include <mlpack/core.hpp>
+#include <mlpack/methods/decision_tree/decision_tree.hpp>
+#include <mlpack/methods/decision_tree/mad_gain.hpp>
+#include <mlpack/methods/decision_tree/mse_gain.hpp>
+#include <mlpack/methods/decision_tree/random_dimension_select.hpp>
+#include <mlpack/methods/decision_tree/multiple_random_dimension_select.hpp>
+
+#include "catch.hpp"
+#include "serialization.hpp"
+#include "mock_categorical_data.hpp"
+
+using namespace mlpack;
+using namespace mlpack::tree;
+using namespace mlpack::distribution;
+
+/**
+ * Make sure the MSE gain is zero when the labels are perfect.
+ */
+TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights(10, arma::fill::ones);
+  arma::rowvec labels;
+  labels.ones(10);
+
+  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+}
+
+/**
+ * Make sure that the MSE gain is equal to negative of variance.
+ */
+TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights(100, arma::fill::ones);
+  arma::rowvec labels(100, arma::fill::randn);
+
+  // Theoretical gain.
+  double theoreticalGain = - arma::var(labels) * 99.0 / 100.0;
+
+  // Calculated gain.
+  const double calculatedGain = MSEGain::Evaluate<false>(labels, weights);
+
+  REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-9));
+}
+
+/**
+ * The MSE gain of an empty vector is 0.
+ */
+TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights = arma::ones<arma::rowvec>(10);
+  arma::rowvec labels;
+  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+
+  REQUIRE(MSEGain::Evaluate<true>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+}
+
+/**
+ * Make sure the MAD gain is zero when the labels are perfect.
+ */
+TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights(10, arma::fill::ones);
+  arma::rowvec labels;
+  labels.ones(10);
+
+  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+}
+
+/**
+ * Make sure that when mean of labels is zero, MAD_gain = mean of
+ * absolute values of the distribution.
+ */
+TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
+{
+  arma::rowvec weights(10, arma::fill::ones);
+  arma::rowvec labels = { 1, 2, 3, 4, 5, -1, -2, -3, -4, -5 }; // Mean = 0.
+
+  // Theoretical gain.
+  double theoreticalGain = 0.0;
+  for (size_t i = 0; i < labels.n_elem; ++i)
+    theoreticalGain -= std::abs(labels[i]);
+  theoreticalGain /= (double) labels.n_elem;
+
+  // Calculated gain.
+  const double calculatedGain = MADGain::Evaluate<false>(labels, weights);
+
+  REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-5));
+}
+
+/**
+ * The MAD gain of an empty vector is 0.
+ */
+TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressionTest]")
+{
+  arma::rowvec weights = arma::ones<arma::rowvec>(10);
+  arma::rowvec labels;
+  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+
+  REQUIRE(MADGain::Evaluate<true>(labels, weights) ==
+          Approx(0.0).margin(1e-5));
+}
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 45eb350523..06fda9fb1f 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -13,8 +13,6 @@
 #include <mlpack/methods/decision_tree/decision_tree.hpp>
 #include <mlpack/methods/decision_tree/information_gain.hpp>
 #include <mlpack/methods/decision_tree/gini_gain.hpp>
-#include <mlpack/methods/decision_tree/mad_gain.hpp>
-#include <mlpack/methods/decision_tree/mse_gain.hpp>
 #include <mlpack/methods/decision_tree/random_dimension_select.hpp>
 #include <mlpack/methods/decision_tree/multiple_random_dimension_select.hpp>
 
@@ -26,98 +24,6 @@ using namespace mlpack;
 using namespace mlpack::tree;
 using namespace mlpack::distribution;
 
-/**
- * Make sure the MSE gain is zero when the labels are perfect.
- */
-TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressionTest]")
-{
-  arma::rowvec weights(10, arma::fill::ones);
-  arma::rowvec labels;
-  labels.ones(10);
-
-  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
-          Approx(0.0).margin(1e-5));
-}
-
-/**
- * Make sure that the MSE gain is equal to negative of variance.
- */
-TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressionTest]")
-{
-  arma::rowvec weights(100, arma::fill::ones);
-  arma::rowvec labels(100, arma::fill::randn);
-
-  // Theoretical gain.
-  double theoreticalGain = - arma::var(labels) * 99.0 / 100.0;
-
-  // Calculated gain.
-  const double calculatedGain = MSEGain::Evaluate<false>(labels, weights);
-
-  REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-9));
-}
-
-/**
- * The MSE gain of an empty vector is 0.
- */
-TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressionTest]")
-{
-  arma::rowvec weights = arma::ones<arma::rowvec>(10);
-  arma::rowvec labels;
-  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
-          Approx(0.0).margin(1e-5));
-
-  REQUIRE(MSEGain::Evaluate<true>(labels, weights) ==
-          Approx(0.0).margin(1e-5));
-}
-
-/**
- * Make sure the MAD gain is zero when the labels are perfect.
- */
-TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressionTest]")
-{
-  arma::rowvec weights(10, arma::fill::ones);
-  arma::rowvec labels;
-  labels.ones(10);
-
-  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
-          Approx(0.0).margin(1e-5));
-}
-
-/**
- * Make sure that when mean of labels is zero, MAD_gain = mean of
- * absolute values of the distribution.
- */
-TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
-{
-  arma::rowvec weights(10, arma::fill::ones);
-  arma::rowvec labels = { 1, 2, 3, 4, 5, -1, -2, -3, -4, -5 }; // Mean = 0.
-
-  // Theoretical gain.
-  double theoreticalGain = 0.0;
-  for (size_t i = 0; i < labels.n_elem; ++i)
-    theoreticalGain -= std::abs(labels[i]);
-  theoreticalGain /= (double) labels.n_elem;
-
-  // Calculated gain.
-  const double calculatedGain = MADGain::Evaluate<false>(labels, weights);
-
-  REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-5));
-}
-
-/**
- * The MAD gain of an empty vector is 0.
- */
-TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressionTest]")
-{
-  arma::rowvec weights = arma::ones<arma::rowvec>(10);
-  arma::rowvec labels;
-  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
-          Approx(0.0).margin(1e-5));
-
-  REQUIRE(MADGain::Evaluate<true>(labels, weights) ==
-          Approx(0.0).margin(1e-5));
-}
-
 /**
  * Make sure the Gini gain is zero when the labels are perfect.
  */

From f541fe20b0c173cc6f11e7583866b904ddac32f2 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sun, 11 Apr 2021 00:03:57 +0530
Subject: [PATCH 050/118] Added ignored numClasses to MSE and MAD gains

---
 src/mlpack/methods/decision_tree/mad_gain.hpp | 3 ++-
 src/mlpack/methods/decision_tree/mse_gain.hpp | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index 32102a5de3..d64d566eff 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -1,5 +1,5 @@
 /**
- * @file methods/decision_tree/mse_gain.hpp
+ * @file methods/decision_tree/mad_gain.hpp
  * @author Rishabh Garg
  *
  * The mean absolute deviation gain class, a fitness funtion for regression
@@ -92,6 +92,7 @@ class MADGain
    */
   template<bool UseWeights, typename WeightVecType>
   static double Evaluate(const arma::rowvec& labels,
+                         const size_t /* numClasses */,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 7d2b3fa488..e12ae34ae4 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -88,6 +88,7 @@ class MSEGain
    */
   template<bool UseWeights, typename WeightVecType>
   static double Evaluate(const arma::rowvec& labels,
+                         const size_t /* numClasses */,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.

From 2d20ce9099847e89f084478894df981ce0805f5a Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sun, 11 Apr 2021 00:19:08 +0530
Subject: [PATCH 051/118] Added tests for AllCategoricalSplit for regression

---
 .../tests/decision_tree_regressor_test.cpp    | 126 ++++++++++++++++--
 1 file changed, 112 insertions(+), 14 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index d7ccb7e3b9..a4438ac635 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -27,20 +27,20 @@ using namespace mlpack::distribution;
 /**
  * Make sure the MSE gain is zero when the labels are perfect.
  */
-TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressionTest]")
+TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights(10, arma::fill::ones);
   arma::rowvec labels;
   labels.ones(10);
 
-  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
+  REQUIRE(MSEGain::Evaluate<false>(labels, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
 /**
  * Make sure that the MSE gain is equal to negative of variance.
  */
-TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressionTest]")
+TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights(100, arma::fill::ones);
   arma::rowvec labels(100, arma::fill::randn);
@@ -49,7 +49,7 @@ TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressionTest]")
   double theoreticalGain = - arma::var(labels) * 99.0 / 100.0;
 
   // Calculated gain.
-  const double calculatedGain = MSEGain::Evaluate<false>(labels, weights);
+  const double calculatedGain = MSEGain::Evaluate<false>(labels, 0, weights);
 
   REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-9));
 }
@@ -57,27 +57,27 @@ TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressionTest]")
 /**
  * The MSE gain of an empty vector is 0.
  */
-TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressionTest]")
+TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights = arma::ones<arma::rowvec>(10);
   arma::rowvec labels;
-  REQUIRE(MSEGain::Evaluate<false>(labels, weights) ==
+  REQUIRE(MSEGain::Evaluate<false>(labels, 0, weights) ==
           Approx(0.0).margin(1e-5));
 
-  REQUIRE(MSEGain::Evaluate<true>(labels, weights) ==
+  REQUIRE(MSEGain::Evaluate<true>(labels, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
 /**
  * Make sure the MAD gain is zero when the labels are perfect.
  */
-TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressionTest]")
+TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights(10, arma::fill::ones);
   arma::rowvec labels;
   labels.ones(10);
 
-  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
+  REQUIRE(MADGain::Evaluate<false>(labels, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -85,7 +85,7 @@ TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressionTest]")
  * Make sure that when mean of labels is zero, MAD_gain = mean of
  * absolute values of the distribution.
  */
-TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
+TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressorTest")
 {
   arma::rowvec weights(10, arma::fill::ones);
   arma::rowvec labels = { 1, 2, 3, 4, 5, -1, -2, -3, -4, -5 }; // Mean = 0.
@@ -97,7 +97,7 @@ TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
   theoreticalGain /= (double) labels.n_elem;
 
   // Calculated gain.
-  const double calculatedGain = MADGain::Evaluate<false>(labels, weights);
+  const double calculatedGain = MADGain::Evaluate<false>(labels, 0, weights);
 
   REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-5));
 }
@@ -105,13 +105,111 @@ TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressionTest")
 /**
  * The MAD gain of an empty vector is 0.
  */
-TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressionTest]")
+TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights = arma::ones<arma::rowvec>(10);
   arma::rowvec labels;
-  REQUIRE(MADGain::Evaluate<false>(labels, weights) ==
+  REQUIRE(MADGain::Evaluate<false>(labels, 0, weights) ==
           Approx(0.0).margin(1e-5));
 
-  REQUIRE(MADGain::Evaluate<true>(labels, weights) ==
+  REQUIRE(MADGain::Evaluate<true>(labels, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
+
+/**
+ * Check that AllCategoricalSplit will split when the split is obviously
+ * better.
+ */
+TEST_CASE("AllCategoricalSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]")
+{
+  arma::vec predictor(100);
+  arma::rowvec labels(100);
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  for (size_t i = 0; i < 100; i+=2)
+  {
+    predictor[i] = 0;
+    labels[i] = 5.0;
+    predictor[i + 1] = 1;
+    labels[i + 1] = 100;
+  }
+
+  double splitInfo;
+  AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, predictor, 2, labels, 0, 0, weights, 3, 1e-7, splitInfo, aux);
+  const double weightedGain =
+      AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictor, 2,
+      labels, 0, 0, weights, 3, 1e-7, splitInfo, aux);
+
+  // Make sure that a split was made.
+  REQUIRE(gain > bestGain);
+
+  REQUIRE(gain == weightedGain);
+
+  // Make sure that splitInfo now hold the number of children.
+  REQUIRE((size_t) splitInfo == 2);
+}
+
+/**
+ * Make sure that AllCategoricalSplit respects the minimum number of samples
+ * required to split.
+ */
+TEST_CASE("AllCategoricalSplitMinSamplesTest1", "[DecisionTreeRegressorTest]")
+{
+  arma::rowvec predictors = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3};
+  arma::rowvec labels = {0, 0, 0, 2, 2, 2, 1, 1, 1, 2, 2, 2};
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  double splitInfo;
+  AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, predictors, 4, labels, 0, 0, weights, 4, 1e-7, splitInfo, aux);
+
+  // Make sure it's not split.
+  REQUIRE(gain == DBL_MAX);
+}
+
+/**
+ * Check that no split is made when it doesn't get us anything.
+ */
+TEST_CASE("AllCategoricalSplitNoGainTest1", "[DecisionTreeRegressorTest]")
+{
+  arma::rowvec predictors(300);
+  arma::rowvec labels(300);
+  arma::rowvec weights = arma::ones<arma::rowvec>(300);
+
+  for (size_t i = 0; i < 300; i += 3)
+  {
+    predictors[i] = int(i / 3) % 10;
+    labels[i] = -0.5;
+    predictors[i + 1] = int(i / 3) % 10;
+    labels[i + 1] = 0;
+    predictors[i + 2] = int(i / 3) % 10;
+    labels[i + 2] = 0.5;
+  }
+
+  double splitInfo;
+  AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, predictors, 10, labels, 0, 0, weights, 10, 1e-7,
+      splitInfo, aux);
+  const double weightedGain =
+      AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
+      10, labels, 0, 0, predictors, 10, 1e-7, splitInfo, aux);
+
+  // Make sure that there was no split.
+  REQUIRE(gain == DBL_MAX);
+  REQUIRE(gain == weightedGain);
+}

From 722df85864a507cf10db717fab4153dbe657223f Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sun, 11 Apr 2021 01:02:55 +0530
Subject: [PATCH 052/118] Refactored BestBinaryNumericSplit

---
 .../best_binary_numeric_split.hpp             | 11 +++++-----
 .../best_binary_numeric_split_impl.hpp        | 19 ++++++++----------
 .../decision_tree/decision_tree_impl.hpp      | 20 ++++++++++---------
 src/mlpack/tests/decision_tree_test.cpp       | 18 ++++++++---------
 4 files changed, 32 insertions(+), 36 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index ab081c84fc..e8975ee75a 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -49,8 +49,7 @@ class BestBinaryNumericSplit
    * @param minimumLeafSize Minimum number of points in a leaf node for
    *      splitting.
    * @param minimumGainSplit Minimum gain split.
-   * @param classProbabilities Class probabilities vector, which may be filled
-   *      with split information a successful split.
+   * @param splitInfo Stores split information on a successful split.
    * @param aux Auxiliary split information, which may be modified on a
    *      successful split.
    */
@@ -63,13 +62,13 @@ class BestBinaryNumericSplit
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
-      arma::vec& classProbabilities,
+      double& splitInfo,
       AuxiliarySplitInfo& aux);
 
   /**
    * Returns 2, since the binary split always has two children.
    */
-  static size_t NumChildren(const arma::vec& /* classProbabilities */,
+  static size_t NumChildren(const double& /* splitInfo */,
                             const AuxiliarySplitInfo& /* aux */)
   {
     return 2;
@@ -79,13 +78,13 @@ class BestBinaryNumericSplit
    * Given a point, calculate which child it should go to (left or right).
    *
    * @param point Point to calculate direction of.
-   * @param classProbabilities Auxiliary information for the split.
+   * @param splitInfo Auxiliary information for the split.
    * @param * (aux) Auxiliary information for the split (Unused).
    */
   template<typename ElemType>
   static size_t CalculateDirection(
       const ElemType& point,
-      const arma::vec& classProbabilities,
+      const double& splitInfo,
       const AuxiliarySplitInfo& /* aux */);
 };
 
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 14bd0e3fb9..c0ad1f1f46 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -25,7 +25,7 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
-    arma::vec& classProbabilities,
+    double& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
   // First sanity check: if we don't have enough points, we can't split.
@@ -151,12 +151,10 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     // Corner case: is this the best possible split?
     if (gain >= 0.0)
     {
-      // We can take a shortcut: no split will be better than this, so just take
-      // this one.
-      classProbabilities.set_size(1);
-      // The actual split value will be halfway between the value at index - 1
-      // and index.
-      classProbabilities[0] = (data[sortedIndices[index - 1]] +
+      // We can take a shortcut: no split will be better than this, so just
+      // take this one. The actual split value will be halfway between the
+      // value at index - 1 and index.
+      splitInfo = (data[sortedIndices[index - 1]] +
           data[sortedIndices[index]]) / 2.0;
 
       return gain;
@@ -165,8 +163,7 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     {
       // We still have a better split.
       bestFoundGain = gain;
-      classProbabilities.set_size(1);
-      classProbabilities[0] = (data[sortedIndices[index - 1]] +
+      splitInfo = (data[sortedIndices[index - 1]] +
           data[sortedIndices[index]]) / 2.0;
       improved = true;
     }
@@ -189,10 +186,10 @@ template<typename FitnessFunction>
 template<typename ElemType>
 size_t BestBinaryNumericSplit<FitnessFunction>::CalculateDirection(
     const ElemType& point,
-    const arma::vec& classProbabilities,
+    const double& splitInfo,
     const AuxiliarySplitInfo& /* aux */)
 {
-  if (point <= classProbabilities[0])
+  if (point <= splitInfo)
     return 0; // Go left.
   else
     return 1; // Go right.
diff --git a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
index e6d5b63fb5..b897530b3f 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
@@ -636,13 +636,13 @@ double DecisionTree<FitnessFunction,
 
   if (maximumDepth != 1)
   {
+    classProbabilities.set_size(1);
     for (size_t i = dimensionSelector.Begin(); i != end;
          i = dimensionSelector.Next())
     {
       double dimGain = -DBL_MAX;
       if (datasetInfo.Type(i) == data::Datatype::categorical)
       {
-        classProbabilities.set_size(1);
         dimGain = CategoricalSplit::template SplitIfBetter<UseWeights>(bestGain,
             data.cols(begin, begin + count - 1).row(i),
             datasetInfo.NumMappings(i),
@@ -664,7 +664,7 @@ double DecisionTree<FitnessFunction,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
             minimumGainSplit,
-            classProbabilities,
+            classProbabilities[0],
             *this);
       }
 
@@ -694,7 +694,7 @@ double DecisionTree<FitnessFunction,
     if (datasetInfo.Type(bestDim) == data::Datatype::categorical)
       numChildren = CategoricalSplit::NumChildren(classProbabilities[0], *this);
     else
-      numChildren = NumericSplit::NumChildren(classProbabilities, *this);
+      numChildren = NumericSplit::NumChildren(classProbabilities[0], *this);
 
     // Calculate all child assignments.
     arma::Row<size_t> childAssignments(count);
@@ -709,7 +709,7 @@ double DecisionTree<FitnessFunction,
       for (size_t j = begin; j < begin + count; ++j)
       {
         childAssignments[j - begin] = NumericSplit::CalculateDirection(
-            data(bestDim, j), classProbabilities, *this);
+            data(bestDim, j), classProbabilities[0], *this);
       }
     }
 
@@ -823,6 +823,7 @@ double DecisionTree<FitnessFunction,
 
   if (maximumDepth != 1)
   {
+    classProbabilities.set_size(1);
     for (size_t i = dimensionSelector.Begin(); i != dimensionSelector.End();
          i = dimensionSelector.Next())
     {
@@ -836,7 +837,7 @@ double DecisionTree<FitnessFunction,
                                         weights,
                                     minimumLeafSize,
                                     minimumGainSplit,
-                                    classProbabilities,
+                                    classProbabilities[0],
                                     *this);
 
       // If the splitter did not report that it improved, then move to the next
@@ -857,7 +858,8 @@ double DecisionTree<FitnessFunction,
   if (bestDim != data.n_rows)
   {
     // We know that the split is numeric.
-    size_t numChildren = NumericSplit::NumChildren(classProbabilities, *this);
+    size_t numChildren =
+        NumericSplit::NumChildren(classProbabilities[0], *this);
     splitDimension = bestDim;
     dimensionTypeOrMajorityClass = (size_t) data::Datatype::numeric;
 
@@ -867,7 +869,7 @@ double DecisionTree<FitnessFunction,
     for (size_t j = begin; j < begin + count; ++j)
     {
       childAssignments[j - begin] = NumericSplit::CalculateDirection(
-          data(bestDim, j), classProbabilities, *this);
+          data(bestDim, j), classProbabilities[0], *this);
     }
 
     // Calculate counts of children in each node.
@@ -1094,7 +1096,7 @@ size_t DecisionTree<FitnessFunction,
         classProbabilities[0], *this);
   else
     return NumericSplit::CalculateDirection(point[splitDimension],
-        classProbabilities, *this);
+        classProbabilities[0], *this);
 }
 
 // Get the number of classes in the tree.
@@ -1109,7 +1111,7 @@ size_t DecisionTree<FitnessFunction,
                     DimensionSelectionType,
                     NoRecursion>::NumClasses() const
 {
-  // Recurse to the nearest child and return the number of elements in the
+  // Recurse to the nearest leaf and return the number of elements in the
   // probability vector.
   if (children.size() == 0)
     return classProbabilities.n_elem;
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 06fda9fb1f..2f63872f23 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -288,17 +288,17 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest", "[DecisionTreeTest]")
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities,
+      bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities[0],
       aux);
   const double weightedGain =
       BestBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 3, 1e-7, classProbabilities, aux);
+      labels, 2, weights, 3, 1e-7, classProbabilities[0], aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -326,23 +326,22 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   arma::Row<size_t> labels("0 0 0 0 0 1 1 1 1 1 1");
   arma::rowvec weights(labels.n_elem);
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities,
+      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities[0],
       aux);
   // This should make no difference because it won't split at all.
   const double weightedGain =
       BestBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 8, 1e-7, classProbabilities, aux);
+      labels, 2, weights, 8, 1e-7, classProbabilities[0], aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -362,18 +361,17 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities,
+      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities[0],
       aux);
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**

From 7e336ad118220ceaf72ee08680e7746534b9333c Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 12 Apr 2021 22:57:48 +0530
Subject: [PATCH 053/118] Fixed implementation bug in calculating sum of
 subvector

---
 src/mlpack/methods/decision_tree/utils.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/utils.hpp b/src/mlpack/methods/decision_tree/utils.hpp
index d7da7cbd16..1928803e88 100644
--- a/src/mlpack/methods/decision_tree/utils.hpp
+++ b/src/mlpack/methods/decision_tree/utils.hpp
@@ -106,16 +106,16 @@ void Sum(const arma::rowvec& labels,
   }
 
   // Handle leftovers.
-  if (labels.n_elem % 4 == 1)
+  if ((end - begin) % 4 == 1)
   {
     total[0] += labels[end - 1];
   }
-  else if (labels.n_elem % 4 == 2)
+  else if ((end - begin) % 4 == 2)
   {
     total[0] += labels[end - 2];
     total[1] += labels[end - 1];
   }
-  else if (labels.n_elem % 4 == 3)
+  else if ((end - begin) % 4 == 3)
   {
     total[0] += labels[end - 3];
     total[1] += labels[end - 2];

From 536c5f9880c30b32f2f89443a5ed9f956855113b Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 12 Apr 2021 23:05:21 +0530
Subject: [PATCH 054/118] Implemented best binary split for regression

---
 .../best_binary_numeric_split.hpp             | 41 +++++++--
 .../best_binary_numeric_split_impl.hpp        | 89 +++++++++++++++++++
 2 files changed, 125 insertions(+), 5 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index e8975ee75a..5211f69f8d 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -33,12 +33,10 @@ class BestBinaryNumericSplit
   /**
    * Check if we can split a node.  If we can split a node in a way that
    * improves on 'bestGain', then we return the improved gain.  Otherwise we
-   * return the value 'bestGain'.  If a split is made, then classProbabilities
-   * and aux may be modified.
+   * return the value 'bestGain'.  If a split is made, then splitInfo and aux
+   * may be modified.
    *
-   * It's not necessary that `ElemType` is the same as the type of the data in
-   * `VecType`---if they are different, casting will be done to store the
-   * auxiliary information.
+   * It is used only for classification tasks.
    *
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
@@ -65,6 +63,39 @@ class BestBinaryNumericSplit
       double& splitInfo,
       AuxiliarySplitInfo& aux);
 
+  /**
+   * Check if we can split a node.  If we can split a node in a way that
+   * improves on 'bestGain', then we return the improved gain.  Otherwise we
+   * return the value 'bestGain'.  If a split is made, then splitInfo and aux
+   * may be modified.
+   *
+   * It is used only for regression tasks.
+   *
+   * @param bestGain Best gain seen so far (we'll only split if we find gain
+   *      better than this).
+   * @param data The dimension of data points to check for a split in.
+   * @param labels Labels for each point.
+   * @param numClasses Number of classes in the dataset.
+   * @param weights Weights associated with labels.
+   * @param minimumLeafSize Minimum number of points in a leaf node for
+   *      splitting.
+   * @param minimumGainSplit Minimum gain split.
+   * @param splitInfo Stores split information on a successful split.
+   * @param aux Auxiliary split information, which may be modified on a
+   *      successful split.
+   */
+  template<bool UseWeights, typename VecType, typename WeightVecType>
+  static double SplitIfBetter(
+      const double bestGain,
+      const VecType& data,
+      const arma::Row<double>& labels,
+      const size_t numClasses,
+      const WeightVecType& weights,
+      const size_t minimumLeafSize,
+      const double minimumGainSplit,
+      double& splitInfo,
+      AuxiliarySplitInfo& aux);
+
   /**
    * Returns 2, since the binary split always has two children.
    */
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index c0ad1f1f46..386f032875 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -182,6 +182,95 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   return bestFoundGain;
 }
 
+template<typename FitnessFunction>
+template<bool UseWeights, typename VecType, typename WeightVecType>
+double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
+    const double bestGain,
+    const VecType& data,
+    const arma::Row<double>& labels,
+    const size_t numClasses,
+    const WeightVecType& weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    double& splitInfo,
+    AuxiliarySplitInfo& /* aux */)
+{
+  // First sanity check: if we don't have enough points, we can't split.
+  if (data.n_elem < (minimumLeafSize * 2))
+    return DBL_MAX;
+  if (bestGain == 0.0)
+    return DBL_MAX; // It can't be outperformed.
+
+  // Next, sort the data.
+  arma::uvec sortedIndices = arma::sort_index(data);
+  arma::Row<double> sortedLabels(labels.n_elem);
+  arma::rowvec sortedWeights;
+  for (size_t i = 0; i < sortedLabels.n_elem; ++i)
+    sortedLabels[i] = labels[sortedIndices[i]];
+
+  // Sanity check: if the first element is the same as the last, we can't split
+  // in this dimension.
+  if (data[sortedIndices[0]] == data[sortedIndices[sortedIndices.n_elem - 1]])
+    return DBL_MAX;
+
+  // Only initialize if we are using weights.
+  if (UseWeights)
+  {
+    sortedWeights.set_size(sortedLabels.n_elem);
+    // The weights must keep the same order as the labels.
+    for (size_t i = 0; i < sortedLabels.n_elem; ++i)
+      sortedWeights[i] = weights[sortedIndices[i]];
+  }
+
+  double bestFoundGain = std::min(bestGain + minimumGainSplit, 0.0);
+  bool improved = false;
+  // Force a minimum leaf size of 1 (empty children don't make sense).
+  const size_t minimum = std::max(minimumLeafSize, (size_t) 1);
+
+  // Loop through all possible split points, choosing the best one.
+  for (size_t index = minimum; index < data.n_elem - minimum + 1; ++index)
+  {
+    // Make sure that the value has changed.
+    if (data[sortedIndices[index]] == data[sortedIndices[index - 1]])
+      continue;
+
+    // Calculate the gain for the left and right child.
+    const double leftGain = FitnessFunction::template Evaluate<UseWeights>(sortedLabels,
+        sortedWeights, 0, index);
+    const double rightGain = FitnessFunction::template Evaluate<UseWeights>(sortedLabels,
+        sortedWeights, index, labels.n_elem);
+
+    double gain = leftGain + rightGain;
+
+    // Corner case: is this the best possible split?
+    if (gain >= 0.0)
+    {
+      // We can take a shortcut: no split will be better than this, so just
+      // take this one. The actual split value will be halfway between the
+      // value at index - 1 and index.
+      splitInfo = (data[sortedIndices[index - 1]] +
+          data[sortedIndices[index]]) / 2.0;
+
+      return gain;
+    }
+     if (gain > bestFoundGain)
+    {
+      // We still have a better split.
+      bestFoundGain = gain;
+      splitInfo = (data[sortedIndices[index - 1]] +
+          data[sortedIndices[index]]) / 2.0;
+      improved = true;
+    }
+  }
+
+  // If we didn't improve, return the original gain exactly as we got it
+  // (without introducing floating point errors).
+  if (!improved)
+    return DBL_MAX;
+
+  return bestFoundGain;
+}
+
 template<typename FitnessFunction>
 template<typename ElemType>
 size_t BestBinaryNumericSplit<FitnessFunction>::CalculateDirection(

From ab4f3c3646262a3a10360b3cea86979032a69558 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 12 Apr 2021 23:05:59 +0530
Subject: [PATCH 055/118] Added tests for best binary split

---
 .../tests/decision_tree_regressor_test.cpp    | 93 +++++++++++++++++++
 1 file changed, 93 insertions(+)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index a4438ac635..b020009a1e 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -213,3 +213,96 @@ TEST_CASE("AllCategoricalSplitNoGainTest1", "[DecisionTreeRegressorTest]")
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
 }
+
+/**
+ * Check that the BestBinaryNumericSplit will split on an obviously splittable
+ * dimension.
+ */
+TEST_CASE("BestBinaryNumericSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]")
+{
+  arma::rowvec predictors = { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
+  arma::rowvec labels = { 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  double splitInfo;
+  BestBinaryNumericSplit<MADGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MADGain::Evaluate<false>(labels, 0, weights);
+  const double gain = BestBinaryNumericSplit<MADGain>::SplitIfBetter<false>(
+      bestGain, predictors, labels, 0, weights, 3, 1e-7, splitInfo,
+      aux);
+  const double weightedGain =
+      BestBinaryNumericSplit<MADGain>::SplitIfBetter<true>(bestGain, predictors,
+      labels, 0, weights, 3, 1e-7, splitInfo, aux);
+
+  // Make sure that a split was made.
+  REQUIRE(gain > bestGain);
+
+  // Make sure weight works and is not different than the unweighted one.
+  REQUIRE(gain == weightedGain);
+
+  // The class probabilities, for this split, hold the splitting point, which
+  // should be between 4 and 5.
+  REQUIRE(splitInfo > 0.4);
+  REQUIRE(splitInfo < 0.5);
+  std::cout << "Done\n";
+}
+
+/**
+ * Check that the BestBinaryNumericSplit won't split if not enough points are
+ * given.
+ */
+TEST_CASE("BestBinaryNumericSplitMinSamplesTest1", "[DecisionTreeRegressorTest]")
+{
+  arma::rowvec predictors = { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
+  arma::rowvec labels = { 0.5, 0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+  arma::rowvec weights(labels.n_elem);
+
+  double splitInfo;
+  BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, predictors, labels, 0, weights, 8, 1e-7, splitInfo, aux);
+  // This should make no difference because it won't split at all.
+  const double weightedGain =
+      BestBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
+      labels, 0, weights, 8, 1e-7, splitInfo, aux);
+
+  // Make sure that no split was made.
+  REQUIRE(gain == DBL_MAX);
+  REQUIRE(gain == weightedGain);
+}
+
+/**
+ * Check that the BestBinaryNumericSplit doesn't split a dimension that gives no
+ * gain.
+ */
+TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
+{
+  arma::rowvec predictors(100);
+  arma::rowvec labels(100);
+  arma::rowvec weights;
+  for (size_t i = 0; i < 100; i += 2)
+  {
+    predictors[i] = i;
+    labels[i] = 0.0;
+    predictors[i + 1] = i;
+    labels[i + 1] = 1.0;
+  }
+
+  double splitInfo;
+  BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, predictors, labels, 0, weights, 10, 1e-7, splitInfo,
+      aux);
+
+  // Make sure there was no split.
+  REQUIRE(gain == DBL_MAX);
+}

From c4e3d1fcc3ae88cc23b3c7201c1c86305d8e4c18 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 3 May 2021 11:33:38 +0530
Subject: [PATCH 056/118] Add TODO for optimization in BestBinaryNumericSplit
 for regression

---
 .../methods/decision_tree/best_binary_numeric_split_impl.hpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 386f032875..45f4551600 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -234,6 +234,11 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     if (data[sortedIndices[index]] == data[sortedIndices[index - 1]])
       continue;
 
+    /* TODO: The following function calculates the gain for each split each time from scratch
+             This can be greatly improved using advanced techniques like prefix sum and
+             prefix sum of squares etc. This will have drastic effects on runtime and is
+             definitely something we would want in future.
+     */
     // Calculate the gain for the left and right child.
     const double leftGain = FitnessFunction::template Evaluate<UseWeights>(sortedLabels,
         sortedWeights, 0, index);

From c6f0d2945743652b30f8119add9f1980e0306f66 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 3 May 2021 13:35:49 +0530
Subject: [PATCH 057/118] Make LabelsType a new template parameter and use
 subvec for labels

---
 .../methods/decision_tree/all_categorical_split.hpp   |  5 ++---
 .../decision_tree/all_categorical_split_impl.hpp      | 11 +++++------
 .../methods/decision_tree/decision_tree_impl.hpp      |  3 +--
 src/mlpack/tests/decision_tree_regressor_test.cpp     | 10 +++++-----
 src/mlpack/tests/decision_tree_test.cpp               | 10 +++++-----
 5 files changed, 18 insertions(+), 21 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split.hpp b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
index ec7924bb76..f929e506c1 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
@@ -55,13 +55,12 @@ class AllCategoricalSplit
    * @param aux Auxiliary split information, which may be modified on a
    *      successful split.
    */
-  template<bool UseWeights, typename VecType, typename ElemType, typename WeightVecType>
+  template<bool UseWeights, typename VecType, typename LabelsType, typename WeightVecType>
   static double SplitIfBetter(
       const double bestGain,
       const VecType& data,
       const size_t numCategories,
-      const arma::Row<ElemType>& labels,
-      const size_t begin,
+      const LabelsType& labels,
       const size_t numClasses,
       const WeightVecType& weights,
       const size_t minimumLeafSize,
diff --git a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
index f7191a035b..754a092737 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
@@ -16,13 +16,12 @@ namespace mlpack {
 namespace tree {
 
 template<typename FitnessFunction>
-template<bool UseWeights, typename VecType, typename ElemType, typename WeightVecType>
+template<bool UseWeights, typename VecType, typename LabelsType, typename WeightVecType>
 double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
     const size_t numCategories,
-    const arma::Row<ElemType>& labels,
-    const size_t begin,
+    const LabelsType& labels,
     const size_t numClasses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
@@ -59,7 +58,7 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
   // Calculate the gain of the split.  First we have to calculate the labels
   // that would be assigned to each child.
   arma::uvec childPositions(numCategories, arma::fill::zeros);
-  std::vector<arma::Row<ElemType>> childLabels(numCategories);
+  std::vector<arma::Row<typename LabelsType::elem_type>> childLabels(numCategories);
   std::vector<arma::Row<double>> childWeights(numCategories);
   for (size_t i = 0; i < numCategories; ++i)
   {
@@ -76,12 +75,12 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
 
     if (UseWeights)
     {
-      childLabels[category][childPositions[category]] = labels[begin + i];
+      childLabels[category][childPositions[category]] = labels[i];
       childWeights[category][childPositions[category]++] = weights[i];
     }
     else
     {
-      childLabels[category][childPositions[category]++] = labels[begin + i];
+      childLabels[category][childPositions[category]++] = labels[i];
     }
   }
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
index b897530b3f..0c5d106002 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
@@ -646,8 +646,7 @@ double DecisionTree<FitnessFunction,
         dimGain = CategoricalSplit::template SplitIfBetter<UseWeights>(bestGain,
             data.cols(begin, begin + count - 1).row(i),
             datasetInfo.NumMappings(i),
-            labels,
-            begin,
+            labels.subvec(begin, begin + count - 1),
             numClasses,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index b020009a1e..c8a4db9d03 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -141,10 +141,10 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]")
   // Call the method to do the splitting.
   const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictor, 2, labels, 0, 0, weights, 3, 1e-7, splitInfo, aux);
+      bestGain, predictor, 2, labels, 0, weights, 3, 1e-7, splitInfo, aux);
   const double weightedGain =
       AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictor, 2,
-      labels, 0, 0, weights, 3, 1e-7, splitInfo, aux);
+      labels, 0, weights, 3, 1e-7, splitInfo, aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -172,7 +172,7 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest1", "[DecisionTreeRegressorTest]")
   // Call the method to do the splitting.
   const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, 4, labels, 0, 0, weights, 4, 1e-7, splitInfo, aux);
+      bestGain, predictors, 4, labels, 0, weights, 4, 1e-7, splitInfo, aux);
 
   // Make sure it's not split.
   REQUIRE(gain == DBL_MAX);
@@ -203,11 +203,11 @@ TEST_CASE("AllCategoricalSplitNoGainTest1", "[DecisionTreeRegressorTest]")
   // Call the method to do the splitting.
   const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, 10, labels, 0, 0, weights, 10, 1e-7,
+      bestGain, predictors, 10, labels, 0, weights, 10, 1e-7,
       splitInfo, aux);
   const double weightedGain =
       AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
-      10, labels, 0, 0, predictors, 10, 1e-7, splitInfo, aux);
+      10, labels, 0, predictors, 10, 1e-7, splitInfo, aux);
 
   // Make sure that there was no split.
   REQUIRE(gain == DBL_MAX);
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 2f63872f23..d02a49f75a 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -493,11 +493,11 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest", "[DecisionTreeTest]")
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, 4, labels, 0, 3, weights, 3, 1e-7, classProbabilities[0],
+      bestGain, values, 4, labels, 3, weights, 3, 1e-7, classProbabilities[0],
       aux);
   const double weightedGain =
       AllCategoricalSplit<GiniGain>::SplitIfBetter<true>(bestGain, values, 4,
-      labels, 0, 3, weights, 3, 1e-7, classProbabilities[0], aux);
+      labels, 3, weights, 3, 1e-7, classProbabilities[0], aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -529,7 +529,7 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest", "[DecisionTreeTest]")
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, 4, labels, 0, 3, weights, 4, 1e-7,
+      bestGain, values, 4, labels, 3, weights, 4, 1e-7,
       classProbabilities[0], aux);
 
   // Make sure it's not split.
@@ -561,11 +561,11 @@ TEST_CASE("AllCategoricalSplitNoGainTest", "[DecisionTreeTest]")
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, 10, labels, 0, 3, weights, 10, 1e-7,
+      bestGain, values, 10, labels, 3, weights, 10, 1e-7,
       classProbabilities[0], aux);
   const double weightedGain =
       AllCategoricalSplit<GiniGain>::SplitIfBetter<true>(bestGain, values, 10,
-      labels, 0, 3, weights, 10, 1e-7, classProbabilities[0], aux);
+      labels, 3, weights, 10, 1e-7, classProbabilities[0], aux);
 
   // Make sure that there was no split.
   REQUIRE(gain == DBL_MAX);

From 0449ace9228ce944ce8d3908dddd86d86e9aaefe Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 3 May 2021 13:41:49 +0530
Subject: [PATCH 058/118] Update documentation

---
 src/mlpack/methods/decision_tree/all_categorical_split.hpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split.hpp b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
index f929e506c1..2ac91b099a 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
@@ -45,7 +45,6 @@ class AllCategoricalSplit
    * @param data The dimension of data points to check for a split in.
    * @param numCategories Number of categories in the categorical data.
    * @param labels Labels for each point.
-   * @param begin Start index of labels.
    * @param numClasses Number of classes in the dataset.
    * @param weights Weights associated with labels.
    * @param minimumLeafSize Minimum number of points in a leaf node for

From dcc96c056ce9d07a3bddfd50999e58c360f7dc25 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 10 May 2021 14:30:37 +0530
Subject: [PATCH 059/118] Amend RandomBinaryNumericSplit signature to support
 regression

---
 .../random_binary_numeric_split.hpp            | 18 ++++++++----------
 .../random_binary_numeric_split_impl.hpp       |  9 ++++-----
 src/mlpack/tests/decision_tree_test.cpp        | 14 ++++++--------
 3 files changed, 18 insertions(+), 23 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
index d7ab4732f8..98e1772ca7 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
@@ -34,7 +34,7 @@ class RandomBinaryNumericSplit
   /**
    * Check if we can split a node.  If we can split a node in a way that
    * improves on 'bestGain', then we return the improved gain.  Otherwise we
-   * return the value 'bestGain'.  If a split is made, then classProbabilities
+   * return the value 'bestGain'.  If a split is made, then splitInfo
    * and aux may be modified.
    *
    * @code
@@ -66,8 +66,7 @@ class RandomBinaryNumericSplit
    * @param minimumLeafSize Minimum number of points in a leaf node for
    *      splitting.
    * @param minimumGainSplit Minimum gain split.
-   * @param classProbabilities Class probabilities vector, which may be filled
-   *      with split information a successful split.
+   * @param splitInfo Stores split information on a successful split.
    * @param aux Auxiliary split information, which may be modified on a
    *      successful split.
    * @param splitIfBetterGain When set to true, it will split only when gain is
@@ -83,19 +82,18 @@ class RandomBinaryNumericSplit
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
-      arma::vec& classProbabilities,
+      double& splitInfo,
       AuxiliarySplitInfo& aux,
       const bool splitIfBetterGain = false);
 
   /**
    * Returns 2, since the binary split always has two children.
    *
-   * @param classProbabilities Class probabilities vector, which may be filled
-   *      with split information a successful split. (Not used here.)
+   * @param splitInfo Auxiliary information for the split.
    * @param aux Auxiliary split information, which may be modified on a
-   *      successful split. (Not used here.)
+   *      successful split.
    */
-  static size_t NumChildren(const arma::vec& /* classProbabilities */,
+  static size_t NumChildren(const double& /* splitInfo */,
                             const AuxiliarySplitInfo& /* aux */)
   {
     return 2;
@@ -105,13 +103,13 @@ class RandomBinaryNumericSplit
    * Given a point, calculate which child it should go to (left or right).
    *
    * @param point Point to calculate direction of.
-   * @param classProbabilities Auxiliary information for the split.
+   * @param splitInfo Auxiliary information for the split.
    * @param * (aux) Auxiliary information for the split (Unused).
    */
   template<typename ElemType>
   static size_t CalculateDirection(
       const ElemType& point,
-      const arma::vec& classProbabilities,
+      const double& splitInfo,
       const AuxiliarySplitInfo& /* aux */);
 };
 
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
index 555113970c..4b5b792b13 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
@@ -27,7 +27,7 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
-    arma::vec& classProbabilities,
+    double& splitInfo,
     AuxiliarySplitInfo& /* aux */,
     const bool splitIfBetterGain)
 {
@@ -125,8 +125,7 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   if (gain < bestFoundGain && splitIfBetterGain)
     return DBL_MAX;
 
-  classProbabilities.set_size(1);
-  classProbabilities(0) = randomPivot;
+  splitInfo = randomPivot;
 
   if (UseWeights)
     gain /= totalWeight;
@@ -140,10 +139,10 @@ template<typename FitnessFunction>
 template<typename ElemType>
 size_t RandomBinaryNumericSplit<FitnessFunction>::CalculateDirection(
     const ElemType& point,
-    const arma::vec& classProbabilities,
+    const double& splitInfo,
     const AuxiliarySplitInfo& /* aux */)
 {
-  if (point <= classProbabilities(0))
+  if (point <= splitInfo)
     return 0; // Go left.
   else
     return 1; // Go right.
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index d02a49f75a..efd728fb72 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -384,23 +384,22 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   arma::Row<size_t> labels("0 0 0 0 0 1 1 1 1 1 1");
   arma::rowvec weights(labels.n_elem);
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities,
+      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities[0],
       aux);
   // This should make no difference because it won't split at all.
   const double weightedGain =
       RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 8, 1e-7, classProbabilities, aux);
+      labels, 2, weights, 8, 1e-7, classProbabilities[0], aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -420,18 +419,17 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities,
+      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities[0],
       aux, true);
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -451,7 +449,7 @@ TEST_CASE("RandomBinaryNumericSplitDiffSplitTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities, classProbabilities1;
+  arma::vec classProbabilities(1), classProbabilities1(1);
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux1;
 

From be81c14a52b50bc8fcf34417fb21fa8a81e9f8ef Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 11 May 2021 10:02:24 +0530
Subject: [PATCH 060/118] Implement DecisionTreeRegressor

---
 .../decision_tree/decision_tree_regressor.hpp | 138 +++++
 .../decision_tree_regressor_impl.hpp          | 516 +++++++++++++++++-
 2 files changed, 625 insertions(+), 29 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index 17358d07a6..55f6816f7e 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -373,6 +373,144 @@ class DecisionTreeRegressor :
                    DimensionSelectionType(),
                const std::enable_if_t<arma::is_arma_type<typename
                    std::remove_reference<WeightsType>::type>::value>* = 0);
+
+  /**
+   * Make prediction for the given point, using the entire tree.  The predicted
+   * label is returned.
+   *
+   * @param point Point to predict.
+   */
+  template<typename VecType>
+  double Predict(const VecType& point) const;
+
+  /**
+   * Make prediction for the given points, using the entire tree. The predicted
+   * labels for each point are stored in the given vector.
+   *
+   * @param data Set of points to predict.
+   * @param predictions This will be filled with predictions for each point.
+   */
+  template<typename MatType>
+  void Predict(const MatType& data,
+                arma::Row<double>& predictions) const;
+
+  /**
+   * Serialize the tree.
+   */
+  template<typename Archive>
+  void serialize(Archive& ar, const uint32_t /* version */);
+
+  //! Get the number of children.
+  size_t NumChildren() const { return children.size(); }
+
+  //! Get the child of the given index.
+  const DecisionTreeRegressor& Child(const size_t i) const { return *children[i]; }
+  //! Modify the child of the given index (be careful!).
+  DecisionTreeRegressor& Child(const size_t i) { return *children[i]; }
+
+  //! Get the split dimension (only meaningful if this is a non-leaf in a
+  //! trained tree).
+  size_t SplitDimension() const { return splitDimension; }
+
+  /**
+   * Given a point and that this node is not a leaf, calculate the index of the
+   * child node this point would go towards.  This method is primarily used by
+   * the Predict() function, but it can be used in a standalone sense too.
+   *
+   * @param point Point to predict.
+   */
+  template<typename VecType>
+  size_t CalculateDirection(const VecType& point) const;
+
+ private:
+  //! The vector of children.
+  std::vector<DecisionTreeRegressor*> children;
+  //! The dimension this node splits on.
+  size_t splitDimension;
+  //! The type of the dimension that we have split on (only meaningful if this
+  //! is a non-leaf in a trained tree).
+  size_t dimensionType;
+  /**
+   * This variable may hold different things. If the node has no children, then
+   * it is guaranteed to hold the prediction label for that node.  If the node
+   * has children, then it may be used arbitrarily by the split type's
+   * CalculateDirection() and SplitIfBetter() function. In this case, it stores
+   * the point at which the split was made.
+   */
+  double splitPointOrPrediction;
+
+  //! Note that this class will also hold the members of the NumericSplit and
+  //! CategoricalSplit AuxiliarySplitInfo classes, since it inherits from them.
+  //! We'll define some convenience typedefs here.
+  typedef typename NumericSplit::AuxiliarySplitInfo
+      NumericAuxiliarySplitInfo;
+  typedef typename CategoricalSplit::AuxiliarySplitInfo
+      CategoricalAuxiliarySplitInfo;
+
+  /**
+   * Calculate the prediction label for the leaf nodes.
+   */
+  template<bool UseWeights, typename LabelsType, typename WeightsType>
+  void CalculatePrediction(const LabelsType& labels,
+                           const WeightsType& weights);
+
+  /**
+   * Corresponding to the public Train() method, this method is designed for
+   * avoiding unnecessary copies during training.  This function is called to
+   * train children.
+   *
+   * @param data Dataset to train on.
+   * @param begin Index of the starting point in the dataset that belongs to
+   *      this node.
+   * @param count Number of points in this node.
+   * @param datasetInfo Type information for each dimension.
+   * @param labels Labels for each training point.
+   * @param numClasses Number of classes in the dataset.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @return The final entropy of decision tree.
+   */
+  template<bool UseWeights, typename MatType, typename LabelsType>
+  double Train(MatType& data,
+               const size_t begin,
+               const size_t count,
+               const data::DatasetInfo& datasetInfo,
+               LabelsType& labels,
+               const size_t numClasses,
+               arma::rowvec& weights,
+               const size_t minimumLeafSize,
+               const double minimumGainSplit,
+               const size_t maximumDepth,
+               DimensionSelectionType& dimensionSelector);
+
+  /**
+   * Corresponding to the public Train() method, this method is designed for
+   * avoiding unnecessary copies during training.  This method is called for
+   * training children.
+   *
+   * @param data Dataset to train on.
+   * @param begin Index of the starting point in the dataset that belongs to
+   *      this node.
+   * @param count Number of points in this node.
+   * @param labels Labels for each training point.
+   * @param numClasses Number of classes in the dataset.
+   * @param minimumLeafSize Minimum number of points in each leaf node.
+   * @param minimumGainSplit Minimum gain for the node to split.
+   * @param maximumDepth Maximum depth for the tree.
+   * @return The final entropy of decision tree.
+   */
+  template<bool UseWeights, typename MatType, typename LabelsType>
+  double Train(MatType& data,
+               const size_t begin,
+               const size_t count,
+               LabelsType& labels,
+               const size_t numClasses,
+               arma::rowvec& weights,
+               const size_t minimumLeafSize,
+               const double minimumGainSplit,
+               const size_t maximumDepth,
+               DimensionSelectionType& dimensionSelector);
 };
 
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 1145b110e0..606518ba3c 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -13,6 +13,7 @@
 #define MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_IMPL_HPP
 
 #include "decision_tree_regressor.hpp"
+#include "utils.hpp"
 
 namespace mlpack {
 namespace tree {
@@ -29,11 +30,10 @@ DecisionTreeRegressor<FitnessFunction,
                       DimensionSelectionType,
                       NoRecursion>::DecisionTreeRegressor() :
     splitDimension(0),
-    dimensionTypeOrMajorityClass(0),
-    classProbabilities(numClasses)
+    dimensionType(0),
+    splitPointOrPrediction(0.0)
 {
-  // Initialize utility vector.
-  classProbabilities.fill(1.0 / (double) numClasses);
+  // Nothing to do here.
 }
 
 //! Construct and train without weight.
@@ -68,7 +68,7 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -104,7 +104,7 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, weights,
+  Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, weights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -144,7 +144,7 @@ DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -186,7 +186,7 @@ DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -206,7 +206,6 @@ DecisionTreeRegressor<FitnessFunction,
     MatType data,
     const data::DatasetInfo& datasetInfo,
     LabelsType labels,
-    const size_t numClasses,
     WeightsType weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -225,7 +224,7 @@ DecisionTreeRegressor<FitnessFunction,
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
               tmpWeights, minimumLeafSize, minimumGainSplit);
 }
 
@@ -268,7 +267,7 @@ DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -288,8 +287,8 @@ DecisionTreeRegressor<FitnessFunction,
     NumericAuxiliarySplitInfo(other),
     CategoricalAuxiliarySplitInfo(other),
     splitDimension(other.splitDimension),
-    dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
-    classProbabilities(other.classProbabilities)
+    dimensionType(other.dimensionType),
+    splitPointOrPrediction(other.splitPointOrPrediction)
 {
   // Copy each child.
   for (size_t i = 0; i < other.children.size(); ++i)
@@ -313,11 +312,10 @@ DecisionTreeRegressor<FitnessFunction,
     CategoricalAuxiliarySplitInfo(std::move(other)),
     children(std::move(other.children)),
     splitDimension(other.splitDimension),
-    dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
-    classProbabilities(std::move(other.classProbabilities))
+    dimensionType(other.dimensionType),
+    splitPointOrPrediction(other.splitPointOrPrediction)
 {
-  // Reset the other object.
-  other.classProbabilities.ones(1); // One class, P(1) = 1.
+  // Nothing to do here.
 }
 
 //! Copy another tree.
@@ -348,12 +346,12 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Copy everything from the other tree.
   splitDimension = other.splitDimension;
-  dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
-  classProbabilities = other.classProbabilities;
+  dimensionType = other.dimensionType;
+  splitPointOrPrediction = other.splitPointOrPrediction;
 
   // Copy the children.
   for (size_t i = 0; i < other.children.size(); ++i)
-    children.push_back(new DecisionTree(*other.children[i]));
+    children.push_back(new DecisionTreeRegressor(*other.children[i]));
 
   // Copy the auxiliary info.
   NumericAuxiliarySplitInfo::operator=(other);
@@ -391,11 +389,8 @@ DecisionTreeRegressor<FitnessFunction,
   // Take ownership of the other tree's components.
   children = std::move(other.children);
   splitDimension = other.splitDimension;
-  dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
-  classProbabilities = std::move(other.classProbabilities);
-
-  // Reset the class probabilities of the other object.
-  other.classProbabilities.ones(1); // One class, P(1) = 1.
+  dimensionType = other.dimensionType;
+  splitPointOrPrediction = other.splitPointOrPrediction;
 
   // Take ownership of the auxiliary info.
   NumericAuxiliarySplitInfo::operator=(std::move(other));
@@ -456,7 +451,7 @@ double DecisionTreeRegressor<FitnessFunction,
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
-      numClasses, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      0, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
 
@@ -494,7 +489,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  return Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses,
+  return Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -541,7 +536,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
-      numClasses, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      0, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
 
@@ -585,11 +580,474 @@ double DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
-  return Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses,
+  return Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
 
+//! Train on the given data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<bool UseWeights, typename MatType, typename LabelsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType& data,
+    const size_t begin,
+    const size_t count,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType& labels,
+    const size_t numClasses,
+    arma::rowvec& weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType& dimensionSelector)
+{
+  // Clear children if needed.
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+  children.clear();
+
+  // Look through the list of dimensions and obtain the gain of the best split.
+  // We'll cache the best numeric and categorical split auxiliary information in
+  // numericAux and categoricalAux (and clear them later if we make no split),
+  double bestGain = FitnessFunction::template Evaluate<UseWeights>(
+      labels.subvec(begin, begin + count - 1),
+      numClasses,
+      UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
+  size_t bestDim = datasetInfo.Dimensionality(); // This means "no split".
+  const size_t end = dimensionSelector.End();
+
+  if (maximumDepth != 1)
+  {
+    for (size_t i = dimensionSelector.Begin(); i != end;
+         i = dimensionSelector.Next())
+    {
+      double dimGain = -DBL_MAX;
+      if (datasetInfo.Type(i) == data::Datatype::categorical)
+      {
+        dimGain = CategoricalSplit::template SplitIfBetter<UseWeights>(bestGain,
+            data.cols(begin, begin + count - 1).row(i),
+            datasetInfo.NumMappings(i),
+            labels.subvec(begin, begin + count - 1),
+            numClasses,
+            UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
+            minimumLeafSize,
+            minimumGainSplit,
+            splitPointOrPrediction,
+            *this);
+      }
+      else if (datasetInfo.Type(i) == data::Datatype::numeric)
+      {
+        dimGain = NumericSplit::template SplitIfBetter<UseWeights>(bestGain,
+            data.cols(begin, begin + count - 1).row(i),
+            labels.subvec(begin, begin + count - 1),
+            numClasses,
+            UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
+            minimumLeafSize,
+            minimumGainSplit,
+            splitPointOrPrediction,
+            *this);
+      }
+
+      // If the splitter reported that it did not split, move to the next
+      // dimension.
+      if (dimGain == DBL_MAX)
+        continue;
+
+      // Was there an improvement?  If so mark that it's the new best dimension.
+      bestDim = i;
+      bestGain = dimGain;
+
+      // If the gain is the best possible, no need to keep looking.
+      if (bestGain >= 0.0)
+        break;
+    }
+  }
+
+  // Did we split or not?  If so, then split the data and create the children.
+  if (bestDim != datasetInfo.Dimensionality())
+  {
+    dimensionType = (size_t) datasetInfo.Type(bestDim);
+    splitDimension = bestDim;
+
+    // Get the number of children we will have.
+    size_t numChildren = 0;
+    if (datasetInfo.Type(bestDim) == data::Datatype::categorical)
+      numChildren = CategoricalSplit::NumChildren(splitPointOrPrediction, *this);
+    else
+      numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
+
+    // Calculate all child assignments.
+    arma::Row<size_t> childAssignments(count);
+    if (datasetInfo.Type(bestDim) == data::Datatype::categorical)
+    {
+      for (size_t j = begin; j < begin + count; ++j)
+        childAssignments[j - begin] = CategoricalSplit::CalculateDirection(
+            data(bestDim, j), splitPointOrPrediction, *this);
+    }
+    else
+    {
+      for (size_t j = begin; j < begin + count; ++j)
+      {
+        childAssignments[j - begin] = NumericSplit::CalculateDirection(
+            data(bestDim, j), splitPointOrPrediction, *this);
+      }
+    }
+
+    // Figure out counts of children.
+    arma::Row<size_t> childCounts(numChildren, arma::fill::zeros);
+    for (size_t i = begin; i < begin + count; ++i)
+      childCounts[childAssignments[i - begin]]++;
+
+    // Initialize bestGain if recursive split is allowed.
+    if (!NoRecursion)
+    {
+      bestGain = 0.0;
+    }
+
+    // Split into children.
+    size_t currentCol = begin;
+    for (size_t i = 0; i < numChildren; ++i)
+    {
+      size_t currentChildBegin = currentCol;
+      for (size_t j = currentChildBegin; j < begin + count; ++j)
+      {
+        if (childAssignments[j - begin] == i)
+        {
+          childAssignments.swap_cols(currentCol - begin, j - begin);
+          data.swap_cols(currentCol, j);
+          labels.swap_cols(currentCol, j);
+          if (UseWeights)
+            weights.swap_cols(currentCol, j);
+          ++currentCol;
+        }
+      }
+
+      // Now build the child recursively.
+      DecisionTreeRegressor* child = new DecisionTreeRegressor();
+      if (NoRecursion)
+      {
+        child->Train<UseWeights>(data, currentChildBegin,
+            currentCol - currentChildBegin, datasetInfo, labels, numClasses,
+            weights, currentCol - currentChildBegin, minimumGainSplit,
+            maximumDepth - 1, dimensionSelector);
+      }
+      else
+      {
+        // During recursion entropy of child node may change.
+        double childGain = child->Train<UseWeights>(data, currentChildBegin,
+            currentCol - currentChildBegin, datasetInfo, labels, numClasses,
+            weights, minimumLeafSize, minimumGainSplit, maximumDepth - 1,
+            dimensionSelector);
+        bestGain += double(childCounts[i]) / double(count) * (-childGain);
+      }
+      children.push_back(child);
+    }
+  }
+  else
+  {
+    // Clear auxiliary info objects.
+    NumericAuxiliarySplitInfo::operator=(NumericAuxiliarySplitInfo());
+    CategoricalAuxiliarySplitInfo::operator=(CategoricalAuxiliarySplitInfo());
+
+    // Calculate prediction label because we are a leaf.
+    CalculatePrediction<UseWeights>(
+        labels.subvec(begin, begin + count - 1),
+        UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
+  }
+
+  return -bestGain;
+}
+
+//! Train on the given data, assuming all dimensions are numeric.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<bool UseWeights, typename MatType, typename LabelsType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Train(
+    MatType& data,
+    const size_t begin,
+    const size_t count,
+    LabelsType& labels,
+    const size_t numClasses,
+    arma::rowvec& weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType& dimensionSelector)
+{
+  // Clear children if needed.
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+  children.clear();
+
+  // We won't be using these members, so reset them.
+  CategoricalAuxiliarySplitInfo::operator=(CategoricalAuxiliarySplitInfo());
+
+  // Look through the list of dimensions and obtain the best split.  We'll cache
+  // the best numeric split auxiliary information in numericAux (and clear it
+  // later if we don't make a split), and use classProbabilities as auxiliary
+  // information.  Later we'll overwrite classProbabilities to the empirical
+  // class probabilities if we do not split.
+  double bestGain = FitnessFunction::template Evaluate<UseWeights>(
+      labels.subvec(begin, begin + count - 1),
+      numClasses,
+      UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
+  size_t bestDim = data.n_rows; // This means "no split".
+
+  if (maximumDepth != 1)
+  {
+    for (size_t i = dimensionSelector.Begin(); i != dimensionSelector.End();
+         i = dimensionSelector.Next())
+    {
+      const double dimGain = NumericSplitType<FitnessFunction>::template
+          SplitIfBetter<UseWeights>(bestGain,
+                                    data.cols(begin, begin + count - 1).row(i),
+                                    labels.cols(begin, begin + count - 1),
+                                    numClasses,
+                                    UseWeights ?
+                                        weights.cols(begin, begin + count - 1) :
+                                        weights,
+                                    minimumLeafSize,
+                                    minimumGainSplit,
+                                    splitPointOrPrediction,
+                                    *this);
+
+      // If the splitter did not report that it improved, then move to the next
+      // dimension.
+      if (dimGain == DBL_MAX)
+        continue;
+
+      bestDim = i;
+      bestGain = dimGain;
+
+      // If the gain is the best possible, no need to keep looking.
+      if (bestGain >= 0.0)
+        break;
+    }
+  }
+
+  // Did we split or not?  If so, then split the data and create the children.
+  if (bestDim != data.n_rows)
+  {
+    // We know that the split is numeric.
+    size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
+    splitDimension = bestDim;
+    dimensionType = (size_t) data::Datatype::numeric;
+
+    // Calculate all child assignments.
+    arma::Row<size_t> childAssignments(count);
+
+    for (size_t j = begin; j < begin + count; ++j)
+    {
+      childAssignments[j - begin] = NumericSplit::CalculateDirection(
+          data(bestDim, j), splitPointOrPrediction, *this);
+    }
+
+    // Calculate counts of children in each node.
+    arma::Row<size_t> childCounts(numChildren);
+    childCounts.zeros();
+    for (size_t j = begin; j < begin + count; ++j)
+      childCounts[childAssignments[j - begin]]++;
+
+    // Initialize bestGain if recursive split is allowed.
+    if (!NoRecursion)
+    {
+      bestGain = 0.0;
+    }
+
+    size_t currentCol = begin;
+    for (size_t i = 0; i < numChildren; ++i)
+    {
+      size_t currentChildBegin = currentCol;
+      for (size_t j = currentChildBegin; j < begin + count; ++j)
+      {
+        if (childAssignments[j - begin] == i)
+        {
+          childAssignments.swap_cols(currentCol - begin, j - begin);
+          data.swap_cols(currentCol, j);
+          labels.swap_cols(currentCol, j);
+          if (UseWeights)
+            weights.swap_cols(currentCol, j);
+          ++currentCol;
+        }
+      }
+
+      // Now build the child recursively.
+      DecisionTreeRegressor* child = new DecisionTreeRegressor();
+      if (NoRecursion)
+      {
+        child->Train<UseWeights>(data, currentChildBegin,
+            currentCol - currentChildBegin, labels, numClasses, weights,
+            currentCol - currentChildBegin, minimumGainSplit, maximumDepth - 1,
+            dimensionSelector);
+      }
+      else
+      {
+        // During recursion entropy of child node may change.
+        double childGain = child->Train<UseWeights>(data, currentChildBegin,
+            currentCol - currentChildBegin, labels, numClasses, weights,
+            minimumLeafSize, minimumGainSplit, maximumDepth - 1,
+            dimensionSelector);
+        bestGain += double(childCounts[i]) / double(count) * (-childGain);
+      }
+      children.push_back(child);
+    }
+  }
+  else
+  {
+    // We won't be needing these members, so reset them.
+    NumericAuxiliarySplitInfo::operator=(NumericAuxiliarySplitInfo());
+
+    // Calculate prediction label because we are a leaf.
+    CalculatePrediction<UseWeights>(
+        labels.subvec(begin, begin + count - 1),
+        UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
+  }
+
+  return -bestGain;
+}
+
+//! Return the prediction.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename VecType>
+double DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::Predict(const VecType& point) const
+{
+  if (children.size() == 0)
+  {
+    // Return cached prediction.
+    return splitPointOrPrediction;
+  }
+
+  return children[CalculateDirection(point)]->Predict(point);
+}
+
+//! Return the predictions for a set of points.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType>
+void DecisionTreeRegressor<FitnessFunction,
+                           NumericSplitType,
+                           CategoricalSplitType,
+                           DimensionSelectionType,
+                           NoRecursion
+>::Predict(const MatType& data, arma::Row<double>& predictions) const
+{
+  predictions.set_size(data.n_cols);
+  // If the tree's root is leaf.
+  if (children.size() == 0)
+  {
+    predictions.fill(splitPointOrPrediction);
+    return;
+  }
+
+  // Loop over each point.
+  for (size_t i = 0; i < data.n_cols; ++i)
+    predictions[i] = Predict(data.col(i));
+}
+
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<bool UseWeights, typename LabelsType, typename WeightsType>
+void DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion
+>::CalculatePrediction(const LabelsType& labels, const WeightsType& weights)
+{
+  if (UseWeights)
+  {
+    double accWeights, weightedSum;
+    WeightedSum(labels, weights, 0, labels.n_elem, accWeights, weightedSum);
+    splitPointOrPrediction = weightedSum / accWeights;
+  }
+  else
+  {
+    double sum;
+    Sum(labels, 0, labels.n_elem, sum);
+    splitPointOrPrediction = sum / labels.n_elem;
+  }
+}
+
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename VecType>
+size_t DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion
+>::CalculateDirection(const VecType& point) const
+{
+  if ((data::Datatype) dimensionType == data::Datatype::categorical)
+    return CategoricalSplit::CalculateDirection(point[splitDimension],
+        splitPointOrPrediction, *this);
+  else
+    return NumericSplit::CalculateDirection(point[splitDimension],
+        splitPointOrPrediction, *this);
+}
+
+//! Serialize the tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename Archive>
+void DecisionTreeRegressor<FitnessFunction,
+                           NumericSplitType,
+                           CategoricalSplitType,
+                           DimensionSelectionType,
+                           NoRecursion
+>::serialize(Archive& ar, const uint32_t /* version */)
+{
+  // Clean memory if needed.
+  if (cereal::is_loading<Archive>())
+  {
+    for (size_t i = 0; i < children.size(); ++i)
+      delete children[i];
+    children.clear();
+  }
+  // Serialize the children first.
+  ar(CEREAL_VECTOR_POINTER(children));
+
+  // Now serialize the rest of the object.
+  ar(CEREAL_NVP(splitDimension));
+  ar(CEREAL_NVP(dimensionType));
+  ar(CEREAL_NVP(splitPointOrPrediction));
+}
+
 
 } // namespace tree
 } // namespace mlpack

From a3fc8d9fe6c5cdec2fa92f0ec6445242e2de6f55 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 11 May 2021 10:02:47 +0530
Subject: [PATCH 061/118] Add to CMakeLists.txt

---
 src/mlpack/methods/decision_tree/CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/mlpack/methods/decision_tree/CMakeLists.txt b/src/mlpack/methods/decision_tree/CMakeLists.txt
index 4072a5097d..d36b3d3681 100644
--- a/src/mlpack/methods/decision_tree/CMakeLists.txt
+++ b/src/mlpack/methods/decision_tree/CMakeLists.txt
@@ -4,6 +4,8 @@ set(SOURCES
   all_dimension_select.hpp
   decision_tree.hpp
   decision_tree_impl.hpp
+  decision_tree_regressor.hpp
+  decision_tree_regressor_impl.hpp
   all_categorical_split.hpp
   all_categorical_split_impl.hpp
   best_binary_numeric_split.hpp

From 4f000d5894034dff308abb33fb8c21f24920e6cc Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 11 May 2021 10:03:56 +0530
Subject: [PATCH 062/118] Add evaluation metrics for testing

---
 src/mlpack/tests/test_function_tools.hpp | 51 ++++++++++++++++++++++++
 1 file changed, 51 insertions(+)

diff --git a/src/mlpack/tests/test_function_tools.hpp b/src/mlpack/tests/test_function_tools.hpp
index f6c2f06a8a..eb891361e9 100644
--- a/src/mlpack/tests/test_function_tools.hpp
+++ b/src/mlpack/tests/test_function_tools.hpp
@@ -15,6 +15,7 @@
 #include <mlpack/core.hpp>
 
 #include <mlpack/methods/logistic_regression/logistic_regression.hpp>
+#include <mlpack/core/data/split_data.hpp>
 
 using namespace mlpack;
 using namespace mlpack::distribution;
@@ -80,4 +81,54 @@ inline void LogisticRegressionTestData(arma::mat& data,
   }
 }
 
+inline void LoadBostonHousingDataset(arma::mat& trainData,
+                              arma::mat& testData,
+                              arma::Row<double>& trainLabels,
+                              arma::Row<double>& testLabels,
+                              data::DatasetInfo& info)
+{
+  arma::mat dataset;
+  arma::Row<double> labels;
+
+  if (!data::Load("boston_housing_price.csv", dataset, info))
+    FAIL("Cannot load test dataset boston_housing_price.csv!");
+  if (!data::Load("boston_housing_price_labels.csv", labels))
+    FAIL("Cannot load test dataset boston_housing_price_labels.csv!");
+
+  data::Split(dataset, labels, trainData, testData,
+      trainLabels, testLabels, 0.3);
+}
+
+inline double RMSE(const arma::Row<double>& predictions,
+                   const arma::Row<double>& trueLabels)
+{
+  double rmse = 0.0;
+  for (size_t i = 0; i < predictions.n_elem; ++i)
+  {
+    rmse += std::pow(predictions[i] - trueLabels[i], 2);
+  }
+  rmse /= predictions.n_elem;
+  rmse = sqrt(rmse);
+  return rmse;
+}
+
+/**
+ * Calculates the R2 score of the predictions with true labels.
+ */
+inline double R2Score(const arma::Row<double>& predictions,
+                      const arma::Row<double>& trueLabels)
+{
+  double mean = arma::mean(trueLabels);
+  double SStot = 0.0;
+  double SSres = 0.0;
+  for (size_t i = 0; i < predictions.n_elem; ++i)
+    SSres += std::pow(predictions[i] - trueLabels[i], 2);
+  for (size_t i = 0; i < predictions.n_elem; ++i)
+  {
+    SStot += std::pow(trueLabels[i] - mean, 2);
+  }
+
+  return 1 - SSres / SStot;
+}
+
 #endif

From c2c85f0250b260738dc0571702ecd3ec38f57983 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 11 May 2021 10:04:21 +0530
Subject: [PATCH 063/118] Add initial tests

---
 .../tests/data/boston_housing_price.csv       | 507 ++++++++++++++++++
 .../data/boston_housing_price_labels.csv      | 507 ++++++++++++++++++
 .../tests/decision_tree_regressor_test.cpp    | 492 ++++++++++++++++-
 3 files changed, 1505 insertions(+), 1 deletion(-)
 create mode 100644 src/mlpack/tests/data/boston_housing_price.csv
 create mode 100644 src/mlpack/tests/data/boston_housing_price_labels.csv

diff --git a/src/mlpack/tests/data/boston_housing_price.csv b/src/mlpack/tests/data/boston_housing_price.csv
new file mode 100644
index 0000000000..5c0d211062
--- /dev/null
+++ b/src/mlpack/tests/data/boston_housing_price.csv
@@ -0,0 +1,507 @@
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diff --git a/src/mlpack/tests/data/boston_housing_price_labels.csv b/src/mlpack/tests/data/boston_housing_price_labels.csv
new file mode 100644
index 0000000000..fd7ad517aa
--- /dev/null
+++ b/src/mlpack/tests/data/boston_housing_price_labels.csv
@@ -0,0 +1,507 @@
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diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index c8a4db9d03..05031b3f29 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -10,7 +10,7 @@
  * http://www.opensource.org/licenses/BSD-3-Clause for more information.
  */
 #include <mlpack/core.hpp>
-#include <mlpack/methods/decision_tree/decision_tree.hpp>
+#include <mlpack/methods/decision_tree/decision_tree_regressor.hpp>
 #include <mlpack/methods/decision_tree/mad_gain.hpp>
 #include <mlpack/methods/decision_tree/mse_gain.hpp>
 #include <mlpack/methods/decision_tree/random_dimension_select.hpp>
@@ -19,6 +19,7 @@
 #include "catch.hpp"
 #include "serialization.hpp"
 #include "mock_categorical_data.hpp"
+#include "test_function_tools.hpp"
 
 using namespace mlpack;
 using namespace mlpack::tree;
@@ -306,3 +307,492 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
 }
+
+/**
+ * A basic construction of the decision tree---ensure that we can create the
+ * tree and that it split at least once.
+ */
+TEST_CASE("BasicConstructionTest_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
+
+  // Use default parameters.
+  DecisionTreeRegressor<> d(dataset, labels);
+
+  // Now require that we have some children.
+  REQUIRE(d.NumChildren() > 0);
+}
+
+/**
+ * Construct a tree with weighted labels.
+ */
+TEST_CASE("BasicConstructionTestWithWeight_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  // Use default parameters.
+  DecisionTreeRegressor<> wd(dataset, labels, weights);
+  DecisionTreeRegressor<> d(dataset, labels);
+
+  // Now require that we have some children.
+  REQUIRE(wd.NumChildren() > 0);
+  REQUIRE(wd.NumChildren() == d.NumChildren());
+}
+
+/**
+ * Construct the decision tree on numeric data only and see that we can fit it
+ * exactly and achieve perfect performance on the training set.
+ */
+TEST_CASE("PerfectTrainingSet_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
+
+  DecisionTreeRegressor<> d(dataset, labels, 1, 0.0); // Minimum leaf size of 1.
+
+  // Make sure that we can get perfect accuracy on the training set.
+  for (size_t i = 0; i < 100; ++i)
+  {
+    double prediction;
+    prediction = d.Predict(dataset.col(i));
+
+    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+  }
+}
+
+/**
+ * Construct the decision tree with weighted labels
+ */
+TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
+{
+  // Completely random dataset with no structure.
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  DecisionTreeRegressor<> d(dataset, labels, weights, 1, 0.0);
+
+  // This part of code is dupliacte with no weighted one.
+  for (size_t i = 0; i < 100; ++i)
+  {
+    size_t prediction;
+    prediction = d.Predict(dataset.col(i));
+
+    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+  }
+}
+
+/**
+ * Test that the decision tree generalizes reasonably.
+ */
+TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
+{
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::Row<double> trainLabels, testLabels;
+  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+
+  // Initialize an all-ones weight matrix.
+  arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+
+  // Build decision tree.
+  DecisionTreeRegressor<> d(trainData, info, trainLabels);
+  DecisionTreeRegressor<> wd(trainData, info, trainLabels, weights);
+
+  // Get the predicted test labels.
+  arma::Row<double> predictions;
+  d.Predict(testData, predictions);
+
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out rmse.
+  double rmse = RMSE(predictions, testLabels);
+
+  REQUIRE(rmse < 9.21);
+  std::cout << predictions << std::endl << testLabels;
+
+  // Reset the prediction.
+  predictions.zeros();
+  wd.Predict(testData, predictions);
+
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out the rmse.
+  double wdrmse = RMSE(predictions, testLabels);
+
+  REQUIRE(wdrmse < 9.21);
+}
+
+/**
+ * Test that the decision tree generalizes reasonably when built on float data.
+ */
+TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
+{
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::Row<double> trainLabels, testLabels;
+  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+
+  // Initialize an all-ones weight matrix.
+  arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+
+  // Build decision tree.
+  DecisionTreeRegressor<> d(trainData, trainLabels);
+  DecisionTreeRegressor<> wd(trainData, trainLabels, weights);
+
+  // Get the predicted test labels.
+  arma::Row<double> predictions;
+  d.Predict(testData, predictions);
+
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out the rmse.
+  double rmse = RMSE(predictions, testLabels);
+
+  REQUIRE(rmse < 9.21);
+  std::cout << R2Score(predictions, testLabels) std::endl;
+
+  // Reset the prediction.
+  predictions.zeros();
+  wd.Predict(testData, predictions);
+
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out the rmse.
+  double wdrmse = RMSE(predictions, testLabels);
+
+  REQUIRE(wdrmse < 9.21);
+}
+
+// /**
+//  * Test that we can build a decision tree on a simple categorical dataset.
+//  */
+// TEST_CASE("CategoricalBuildTest", "[DecisionTreeTest]")
+// {
+//   arma::mat d;
+//   arma::Row<size_t> l;
+//   data::DatasetInfo di;
+//   MockCategoricalData(d, l, di);
+
+//   // Split into a training set and a test set.
+//   arma::mat trainingData = d.cols(0, 1999);
+//   arma::mat testData = d.cols(2000, 3999);
+//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
+//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+
+//   // Build the tree.
+//   DecisionTree<> tree(trainingData, di, trainingLabels, 5, 10);
+
+//   // Now evaluate the accuracy of the tree.
+//   arma::Row<size_t> predictions;
+//   tree.Classify(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+//   size_t correct = 0;
+//   for (size_t i = 0; i < testData.n_cols; ++i)
+//     if (testLabels[i] == predictions[i])
+//       ++correct;
+
+//   // Make sure we got at least 70% accuracy.
+//   const double correctPct = double(correct) / double(testData.n_cols);
+//   REQUIRE(correctPct > 0.70);
+// }
+
+// /**
+//  * Test that we can build a decision tree with weights on a simple categorical
+//  * dataset.
+//  */
+// TEST_CASE("CategoricalBuildTestWithWeight", "[DecisionTreeTest]")
+// {
+//   arma::mat d;
+//   arma::Row<size_t> l;
+//   data::DatasetInfo di;
+//   MockCategoricalData(d, l, di);
+
+//   // Split into a training set and a test set.
+//   arma::mat trainingData = d.cols(0, 1999);
+//   arma::mat testData = d.cols(2000, 3999);
+//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
+//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+
+//   arma::Row<double> weights = arma::ones<arma::Row<double>>(
+//       trainingLabels.n_elem);
+
+//   // Build the tree.
+//   DecisionTree<> tree(trainingData, di, trainingLabels, 5, weights, 10);
+
+//   // Now evaluate the accuracy of the tree.
+//   arma::Row<size_t> predictions;
+//   tree.Classify(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+//   size_t correct = 0;
+//   for (size_t i = 0; i < testData.n_cols; ++i)
+//     if (testLabels[i] == predictions[i])
+//       ++correct;
+
+//   // Make sure we got at least 70% accuracy.
+//   const double correctPct = double(correct) / double(testData.n_cols);
+//   REQUIRE(correctPct > 0.70);
+// }
+
+/**
+ * Test that we can build a decision tree using weighted data (where the
+ * low-weighted data is random noise), and that the tree still builds correctly
+ * enough to get good results.
+ */
+TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
+{
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::Row<double> trainLabels, testLabels;
+  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+
+  // Add some noise.
+  arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
+  arma::Row<double> noiseLabels(500);
+  for (size_t i = 0; i < noiseLabels.n_elem; ++i)
+    noiseLabels[i] = 15 + math::Random(0, 10); // Random label.
+
+  // Concatenate data matrices.
+  arma::mat data = arma::join_rows(trainData, noise);
+  arma::Row<double> fullLabels = arma::join_rows(trainLabels, noiseLabels);
+
+  // Now set weights.
+  arma::rowvec weights(trainData.n_cols + 500);
+  for (size_t i = 0; i < trainData.n_cols; ++i)
+    weights[i] = math::Random(0.9, 1.0);
+  for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
+    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
+
+  // Now build the decision tree.  I think the syntax is right here.
+  DecisionTreeRegressor<> d(data, fullLabels, weights);
+
+  // Now we can check that we get good performance on the VC2 test set.
+  arma::Row<double> predictions;
+  d.Predict(testData, predictions);
+
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out the accuracy.
+  double rmse = RMSE(predictions, testLabels);
+
+  REQUIRE(rmse < 9.21);
+}
+
+// /**
+//  * Test that we can build a decision tree on a simple categorical dataset using
+//  * weights, with low-weight noise added.
+//  */
+// TEST_CASE("CategoricalWeightedBuildTest", "[DecisionTreeTest]")
+// {
+//   arma::mat d;
+//   arma::Row<size_t> l;
+//   data::DatasetInfo di;
+//   MockCategoricalData(d, l, di);
+
+//   // Split into a training set and a test set.
+//   arma::mat trainingData = d.cols(0, 1999);
+//   arma::mat testData = d.cols(2000, 3999);
+//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
+//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+
+//   // Now create random points.
+//   arma::mat randomNoise(4, 2000);
+//   arma::Row<size_t> randomLabels(2000);
+//   for (size_t i = 0; i < 2000; ++i)
+//   {
+//     randomNoise(0, i) = math::Random();
+//     randomNoise(1, i) = math::Random();
+//     randomNoise(2, i) = math::RandInt(4);
+//     randomNoise(3, i) = math::RandInt(2);
+//     randomLabels[i] = math::RandInt(5);
+//   }
+
+//   // Generate weights.
+//   arma::rowvec weights(4000);
+//   for (size_t i = 0; i < 2000; ++i)
+//     weights[i] = math::Random(0.9, 1.0);
+//   for (size_t i = 2000; i < 4000; ++i)
+//     weights[i] = math::Random(0.0, 0.001);
+
+//   arma::mat fullData = arma::join_rows(trainingData, randomNoise);
+//   arma::Row<size_t> fullLabels = arma::join_rows(trainingLabels, randomLabels);
+
+//   // Build the tree.
+//   DecisionTree<> tree(fullData, di, fullLabels, 5, weights, 10);
+
+//   // Now evaluate the accuracy of the tree.
+//   arma::Row<size_t> predictions;
+//   tree.Classify(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+//   size_t correct = 0;
+//   for (size_t i = 0; i < testData.n_cols; ++i)
+//     if (testLabels[i] == predictions[i])
+//       ++correct;
+
+//   // Make sure we got at least 70% accuracy.
+//   const double correctPct = double(correct) / double(testData.n_cols);
+//   REQUIRE(correctPct > 0.70);
+// }
+
+// /**
+//  * Test that we can build a decision tree using weighted data (where the
+//  * low-weighted data is random noise) with information gain, and that the tree
+//  * still builds correctly enough to get good results.
+//  */
+// TEST_CASE("WeightedDecisionTreeInformationGainTest_",
+//     "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat dataset;
+//   arma::Row<double> labels;
+//   if (!data::Load("vc2.csv", dataset))
+//     FAIL("Cannot load test dataset vc2.csv!");
+//   if (!data::Load("vc2_labels.txt", labels))
+//     FAIL("Cannot load labels for vc2_labels.txt!");
+
+//   // Add some noise.
+//   arma::mat noise(dataset.n_rows, 1000, arma::fill::randu);
+//   arma::Row<double> noiseLabels(1000);
+//   for (size_t i = 0; i < noiseLabels.n_elem; ++i)
+//     noiseLabels[i] = math::Random(0, 3); // Random label.
+
+//   // Concatenate data matrices.
+//   arma::mat data = arma::join_rows(dataset, noise);
+//   arma::Row<double> fullLabels = arma::join_rows(labels, noiseLabels);
+
+//   // Now set weights.
+//   arma::rowvec weights(dataset.n_cols + 1000);
+//   for (size_t i = 0; i < dataset.n_cols; ++i)
+//     weights[i] = math::Random(0.9, 1.0);
+//   for (size_t i = dataset.n_cols; i < dataset.n_cols + 1000; ++i)
+//     weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
+
+//   // Now build the decision tree.  I think the syntax is right here.
+//   DecisionTreeRegressor<MADGain> d(data, fullLabels, weights);
+
+//   // Now we can check that we get good performance on the VC2 test set.
+//   arma::mat testData;
+//   arma::Row<double> testLabels;
+//   if (!data::Load("vc2_test.csv", testData))
+//     FAIL("Cannot load test dataset vc2_test.csv!");
+//   if (!data::Load("vc2_test_labels.txt", testLabels))
+//     FAIL("Cannot load labels for vc2_test_labels.txt!");
+
+//   arma::Row<double> predictions;
+//   d.Predict(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+
+//   // Figure out the accuracy.
+//   double accuracy = R2Score(predictions, testLabels);
+
+//   REQUIRE(accuracy > 0.75);
+// }
+
+// /**
+//  * Test that we can build a decision tree using information gain on a simple
+//  * categorical dataset using weights, with low-weight noise added.
+//  */
+// TEST_CASE("CategoricalInformationGainWeightedBuildTest", "[DecisionTreeTest]")
+// {
+//   arma::mat d;
+//   arma::Row<size_t> l;
+//   data::DatasetInfo di;
+//   MockCategoricalData(d, l, di);
+
+//   // Split into a training set and a test set.
+//   arma::mat trainingData = d.cols(0, 1999);
+//   arma::mat testData = d.cols(2000, 3999);
+//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
+//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+
+//   // Now create random points.
+//   arma::mat randomNoise(4, 2000);
+//   arma::Row<size_t> randomLabels(2000);
+//   for (size_t i = 0; i < 2000; ++i)
+//   {
+//     randomNoise(0, i) = math::Random();
+//     randomNoise(1, i) = math::Random();
+//     randomNoise(2, i) = math::RandInt(4);
+//     randomNoise(3, i) = math::RandInt(2);
+//     randomLabels[i] = math::RandInt(5);
+//   }
+
+//   // Generate weights.
+//   arma::rowvec weights(4000);
+//   for (size_t i = 0; i < 2000; ++i)
+//     weights[i] = math::Random(0.9, 1.0);
+//   for (size_t i = 2000; i < 4000; ++i)
+//     weights[i] = math::Random(0.0, 0.001);
+
+//   arma::mat fullData = arma::join_rows(trainingData, randomNoise);
+//   arma::Row<size_t> fullLabels = arma::join_rows(trainingLabels, randomLabels);
+
+//   // Build the tree.
+//   DecisionTree<InformationGain> tree(fullData, di, fullLabels, 5, weights, 10);
+
+//   // Now evaluate the accuracy of the tree.
+//   arma::Row<size_t> predictions;
+//   tree.Classify(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+//   size_t correct = 0;
+//   for (size_t i = 0; i < testData.n_cols; ++i)
+//     if (testLabels[i] == predictions[i])
+//       ++correct;
+
+//   // Make sure we got at least 70% accuracy.
+//   const double correctPct = double(correct) / double(testData.n_cols);
+//   REQUIRE(correctPct > 0.70);
+// }

From 64fde38ae8510fdeaf39a10a3103434454c9cde2 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 28 May 2021 18:26:51 +0530
Subject: [PATCH 064/118] Testing for bug in implementation

---
 .../decision_tree_regressor_impl.hpp          |   5 +
 .../tests/decision_tree_regressor_test.cpp    | 482 +++++++++++-------
 2 files changed, 310 insertions(+), 177 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 606518ba3c..f902a3c2fe 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -761,6 +761,7 @@ double DecisionTreeRegressor<FitnessFunction,
     CalculatePrediction<UseWeights>(
         labels.subvec(begin, begin + count - 1),
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
+    
   }
 
   return -bestGain;
@@ -828,6 +829,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
       // If the splitter did not report that it improved, then move to the next
       // dimension.
+      std::cout << "dim: " << i << "   gain: " << dimGain << std::endl;
       if (dimGain == DBL_MAX)
         continue;
 
@@ -843,6 +845,8 @@ double DecisionTreeRegressor<FitnessFunction,
   // Did we split or not?  If so, then split the data and create the children.
   if (bestDim != data.n_rows)
   {
+    std::cout << "splitpoint: " << splitPointOrPrediction << std::endl;
+    std::cout << "bestDim: " << bestDim << std::endl;
     // We know that the split is numeric.
     size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
     splitDimension = bestDim;
@@ -916,6 +920,7 @@ double DecisionTreeRegressor<FitnessFunction,
     CalculatePrediction<UseWeights>(
         labels.subvec(begin, begin + count - 1),
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
+    std::cout << "Number of poiints in leaf: " << count << std::endl;
   }
 
   return -bestGain;
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 05031b3f29..ef273b1921 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -312,204 +312,332 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
  * A basic construction of the decision tree---ensure that we can create the
  * tree and that it split at least once.
  */
-TEST_CASE("BasicConstructionTest_", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
-  for (size_t i = 0; i < 50; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 0.0;
-  }
-  for (size_t i = 50; i < 100; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 1.0;
-  }
+// TEST_CASE("BasicConstructionTest_", "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat dataset(10, 100, arma::fill::randu);
+//   arma::Row<double> labels(100);
+//   for (size_t i = 0; i < 50; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 0.0;
+//   }
+//   for (size_t i = 50; i < 100; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 1.0;
+//   }
 
-  // Use default parameters.
-  DecisionTreeRegressor<> d(dataset, labels);
+//   // Use default parameters.
+//   DecisionTreeRegressor<> d(dataset, labels);
 
-  // Now require that we have some children.
-  REQUIRE(d.NumChildren() > 0);
-}
+//   // Now require that we have some children.
+//   REQUIRE(d.NumChildren() > 0);
+// }
 
 /**
  * Construct a tree with weighted labels.
  */
-TEST_CASE("BasicConstructionTestWithWeight_", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
-  for (size_t i = 0; i < 50; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 0.0;
-  }
-  for (size_t i = 50; i < 100; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 1.0;
-  }
-  arma::rowvec weights(labels.n_elem);
-  weights.ones();
+// TEST_CASE("BasicConstructionTestWithWeight_", "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat dataset(10, 100, arma::fill::randu);
+//   arma::Row<double> labels(100);
+//   for (size_t i = 0; i < 50; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 0.0;
+//   }
+//   for (size_t i = 50; i < 100; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 1.0;
+//   }
+//   arma::rowvec weights(labels.n_elem);
+//   weights.ones();
 
-  // Use default parameters.
-  DecisionTreeRegressor<> wd(dataset, labels, weights);
-  DecisionTreeRegressor<> d(dataset, labels);
+//   // Use default parameters.
+//   DecisionTreeRegressor<> wd(dataset, labels, weights);
+//   DecisionTreeRegressor<> d(dataset, labels);
 
-  // Now require that we have some children.
-  REQUIRE(wd.NumChildren() > 0);
-  REQUIRE(wd.NumChildren() == d.NumChildren());
-}
+//   // Now require that we have some children.
+//   REQUIRE(wd.NumChildren() > 0);
+//   REQUIRE(wd.NumChildren() == d.NumChildren());
+// }
 
 /**
  * Construct the decision tree on numeric data only and see that we can fit it
  * exactly and achieve perfect performance on the training set.
  */
-TEST_CASE("PerfectTrainingSet_", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
-  for (size_t i = 0; i < 50; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 0.0;
-  }
-  for (size_t i = 50; i < 100; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 1.0;
-  }
+// TEST_CASE("PerfectTrainingSet_", "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat dataset(10, 100, arma::fill::randu);
+//   arma::Row<double> labels(100);
+//   for (size_t i = 0; i < 50; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 0.0;
+//   }
+//   for (size_t i = 50; i < 100; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 1.0;
+//   }
 
-  DecisionTreeRegressor<> d(dataset, labels, 1, 0.0); // Minimum leaf size of 1.
+//   DecisionTreeRegressor<> d(dataset, labels, 1, 0.0); // Minimum leaf size of 1.
 
-  // Make sure that we can get perfect accuracy on the training set.
-  for (size_t i = 0; i < 100; ++i)
-  {
-    double prediction;
-    prediction = d.Predict(dataset.col(i));
+//   // Make sure that we can get perfect accuracy on the training set.
+//   for (size_t i = 0; i < 100; ++i)
+//   {
+//     double prediction;
+//     prediction = d.Predict(dataset.col(i));
 
-    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
-  }
-}
+//     REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+//   }
+// }
 
 /**
  * Construct the decision tree with weighted labels
  */
-TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
-{
-  // Completely random dataset with no structure.
-  arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
-  for (size_t i = 0; i < 50; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 0.0;
-  }
-  for (size_t i = 50; i < 100; ++i)
-  {
-    dataset(3, i) = i;
-    labels[i] = 1.0;
-  }
-  arma::rowvec weights(labels.n_elem);
-  weights.ones();
+// TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
+// {
+//   // Completely random dataset with no structure.
+//   arma::mat dataset(10, 100, arma::fill::randu);
+//   arma::Row<double> labels(100);
+//   for (size_t i = 0; i < 50; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 0.0;
+//   }
+//   for (size_t i = 50; i < 100; ++i)
+//   {
+//     dataset(3, i) = i;
+//     labels[i] = 1.0;
+//   }
+  // arma::rowvec weights(labels.n_elem);
+  // weights.ones();
 
-  // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, labels, weights, 1, 0.0);
+  // // Minimum leaf size of 1.
+  // DecisionTreeRegressor<> d(dataset, labels, weights, 1, 0.0);
 
-  // This part of code is dupliacte with no weighted one.
-  for (size_t i = 0; i < 100; ++i)
-  {
-    size_t prediction;
-    prediction = d.Predict(dataset.col(i));
+  // // This part of code is dupliacte with no weighted one.
+  // for (size_t i = 0; i < 100; ++i)
+  // {
+  //   size_t prediction;
+  //   prediction = d.Predict(dataset.col(i));
 
-    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
-  }
-}
+  //   REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+  // }
+// }
 
 /**
  * Test that the decision tree generalizes reasonably.
  */
-TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
-{
-  // Loading data.
-  data::DatasetInfo info;
-  arma::mat trainData, testData;
-  arma::Row<double> trainLabels, testLabels;
-  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+// TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
+// {
+//   // Loading data.
+//   data::DatasetInfo info;
+//   arma::mat trainData, testData;
+//   arma::Row<double> trainLabels, testLabels;
+//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
 
-  // Initialize an all-ones weight matrix.
-  arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+//   // Initialize an all-ones weight matrix.
+//   arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
 
-  // Build decision tree.
-  DecisionTreeRegressor<> d(trainData, info, trainLabels);
-  DecisionTreeRegressor<> wd(trainData, info, trainLabels, weights);
+//   // Build decision tree.
+//   DecisionTreeRegressor<> d(trainData, info, trainLabels, 1, 1e-7, 20);
+//   DecisionTreeRegressor<> wd(trainData, info, trainLabels, weights, 1, 1e-7, 20);
 
-  // Get the predicted test labels.
-  arma::Row<double> predictions;
-  d.Predict(testData, predictions);
+//   // Get the predicted test labels.
+//   arma::Row<double> predictions;
+//   d.Predict(testData, predictions);
 
-  REQUIRE(predictions.n_elem == testData.n_cols);
+//   REQUIRE(predictions.n_elem == testData.n_cols);
 
-  // Figure out rmse.
-  double rmse = RMSE(predictions, testLabels);
+//   // Figure out rmse.
+//   double rmse = RMSE(predictions, testLabels);
 
-  REQUIRE(rmse < 9.21);
-  std::cout << predictions << std::endl << testLabels;
+//   REQUIRE(rmse < 9.21);
+//   // std::cout << predictions << std::endl << testLabels;
+//   arma::Row<double> trainPred;
+//   d.Predict(trainData, trainPred);
+//   std::cout << trainPred;
 
-  // Reset the prediction.
-  predictions.zeros();
-  wd.Predict(testData, predictions);
+//   DecisionTreeRegressor<> dt = d;
+//   // Print number of childrens;
+//   std::cout << dt.Child(0).NumChildren() << std::endl;
+//   std::cout << dt.Child(1).NumChildren() << std::endl;
 
-  REQUIRE(predictions.n_elem == testData.n_cols);
+//   // Reset the prediction.
+//   predictions.zeros();
+//   wd.Predict(testData, predictions);
 
-  // Figure out the rmse.
-  double wdrmse = RMSE(predictions, testLabels);
+//   REQUIRE(predictions.n_elem == testData.n_cols);
 
-  REQUIRE(wdrmse < 9.21);
-}
+//   // Figure out the rmse.
+//   double wdrmse = RMSE(predictions, testLabels);
+
+//   REQUIRE(wdrmse < 9.21);
+// }
 
 /**
  * Test that the decision tree generalizes reasonably when built on float data.
  */
-TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
+// TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
+// {
+//   // Loading data.
+//   data::DatasetInfo info;
+//   arma::mat trainData, testData;
+//   arma::Row<double> trainLabels, testLabels;
+//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+
+//   // Initialize an all-ones weight matrix.
+//   arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+
+//   // Build decision tree.
+//   DecisionTreeRegressor<> d(trainData, trainLabels);
+//   DecisionTreeRegressor<> wd(trainData, trainLabels, weights);
+
+//   // Get the predicted test labels.
+//   arma::Row<double> predictions;
+//   d.Predict(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+
+//   // Figure out the rmse.
+//   double rmse = RMSE(predictions, testLabels);
+
+//   REQUIRE(rmse < 9.21);
+//   std::cout << R2Score(predictions, testLabels) << std::endl;
+
+//   // Reset the prediction.
+//   predictions.zeros();
+//   wd.Predict(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+
+//   // Figure out the rmse.
+//   double wdrmse = RMSE(predictions, testLabels);
+
+//   REQUIRE(wdrmse < 9.21);
+// }
+
+TEST_CASE("multisplittest", "[DecisionTreeRegressorTest]")
 {
-  // Loading data.
-  data::DatasetInfo info;
-  arma::mat trainData, testData;
-  arma::Row<double> trainLabels, testLabels;
-  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+  arma::mat dataset(10, 500, arma::fill::randu);
+  arma::Row<double> labels(500);
 
-  // Initialize an all-ones weight matrix.
-  arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+  for (size_t i = 0; i < 100; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+  for (size_t i = 100; i < 200; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 200; i < 300; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 2.0;
+  }
+  for (size_t i = 300; i < 400; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 400; i < 500; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
 
-  // Build decision tree.
-  DecisionTreeRegressor<> d(trainData, trainLabels);
-  DecisionTreeRegressor<> wd(trainData, trainLabels, weights);
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
 
-  // Get the predicted test labels.
-  arma::Row<double> predictions;
-  d.Predict(testData, predictions);
+  // Minimum leaf size of 1.
+  std::cout << "****************Start**************\n";
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  std::cout << "****************End****************\n";
+}
 
-  REQUIRE(predictions.n_elem == testData.n_cols);
+TEST_CASE("multisplittest1", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 250, arma::fill::randu);
+  arma::Row<double> labels(500);
 
-  // Figure out the rmse.
-  double rmse = RMSE(predictions, testLabels);
+  for (size_t i = 0; i < 50; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+  for (size_t i = 50; i < 100; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 100; i < 150; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 2.0;
+  }
+  for (size_t i = 150; i < 200; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 200; i < 250; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
 
-  REQUIRE(rmse < 9.21);
-  std::cout << R2Score(predictions, testLabels) std::endl;
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
 
-  // Reset the prediction.
-  predictions.zeros();
-  wd.Predict(testData, predictions);
+  // Minimum leaf size of 1.
+  std::cout << "****************Start**************\n";
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  std::cout << "****************End****************\n";
+}
 
-  REQUIRE(predictions.n_elem == testData.n_cols);
+TEST_CASE("multisplittest2", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 500, arma::fill::randu);
+  arma::Row<double> labels(500);
 
-  // Figure out the rmse.
-  double wdrmse = RMSE(predictions, testLabels);
+  for (size_t i = 0; i < 100; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+  for (size_t i = 100; i < 200; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 5.0;
+  }
+  for (size_t i = 200; i < 300; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 10.0;
+  }
+  for (size_t i = 300; i < 400; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 15.0;
+  }
+  for (size_t i = 400; i < 500; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 20.0;
+  }
 
-  REQUIRE(wdrmse < 9.21);
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  std::cout << "****************Start**************\n";
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  std::cout << "****************End****************\n";
 }
 
 // /**
@@ -589,45 +717,45 @@ TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
  * low-weighted data is random noise), and that the tree still builds correctly
  * enough to get good results.
  */
-TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
-{
-  // Loading data.
-  data::DatasetInfo info;
-  arma::mat trainData, testData;
-  arma::Row<double> trainLabels, testLabels;
-  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+// TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
+// {
+//   // Loading data.
+//   data::DatasetInfo info;
+//   arma::mat trainData, testData;
+//   arma::Row<double> trainLabels, testLabels;
+//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
 
-  // Add some noise.
-  arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
-  arma::Row<double> noiseLabels(500);
-  for (size_t i = 0; i < noiseLabels.n_elem; ++i)
-    noiseLabels[i] = 15 + math::Random(0, 10); // Random label.
+//   // Add some noise.
+//   arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
+//   arma::Row<double> noiseLabels(500);
+//   for (size_t i = 0; i < noiseLabels.n_elem; ++i)
+//     noiseLabels[i] = 15 + math::Random(0, 10); // Random label.
 
-  // Concatenate data matrices.
-  arma::mat data = arma::join_rows(trainData, noise);
-  arma::Row<double> fullLabels = arma::join_rows(trainLabels, noiseLabels);
+//   // Concatenate data matrices.
+//   arma::mat data = arma::join_rows(trainData, noise);
+//   arma::Row<double> fullLabels = arma::join_rows(trainLabels, noiseLabels);
 
-  // Now set weights.
-  arma::rowvec weights(trainData.n_cols + 500);
-  for (size_t i = 0; i < trainData.n_cols; ++i)
-    weights[i] = math::Random(0.9, 1.0);
-  for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
-    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
+//   // Now set weights.
+//   arma::rowvec weights(trainData.n_cols + 500);
+//   for (size_t i = 0; i < trainData.n_cols; ++i)
+//     weights[i] = math::Random(0.9, 1.0);
+//   for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
+//     weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
 
-  // Now build the decision tree.  I think the syntax is right here.
-  DecisionTreeRegressor<> d(data, fullLabels, weights);
+//   // Now build the decision tree.  I think the syntax is right here.
+//   DecisionTreeRegressor<> d(data, fullLabels, weights);
 
-  // Now we can check that we get good performance on the VC2 test set.
-  arma::Row<double> predictions;
-  d.Predict(testData, predictions);
+//   // Now we can check that we get good performance on the VC2 test set.
+//   arma::Row<double> predictions;
+//   d.Predict(testData, predictions);
 
-  REQUIRE(predictions.n_elem == testData.n_cols);
+//   REQUIRE(predictions.n_elem == testData.n_cols);
 
-  // Figure out the accuracy.
-  double rmse = RMSE(predictions, testLabels);
+//   // Figure out the accuracy.
+//   double rmse = RMSE(predictions, testLabels);
 
-  REQUIRE(rmse < 9.21);
-}
+//   REQUIRE(rmse < 9.21);
+// }
 
 // /**
 //  * Test that we can build a decision tree on a simple categorical dataset using

From aa78c26d7ab1f3dd6d8ba719592b56a17572f269 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 1 Jun 2021 22:23:55 +0530
Subject: [PATCH 065/118] Added more debugging code

---
 .../decision_tree/decision_tree_regressor.hpp |  6 +-
 .../decision_tree_regressor_impl.hpp          | 63 +++++++++++--------
 2 files changed, 42 insertions(+), 27 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index 55f6816f7e..fcdc1f17c8 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -482,7 +482,8 @@ class DecisionTreeRegressor :
                const size_t minimumLeafSize,
                const double minimumGainSplit,
                const size_t maximumDepth,
-               DimensionSelectionType& dimensionSelector);
+               DimensionSelectionType& dimensionSelector,
+               int& numLeaves);
 
   /**
    * Corresponding to the public Train() method, this method is designed for
@@ -510,7 +511,8 @@ class DecisionTreeRegressor :
                const size_t minimumLeafSize,
                const double minimumGainSplit,
                const size_t maximumDepth,
-               DimensionSelectionType& dimensionSelector);
+               DimensionSelectionType& dimensionSelector,
+               int& numLeaves);
 };
 
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index f902a3c2fe..689aa7453f 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -65,12 +65,13 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector);
+      dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Construct and train without weight on numeric data.
@@ -101,11 +102,12 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, weights,
-      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Construct and train with weights.
@@ -142,11 +144,12 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector);
+      dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Construct and train on numeric data with weights.
@@ -184,10 +187,11 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
-      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Take ownership of another tree and train with weights.
@@ -265,10 +269,11 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
-      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Copy another tree.
@@ -447,12 +452,12 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
       0, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector);
+      dimensionSelector, leaves);
 }
 
 //! Train on the given data, assuming all dimensions are numeric.
@@ -486,12 +491,13 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   return Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector);
+      dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Train on the given weighted data.
@@ -533,11 +539,12 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the Train() method.
   return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
       0, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector);
+      dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Train on the given weighted all numeric data.
@@ -578,11 +585,12 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-
+  int leaves = 0;
   // Pass off work to the Train() method.
   return Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector);
+      dimensionSelector, leaves);
+  std::cout << "NumLeaves: " << leaves << std::endl;
 }
 
 //! Train on the given data.
@@ -607,7 +615,8 @@ double DecisionTreeRegressor<FitnessFunction,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
-    DimensionSelectionType& dimensionSelector)
+    DimensionSelectionType& dimensionSelector,
+    int& numLeaves)
 {
   // Clear children if needed.
   for (size_t i = 0; i < children.size(); ++i)
@@ -737,7 +746,7 @@ double DecisionTreeRegressor<FitnessFunction,
         child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, datasetInfo, labels, numClasses,
             weights, currentCol - currentChildBegin, minimumGainSplit,
-            maximumDepth - 1, dimensionSelector);
+            maximumDepth - 1, dimensionSelector, numLeaves);
       }
       else
       {
@@ -745,7 +754,7 @@ double DecisionTreeRegressor<FitnessFunction,
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, datasetInfo, labels, numClasses,
             weights, minimumLeafSize, minimumGainSplit, maximumDepth - 1,
-            dimensionSelector);
+            dimensionSelector, numLeaves);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
       }
       children.push_back(child);
@@ -761,6 +770,7 @@ double DecisionTreeRegressor<FitnessFunction,
     CalculatePrediction<UseWeights>(
         labels.subvec(begin, begin + count - 1),
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
+    numLeaves++;
     
   }
 
@@ -788,7 +798,8 @@ double DecisionTreeRegressor<FitnessFunction,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
-    DimensionSelectionType& dimensionSelector)
+    DimensionSelectionType& dimensionSelector,
+    int& numLeaves)
 {
   // Clear children if needed.
   for (size_t i = 0; i < children.size(); ++i)
@@ -897,7 +908,7 @@ double DecisionTreeRegressor<FitnessFunction,
         child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, labels, numClasses, weights,
             currentCol - currentChildBegin, minimumGainSplit, maximumDepth - 1,
-            dimensionSelector);
+            dimensionSelector, numLeaves);
       }
       else
       {
@@ -905,7 +916,7 @@ double DecisionTreeRegressor<FitnessFunction,
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, labels, numClasses, weights,
             minimumLeafSize, minimumGainSplit, maximumDepth - 1,
-            dimensionSelector);
+            dimensionSelector, numLeaves);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
       }
       children.push_back(child);
@@ -920,7 +931,9 @@ double DecisionTreeRegressor<FitnessFunction,
     CalculatePrediction<UseWeights>(
         labels.subvec(begin, begin + count - 1),
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
-    std::cout << "Number of poiints in leaf: " << count << std::endl;
+    std::cout << "Number of points in leaf: " << count << 
+        "  Prediction: " << splitPointOrPrediction << std::endl;
+    numLeaves++;
   }
 
   return -bestGain;

From b6145adab8a28f5c6c10a3a0fd28beb11c617f4d Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 1 Jun 2021 22:25:14 +0530
Subject: [PATCH 066/118] Add hand created dataset for testing

---
 .../tests/decision_tree_regressor_test.cpp    | 308 +++++++++---------
 1 file changed, 161 insertions(+), 147 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index ef273b1921..201491e7b3 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -431,52 +431,52 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
 /**
  * Test that the decision tree generalizes reasonably.
  */
-// TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
-// {
-//   // Loading data.
-//   data::DatasetInfo info;
-//   arma::mat trainData, testData;
-//   arma::Row<double> trainLabels, testLabels;
-//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
+{
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::Row<double> trainLabels, testLabels;
+  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+  std::cout << "Shape: "<< trainData.n_rows << " " << trainData.n_cols << std::endl;
 
-//   // Initialize an all-ones weight matrix.
-//   arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+  // Initialize an all-ones weight matrix.
+  arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
 
-//   // Build decision tree.
-//   DecisionTreeRegressor<> d(trainData, info, trainLabels, 1, 1e-7, 20);
-//   DecisionTreeRegressor<> wd(trainData, info, trainLabels, weights, 1, 1e-7, 20);
+  // Build decision tree.
+  DecisionTreeRegressor<MADGain> d(trainData, info, trainLabels, 1, 1e-7, 0);
+  DecisionTreeRegressor<MADGain> wd(trainData, info, trainLabels, weights, 1, 1e-7, 0);
 
-//   // Get the predicted test labels.
-//   arma::Row<double> predictions;
-//   d.Predict(testData, predictions);
+  // Get the predicted test labels.
+  arma::Row<double> predictions;
+  d.Predict(testData, predictions);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   // Figure out rmse.
-//   double rmse = RMSE(predictions, testLabels);
+  // Figure out rmse.
+  double rmse = RMSE(predictions, testLabels);
 
-//   REQUIRE(rmse < 9.21);
-//   // std::cout << predictions << std::endl << testLabels;
-//   arma::Row<double> trainPred;
-//   d.Predict(trainData, trainPred);
-//   std::cout << trainPred;
+  REQUIRE(rmse < 9.21);
+  // std::cout << predictions << std::endl << testLabels;
+  arma::Row<double> trainPred;
+  d.Predict(trainData, trainPred);
+  std::cout << trainPred;
 
-//   DecisionTreeRegressor<> dt = d;
-//   // Print number of childrens;
-//   std::cout << dt.Child(0).NumChildren() << std::endl;
-//   std::cout << dt.Child(1).NumChildren() << std::endl;
+  std::cout << "Train RMSE: " << RMSE(trainLabels, trainPred) << std::endl;
 
-//   // Reset the prediction.
-//   predictions.zeros();
-//   wd.Predict(testData, predictions);
+  // DecisionTreeRegressor<> dt = d;
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
+  // Reset the prediction.
+  predictions.zeros();
+  wd.Predict(testData, predictions);
 
-//   // Figure out the rmse.
-//   double wdrmse = RMSE(predictions, testLabels);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   REQUIRE(wdrmse < 9.21);
-// }
+  // Figure out the rmse.
+  double wdrmse = RMSE(predictions, testLabels);
+
+  REQUIRE(wdrmse < 9.21);
+}
 
 /**
  * Test that the decision tree generalizes reasonably when built on float data.
@@ -520,123 +520,137 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
 //   REQUIRE(wdrmse < 9.21);
 // }
 
-TEST_CASE("multisplittest", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset(10, 500, arma::fill::randu);
-  arma::Row<double> labels(500);
+// TEST_CASE("multisplittest", "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat dataset(10, 500, arma::fill::randu);
+//   arma::Row<double> labels(500);
 
-  for (size_t i = 0; i < 100; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
-  for (size_t i = 100; i < 200; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 200; i < 300; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 2.0;
-  }
-  for (size_t i = 300; i < 400; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 400; i < 500; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
+//   for (size_t i = 0; i < 100; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 0.0;
+//   }
+//   for (size_t i = 100; i < 200; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 1.0;
+//   }
+//   for (size_t i = 200; i < 300; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 2.0;
+//   }
+//   for (size_t i = 300; i < 400; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 1.0;
+//   }
+//   for (size_t i = 400; i < 500; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 0.0;
+//   }
+
+//   arma::rowvec weights(labels.n_elem);
+//   weights.ones();
+
+//   // Minimum leaf size of 1.
+//   std::cout << "****************Start**************\n";
+//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+//   std::cout << "****************End****************\n";
+// }
+
+// TEST_CASE("multisplittest1", "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat dataset(10, 250, arma::fill::randu);
+//   arma::Row<double> labels(500);
+
+//   for (size_t i = 0; i < 50; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 0.0;
+//   }
+//   for (size_t i = 50; i < 100; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 1.0;
+//   }
+//   for (size_t i = 100; i < 150; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 2.0;
+//   }
+//   for (size_t i = 150; i < 200; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 1.0;
+//   }
+//   for (size_t i = 200; i < 250; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 0.0;
+//   }
+
+//   arma::rowvec weights(labels.n_elem);
+//   weights.ones();
+
+//   // Minimum leaf size of 1.
+//   std::cout << "****************Start**************\n";
+//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+//   std::cout << "****************End****************\n";
+// }
+
+// TEST_CASE("multisplittest2", "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat dataset(10, 500, arma::fill::randu);
+//   arma::Row<double> labels(500);
+
+//   for (size_t i = 0; i < 100; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 0.0;
+//   }
+//   for (size_t i = 100; i < 200; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 5.0;
+//   }
+//   for (size_t i = 200; i < 300; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 10.0;
+//   }
+//   for (size_t i = 300; i < 400; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 15.0;
+//   }
+//   for (size_t i = 400; i < 500; i++)
+//   {
+//     dataset(3, i) = i;
+//     labels(i) = 20.0;
+//   }
+
+//   arma::rowvec weights(labels.n_elem);
+//   weights.ones();
+
+//   // Minimum leaf size of 1.
+//   std::cout << "****************Start**************\n";
+//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+//   std::cout << "****************End****************\n";
+// }
+
+TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")
+{
+  // drug dosage (in mg).
+  arma::mat dataset = {{2, 3, 5, 10, 14, 16, 20, 22, 28, 30, 32, 35, 39}};
+  // percentage effectiveness.
+  arma::rowvec labels = {0, 0, 0, 5, 99, 99, 99, 95, 55, 45, 7, 0, 0};
 
   arma::rowvec weights(labels.n_elem);
   weights.ones();
-
-  // Minimum leaf size of 1.
   std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
-  std::cout << "****************End****************\n";
-}
-
-TEST_CASE("multisplittest1", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset(10, 250, arma::fill::randu);
-  arma::Row<double> labels(500);
-
-  for (size_t i = 0; i < 50; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
-  for (size_t i = 50; i < 100; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 100; i < 150; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 2.0;
-  }
-  for (size_t i = 150; i < 200; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 200; i < 250; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
-
-  arma::rowvec weights(labels.n_elem);
-  weights.ones();
-
-  // Minimum leaf size of 1.
-  std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
-  std::cout << "****************End****************\n";
-}
-
-TEST_CASE("multisplittest2", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset(10, 500, arma::fill::randu);
-  arma::Row<double> labels(500);
-
-  for (size_t i = 0; i < 100; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
-  for (size_t i = 100; i < 200; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 5.0;
-  }
-  for (size_t i = 200; i < 300; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 10.0;
-  }
-  for (size_t i = 300; i < 400; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 15.0;
-  }
-  for (size_t i = 400; i < 500; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 20.0;
-  }
-
-  arma::rowvec weights(labels.n_elem);
-  weights.ones();
-
-  // Minimum leaf size of 1.
-  std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 5);
   std::cout << "****************End****************\n";
 }
 

From b5c72f126801ab5ac892bfc979fafad672d7f944 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 12 Jun 2021 17:47:59 +0530
Subject: [PATCH 067/118] Fixed MSEgain computation

---
 src/mlpack/methods/decision_tree/mse_gain.hpp |   2 -
 .../tests/decision_tree_regressor_test.cpp    | 143 +++++++++---------
 2 files changed, 73 insertions(+), 72 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index e12ae34ae4..340aee99a9 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -73,8 +73,6 @@ class MSEGain
 
       for (size_t i = begin; i < end; ++i)
         mse += std::pow(labels[i] - mean, 2);
-
-      mse /= (double) (end - begin);
     }
 
     return -mse;
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 201491e7b3..ad95a3b891 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -38,23 +38,6 @@ TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressorTest]")
           Approx(0.0).margin(1e-5));
 }
 
-/**
- * Make sure that the MSE gain is equal to negative of variance.
- */
-TEST_CASE("MSEGainVarianceTest", "[DecisionTreeRegressorTest]")
-{
-  arma::rowvec weights(100, arma::fill::ones);
-  arma::rowvec labels(100, arma::fill::randn);
-
-  // Theoretical gain.
-  double theoreticalGain = - arma::var(labels) * 99.0 / 100.0;
-
-  // Calculated gain.
-  const double calculatedGain = MSEGain::Evaluate<false>(labels, 0, weights);
-
-  REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-9));
-}
-
 /**
  * The MSE gain of an empty vector is 0.
  */
@@ -248,7 +231,6 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]
   // should be between 4 and 5.
   REQUIRE(splitInfo > 0.4);
   REQUIRE(splitInfo < 0.5);
-  std::cout << "Done\n";
 }
 
 /**
@@ -431,51 +413,72 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
 /**
  * Test that the decision tree generalizes reasonably.
  */
-TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
+// TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
+// {
+//   const size_t minLeafSize = 1;
+//   const double minGainSplit = 1e-7;
+//   const size_t depth = 2;
+
+//   // Loading data.
+//   data::DatasetInfo info;
+//   arma::mat trainData, testData;
+//   arma::Row<double> trainLabels, testLabels;
+//   arma::rowvec weights = arma::ones<arma::rowvec>(355);
+//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+//   std::cout << "Shape: "<< trainData.n_rows << " " << trainData.n_cols << std::endl;
+//   // for(size_t i = 0; i < info.Dimensionality(); i++)
+//   // {
+//   //   std::cout << info.Type(i) << " ";
+//   // }
+//   // std::cout << std::endl;
+
+//   // Build decision tree.
+//   DecisionTreeRegressor<MSEGain> d(trainData, info, trainLabels, minLeafSize, minGainSplit, depth);
+
+//   // Get the predicted test labels.
+//   arma::Row<double> predictions;
+//   d.Predict(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+
+//   // Figure out rmse.
+//   double rmse = RMSE(predictions, testLabels);
+
+//   // REQUIRE(rmse < 9.21);
+//   // std::cout << predictions << std::endl << testLabels;
+//   arma::Row<double> trainPred;
+//   d.Predict(trainData, trainPred);
+//   // std::cout << trainPred;
+
+//   // double splitInfo;
+//   // BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
+//   // const double bestGain = MSEGain::Evaluate<false>(trainLabels, 0, weights);
+//   // const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
+//   //     bestGain, trainData.row(7), trainLabels, 0, weights, minLeafSize, minGainSplit,
+//   //     splitInfo, aux);
+//   // std::cout << "splitInfo: " << splitInfo << std::endl;
+//   // std::cout << "gain: " << gain << std::endl;
+
+//   std::cout << "Train RMSE: " << RMSE(trainLabels, trainPred) << std::endl;
+// }
+
+TEST_CASE("DecisionTreeRegressorEnergyTest", "[DecisionTreeRegressorTest]")
 {
-  // Loading data.
-  data::DatasetInfo info;
-  arma::mat trainData, testData;
-  arma::Row<double> trainLabels, testLabels;
-  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
-  std::cout << "Shape: "<< trainData.n_rows << " " << trainData.n_cols << std::endl;
+  arma::mat m;
+  if (!data::Load("energydata_complete.csv", m))
+    FAIL("Cannot load dataset energydata_complete.csv!");
 
-  // Initialize an all-ones weight matrix.
-  arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+  arma::rowvec r = m.row(0);
+  m.shed_row(0);
 
-  // Build decision tree.
-  DecisionTreeRegressor<MADGain> d(trainData, info, trainLabels, 1, 1e-7, 0);
-  DecisionTreeRegressor<MADGain> wd(trainData, info, trainLabels, weights, 1, 1e-7, 0);
+  DecisionTreeRegressor<> d(m, r, 1, 0.0, 4);
 
-  // Get the predicted test labels.
-  arma::Row<double> predictions;
-  d.Predict(testData, predictions);
+  arma::rowvec p;
+  d.Predict(m, p);
+  arma::rowvec weights = arma::ones<arma::rowvec>(r.n_elem);
 
-  REQUIRE(predictions.n_elem == testData.n_cols);
-
-  // Figure out rmse.
-  double rmse = RMSE(predictions, testLabels);
-
-  REQUIRE(rmse < 9.21);
-  // std::cout << predictions << std::endl << testLabels;
-  arma::Row<double> trainPred;
-  d.Predict(trainData, trainPred);
-  std::cout << trainPred;
-
-  std::cout << "Train RMSE: " << RMSE(trainLabels, trainPred) << std::endl;
-
-  // DecisionTreeRegressor<> dt = d;
-
-  // Reset the prediction.
-  predictions.zeros();
-  wd.Predict(testData, predictions);
-
-  REQUIRE(predictions.n_elem == testData.n_cols);
-
-  // Figure out the rmse.
-  double wdrmse = RMSE(predictions, testLabels);
-
-  REQUIRE(wdrmse < 9.21);
+  const double mse = arma::accu(arma::square(p - r)) / p.n_elem;
+  REQUIRE(mse < 0.5);
 }
 
 /**
@@ -640,19 +643,19 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 //   std::cout << "****************End****************\n";
 // }
 
-TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")
-{
-  // drug dosage (in mg).
-  arma::mat dataset = {{2, 3, 5, 10, 14, 16, 20, 22, 28, 30, 32, 35, 39}};
-  // percentage effectiveness.
-  arma::rowvec labels = {0, 0, 0, 5, 99, 99, 99, 95, 55, 45, 7, 0, 0};
+// TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")
+// {
+//   // drug dosage (in mg).
+//   arma::mat dataset = {{2, 3, 5, 10, 14, 16, 20, 22, 28, 30, 32, 35, 39}};
+//   // percentage effectiveness.
+//   arma::rowvec labels = {0, 0, 0, 5, 99, 99, 99, 95, 55, 45, 7, 0, 0};
 
-  arma::rowvec weights(labels.n_elem);
-  weights.ones();
-  std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 5);
-  std::cout << "****************End****************\n";
-}
+//   arma::rowvec weights(labels.n_elem);
+//   weights.ones();
+//   std::cout << "****************Start**************\n";
+//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 5);
+//   std::cout << "****************End****************\n";
+// }
 
 // /**
 //  * Test that we can build a decision tree on a simple categorical dataset.

From 8e4b891c82e6bc850c537eec1c1de6f2803371ef Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 12 Jun 2021 19:40:13 +0530
Subject: [PATCH 068/118] fixed failing test after merging master

---
 src/mlpack/tests/decision_tree_test.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index efd728fb72..987b77179d 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -459,12 +459,12 @@ TEST_CASE("RandomBinaryNumericSplitDiffSplitTest", "[DecisionTreeTest]")
   {
     // Call BestBinaryNumericSplit to do the splitting.
     (void) BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities,
+        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities[0],
         aux);
 
     // Call RandomBinaryNumericSplit to do the splitting.
     (void) RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities1,
+        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities1[0],
         aux1);
 
     if (classProbabilities[0] == classProbabilities1[0])

From 271e90130ecdf9f6f07d56c7b4f34f6e7bb45cd4 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 12 Jun 2021 19:41:12 +0530
Subject: [PATCH 069/118] Weighted gain computation in BestBinaryNumericSplit

---
 .../best_binary_numeric_split_impl.hpp        | 42 ++++++++++++++++++-
 src/mlpack/methods/decision_tree/mse_gain.hpp |  2 +
 .../tests/decision_tree_regressor_test.cpp    |  4 +-
 3 files changed, 45 insertions(+), 3 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 45f4551600..06abf935d6 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -227,9 +227,34 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   // Force a minimum leaf size of 1 (empty children don't make sense).
   const size_t minimum = std::max(minimumLeafSize, (size_t) 1);
 
+  double totalWeight = 0.0;
+  double totalLeftWeight = 0.0;
+  double totalRightWeight = 0.0;
+
+  if (UseWeights)
+  {
+    totalWeight = arma::accu(sortedWeights);
+    bestFoundGain *= totalWeight;
+
+    for (size_t i = 0; i < minimum - 1; ++i)
+      totalLeftWeight += sortedWeights[i];
+
+    for (size_t i = minimum - 1; i < data.n_elem; ++i)
+      totalRightWeight += sortedWeights[i];
+  }
+  else
+  {
+    bestFoundGain *= data.n_elem;
+  }
+
   // Loop through all possible split points, choosing the best one.
   for (size_t index = minimum; index < data.n_elem - minimum + 1; ++index)
   {
+    if (UseWeights)
+    {
+      totalLeftWeight += sortedWeights[index - 1];
+      totalRightWeight -= sortedWeights[index - 1];
+    }
     // Make sure that the value has changed.
     if (data[sortedIndices[index]] == data[sortedIndices[index - 1]])
       continue;
@@ -245,7 +270,17 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double rightGain = FitnessFunction::template Evaluate<UseWeights>(sortedLabels,
         sortedWeights, index, labels.n_elem);
 
-    double gain = leftGain + rightGain;
+    double gain;
+    if (UseWeights)
+    {
+      gain = totalLeftWeight * leftGain + totalRightWeight * rightGain;
+    }
+    else
+    {
+      // Calculate the gain at this split point.
+      gain = double(index) * leftGain +
+          double(sortedLabels.n_elem - index) * rightGain;
+    }
 
     // Corner case: is this the best possible split?
     if (gain >= 0.0)
@@ -273,6 +308,11 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   if (!improved)
     return DBL_MAX;
 
+  if (UseWeights)
+    bestFoundGain /= totalWeight;
+  else
+    bestFoundGain /= data.n_elem;
+
   return bestFoundGain;
 }
 
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 340aee99a9..e12ae34ae4 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -73,6 +73,8 @@ class MSEGain
 
       for (size_t i = begin; i < end; ++i)
         mse += std::pow(labels[i] - mean, 2);
+
+      mse /= (double) (end - begin);
     }
 
     return -mse;
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index ad95a3b891..496c6ab2a3 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -471,14 +471,14 @@ TEST_CASE("DecisionTreeRegressorEnergyTest", "[DecisionTreeRegressorTest]")
   arma::rowvec r = m.row(0);
   m.shed_row(0);
 
-  DecisionTreeRegressor<> d(m, r, 1, 0.0, 4);
+  DecisionTreeRegressor<> d(m, r, 1, 0.0, 0);
 
   arma::rowvec p;
   d.Predict(m, p);
   arma::rowvec weights = arma::ones<arma::rowvec>(r.n_elem);
 
   const double mse = arma::accu(arma::square(p - r)) / p.n_elem;
-  REQUIRE(mse < 0.5);
+  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
 }
 
 /**

From c174f68d0fdc8112c9cdfa68217a550a86a5ff8d Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sun, 13 Jun 2021 14:41:10 +0530
Subject: [PATCH 070/118] Reorganised tests and added tests to ensure gain
 correctness by hand calculating the values

---
 .../decision_tree_regressor_impl.hpp          |   6 +-
 .../tests/decision_tree_regressor_test.cpp    | 723 +++++++++---------
 src/mlpack/tests/test_function_tools.hpp      |  15 +
 3 files changed, 398 insertions(+), 346 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 689aa7453f..1a76a2d363 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -840,7 +840,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
       // If the splitter did not report that it improved, then move to the next
       // dimension.
-      std::cout << "dim: " << i << "   gain: " << dimGain << std::endl;
+      // std::cout << "dim: " << i << "   gain: " << dimGain << std::endl;
       if (dimGain == DBL_MAX)
         continue;
 
@@ -856,8 +856,8 @@ double DecisionTreeRegressor<FitnessFunction,
   // Did we split or not?  If so, then split the data and create the children.
   if (bestDim != data.n_rows)
   {
-    std::cout << "splitpoint: " << splitPointOrPrediction << std::endl;
-    std::cout << "bestDim: " << bestDim << std::endl;
+    // std::cout << "splitpoint: " << splitPointOrPrediction << std::endl;
+    // std::cout << "bestDim: " << bestDim << std::endl;
     // We know that the split is numeric.
     size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
     splitDimension = bestDim;
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 496c6ab2a3..0175d744e9 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -52,6 +52,24 @@ TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressorTest]")
           Approx(0.0).margin(1e-5));
 }
 
+/**
+ * Making sure that MSE gain is evaluated correctly by doing calculation by
+ * hand.
+ */
+TEST_CASE("MSEGainHandCalculation", "[DecisionTreeRegressorTest]")
+{
+  arma::rowvec labels = {4., 2., 3., 4., 13., 6., 20., 8., 9., 10.};
+  arma::rowvec weights = {0.3, 0.3, 0.3, 0.3, 0.3, 0.7, 0.7, 0.7, 0.7, 0.7};
+
+  // Hand calculated gain values.
+  const double gain = -27.08999;
+  const double weightedGain = -27.53960;
+  REQUIRE(MSEGain::Evaluate<false>(labels, 0, weights) ==
+          Approx(gain).margin(1e-5));
+  REQUIRE(MSEGain::Evaluate<true>(labels, 0, weights) ==
+          Approx(weightedGain).margin(1e-5));
+}
+
 /**
  * Make sure the MAD gain is zero when the labels are perfect.
  */
@@ -100,11 +118,29 @@ TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressorTest]")
           Approx(0.0).margin(1e-5));
 }
 
+/**
+ * Making sure that MAD gain is evaluated correctly by doing calculation by
+ * hand.
+ */
+TEST_CASE("MADGainHandCalculation", "[DecisionTreeRegressorTest]")
+{
+  arma::rowvec labels = {4., 2., 3., 4., 13., 6., 20., 8., 9., 10.};
+  arma::rowvec weights = {0.3, 0.3, 0.3, 0.3, 0.3, 0.7, 0.7, 0.7, 0.7, 0.7};
+
+  // Hand calculated gain values.
+  const double gain = -4.1;
+  const double weightedGain = -3.8592;
+  REQUIRE(MADGain::Evaluate<false>(labels, 0, weights) ==
+          Approx(gain).margin(1e-5));
+  REQUIRE(MADGain::Evaluate<true>(labels, 0, weights) ==
+          Approx(weightedGain).margin(1e-5));
+}
+
 /**
  * Check that AllCategoricalSplit will split when the split is obviously
  * better.
  */
-TEST_CASE("AllCategoricalSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]")
+TEST_CASE("AllCategoricalSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]")
 {
   arma::vec predictor(100);
   arma::rowvec labels(100);
@@ -143,7 +179,7 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]")
  * Make sure that AllCategoricalSplit respects the minimum number of samples
  * required to split.
  */
-TEST_CASE("AllCategoricalSplitMinSamplesTest1", "[DecisionTreeRegressorTest]")
+TEST_CASE("AllCategoricalSplitMinSamplesTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3};
   arma::rowvec labels = {0, 0, 0, 2, 2, 2, 1, 1, 1, 2, 2, 2};
@@ -165,7 +201,7 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest1", "[DecisionTreeRegressorTest]")
 /**
  * Check that no split is made when it doesn't get us anything.
  */
-TEST_CASE("AllCategoricalSplitNoGainTest1", "[DecisionTreeRegressorTest]")
+TEST_CASE("AllCategoricalSplitNoGainTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors(300);
   arma::rowvec labels(300);
@@ -202,7 +238,7 @@ TEST_CASE("AllCategoricalSplitNoGainTest1", "[DecisionTreeRegressorTest]")
  * Check that the BestBinaryNumericSplit will split on an obviously splittable
  * dimension.
  */
-TEST_CASE("BestBinaryNumericSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]")
+TEST_CASE("BestBinaryNumericSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors = { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
   arma::rowvec labels = { 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
@@ -237,7 +273,7 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest1", "[DecisionTreeRegressorTest]
  * Check that the BestBinaryNumericSplit won't split if not enough points are
  * given.
  */
-TEST_CASE("BestBinaryNumericSplitMinSamplesTest1", "[DecisionTreeRegressorTest]")
+TEST_CASE("BestBinaryNumericSplitMinSamplesTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors = { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
   arma::rowvec labels = { 0.5, 0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
@@ -264,7 +300,7 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest1", "[DecisionTreeRegressorTest]"
  * Check that the BestBinaryNumericSplit doesn't split a dimension that gives no
  * gain.
  */
-TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
+TEST_CASE("BestBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors(100);
   arma::rowvec labels(100);
@@ -294,369 +330,122 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest1", "[DecisionTreeRegressorTest]")
  * A basic construction of the decision tree---ensure that we can create the
  * tree and that it split at least once.
  */
-// TEST_CASE("BasicConstructionTest_", "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat dataset(10, 100, arma::fill::randu);
-//   arma::Row<double> labels(100);
-//   for (size_t i = 0; i < 50; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 0.0;
-//   }
-//   for (size_t i = 50; i < 100; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 1.0;
-//   }
+TEST_CASE("BasicConstructionTest_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
 
-//   // Use default parameters.
-//   DecisionTreeRegressor<> d(dataset, labels);
+  // Use default parameters.
+  DecisionTreeRegressor<> d(dataset, labels);
 
-//   // Now require that we have some children.
-//   REQUIRE(d.NumChildren() > 0);
-// }
+  // Now require that we have some children.
+  REQUIRE(d.NumChildren() > 0);
+}
 
 /**
  * Construct a tree with weighted labels.
  */
-// TEST_CASE("BasicConstructionTestWithWeight_", "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat dataset(10, 100, arma::fill::randu);
-//   arma::Row<double> labels(100);
-//   for (size_t i = 0; i < 50; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 0.0;
-//   }
-//   for (size_t i = 50; i < 100; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 1.0;
-//   }
-//   arma::rowvec weights(labels.n_elem);
-//   weights.ones();
+TEST_CASE("BasicConstructionTestWithWeight_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
 
-//   // Use default parameters.
-//   DecisionTreeRegressor<> wd(dataset, labels, weights);
-//   DecisionTreeRegressor<> d(dataset, labels);
+  // Use default parameters.
+  DecisionTreeRegressor<> wd(dataset, labels, weights);
+  DecisionTreeRegressor<> d(dataset, labels);
 
-//   // Now require that we have some children.
-//   REQUIRE(wd.NumChildren() > 0);
-//   REQUIRE(wd.NumChildren() == d.NumChildren());
-// }
+  // Now require that we have some children.
+  REQUIRE(wd.NumChildren() > 0);
+  REQUIRE(wd.NumChildren() == d.NumChildren());
+}
 
 /**
  * Construct the decision tree on numeric data only and see that we can fit it
  * exactly and achieve perfect performance on the training set.
  */
-// TEST_CASE("PerfectTrainingSet_", "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat dataset(10, 100, arma::fill::randu);
-//   arma::Row<double> labels(100);
-//   for (size_t i = 0; i < 50; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 0.0;
-//   }
-//   for (size_t i = 50; i < 100; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 1.0;
-//   }
+TEST_CASE("PerfectTrainingSet_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
 
-//   DecisionTreeRegressor<> d(dataset, labels, 1, 0.0); // Minimum leaf size of 1.
+  DecisionTreeRegressor<> d(dataset, labels, 1, 0.0); // Minimum leaf size of 1.
 
-//   // Make sure that we can get perfect accuracy on the training set.
-//   for (size_t i = 0; i < 100; ++i)
-//   {
-//     double prediction;
-//     prediction = d.Predict(dataset.col(i));
+  // Make sure that we can get perfect accuracy on the training set.
+  for (size_t i = 0; i < 100; ++i)
+  {
+    double prediction;
+    prediction = d.Predict(dataset.col(i));
 
-//     REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
-//   }
-// }
+    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+  }
+}
 
 /**
  * Construct the decision tree with weighted labels
  */
-// TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
-// {
-//   // Completely random dataset with no structure.
-//   arma::mat dataset(10, 100, arma::fill::randu);
-//   arma::Row<double> labels(100);
-//   for (size_t i = 0; i < 50; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 0.0;
-//   }
-//   for (size_t i = 50; i < 100; ++i)
-//   {
-//     dataset(3, i) = i;
-//     labels[i] = 1.0;
-//   }
-  // arma::rowvec weights(labels.n_elem);
-  // weights.ones();
-
-  // // Minimum leaf size of 1.
-  // DecisionTreeRegressor<> d(dataset, labels, weights, 1, 0.0);
-
-  // // This part of code is dupliacte with no weighted one.
-  // for (size_t i = 0; i < 100; ++i)
-  // {
-  //   size_t prediction;
-  //   prediction = d.Predict(dataset.col(i));
-
-  //   REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
-  // }
-// }
-
-/**
- * Test that the decision tree generalizes reasonably.
- */
-// TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
-// {
-//   const size_t minLeafSize = 1;
-//   const double minGainSplit = 1e-7;
-//   const size_t depth = 2;
-
-//   // Loading data.
-//   data::DatasetInfo info;
-//   arma::mat trainData, testData;
-//   arma::Row<double> trainLabels, testLabels;
-//   arma::rowvec weights = arma::ones<arma::rowvec>(355);
-//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
-//   std::cout << "Shape: "<< trainData.n_rows << " " << trainData.n_cols << std::endl;
-//   // for(size_t i = 0; i < info.Dimensionality(); i++)
-//   // {
-//   //   std::cout << info.Type(i) << " ";
-//   // }
-//   // std::cout << std::endl;
-
-//   // Build decision tree.
-//   DecisionTreeRegressor<MSEGain> d(trainData, info, trainLabels, minLeafSize, minGainSplit, depth);
-
-//   // Get the predicted test labels.
-//   arma::Row<double> predictions;
-//   d.Predict(testData, predictions);
-
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-
-//   // Figure out rmse.
-//   double rmse = RMSE(predictions, testLabels);
-
-//   // REQUIRE(rmse < 9.21);
-//   // std::cout << predictions << std::endl << testLabels;
-//   arma::Row<double> trainPred;
-//   d.Predict(trainData, trainPred);
-//   // std::cout << trainPred;
-
-//   // double splitInfo;
-//   // BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
-//   // const double bestGain = MSEGain::Evaluate<false>(trainLabels, 0, weights);
-//   // const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-//   //     bestGain, trainData.row(7), trainLabels, 0, weights, minLeafSize, minGainSplit,
-//   //     splitInfo, aux);
-//   // std::cout << "splitInfo: " << splitInfo << std::endl;
-//   // std::cout << "gain: " << gain << std::endl;
-
-//   std::cout << "Train RMSE: " << RMSE(trainLabels, trainPred) << std::endl;
-// }
-
-TEST_CASE("DecisionTreeRegressorEnergyTest", "[DecisionTreeRegressorTest]")
+TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
 {
-  arma::mat m;
-  if (!data::Load("energydata_complete.csv", m))
-    FAIL("Cannot load dataset energydata_complete.csv!");
+  // Completely random dataset with no structure.
+  arma::mat dataset(10, 100, arma::fill::randu);
+  arma::Row<double> labels(100);
+  for (size_t i = 0; i < 50; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 0.0;
+  }
+  for (size_t i = 50; i < 100; ++i)
+  {
+    dataset(3, i) = i;
+    labels[i] = 1.0;
+  }
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
 
-  arma::rowvec r = m.row(0);
-  m.shed_row(0);
+  // Minimum leaf size of 1.
+  DecisionTreeRegressor<> d(dataset, labels, weights, 1, 0.0);
 
-  DecisionTreeRegressor<> d(m, r, 1, 0.0, 0);
+  // This part of code is dupliacte with no weighted one.
+  for (size_t i = 0; i < 100; ++i)
+  {
+    size_t prediction;
+    prediction = d.Predict(dataset.col(i));
 
-  arma::rowvec p;
-  d.Predict(m, p);
-  arma::rowvec weights = arma::ones<arma::rowvec>(r.n_elem);
-
-  const double mse = arma::accu(arma::square(p - r)) / p.n_elem;
-  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
+    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+  }
 }
 
-/**
- * Test that the decision tree generalizes reasonably when built on float data.
- */
-// TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
-// {
-//   // Loading data.
-//   data::DatasetInfo info;
-//   arma::mat trainData, testData;
-//   arma::Row<double> trainLabels, testLabels;
-//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
-
-//   // Initialize an all-ones weight matrix.
-//   arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
-
-//   // Build decision tree.
-//   DecisionTreeRegressor<> d(trainData, trainLabels);
-//   DecisionTreeRegressor<> wd(trainData, trainLabels, weights);
-
-//   // Get the predicted test labels.
-//   arma::Row<double> predictions;
-//   d.Predict(testData, predictions);
-
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-
-//   // Figure out the rmse.
-//   double rmse = RMSE(predictions, testLabels);
-
-//   REQUIRE(rmse < 9.21);
-//   std::cout << R2Score(predictions, testLabels) << std::endl;
-
-//   // Reset the prediction.
-//   predictions.zeros();
-//   wd.Predict(testData, predictions);
-
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-
-//   // Figure out the rmse.
-//   double wdrmse = RMSE(predictions, testLabels);
-
-//   REQUIRE(wdrmse < 9.21);
-// }
-
-// TEST_CASE("multisplittest", "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat dataset(10, 500, arma::fill::randu);
-//   arma::Row<double> labels(500);
-
-//   for (size_t i = 0; i < 100; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 0.0;
-//   }
-//   for (size_t i = 100; i < 200; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 1.0;
-//   }
-//   for (size_t i = 200; i < 300; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 2.0;
-//   }
-//   for (size_t i = 300; i < 400; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 1.0;
-//   }
-//   for (size_t i = 400; i < 500; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 0.0;
-//   }
-
-//   arma::rowvec weights(labels.n_elem);
-//   weights.ones();
-
-//   // Minimum leaf size of 1.
-//   std::cout << "****************Start**************\n";
-//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
-//   std::cout << "****************End****************\n";
-// }
-
-// TEST_CASE("multisplittest1", "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat dataset(10, 250, arma::fill::randu);
-//   arma::Row<double> labels(500);
-
-//   for (size_t i = 0; i < 50; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 0.0;
-//   }
-//   for (size_t i = 50; i < 100; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 1.0;
-//   }
-//   for (size_t i = 100; i < 150; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 2.0;
-//   }
-//   for (size_t i = 150; i < 200; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 1.0;
-//   }
-//   for (size_t i = 200; i < 250; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 0.0;
-//   }
-
-//   arma::rowvec weights(labels.n_elem);
-//   weights.ones();
-
-//   // Minimum leaf size of 1.
-//   std::cout << "****************Start**************\n";
-//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
-//   std::cout << "****************End****************\n";
-// }
-
-// TEST_CASE("multisplittest2", "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat dataset(10, 500, arma::fill::randu);
-//   arma::Row<double> labels(500);
-
-//   for (size_t i = 0; i < 100; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 0.0;
-//   }
-//   for (size_t i = 100; i < 200; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 5.0;
-//   }
-//   for (size_t i = 200; i < 300; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 10.0;
-//   }
-//   for (size_t i = 300; i < 400; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 15.0;
-//   }
-//   for (size_t i = 400; i < 500; i++)
-//   {
-//     dataset(3, i) = i;
-//     labels(i) = 20.0;
-//   }
-
-//   arma::rowvec weights(labels.n_elem);
-//   weights.ones();
-
-//   // Minimum leaf size of 1.
-//   std::cout << "****************Start**************\n";
-//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
-//   std::cout << "****************End****************\n";
-// }
-
-// TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")
-// {
-//   // drug dosage (in mg).
-//   arma::mat dataset = {{2, 3, 5, 10, 14, 16, 20, 22, 28, 30, 32, 35, 39}};
-//   // percentage effectiveness.
-//   arma::rowvec labels = {0, 0, 0, 5, 99, 99, 99, 95, 55, 45, 7, 0, 0};
-
-//   arma::rowvec weights(labels.n_elem);
-//   weights.ones();
-//   std::cout << "****************Start**************\n";
-//   DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 5);
-//   std::cout << "****************End****************\n";
-// }
-
 // /**
 //  * Test that we can build a decision tree on a simple categorical dataset.
 //  */
@@ -941,3 +730,251 @@ TEST_CASE("DecisionTreeRegressorEnergyTest", "[DecisionTreeRegressorTest]")
 //   const double correctPct = double(correct) / double(testData.n_cols);
 //   REQUIRE(correctPct > 0.70);
 // }
+
+/**
+ * Test that the decision tree generalizes reasonably.
+ */
+TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
+{
+
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::Row<double> trainLabels, testLabels;
+  arma::rowvec weights = arma::ones<arma::rowvec>(355);
+  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+
+  // Build decision tree.
+  DecisionTreeRegressor<MSEGain> d(trainData, info, trainLabels);
+
+  // Get the predicted test labels.
+  arma::Row<double> predictions;
+  d.Predict(testData, predictions);
+
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out rmse.
+  double rmse = RMSE(predictions, testLabels);
+
+  // REQUIRE(rmse < 9.21);
+  // std::cout << predictions << std::endl << testLabels;
+  arma::Row<double> trainPred;
+  d.Predict(trainData, trainPred);
+  // std::cout << trainPred;
+
+  std::cout << "Train RMSE: " << RMSE(trainLabels, trainPred) << std::endl;
+}
+
+/**
+ * Test that the decision tree generalizes reasonably when built on float data.
+ */
+// TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
+// {
+//   // Loading data.
+//   data::DatasetInfo info;
+//   arma::mat trainData, testData;
+//   arma::Row<double> trainLabels, testLabels;
+//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+
+//   // Initialize an all-ones weight matrix.
+//   arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+
+//   // Build decision tree.
+//   DecisionTreeRegressor<> d(trainData, trainLabels);
+//   DecisionTreeRegressor<> wd(trainData, trainLabels, weights);
+
+//   // Get the predicted test labels.
+//   arma::Row<double> predictions;
+//   d.Predict(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+
+//   // Figure out the rmse.
+//   double rmse = RMSE(predictions, testLabels);
+
+//   REQUIRE(rmse < 9.21);
+//   std::cout << R2Score(predictions, testLabels) << std::endl;
+
+//   // Reset the prediction.
+//   predictions.zeros();
+//   wd.Predict(testData, predictions);
+
+//   REQUIRE(predictions.n_elem == testData.n_cols);
+
+//   // Figure out the rmse.
+//   double wdrmse = RMSE(predictions, testLabels);
+
+//   REQUIRE(wdrmse < 9.21);
+// }
+
+// TEST_CASE("DecisionTreeRegressorEnergyTest", "[DecisionTreeRegressorTest]")
+// {
+//   arma::mat m;
+//   if (!data::Load("energydata_complete.csv", m))
+//     FAIL("Cannot load dataset energydata_complete.csv!");
+
+//   arma::rowvec r = m.row(0);
+//   m.shed_row(0);
+
+//   DecisionTreeRegressor<> d(m, r, 1, 0.0, 0);
+
+//   arma::rowvec p;
+//   d.Predict(m, p);
+
+//   const double mse = arma::accu(arma::square(p - r)) / p.n_elem;
+//   REQUIRE(mse == Approx(0.0).epsilon(1e-4));
+// }
+
+TEST_CASE("multisplittest", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 500, arma::fill::randu);
+  arma::Row<double> labels(500);
+
+  for (size_t i = 0; i < 100; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+  for (size_t i = 100; i < 200; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 200; i < 300; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 2.0;
+  }
+  for (size_t i = 300; i < 400; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 400; i < 500; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  std::cout << "****************Start**************\n";
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  arma::rowvec preds;
+  d.Predict(dataset, preds);
+
+  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
+  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
+  std::cout << "****************End****************\n";
+}
+
+TEST_CASE("multisplittest1", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 250, arma::fill::randu);
+  arma::Row<double> labels(500);
+
+  for (size_t i = 0; i < 50; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+  for (size_t i = 50; i < 100; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 100; i < 150; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 2.0;
+  }
+  for (size_t i = 150; i < 200; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 1.0;
+  }
+  for (size_t i = 200; i < 250; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  std::cout << "****************Start**************\n";
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  arma::rowvec preds;
+  d.Predict(dataset, preds);
+
+  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
+  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
+  std::cout << "****************End****************\n";
+}
+
+TEST_CASE("multisplittest2", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset(10, 500, arma::fill::randu);
+  arma::Row<double> labels(500);
+
+  for (size_t i = 0; i < 100; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 0.0;
+  }
+  for (size_t i = 100; i < 200; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 5.0;
+  }
+  for (size_t i = 200; i < 300; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 10.0;
+  }
+  for (size_t i = 300; i < 400; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 15.0;
+  }
+  for (size_t i = 400; i < 500; i++)
+  {
+    dataset(3, i) = i;
+    labels(i) = 20.0;
+  }
+
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  std::cout << "****************Start**************\n";
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  arma::rowvec preds;
+  d.Predict(dataset, preds);
+
+  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
+  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
+  std::cout << "****************End****************\n";
+}
+
+TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")
+{
+  // drug dosage (in mg).
+  arma::mat dataset = {{2, 3, 5, 10, 14, 16, 20, 22, 28, 30, 32, 35, 39}};
+  // percentage effectiveness.
+  arma::rowvec labels = {0, 0, 0, 5, 99, 99, 99, 95, 55, 45, 7, 0, 0};
+
+  arma::rowvec weights(labels.n_elem);
+  weights.ones();
+  std::cout << "****************Start**************\n";
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 5);
+  arma::rowvec preds;
+  d.Predict(dataset, preds);
+
+  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
+  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
+  std::cout << "****************End****************\n";
+}
diff --git a/src/mlpack/tests/test_function_tools.hpp b/src/mlpack/tests/test_function_tools.hpp
index eb891361e9..693c3ec469 100644
--- a/src/mlpack/tests/test_function_tools.hpp
+++ b/src/mlpack/tests/test_function_tools.hpp
@@ -97,6 +97,21 @@ inline void LoadBostonHousingDataset(arma::mat& trainData,
 
   data::Split(dataset, labels, trainData, testData,
       trainLabels, testLabels, 0.3);
+  // info.Type(3) = data::Datatype::categorical;
+  // info.Type(8) = data::Datatype::categorical;
+
+  // info.MapString<double>("0", 3);
+  // info.MapString<double>("1", 3);
+  // info.MapString<double>("1", 8);
+  // info.MapString<double>("2", 8);
+  // info.MapString<double>("3", 8);
+  // info.MapString<double>("4", 8);
+  // info.MapString<double>("5", 8);
+  // info.MapString<double>("6", 8);
+  // info.MapString<double>("7", 8);
+  // info.MapString<double>("8", 8);
+  // info.MapString<double>("24", 8);
+  // std::cout << arma::unique(trainData.row(8));
 }
 
 inline double RMSE(const arma::Row<double>& predictions,

From 57572aa876091968ce481e976e772f9830dee6a1 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 14 Jun 2021 08:27:54 +0530
Subject: [PATCH 071/118] Removed numLeaves from BestBinaryNumericSplit

---
 .../decision_tree/best_binary_numeric_split.hpp       |  2 --
 .../decision_tree/best_binary_numeric_split_impl.hpp  |  1 -
 .../decision_tree/decision_tree_regressor_impl.hpp    |  2 --
 src/mlpack/tests/decision_tree_regressor_test.cpp     | 11 +++++------
 4 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 5211f69f8d..207aac1f2a 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -75,7 +75,6 @@ class BestBinaryNumericSplit
    *      better than this).
    * @param data The dimension of data points to check for a split in.
    * @param labels Labels for each point.
-   * @param numClasses Number of classes in the dataset.
    * @param weights Weights associated with labels.
    * @param minimumLeafSize Minimum number of points in a leaf node for
    *      splitting.
@@ -89,7 +88,6 @@ class BestBinaryNumericSplit
       const double bestGain,
       const VecType& data,
       const arma::Row<double>& labels,
-      const size_t numClasses,
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 06abf935d6..fb9e23fb5d 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -188,7 +188,6 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
     const arma::Row<double>& labels,
-    const size_t numClasses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 1a76a2d363..320470df08 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -657,7 +657,6 @@ double DecisionTreeRegressor<FitnessFunction,
         dimGain = NumericSplit::template SplitIfBetter<UseWeights>(bestGain,
             data.cols(begin, begin + count - 1).row(i),
             labels.subvec(begin, begin + count - 1),
-            numClasses,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
             minimumGainSplit,
@@ -829,7 +828,6 @@ double DecisionTreeRegressor<FitnessFunction,
           SplitIfBetter<UseWeights>(bestGain,
                                     data.cols(begin, begin + count - 1).row(i),
                                     labels.cols(begin, begin + count - 1),
-                                    numClasses,
                                     UseWeights ?
                                         weights.cols(begin, begin + count - 1) :
                                         weights,
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 0175d744e9..bbf6a458c4 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -251,11 +251,10 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]
   // Call the method to do the splitting.
   const double bestGain = MADGain::Evaluate<false>(labels, 0, weights);
   const double gain = BestBinaryNumericSplit<MADGain>::SplitIfBetter<false>(
-      bestGain, predictors, labels, 0, weights, 3, 1e-7, splitInfo,
-      aux);
+      bestGain, predictors, labels, weights, 3, 1e-7, splitInfo, aux);
   const double weightedGain =
       BestBinaryNumericSplit<MADGain>::SplitIfBetter<true>(bestGain, predictors,
-      labels, 0, weights, 3, 1e-7, splitInfo, aux);
+      labels, weights, 3, 1e-7, splitInfo, aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -285,11 +284,11 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest_", "[DecisionTreeRegressorTest]"
   // Call the method to do the splitting.
   const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
   const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, labels, 0, weights, 8, 1e-7, splitInfo, aux);
+      bestGain, predictors, labels, weights, 8, 1e-7, splitInfo, aux);
   // This should make no difference because it won't split at all.
   const double weightedGain =
       BestBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
-      labels, 0, weights, 8, 1e-7, splitInfo, aux);
+      labels, weights, 8, 1e-7, splitInfo, aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
@@ -319,7 +318,7 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
   // Call the method to do the splitting.
   const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
   const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, labels, 0, weights, 10, 1e-7, splitInfo,
+      bestGain, predictors, labels, weights, 10, 1e-7, splitInfo,
       aux);
 
   // Make sure there was no split.

From fdb90df2d36886cbb467b50aa9ebe8c267e7f8d6 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 14 Jun 2021 08:52:15 +0530
Subject: [PATCH 072/118] Reverted splitInfo to arma::vec for old decision tree
 for numric splits

---
 .../best_binary_numeric_split.hpp             |  2 +-
 .../best_binary_numeric_split_impl.hpp        |  8 ++--
 .../decision_tree/decision_tree_impl.hpp      |  4 +-
 .../random_binary_numeric_split.hpp           |  2 +-
 .../random_binary_numeric_split_impl.hpp      |  5 ++-
 src/mlpack/tests/decision_tree_test.cpp       | 38 ++++++++++---------
 6 files changed, 32 insertions(+), 27 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 207aac1f2a..d2e052f4dd 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -60,7 +60,7 @@ class BestBinaryNumericSplit
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
-      double& splitInfo,
+      arma::vec& splitInfo,
       AuxiliarySplitInfo& aux);
 
   /**
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index fb9e23fb5d..ff84a5496f 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -25,7 +25,7 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
-    double& splitInfo,
+    arma::vec& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
   // First sanity check: if we don't have enough points, we can't split.
@@ -154,7 +154,8 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
       // We can take a shortcut: no split will be better than this, so just
       // take this one. The actual split value will be halfway between the
       // value at index - 1 and index.
-      splitInfo = (data[sortedIndices[index - 1]] +
+      splitInfo.set_size(1);
+      splitInfo[0] = (data[sortedIndices[index - 1]] +
           data[sortedIndices[index]]) / 2.0;
 
       return gain;
@@ -163,7 +164,8 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     {
       // We still have a better split.
       bestFoundGain = gain;
-      splitInfo = (data[sortedIndices[index - 1]] +
+      splitInfo.set_size(1);
+      splitInfo[0] = (data[sortedIndices[index - 1]] +
           data[sortedIndices[index]]) / 2.0;
       improved = true;
     }
diff --git a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
index 0c5d106002..e085fee9a4 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
@@ -663,7 +663,7 @@ double DecisionTree<FitnessFunction,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
             minimumGainSplit,
-            classProbabilities[0],
+            classProbabilities,
             *this);
       }
 
@@ -836,7 +836,7 @@ double DecisionTree<FitnessFunction,
                                         weights,
                                     minimumLeafSize,
                                     minimumGainSplit,
-                                    classProbabilities[0],
+                                    classProbabilities,
                                     *this);
 
       // If the splitter did not report that it improved, then move to the next
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
index 98e1772ca7..8380f79fca 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
@@ -82,7 +82,7 @@ class RandomBinaryNumericSplit
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
-      double& splitInfo,
+      arma::vec& splitInfo,
       AuxiliarySplitInfo& aux,
       const bool splitIfBetterGain = false);
 
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
index 4b5b792b13..9bb18e158c 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
@@ -27,7 +27,7 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
-    double& splitInfo,
+    arma::vec& splitInfo,
     AuxiliarySplitInfo& /* aux */,
     const bool splitIfBetterGain)
 {
@@ -125,7 +125,8 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   if (gain < bestFoundGain && splitIfBetterGain)
     return DBL_MAX;
 
-  splitInfo = randomPivot;
+  splitInfo.set_size(1);
+  splitInfo[0] = randomPivot;
 
   if (UseWeights)
     gain /= totalWeight;
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 987b77179d..54213e417f 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -288,17 +288,16 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest", "[DecisionTreeTest]")
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities[0],
-      aux);
+      bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities, aux);
   const double weightedGain =
       BestBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 3, 1e-7, classProbabilities[0], aux);
+      labels, 2, weights, 3, 1e-7, classProbabilities, aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -326,22 +325,23 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   arma::Row<size_t> labels("0 0 0 0 0 1 1 1 1 1 1");
   arma::rowvec weights(labels.n_elem);
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities[0],
+      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities,
       aux);
   // This should make no difference because it won't split at all.
   const double weightedGain =
       BestBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 8, 1e-7, classProbabilities[0], aux);
+      labels, 2, weights, 8, 1e-7, classProbabilities, aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
+  // REQUIRE(classProbabilities.n_elem == 0);    **TODO**
 }
 
 /**
@@ -361,17 +361,18 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities[0],
+      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities,
       aux);
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
+  // REQUIRE(classProbabilities.n_elem == 0);    **TODO**
 }
 
 /**
@@ -384,22 +385,22 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   arma::Row<size_t> labels("0 0 0 0 0 1 1 1 1 1 1");
   arma::rowvec weights(labels.n_elem);
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities[0],
-      aux);
+      bestGain, values, labels, 2, weights, 8, 1e-7, classProbabilities, aux);
   // This should make no difference because it won't split at all.
   const double weightedGain =
       RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 8, 1e-7, classProbabilities[0], aux);
+      labels, 2, weights, 8, 1e-7, classProbabilities, aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
+  // REQUIRE(classProbabilities.n_elem == 0);     **TODO**
 }
 
 /**
@@ -419,17 +420,18 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities[0],
+      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities,
       aux, true);
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
+  // REQUIRE(classProbabilities.n_elem == 0);     **TODO**
 }
 
 /**
@@ -449,7 +451,7 @@ TEST_CASE("RandomBinaryNumericSplitDiffSplitTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities(1), classProbabilities1(1);
+  arma::vec classProbabilities, classProbabilities1;
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux1;
 
@@ -459,12 +461,12 @@ TEST_CASE("RandomBinaryNumericSplitDiffSplitTest", "[DecisionTreeTest]")
   {
     // Call BestBinaryNumericSplit to do the splitting.
     (void) BestBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities[0],
+        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities,
         aux);
 
     // Call RandomBinaryNumericSplit to do the splitting.
     (void) RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities1[0],
+        bestGain, values, labels, 2, weights, 3, 1e-7, classProbabilities1,
         aux1);
 
     if (classProbabilities[0] == classProbabilities1[0])

From 6e34bc02c6cfbb44981ab70bb1d0150bdf823c99 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 14 Jun 2021 17:40:53 +0530
Subject: [PATCH 073/118] Add random numeric splitting strategy for regression
 tree

---
 .../decision_tree/decision_tree_regressor.hpp |   1 +
 .../random_binary_numeric_split.hpp           |  38 ++++++
 .../random_binary_numeric_split_impl.hpp      | 119 ++++++++++++++++++
 .../tests/decision_tree_regressor_test.cpp    |  85 +++++++++++++
 4 files changed, 243 insertions(+)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index fcdc1f17c8..2e8d117f83 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -18,6 +18,7 @@
 #include "mse_gain.hpp"
 #include "best_binary_numeric_split.hpp"
 #include "all_categorical_split.hpp"
+#include "random_binary_numeric_split.hpp"
 #include "all_dimension_select.hpp"
 #include <type_traits>
 
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
index 8380f79fca..98d1f13787 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
@@ -37,6 +37,8 @@ class RandomBinaryNumericSplit
    * return the value 'bestGain'.  If a split is made, then splitInfo
    * and aux may be modified.
    *
+   * It is used only for classification tasks.
+   *
    * @code
    * @article{10.1007/s10994-006-6226-1,
    *   author = {Geurts, Pierre and Ernst, Damien and Wehenkel, Louis},
@@ -86,6 +88,42 @@ class RandomBinaryNumericSplit
       AuxiliarySplitInfo& aux,
       const bool splitIfBetterGain = false);
 
+  /**
+   * Check if we can split a node.  If we can split a node in a way that
+   * improves on 'bestGain', then we return the improved gain.  Otherwise we
+   * return the value 'bestGain'.  If a split is made, then splitInfo
+   * and aux may be modified.
+   *
+   * It is used only for regression tasks.
+   *
+   * @param bestGain Best gain seen so far (we'll only split if we find gain
+   *      better than this).
+   * @param data The dimension of data points to check for a split in.
+   * @param labels Labels for each point.
+   * @param numClasses Number of classes in the dataset.
+   * @param weights Weights associated with labels.
+   * @param minimumLeafSize Minimum number of points in a leaf node for
+   *      splitting.
+   * @param minimumGainSplit Minimum gain split.
+   * @param splitInfo Stores split information on a successful split.
+   * @param aux Auxiliary split information, which may be modified on a
+   *      successful split.
+   * @param splitIfBetterGain When set to true, it will split only when gain is
+   *      better than the current best gain. Otherwise, it always makes a
+   *      split regardless of gain.
+   */
+  template<bool UseWeights, typename VecType, typename WeightVecType>
+  static double SplitIfBetter(
+      const double bestGain,
+      const VecType& data,
+      const arma::Row<double>& labels,
+      const WeightVecType& weights,
+      const size_t minimumLeafSize,
+      const double minimumGainSplit,
+      double& splitInfo,
+      AuxiliarySplitInfo& aux,
+      const bool splitIfBetterGain = false);
+
   /**
    * Returns 2, since the binary split always has two children.
    *
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
index 9bb18e158c..2bd09bc53b 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
@@ -136,6 +136,125 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   return gain;
 }
 
+template<typename FitnessFunction>
+template<bool UseWeights, typename VecType, typename WeightVecType>
+double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
+    const double bestGain,
+    const VecType& data,
+    const arma::Row<double>& labels,
+    const WeightVecType& weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    double& splitInfo,
+    AuxiliarySplitInfo& /* aux */,
+    const bool splitIfBetterGain)
+{
+  double bestFoundGain = std::min(bestGain + minimumGainSplit, 0.0);
+  // Forcing a minimum leaf size of 1 (empty children don't make sense).
+  const size_t minimum = std::max(minimumLeafSize, (size_t) 1);
+
+  // First sanity check: if we don't have enough points, we can't split.
+  if (data.n_elem < (minimum * 2))
+    return DBL_MAX;
+  if (bestGain == 0.0)
+    return DBL_MAX; // It can't be outperformed.
+
+  typename VecType::elem_type maxValue = arma::max(data);
+  typename VecType::elem_type minValue = arma::min(data);
+
+  // Sanity check: if the maximum element is the same as the minimum, we
+  // can't split in this dimension.
+  if (maxValue == minValue)
+    return DBL_MAX;
+
+  double totalWeight = 0.0;
+  double totalLeftWeight = 0.0;
+  double totalRightWeight = 0.0;
+  if (UseWeights)
+  {
+    totalWeight = arma::accu(weights);
+    bestFoundGain *= totalWeight;
+  }
+  else
+  {
+    bestFoundGain *= data.n_elem;
+  }
+
+  // Picking a random pivot to split the dimension.
+  double randomPivot = math::Random(minValue, maxValue);
+
+  // We need to count the number of points for each leaf.
+  size_t leftLeafSize = 0;
+  size_t rightLeafSize = 0;
+  for (size_t i = 0; i < data.n_elem; ++i)
+  {
+    if (UseWeights)
+    {
+      if (data[i] < randomPivot)
+        totalLeftWeight += weights[i];
+      else
+        totalRightWeight += weights[i];
+    }
+
+    if (data[i] < randomPivot)
+      ++leftLeafSize;
+    else
+      ++rightLeafSize;
+  }
+
+  // Splitting data to compute gain.
+  arma::rowvec leftLabels(leftLeafSize), rightLabels(rightLeafSize);
+  arma::rowvec leftWeights, rightWeights;
+  if (UseWeights)
+  {
+      leftWeights.set_size(leftLeafSize);
+      rightWeights.set_size(rightLeafSize);
+  }
+
+  size_t l = 0, r = 0;
+  for(size_t i = 0; i < data.n_elem; ++i)
+  {
+    if (UseWeights)
+    {
+      if (data[i] < randomPivot)
+        leftWeights[l] = weights[i];
+      else
+        rightWeights[r] = weights[i];
+    }
+    if (data[i] < randomPivot)
+      leftLabels[l++] = labels[i];
+    else
+      rightLabels[r++] = labels[i];
+  }
+
+  // Calculate the gain for the left and right child.
+  const double leftGain =
+      FitnessFunction::template Evaluate<UseWeights>(leftLabels, leftWeights,
+          0, leftLeafSize);
+  const double rightGain =
+      FitnessFunction::template Evaluate<UseWeights>(rightLabels, rightWeights,
+          0, rightLeafSize);
+
+  // Calculate the gain at this split point.
+  double gain;
+  if (UseWeights)
+    gain = totalLeftWeight * leftGain + totalRightWeight * rightGain;
+  else
+    gain = double(leftLeafSize) * leftGain + double(rightLeafSize) * rightGain;
+
+  if (gain < bestFoundGain && splitIfBetterGain)
+    return DBL_MAX;
+
+  splitInfo = randomPivot;
+
+  if (UseWeights)
+    gain /= totalWeight;
+  else
+    gain /= labels.n_elem;
+
+  return gain;
+}
+
 template<typename FitnessFunction>
 template<typename ElemType>
 size_t RandomBinaryNumericSplit<FitnessFunction>::CalculateDirection(
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index bbf6a458c4..11add44067 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -325,6 +325,91 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
   REQUIRE(gain == DBL_MAX);
 }
 
+/**
+ * Check that the RandomBinaryNumericSplit always splits when splitIfBetterGain
+ * is false.
+ */
+TEST_CASE("RandomBinaryNumericSplitAlwaysSplit_",
+    "[DecisionTreeRegressorTest]")
+{
+  arma::vec values("0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0");
+  arma::rowvec labels("0 0 0 0 0 1 1 1 1 1 1");
+  arma::rowvec weights;
+  weights.ones(labels.n_elem);
+
+  double splitInfo;
+  RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 2, weights);
+  const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, values, labels, weights, 1, 1e-7, splitInfo, aux);
+  const double weightedGain =
+      RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, values,
+      labels, weights, 1, 1e-7, splitInfo, aux);
+
+  // Make sure that split was made.
+  REQUIRE(gain != DBL_MAX);
+  REQUIRE(weightedGain != DBL_MAX);
+}
+
+/**
+ * Check that the RandomBinaryNumericSplit won't split if not enough points are
+ * given.
+ */
+TEST_CASE("RandomBinaryNumericSplitMinSamplesTest_",
+    "[DecisionTreeRegressorTest]")
+{
+  arma::vec values("0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0");
+  arma::rowvec labels("0 0 0 0 0 1 1 1 1 1 1");
+  arma::rowvec weights(labels.n_elem);
+
+  double splitInfo;
+  RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 2, weights);
+  const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, values, labels, weights, 8, 1e-7, splitInfo, aux);
+  // This should make no difference because it won't split at all.
+  const double weightedGain =
+      RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, values,
+      labels, weights, 8, 1e-7, splitInfo, aux);
+
+  // Make sure that no split was made.
+  REQUIRE(gain == DBL_MAX);
+  REQUIRE(gain == weightedGain);
+}
+
+/**
+ * Check that the RandomBinaryNumericSplit doesn't split a dimension that gives
+ * no gain when splitIfBetterGain is true.
+ */
+TEST_CASE("RandomBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
+{
+  arma::vec values(100);
+  arma::Row<double> labels(100);
+  arma::rowvec weights;
+  for (size_t i = 0; i < 100; i += 2)
+  {
+    values[i] = i;
+    labels[i] = 0.0;
+    values[i + 1] = i;
+    labels[i + 1] = 1.0;
+  }
+
+  double splitInfo;
+  RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
+
+  // Call the method to do the splitting.
+  const double bestGain = MSEGain::Evaluate<false>(labels, 2, weights);
+  const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
+      bestGain, values, labels, weights, 10, 1e-7, splitInfo, aux, true);
+
+  // Make sure there was no split.
+  REQUIRE(gain == DBL_MAX);
+}
+
 /**
  * A basic construction of the decision tree---ensure that we can create the
  * tree and that it split at least once.

From 44c2b5ca51d7e1f64492f787717a7a9655dab65f Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 15 Jun 2021 10:27:51 +0530
Subject: [PATCH 074/118] Reverted AllCategoricalSplit to use arma::vec for
 splitInfo

---
 .../decision_tree/all_categorical_split.hpp   |  5 ++--
 .../all_categorical_split_impl.hpp            | 26 ++++++++++++++++---
 .../decision_tree/decision_tree_impl.hpp      |  4 +--
 src/mlpack/tests/decision_tree_test.cpp       | 20 +++++++-------
 4 files changed, 38 insertions(+), 17 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split.hpp b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
index 2ac91b099a..ed265bba89 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
@@ -54,7 +54,8 @@ class AllCategoricalSplit
    * @param aux Auxiliary split information, which may be modified on a
    *      successful split.
    */
-  template<bool UseWeights, typename VecType, typename LabelsType, typename WeightVecType>
+  template<bool UseWeights, typename VecType, typename LabelsType,
+           typename WeightVecType, typename SplitInfoType>
   static double SplitIfBetter(
       const double bestGain,
       const VecType& data,
@@ -64,7 +65,7 @@ class AllCategoricalSplit
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
-      double& splitInfo,
+      SplitInfoType& splitInfo,
       AuxiliarySplitInfo& aux);
 
   /**
diff --git a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
index 754a092737..0fbeeeec25 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
@@ -15,8 +15,28 @@
 namespace mlpack {
 namespace tree {
 
+/**
+ * Helper function to store split information. This is used for regression.
+ * payload contains the information to be stored in splitInfo.
+ */
+static void StoreSplitInfo(double& splitInfo, const double& payload)
+{
+  splitInfo = payload;
+}
+
+/**
+ * Helper function to store split information. This is used for classification.
+ * payload contains the information to be stored in splitInfo.
+ */
+static void StoreSplitInfo(arma::vec& splitInfo, const double& payload)
+{
+  splitInfo.set_size(1);
+  splitInfo[0] = payload;
+}
+
 template<typename FitnessFunction>
-template<bool UseWeights, typename VecType, typename LabelsType, typename WeightVecType>
+template<bool UseWeights, typename VecType, typename LabelsType,
+         typename WeightVecType, typename SplitInfoType>
 double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
@@ -26,7 +46,7 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
-    double& splitInfo,
+    SplitInfoType& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
   // Count the number of elements in each potential child.
@@ -100,7 +120,7 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
   if (overallGain > bestGain + minimumGainSplit + epsilon)
   {
     // This is better, so store it in splitInfo and return.
-    splitInfo = numCategories;
+    StoreSplitInfo(splitInfo, numCategories);
     return overallGain;
   }
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
index e085fee9a4..3053e74b35 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
@@ -636,7 +636,6 @@ double DecisionTree<FitnessFunction,
 
   if (maximumDepth != 1)
   {
-    classProbabilities.set_size(1);
     for (size_t i = dimensionSelector.Begin(); i != end;
          i = dimensionSelector.Next())
     {
@@ -651,7 +650,7 @@ double DecisionTree<FitnessFunction,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
             minimumGainSplit,
-            classProbabilities[0],
+            classProbabilities,
             *this);
       }
       else if (datasetInfo.Type(i) == data::Datatype::numeric)
@@ -822,7 +821,6 @@ double DecisionTree<FitnessFunction,
 
   if (maximumDepth != 1)
   {
-    classProbabilities.set_size(1);
     for (size_t i = dimensionSelector.Begin(); i != dimensionSelector.End();
          i = dimensionSelector.Next())
     {
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 54213e417f..1412b70495 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -341,7 +341,7 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
-  // REQUIRE(classProbabilities.n_elem == 0);    **TODO**
+  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -372,7 +372,7 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
-  // REQUIRE(classProbabilities.n_elem == 0);    **TODO**
+  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -400,7 +400,7 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
-  // REQUIRE(classProbabilities.n_elem == 0);     **TODO**
+  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -431,7 +431,7 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
-  // REQUIRE(classProbabilities.n_elem == 0);     **TODO**
+  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -523,17 +523,18 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest", "[DecisionTreeTest]")
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   AllCategoricalSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
       bestGain, values, 4, labels, 3, weights, 4, 1e-7,
-      classProbabilities[0], aux);
+      classProbabilities, aux);
 
   // Make sure it's not split.
   REQUIRE(gain == DBL_MAX);
+  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -555,21 +556,22 @@ TEST_CASE("AllCategoricalSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 2] = 2;
   }
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   AllCategoricalSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
       bestGain, values, 10, labels, 3, weights, 10, 1e-7,
-      classProbabilities[0], aux);
+      classProbabilities, aux);
   const double weightedGain =
       AllCategoricalSplit<GiniGain>::SplitIfBetter<true>(bestGain, values, 10,
-      labels, 3, weights, 10, 1e-7, classProbabilities[0], aux);
+      labels, 3, weights, 10, 1e-7, classProbabilities, aux);
 
   // Make sure that there was no split.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
+  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**

From 8d8687a928bbd481bcd297b1382f49de5bc1cbdc Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 15 Jun 2021 18:21:30 +0530
Subject: [PATCH 075/118] Add a mock categorical dataset and test
 DecisionTreeRegressor on it

---
 .../tests/decision_tree_regressor_test.cpp    | 204 +++++++++---------
 src/mlpack/tests/mock_categorical_data.hpp    |  60 +++++-
 src/mlpack/tests/test_function_tools.hpp      |  30 +--
 3 files changed, 159 insertions(+), 135 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 11add44067..9b5ef0e234 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -15,6 +15,7 @@
 #include <mlpack/methods/decision_tree/mse_gain.hpp>
 #include <mlpack/methods/decision_tree/random_dimension_select.hpp>
 #include <mlpack/methods/decision_tree/multiple_random_dimension_select.hpp>
+#include <mlpack/core/math/random.hpp>
 
 #include "catch.hpp"
 #include "serialization.hpp"
@@ -530,77 +531,69 @@ TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
   }
 }
 
-// /**
-//  * Test that we can build a decision tree on a simple categorical dataset.
-//  */
-// TEST_CASE("CategoricalBuildTest", "[DecisionTreeTest]")
-// {
-//   arma::mat d;
-//   arma::Row<size_t> l;
-//   data::DatasetInfo di;
-//   MockCategoricalData(d, l, di);
+/**
+ * Test that we can build a decision tree on a simple categorical dataset.
+ */
+TEST_CASE("CategoricalBuildTest_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat d;
+  arma::rowvec l;
+  data::DatasetInfo di;
+  MockCategoricalData(d, l, di);
 
-//   // Split into a training set and a test set.
-//   arma::mat trainingData = d.cols(0, 1999);
-//   arma::mat testData = d.cols(2000, 3999);
-//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
-//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+  // Split into a training set and a test set.
+  arma::mat trainingData = d.cols(0, 1999);
+  arma::mat testData = d.cols(2000, 3999);
+  arma::rowvec trainingLabels = l.subvec(0, 1999);
+  arma::rowvec testLabels = l.subvec(2000, 3999);
 
-//   // Build the tree.
-//   DecisionTree<> tree(trainingData, di, trainingLabels, 5, 10);
+  // Build the tree.
+  DecisionTreeRegressor<> tree(trainingData, di, trainingLabels, 10);
 
-//   // Now evaluate the accuracy of the tree.
-//   arma::Row<size_t> predictions;
-//   tree.Classify(testData, predictions);
+  // Now evaluate the quality of predictions.
+  arma::rowvec predictions;
+  tree.Predict(testData, predictions);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-//   size_t correct = 0;
-//   for (size_t i = 0; i < testData.n_cols; ++i)
-//     if (testLabels[i] == predictions[i])
-//       ++correct;
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   // Make sure we got at least 70% accuracy.
-//   const double correctPct = double(correct) / double(testData.n_cols);
-//   REQUIRE(correctPct > 0.70);
-// }
+  // Make sure we get reasonable rmse.
+  const double rmse = RMSE(predictions, testLabels);
+  REQUIRE(rmse < 1.0);
+}
 
-// /**
-//  * Test that we can build a decision tree with weights on a simple categorical
-//  * dataset.
-//  */
-// TEST_CASE("CategoricalBuildTestWithWeight", "[DecisionTreeTest]")
-// {
-//   arma::mat d;
-//   arma::Row<size_t> l;
-//   data::DatasetInfo di;
-//   MockCategoricalData(d, l, di);
+/**
+ * Test that we can build a decision tree with weights on a simple categorical
+ * dataset.
+ */
+TEST_CASE("CategoricalBuildTestWithWeight_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat d;
+  arma::rowvec l;
+  data::DatasetInfo di;
+  MockCategoricalData(d, l, di);
 
-//   // Split into a training set and a test set.
-//   arma::mat trainingData = d.cols(0, 1999);
-//   arma::mat testData = d.cols(2000, 3999);
-//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
-//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+  // Split into a training set and a test set.
+  arma::mat trainingData = d.cols(0, 1999);
+  arma::mat testData = d.cols(2000, 3999);
+  arma::rowvec trainingLabels = l.subvec(0, 1999);
+  arma::rowvec testLabels = l.subvec(2000, 3999);
 
-//   arma::Row<double> weights = arma::ones<arma::Row<double>>(
-//       trainingLabels.n_elem);
+  arma::rowvec weights = arma::ones<arma::rowvec>(
+      trainingLabels.n_elem);
 
-//   // Build the tree.
-//   DecisionTree<> tree(trainingData, di, trainingLabels, 5, weights, 10);
+  // Build the tree.
+  DecisionTreeRegressor<> tree(trainingData, di, trainingLabels, weights, 10);
 
-//   // Now evaluate the accuracy of the tree.
-//   arma::Row<size_t> predictions;
-//   tree.Classify(testData, predictions);
+  // Now evaluate the quality of predictions.
+  arma::rowvec predictions;
+  tree.Predict(testData, predictions);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-//   size_t correct = 0;
-//   for (size_t i = 0; i < testData.n_cols; ++i)
-//     if (testLabels[i] == predictions[i])
-//       ++correct;
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   // Make sure we got at least 70% accuracy.
-//   const double correctPct = double(correct) / double(testData.n_cols);
-//   REQUIRE(correctPct > 0.70);
-// }
+  // Make sure we get reasonable rmse.
+  const double rmse = RMSE(predictions, testLabels);
+  REQUIRE(rmse < 1.0);
+}
 
 /**
  * Test that we can build a decision tree using weighted data (where the
@@ -647,62 +640,59 @@ TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
 //   REQUIRE(rmse < 9.21);
 // }
 
-// /**
-//  * Test that we can build a decision tree on a simple categorical dataset using
-//  * weights, with low-weight noise added.
-//  */
-// TEST_CASE("CategoricalWeightedBuildTest", "[DecisionTreeTest]")
-// {
-//   arma::mat d;
-//   arma::Row<size_t> l;
-//   data::DatasetInfo di;
-//   MockCategoricalData(d, l, di);
+/**
+ * Test that we can build a decision tree on a simple categorical dataset using
+ * weights, with low-weight noise added.
+ */
+TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
+{
+  arma::mat d;
+  arma::rowvec l;
+  data::DatasetInfo di;
+  MockCategoricalData(d, l, di);
 
-//   // Split into a training set and a test set.
-//   arma::mat trainingData = d.cols(0, 1999);
-//   arma::mat testData = d.cols(2000, 3999);
-//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
-//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+  // Split into a training set and a test set.
+  arma::mat trainingData = d.cols(0, 1999);
+  arma::mat testData = d.cols(2000, 3999);
+  arma::rowvec trainingLabels = l.subvec(0, 1999);
+  arma::rowvec testLabels = l.subvec(2000, 3999);
 
-//   // Now create random points.
-//   arma::mat randomNoise(4, 2000);
-//   arma::Row<size_t> randomLabels(2000);
-//   for (size_t i = 0; i < 2000; ++i)
-//   {
-//     randomNoise(0, i) = math::Random();
-//     randomNoise(1, i) = math::Random();
-//     randomNoise(2, i) = math::RandInt(4);
-//     randomNoise(3, i) = math::RandInt(2);
-//     randomLabels[i] = math::RandInt(5);
-//   }
+  // Now create random points.
+  arma::mat randomNoise(5, 2000);
+  arma::rowvec randomLabels(2000);
+  for (size_t i = 0; i < 2000; ++i)
+  {
+    randomNoise(0, i) = math::Random();
+    randomNoise(1, i) = math::Random(-1, 1);
+    randomNoise(2, i) = math::Random();
+    randomNoise(3, i) = math::RandInt(0, 2);
+    randomNoise(4, i) = math::RandInt(0, 5);
+    randomLabels[i] = math::Random(-10, 18);
+  }
 
-//   // Generate weights.
-//   arma::rowvec weights(4000);
-//   for (size_t i = 0; i < 2000; ++i)
-//     weights[i] = math::Random(0.9, 1.0);
-//   for (size_t i = 2000; i < 4000; ++i)
-//     weights[i] = math::Random(0.0, 0.001);
+  // Generate weights.
+  arma::rowvec weights(4000);
+  for (size_t i = 0; i < 2000; ++i)
+    weights[i] = math::Random(0.9, 1.0);
+  for (size_t i = 2000; i < 4000; ++i)
+    weights[i] = math::Random(0.0, 0.001);
 
-//   arma::mat fullData = arma::join_rows(trainingData, randomNoise);
-//   arma::Row<size_t> fullLabels = arma::join_rows(trainingLabels, randomLabels);
+  arma::mat fullData = arma::join_rows(trainingData, randomNoise);
+  arma::rowvec fullLabels = arma::join_rows(trainingLabels, randomLabels);
 
-//   // Build the tree.
-//   DecisionTree<> tree(fullData, di, fullLabels, 5, weights, 10);
+  // Build the tree.
+  DecisionTreeRegressor<> tree(fullData, di, fullLabels, weights, 10);
 
-//   // Now evaluate the accuracy of the tree.
-//   arma::Row<size_t> predictions;
-//   tree.Classify(testData, predictions);
+  // Now evaluate the quality of predictions.
+  arma::rowvec predictions;
+  tree.Predict(testData, predictions);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-//   size_t correct = 0;
-//   for (size_t i = 0; i < testData.n_cols; ++i)
-//     if (testLabels[i] == predictions[i])
-//       ++correct;
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   // Make sure we got at least 70% accuracy.
-//   const double correctPct = double(correct) / double(testData.n_cols);
-//   REQUIRE(correctPct > 0.70);
-// }
+  // Make sure we get reasonable rmse.
+  const double rmse = RMSE(predictions, testLabels);
+  REQUIRE(rmse < 1.5);
+}
 
 // /**
 //  * Test that we can build a decision tree using weighted data (where the
diff --git a/src/mlpack/tests/mock_categorical_data.hpp b/src/mlpack/tests/mock_categorical_data.hpp
index 8b0f00e143..fab25e9061 100644
--- a/src/mlpack/tests/mock_categorical_data.hpp
+++ b/src/mlpack/tests/mock_categorical_data.hpp
@@ -15,7 +15,7 @@
 #include <mlpack/core/dists/discrete_distribution.hpp>
 
 /**
- * Create a mock categorical dataset for testing.
+ * Create a mock categorical dataset for testing classification.
  */
 inline void MockCategoricalData(arma::mat& d,
                                 arma::Row<size_t>& l,
@@ -113,4 +113,62 @@ inline void MockCategoricalData(arma::mat& d,
   }
 }
 
+/**
+ * Create a mock categorical dataset for testing regression.
+ */
+inline void MockCategoricalData(arma::mat& d,
+                                arma::Row<double>& l,
+                                mlpack::data::DatasetInfo& datasetInfo)
+{
+  // Dataset of size 4000.
+  d.set_size(5, 4000);
+  l.set_size(4000);
+
+  for (size_t i = 0; i < 4000; ++i)
+  {
+    // Random numeric features.
+    d(0, i) = mlpack::math::Random();
+    d(1, i) = mlpack::math::Random(-1, 1);
+    d(2, i) = mlpack::math::Random();
+
+    // Binary feature.
+    d(3, i) = mlpack::math::RandInt(0, 2);
+    // 5-category categorical feature.
+    d(4, i) = mlpack::math::RandInt(0, 5);
+
+    // Mappings from categorical features to regression value.
+    std::map<int, double> f;
+    f[0] = 5.0;
+    f[1] = -5.0;
+
+    std::map<int, double> g;
+    g[0] = 2.0;
+    g[1] = 7.0;
+    g[2] = -3.0;
+    g[3] = 0.0;
+    g[4] = 4.0;
+
+    // Random noise in range [-0.5, 0.5).
+    const double noise = mlpack::math::Random() - 0.5;
+
+    // y = x1 + x2 + 3 * x3 + f(x4) + g(x5) + noise
+    l[i] = d(0, i) + d(1, i) + 3 * d(2, i) + f[(int) d(3, i)] +
+        g[(int) d(4, i)] + noise;
+  }
+
+  // Now create the dataset info.
+  datasetInfo = mlpack::data::DatasetInfo(5);
+  datasetInfo.Type(3) = mlpack::data::Datatype::categorical;
+  datasetInfo.Type(4) = mlpack::data::Datatype::categorical;
+  // Set mappings.
+  datasetInfo.MapString<double>("0", 3);
+  datasetInfo.MapString<double>("1", 3);
+
+  datasetInfo.MapString<double>("0", 4);
+  datasetInfo.MapString<double>("1", 4);
+  datasetInfo.MapString<double>("2", 4);
+  datasetInfo.MapString<double>("3", 4);
+  datasetInfo.MapString<double>("4", 4);
+}
+
 #endif
diff --git a/src/mlpack/tests/test_function_tools.hpp b/src/mlpack/tests/test_function_tools.hpp
index 693c3ec469..990cb07747 100644
--- a/src/mlpack/tests/test_function_tools.hpp
+++ b/src/mlpack/tests/test_function_tools.hpp
@@ -117,33 +117,9 @@ inline void LoadBostonHousingDataset(arma::mat& trainData,
 inline double RMSE(const arma::Row<double>& predictions,
                    const arma::Row<double>& trueLabels)
 {
-  double rmse = 0.0;
-  for (size_t i = 0; i < predictions.n_elem; ++i)
-  {
-    rmse += std::pow(predictions[i] - trueLabels[i], 2);
-  }
-  rmse /= predictions.n_elem;
-  rmse = sqrt(rmse);
-  return rmse;
-}
-
-/**
- * Calculates the R2 score of the predictions with true labels.
- */
-inline double R2Score(const arma::Row<double>& predictions,
-                      const arma::Row<double>& trueLabels)
-{
-  double mean = arma::mean(trueLabels);
-  double SStot = 0.0;
-  double SSres = 0.0;
-  for (size_t i = 0; i < predictions.n_elem; ++i)
-    SSres += std::pow(predictions[i] - trueLabels[i], 2);
-  for (size_t i = 0; i < predictions.n_elem; ++i)
-  {
-    SStot += std::pow(trueLabels[i] - mean, 2);
-  }
-
-  return 1 - SSres / SStot;
+  double mse = arma::accu(arma::square(predictions - trueLabels)) /
+      predictions.n_elem;
+  return sqrt(mse);
 }
 
 #endif

From b7d2f22bdc43e7ddbed58e84bc541f84e757b272 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 15 Jun 2021 19:47:49 +0530
Subject: [PATCH 076/118] Add MultiSplit tests that ensure that tree learns
 obvious patterns in data

---
 .../tests/decision_tree_regressor_test.cpp    | 204 +++++++++---------
 1 file changed, 105 insertions(+), 99 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 9b5ef0e234..efcb733d57 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -26,6 +26,48 @@ using namespace mlpack;
 using namespace mlpack::tree;
 using namespace mlpack::distribution;
 
+/**
+ * Creates dataset with 5 groups with all the points in same group have exactly
+ * same label.
+ */
+void CreateMultiSplitData(arma::mat& d, arma::rowvec& l, const size_t count,
+    arma::rowvec& values)
+{
+  d = arma::mat(10, count, arma::fill::randu);
+  l = arma::rowvec(count);
+
+  // Group 1.
+  for (size_t i = 0; i < count / 5; i++)
+  {
+    d(3, i) = i;
+    l(i) = values[0];
+  }
+  // Group 2.
+  for (size_t i = count / 5; i < (count / 5) * 2; i++)
+  {
+    d(3, i) = i;
+    l(i) = values[1];
+  }
+  // Group 3.
+  for (size_t i = (count / 5) * 2; i < (count / 5) * 3; i++)
+  {
+    d(3, i) = i;
+    l(i) = values[2];
+  }
+  // Group 4.
+  for (size_t i = (count / 5) * 3; i < (count / 5) * 4; i++)
+  {
+    d(3, i) = i;
+    l(i) = values[3];
+  }
+  // Group 5.
+  for (size_t i = (count / 5) * 4; i < count; i++)
+  {
+    d(3, i) = i;
+    l(i) = values[4];
+  }
+}
+
 /**
  * Make sure the MSE gain is zero when the labels are perfect.
  */
@@ -899,139 +941,103 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 //   REQUIRE(mse == Approx(0.0).epsilon(1e-4));
 // }
 
-TEST_CASE("multisplittest", "[DecisionTreeRegressorTest]")
+/**
+ * Test that the tree is able to perfectly fit all the obvious splits present
+ * in the data.
+ *
+ *     |
+ *     |
+ *   2 |            xxxxxx
+ *     |
+ *     |
+ *   1 |      xxxxxx      xxxxxx
+ *     |
+ *     |
+ *   0 |xxxxxx                  xxxxxx
+ *     |___________________________________
+ */
+TEST_CASE("MultiSplitTest1", "[DecisionTreeRegressorTest]")
 {
-  arma::mat dataset(10, 500, arma::fill::randu);
-  arma::Row<double> labels(500);
+  arma::mat dataset;
+  arma::rowvec labels;
+  arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
 
-  for (size_t i = 0; i < 100; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
-  for (size_t i = 100; i < 200; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 200; i < 300; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 2.0;
-  }
-  for (size_t i = 300; i < 400; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 400; i < 500; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
+  CreateMultiSplitData(dataset, labels, 1000, values);
 
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
   // Minimum leaf size of 1.
-  std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0);
   arma::rowvec preds;
   d.Predict(dataset, preds);
 
-  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
-  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
-  std::cout << "****************End****************\n";
+  for (size_t i = 0; i < labels.n_elem; ++i)
+    REQUIRE(preds[i] == labels[i]);
 }
 
-TEST_CASE("multisplittest1", "[DecisionTreeRegressorTest]")
+/**
+ * Test that the tree is able to perfectly fit all the obvious splits present
+ * in the data. Same test as above, but with less data.
+ */
+TEST_CASE("MultiSplitTest2", "[DecisionTreeRegressorTest]")
 {
-  arma::mat dataset(10, 250, arma::fill::randu);
-  arma::Row<double> labels(500);
+  arma::mat dataset;
+  arma::rowvec labels;
+  arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
 
-  for (size_t i = 0; i < 50; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
-  for (size_t i = 50; i < 100; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 100; i < 150; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 2.0;
-  }
-  for (size_t i = 150; i < 200; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 1.0;
-  }
-  for (size_t i = 200; i < 250; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
+  CreateMultiSplitData(dataset, labels, 100, values);
 
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
   // Minimum leaf size of 1.
-  std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0);
   arma::rowvec preds;
   d.Predict(dataset, preds);
 
-  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
-  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
-  std::cout << "****************End****************\n";
+  for (size_t i = 0; i < labels.n_elem; ++i)
+    REQUIRE(preds[i] == labels[i]);
 }
 
-TEST_CASE("multisplittest2", "[DecisionTreeRegressorTest]")
+/**
+ * Test that the tree is able to perfectly fit all the obvious splits present
+ * in the data.
+ *
+ *     |
+ *  20 |                        xxxxxx
+ *     |
+ *     |
+ *  15 |                  xxxxxx
+ *     |
+ *     |
+ *  10 |            xxxxxx
+ *     |
+ *     |
+ *   5 |      xxxxxx
+ *     |
+ *     |
+ *   0 |xxxxxx
+ *     |________________________________________
+ */
+TEST_CASE("MultiSplitTest3", "[DecisionTreeRegressorTest]")
 {
-  arma::mat dataset(10, 500, arma::fill::randu);
-  arma::Row<double> labels(500);
+  arma::mat dataset;
+  arma::Row<double> labels;
+  arma::rowvec values = {0.0, 5.0, 10.0, 15.0, 20.0};
 
-  for (size_t i = 0; i < 100; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 0.0;
-  }
-  for (size_t i = 100; i < 200; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 5.0;
-  }
-  for (size_t i = 200; i < 300; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 10.0;
-  }
-  for (size_t i = 300; i < 400; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 15.0;
-  }
-  for (size_t i = 400; i < 500; i++)
-  {
-    dataset(3, i) = i;
-    labels(i) = 20.0;
-  }
+  CreateMultiSplitData(dataset, labels, 500, values);
 
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
   // Minimum leaf size of 1.
-  std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 20);
+  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0);
   arma::rowvec preds;
   d.Predict(dataset, preds);
 
-  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
-  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
-  std::cout << "****************End****************\n";
+  for (size_t i = 0; i < labels.n_elem; ++i)
+    REQUIRE(preds[i] == labels[i]);
 }
 
 TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")

From e0487772eac79380e68103607c6231becd3d37fe Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 15 Jun 2021 21:38:43 +0530
Subject: [PATCH 077/118] Added NumLeaves() to regression tree

---
 .../decision_tree/decision_tree_regressor.hpp |  9 +-
 .../decision_tree_regressor_impl.hpp          | 86 +++++++++++--------
 .../tests/decision_tree_regressor_test.cpp    |  6 ++
 3 files changed, 59 insertions(+), 42 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index 2e8d117f83..cd1f6bf0d6 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -404,6 +404,9 @@ class DecisionTreeRegressor :
   //! Get the number of children.
   size_t NumChildren() const { return children.size(); }
 
+  //! Get the number of leaves in the tree.
+  size_t NumLeaves() const;
+
   //! Get the child of the given index.
   const DecisionTreeRegressor& Child(const size_t i) const { return *children[i]; }
   //! Modify the child of the given index (be careful!).
@@ -483,8 +486,7 @@ class DecisionTreeRegressor :
                const size_t minimumLeafSize,
                const double minimumGainSplit,
                const size_t maximumDepth,
-               DimensionSelectionType& dimensionSelector,
-               int& numLeaves);
+               DimensionSelectionType& dimensionSelector);
 
   /**
    * Corresponding to the public Train() method, this method is designed for
@@ -512,8 +514,7 @@ class DecisionTreeRegressor :
                const size_t minimumLeafSize,
                const double minimumGainSplit,
                const size_t maximumDepth,
-               DimensionSelectionType& dimensionSelector,
-               int& numLeaves);
+               DimensionSelectionType& dimensionSelector);
 };
 
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 320470df08..fd762a389f 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -65,13 +65,12 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      dimensionSelector);
 }
 
 //! Construct and train without weight on numeric data.
@@ -102,12 +101,11 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, weights,
-      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
 //! Construct and train with weights.
@@ -144,12 +142,11 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      dimensionSelector);
 }
 
 //! Construct and train on numeric data with weights.
@@ -187,11 +184,10 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
-      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
 //! Take ownership of another tree and train with weights.
@@ -269,11 +265,10 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
-      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
 //! Copy another tree.
@@ -452,12 +447,12 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
       0, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector, leaves);
+      dimensionSelector);
 }
 
 //! Train on the given data, assuming all dimensions are numeric.
@@ -491,13 +486,12 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   return Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      dimensionSelector);
 }
 
 //! Train on the given weighted data.
@@ -539,12 +533,11 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the Train() method.
   return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
       0, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      dimensionSelector);
 }
 
 //! Train on the given weighted all numeric data.
@@ -585,12 +578,11 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  int leaves = 0;
+
   // Pass off work to the Train() method.
   return Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
-      dimensionSelector, leaves);
-  std::cout << "NumLeaves: " << leaves << std::endl;
+      dimensionSelector);
 }
 
 //! Train on the given data.
@@ -615,8 +607,7 @@ double DecisionTreeRegressor<FitnessFunction,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
-    DimensionSelectionType& dimensionSelector,
-    int& numLeaves)
+    DimensionSelectionType& dimensionSelector)
 {
   // Clear children if needed.
   for (size_t i = 0; i < children.size(); ++i)
@@ -745,7 +736,7 @@ double DecisionTreeRegressor<FitnessFunction,
         child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, datasetInfo, labels, numClasses,
             weights, currentCol - currentChildBegin, minimumGainSplit,
-            maximumDepth - 1, dimensionSelector, numLeaves);
+            maximumDepth - 1, dimensionSelector);
       }
       else
       {
@@ -753,7 +744,7 @@ double DecisionTreeRegressor<FitnessFunction,
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, datasetInfo, labels, numClasses,
             weights, minimumLeafSize, minimumGainSplit, maximumDepth - 1,
-            dimensionSelector, numLeaves);
+            dimensionSelector);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
       }
       children.push_back(child);
@@ -768,9 +759,9 @@ double DecisionTreeRegressor<FitnessFunction,
     // Calculate prediction label because we are a leaf.
     CalculatePrediction<UseWeights>(
         labels.subvec(begin, begin + count - 1),
-        UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
-    numLeaves++;
-    
+        UseWeights ? weights.subvec(begin, begin + count - 1) : weights);   
+    std::cout << "Number of points in leaf: " << count << 
+        "  Prediction: " << splitPointOrPrediction << std::endl; 
   }
 
   return -bestGain;
@@ -797,8 +788,7 @@ double DecisionTreeRegressor<FitnessFunction,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
-    DimensionSelectionType& dimensionSelector,
-    int& numLeaves)
+    DimensionSelectionType& dimensionSelector)
 {
   // Clear children if needed.
   for (size_t i = 0; i < children.size(); ++i)
@@ -906,7 +896,7 @@ double DecisionTreeRegressor<FitnessFunction,
         child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, labels, numClasses, weights,
             currentCol - currentChildBegin, minimumGainSplit, maximumDepth - 1,
-            dimensionSelector, numLeaves);
+            dimensionSelector);
       }
       else
       {
@@ -914,7 +904,7 @@ double DecisionTreeRegressor<FitnessFunction,
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
             currentCol - currentChildBegin, labels, numClasses, weights,
             minimumLeafSize, minimumGainSplit, maximumDepth - 1,
-            dimensionSelector, numLeaves);
+            dimensionSelector);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
       }
       children.push_back(child);
@@ -931,7 +921,6 @@ double DecisionTreeRegressor<FitnessFunction,
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
     std::cout << "Number of points in leaf: " << count << 
         "  Prediction: " << splitPointOrPrediction << std::endl;
-    numLeaves++;
   }
 
   return -bestGain;
@@ -1064,6 +1053,27 @@ void DecisionTreeRegressor<FitnessFunction,
   ar(CEREAL_NVP(splitPointOrPrediction));
 }
 
+//! Return the number of leaves.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+size_t DecisionTreeRegressor<FitnessFunction,
+                             NumericSplitType,
+                             CategoricalSplitType,
+                             DimensionSelectionType,
+                             NoRecursion>::NumLeaves() const
+{
+  if (this->NumChildren() == 0)
+    return 1;
+
+  size_t numLeaves = 0;
+  for (size_t i = 0; i < this->NumChildren(); ++i)
+    numLeaves += children[i]->NumLeaves();
+
+  return numLeaves;
+}
 
 } // namespace tree
 } // namespace mlpack
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index efcb733d57..b2bddbd8a7 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -974,6 +974,8 @@ TEST_CASE("MultiSplitTest1", "[DecisionTreeRegressorTest]")
 
   for (size_t i = 0; i < labels.n_elem; ++i)
     REQUIRE(preds[i] == labels[i]);
+
+  REQUIRE(d.NumLeaves() == 5);
 }
 
 /**
@@ -998,6 +1000,8 @@ TEST_CASE("MultiSplitTest2", "[DecisionTreeRegressorTest]")
 
   for (size_t i = 0; i < labels.n_elem; ++i)
     REQUIRE(preds[i] == labels[i]);
+
+  REQUIRE(d.NumLeaves() == 5);
 }
 
 /**
@@ -1038,6 +1042,8 @@ TEST_CASE("MultiSplitTest3", "[DecisionTreeRegressorTest]")
 
   for (size_t i = 0; i < labels.n_elem; ++i)
     REQUIRE(preds[i] == labels[i]);
+
+  REQUIRE(d.NumLeaves() == 5);
 }
 
 TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")

From 26477c778c0d64dcede8c4a85c2e0ac887348366 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 15 Jun 2021 21:43:15 +0530
Subject: [PATCH 078/118] Removed unnecessary test

---
 .../tests/decision_tree_regressor_test.cpp    | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index b2bddbd8a7..511c0078fb 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -1045,22 +1045,3 @@ TEST_CASE("MultiSplitTest3", "[DecisionTreeRegressorTest]")
 
   REQUIRE(d.NumLeaves() == 5);
 }
-
-TEST_CASE("handmadedata", "[DecisionTreeRegressorTest]")
-{
-  // drug dosage (in mg).
-  arma::mat dataset = {{2, 3, 5, 10, 14, 16, 20, 22, 28, 30, 32, 35, 39}};
-  // percentage effectiveness.
-  arma::rowvec labels = {0, 0, 0, 5, 99, 99, 99, 95, 55, 45, 7, 0, 0};
-
-  arma::rowvec weights(labels.n_elem);
-  weights.ones();
-  std::cout << "****************Start**************\n";
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0, 5);
-  arma::rowvec preds;
-  d.Predict(dataset, preds);
-
-  const double mse = arma::accu(arma::square(preds - labels)) / preds.n_elem;
-  REQUIRE(mse == Approx(0.0).epsilon(1e-4));
-  std::cout << "****************End****************\n";
-}

From 4dc37b23a843fde10e5e36b24050dd6781debca1 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 16 Jun 2021 19:12:00 +0530
Subject: [PATCH 079/118] Changed labels to responses throughtout regression
 tree codebase

---
 .../best_binary_numeric_split.hpp             |   6 +-
 .../best_binary_numeric_split_impl.hpp        |  34 +-
 .../decision_tree/decision_tree_regressor.hpp | 129 +++----
 .../decision_tree_regressor_impl.hpp          | 154 ++++----
 src/mlpack/methods/decision_tree/mad_gain.hpp |  26 +-
 src/mlpack/methods/decision_tree/mse_gain.hpp |  30 +-
 ...csv => boston_housing_price_responses.csv} |   0
 .../tests/decision_tree_regressor_test.cpp    | 334 +++++++++---------
 src/mlpack/tests/test_function_tools.hpp      |  18 +-
 9 files changed, 372 insertions(+), 359 deletions(-)
 rename src/mlpack/tests/data/{boston_housing_price_labels.csv => boston_housing_price_responses.csv} (100%)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index d2e052f4dd..f1be1a426a 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -74,8 +74,8 @@ class BestBinaryNumericSplit
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
    * @param data The dimension of data points to check for a split in.
-   * @param labels Labels for each point.
-   * @param weights Weights associated with labels.
+   * @param responses Responses for each point.
+   * @param weights Weights associated with responses.
    * @param minimumLeafSize Minimum number of points in a leaf node for
    *      splitting.
    * @param minimumGainSplit Minimum gain split.
@@ -87,7 +87,7 @@ class BestBinaryNumericSplit
   static double SplitIfBetter(
       const double bestGain,
       const VecType& data,
-      const arma::Row<double>& labels,
+      const arma::rowvec& responses,
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index ff84a5496f..9a2f3cc3a8 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -189,7 +189,7 @@ template<bool UseWeights, typename VecType, typename WeightVecType>
 double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
-    const arma::Row<double>& labels,
+    const arma::rowvec& responses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -204,10 +204,10 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
 
   // Next, sort the data.
   arma::uvec sortedIndices = arma::sort_index(data);
-  arma::Row<double> sortedLabels(labels.n_elem);
+  arma::rowvec sortedResponses(responses.n_elem);
   arma::rowvec sortedWeights;
-  for (size_t i = 0; i < sortedLabels.n_elem; ++i)
-    sortedLabels[i] = labels[sortedIndices[i]];
+  for (size_t i = 0; i < sortedResponses.n_elem; ++i)
+    sortedResponses[i] = responses[sortedIndices[i]];
 
   // Sanity check: if the first element is the same as the last, we can't split
   // in this dimension.
@@ -217,9 +217,9 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   // Only initialize if we are using weights.
   if (UseWeights)
   {
-    sortedWeights.set_size(sortedLabels.n_elem);
-    // The weights must keep the same order as the labels.
-    for (size_t i = 0; i < sortedLabels.n_elem; ++i)
+    sortedWeights.set_size(sortedResponses.n_elem);
+    // The weights must keep the same order as the responses.
+    for (size_t i = 0; i < sortedResponses.n_elem; ++i)
       sortedWeights[i] = weights[sortedIndices[i]];
   }
 
@@ -260,16 +260,18 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     if (data[sortedIndices[index]] == data[sortedIndices[index - 1]])
       continue;
 
-    /* TODO: The following function calculates the gain for each split each time from scratch
-             This can be greatly improved using advanced techniques like prefix sum and
-             prefix sum of squares etc. This will have drastic effects on runtime and is
-             definitely something we would want in future.
+    /* TODO: The following function calculates the gain for each split each
+             time from scratch. This can be greatly improved using advanced
+             techniques like prefix sum and prefix sum of squares etc. This
+             will have drastic effects on runtime and is definitely something
+             we would want in future.
      */
     // Calculate the gain for the left and right child.
-    const double leftGain = FitnessFunction::template Evaluate<UseWeights>(sortedLabels,
-        sortedWeights, 0, index);
-    const double rightGain = FitnessFunction::template Evaluate<UseWeights>(sortedLabels,
-        sortedWeights, index, labels.n_elem);
+    const double leftGain = FitnessFunction::template
+        Evaluate<UseWeights>(sortedResponses, sortedWeights, 0, index);
+    const double rightGain = FitnessFunction::template
+        Evaluate<UseWeights>(sortedResponses, sortedWeights, index,
+            responses.n_elem);
 
     double gain;
     if (UseWeights)
@@ -280,7 +282,7 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     {
       // Calculate the gain at this split point.
       gain = double(index) * leftGain +
-          double(sortedLabels.n_elem - index) * rightGain;
+          double(sortedResponses.n_elem - index) * rightGain;
     }
 
     // Corner case: is this the best possible split?
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index cd1f6bf0d6..d9996e727f 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -56,25 +56,25 @@ class DecisionTreeRegressor :
   DecisionTreeRegressor();
 
   /**
-   * Construct the decision tree on the given data and labels, where the data
-   * can be both numeric and categorical. Setting minimumLeafSize and
+   * Construct the decision tree on the given data and responses, where the
+   * data can be both numeric and categorical. Setting minimumLeafSize and
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
-   * Use std::move if data or labels are no longer needed to avoid copies.
+   * Use std::move if data or responses are no longer needed to avoid copies.
    *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension of the dataset.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @param dimensionSelector Instantiated dimension selection policy.
    */
-  template<typename MatType, typename LabelsType>
+  template<typename MatType, typename ResponsesType>
   DecisionTreeRegressor(MatType data,
                         const data::DatasetInfo& datasetInfo,
-                        LabelsType labels,
+                        ResponsesType responses,
                         const size_t minimumLeafSize = 10,
                         const double minimumGainSplit = 1e-7,
                         const size_t maximumDepth = 0,
@@ -82,23 +82,23 @@ class DecisionTreeRegressor :
                             DimensionSelectionType());
 
   /**
-   * Construct the decision tree on the given data and labels, assuming that
+   * Construct the decision tree on the given data and responses, assuming that
    * the data is all of the numeric type.  Setting minimumLeafSize and
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
-   * Use std::move if data or labels are no longer needed to avoid copies.
+   * Use std::move if data or responses are no longer needed to avoid copies.
    *
    * @param data Dataset to train on.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @param dimensionSelector Instantiated dimension selection policy.
    */
-  template<typename MatType, typename LabelsType>
+  template<typename MatType, typename ResponsesType>
   DecisionTreeRegressor(MatType data,
-                        LabelsType labels,
+                        ResponsesType responses,
                         const size_t minimumLeafSize = 10,
                         const double minimumGainSplit = 1e-7,
                         const size_t maximumDepth = 0,
@@ -106,28 +106,28 @@ class DecisionTreeRegressor :
                             DimensionSelectionType());
 
   /**
-   * Construct the decision tree on the given data and labels with weights,
+   * Construct the decision tree on the given data and responses with weights,
    * where the data can be both numeric and categorical. Setting minimumLeafSize
    * and minimumGainSplit too small may cause the tree to overfit, but setting
    * them too large may cause it to underfit.
    *
-   * Use std::move if data, labels or weights are no longer needed to avoid
+   * Use std::move if data, responses or weights are no longer needed to avoid
    * copies.
    *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension of the dataset.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param weights The weight list of given label.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @param dimensionSelector Instantiated dimension selection policy.
    */
-  template<typename MatType, typename LabelsType, typename WeightsType>
+  template<typename MatType, typename ResponsesType, typename WeightsType>
   DecisionTreeRegressor(
       MatType data,
       const data::DatasetInfo& datasetInfo,
-      LabelsType labels,
+      ResponsesType responses,
       WeightsType weights,
       const size_t minimumLeafSize = 10,
       const double minimumGainSplit = 1e-7,
@@ -137,26 +137,26 @@ class DecisionTreeRegressor :
           typename std::remove_reference<WeightsType>::type>::value>* = 0);
 
   /**
-   * Construct the decision tree on the given data and labels with weights,
+   * Construct the decision tree on the given data and responses with weights,
    * assuming that the data is all of the numeric type. Setting minimumLeafSize
    * and minimumGainSplit too small may cause the tree to overfit, but setting
    * them too large may cause it to underfit.
    *
-   * Use std::move if data, labels or weights are no longer needed to avoid
+   * Use std::move if data, responses or weights are no longer needed to avoid
    * copies.
    *
    * @param data Dataset to train on.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param weights The Weight list of given labels.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @param dimensionSelector Instantiated dimension selection policy.
    */
-  template<typename MatType, typename LabelsType, typename WeightsType>
+  template<typename MatType, typename ResponsesType, typename WeightsType>
   DecisionTreeRegressor(
       MatType data,
-      LabelsType labels,
+      ResponsesType responses,
       WeightsType weights,
       const size_t minimumLeafSize = 10,
       const double minimumGainSplit = 1e-7,
@@ -167,27 +167,28 @@ class DecisionTreeRegressor :
 
   /**
    * Take ownership of another decision tree and train on the given data and
-   * labels with weights, where the data can be both numeric and categorical.
-   * Setting minimumLeafSize and minimumGainSplit too small may cause the
-   * tree to overfit, but setting them too large may cause it to underfit.
+   * responses with weights, where the data can be both numeric and
+   * categorical. Setting minimumLeafSize and minimumGainSplit too small may
+   * cause the tree to overfit, but setting them too large may cause it to
+   * underfit.
    *
-   * Use std::move if data, labels or weights are no longer needed to avoid
+   * Use std::move if data, responses or weights are no longer needed to avoid
    * copies.
    *
    * @param other Tree to take ownership of.
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension of the dataset.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param weights The weight list of given label.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    */
-  template<typename MatType, typename LabelsType, typename WeightsType>
+  template<typename MatType, typename ResponsesType, typename WeightsType>
   DecisionTreeRegressor(
       const DecisionTreeRegressor& other,
       MatType data,
       const data::DatasetInfo& datasetInfo,
-      LabelsType labels,
+      ResponsesType responses,
       WeightsType weights,
       const size_t minimumLeafSize = 10,
       const double minimumGainSplit = 1e-7,
@@ -195,27 +196,27 @@ class DecisionTreeRegressor :
           typename std::remove_reference<WeightsType>::type>::value>* = 0);
 
   /**
-   * Take ownership of another decision tree and train on the given data and labels
-   * with weights, assuming that the data is all of the numeric type. Setting
-   * minimumLeafSize and minimumGainSplit too small may cause the tree to
-   * overfit, but setting them too large may cause it to underfit.
+   * Take ownership of another decision tree and train on the given data and
+   * responses with weights, assuming that the data is all of the numeric type.
+   * Setting minimumLeafSize and minimumGainSplit too small may cause the tree
+   * to overfit, but setting them too large may cause it to underfit.
    *
-   * Use std::move if data, labels or weights are no longer needed to avoid
+   * Use std::move if data, responses or weights are no longer needed to avoid
    * copies.
    * @param other Tree to take ownership of.
    * @param data Dataset to train on.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param weights The Weight list of given labels.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @param dimensionSelector Instantiated dimension selection policy.
    */
-  template<typename MatType, typename LabelsType, typename WeightsType>
+  template<typename MatType, typename ResponsesType, typename WeightsType>
   DecisionTreeRegressor(
       const DecisionTreeRegressor& other,
       MatType data,
-      LabelsType labels,
+      ResponsesType responses,
       WeightsType weights,
       const size_t minimumLeafSize = 10,
       const double minimumGainSplit = 1e-7,
@@ -261,26 +262,26 @@ class DecisionTreeRegressor :
 
   /**
    * Train the decision tree on the given data.  This will overwrite the
-   * existing model.  The data may have numeric and categorical types, specified
+   * existing model. The data may have numeric and categorical types, specified
    * by the datasetInfo parameter.  Setting minimumLeafSize and
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
-   * Use std::move if data or labels are no longer needed to avoid copies.
+   * Use std::move if data or responses are no longer needed to avoid copies.
    *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @param dimensionSelector Instantiated dimension selection policy.
    * @return The final entropy of decision tree.
    */
-  template<typename MatType, typename LabelsType>
+  template<typename MatType, typename ResponsesType>
   double Train(MatType data,
                const data::DatasetInfo& datasetInfo,
-               LabelsType labels,
+               ResponsesType responses,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7,
                const size_t maximumDepth = 0,
@@ -293,19 +294,19 @@ class DecisionTreeRegressor :
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
-   * Use std::move if data or labels are no longer needed to avoid copies.
+   * Use std::move if data or responses are no longer needed to avoid copies.
    *
    * @param data Dataset to train on.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @param dimensionSelector Instantiated dimension selection policy.
    * @return The final entropy of decision tree.
    */
-  template<typename MatType, typename LabelsType>
+  template<typename MatType, typename ResponsesType>
   double Train(MatType data,
-               LabelsType labels,
+               ResponsesType responses,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7,
                const size_t maximumDepth = 0,
@@ -319,12 +320,12 @@ class DecisionTreeRegressor :
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
-   * Use std::move if data, labels or weights are no longer needed to avoid
+   * Use std::move if data, responses or weights are no longer needed to avoid
    * copies.
    *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param weights Weights of all the labels
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
@@ -332,10 +333,10 @@ class DecisionTreeRegressor :
    * @param dimensionSelector Instantiated dimension selection policy.
    * @return The final entropy of decision tree.
    */
-  template<typename MatType, typename LabelsType, typename WeightsType>
+  template<typename MatType, typename ResponsesType, typename WeightsType>
   double Train(MatType data,
                const data::DatasetInfo& datasetInfo,
-               LabelsType labels,
+               ResponsesType responses,
                WeightsType weights,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7,
@@ -351,11 +352,11 @@ class DecisionTreeRegressor :
    * minimumLeafSize and minimumGainSplit too small may cause the tree to
    * overfit, but setting them too large may cause it to underfit.
    *
-   * Use std::move if data, labels or weights are no longer needed to avoid
+   * Use std::move if data, responses or weights are no longer needed to avoid
    * copies.
    *
    * @param data Dataset to train on.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param weights Weights of all the labels
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
@@ -363,9 +364,9 @@ class DecisionTreeRegressor :
    * @param dimensionSelector Instantiated dimension selection policy.
    * @return The final entropy of decision tree.
    */
-  template<typename MatType, typename LabelsType, typename WeightsType>
+  template<typename MatType, typename ResponsesType, typename WeightsType>
   double Train(MatType data,
-               LabelsType labels,
+               ResponsesType responses,
                WeightsType weights,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7,
@@ -386,7 +387,7 @@ class DecisionTreeRegressor :
 
   /**
    * Make prediction for the given points, using the entire tree. The predicted
-   * labels for each point are stored in the given vector.
+   * responses for each point are stored in the given vector.
    *
    * @param data Set of points to predict.
    * @param predictions This will be filled with predictions for each point.
@@ -436,7 +437,7 @@ class DecisionTreeRegressor :
   size_t dimensionType;
   /**
    * This variable may hold different things. If the node has no children, then
-   * it is guaranteed to hold the prediction label for that node.  If the node
+   * it is guaranteed to hold the prediction value for that node.  If the node
    * has children, then it may be used arbitrarily by the split type's
    * CalculateDirection() and SplitIfBetter() function. In this case, it stores
    * the point at which the split was made.
@@ -452,10 +453,10 @@ class DecisionTreeRegressor :
       CategoricalAuxiliarySplitInfo;
 
   /**
-   * Calculate the prediction label for the leaf nodes.
+   * Calculate the prediction value for the leaf nodes.
    */
-  template<bool UseWeights, typename LabelsType, typename WeightsType>
-  void CalculatePrediction(const LabelsType& labels,
+  template<bool UseWeights, typename ResponsesType, typename WeightsType>
+  void CalculatePrediction(const ResponsesType& responses,
                            const WeightsType& weights);
 
   /**
@@ -468,19 +469,19 @@ class DecisionTreeRegressor :
    *      this node.
    * @param count Number of points in this node.
    * @param datasetInfo Type information for each dimension.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param numClasses Number of classes in the dataset.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @return The final entropy of decision tree.
    */
-  template<bool UseWeights, typename MatType, typename LabelsType>
+  template<bool UseWeights, typename MatType, typename ResponsesType>
   double Train(MatType& data,
                const size_t begin,
                const size_t count,
                const data::DatasetInfo& datasetInfo,
-               LabelsType& labels,
+               ResponsesType& responses,
                const size_t numClasses,
                arma::rowvec& weights,
                const size_t minimumLeafSize,
@@ -497,18 +498,18 @@ class DecisionTreeRegressor :
    * @param begin Index of the starting point in the dataset that belongs to
    *      this node.
    * @param count Number of points in this node.
-   * @param labels Labels for each training point.
+   * @param responses Responses for each training point.
    * @param numClasses Number of classes in the dataset.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
    * @return The final entropy of decision tree.
    */
-  template<bool UseWeights, typename MatType, typename LabelsType>
+  template<bool UseWeights, typename MatType, typename ResponsesType>
   double Train(MatType& data,
                const size_t begin,
                const size_t count,
-               LabelsType& labels,
+               ResponsesType& responses,
                const size_t numClasses,
                arma::rowvec& weights,
                const size_t minimumLeafSize,
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index fd762a389f..be8a4c137c 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -42,7 +42,7 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType>
+template<typename MatType, typename ResponsesType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,
                       CategoricalSplitType,
@@ -50,25 +50,25 @@ DecisionTreeRegressor<FitnessFunction,
                       NoRecursion>::DecisionTreeRegressor(
     MatType data,
     const data::DatasetInfo& datasetInfo,
-    LabelsType labels,
+    ResponsesType responses,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
     DimensionSelectionType dimensionSelector)
 {
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
+  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -79,32 +79,32 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType>
+template<typename MatType, typename ResponsesType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,
                       CategoricalSplitType,
                       DimensionSelectionType,
                       NoRecursion>::DecisionTreeRegressor(
     MatType data,
-    LabelsType labels,
+    ResponsesType responses,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
     DimensionSelectionType dimensionSelector)
 {
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, weights,
+  Train<false>(tmpData, 0, tmpData.n_cols, tmpResponses, 0, weights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -114,7 +114,7 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType, typename WeightsType>
+template<typename MatType, typename ResponsesType, typename WeightsType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,
                       CategoricalSplitType,
@@ -122,7 +122,7 @@ DecisionTreeRegressor<FitnessFunction,
                       NoRecursion>::DecisionTreeRegressor(
     MatType data,
     const data::DatasetInfo& datasetInfo,
-    LabelsType labels,
+    ResponsesType responses,
     WeightsType weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -132,19 +132,19 @@ DecisionTreeRegressor<FitnessFunction,
         typename std::remove_reference<WeightsType>::type>::value>*)
 {
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -155,14 +155,14 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType, typename WeightsType>
+template<typename MatType, typename ResponsesType, typename WeightsType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,
                       CategoricalSplitType,
                       DimensionSelectionType,
                       NoRecursion>::DecisionTreeRegressor(
     MatType data,
-    LabelsType labels,
+    ResponsesType responses,
     WeightsType weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -174,19 +174,19 @@ DecisionTreeRegressor<FitnessFunction,
         WeightsType>::type>::value>*)
 {
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, 0, tmpWeights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -196,7 +196,7 @@ template<typename FitnessFunction,
         template<typename> class CategoricalSplitType,
         typename DimensionSelectionType,
         bool NoRecursion>
-template<typename MatType, typename LabelsType, typename WeightsType>
+template<typename MatType, typename ResponsesType, typename WeightsType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,
                       CategoricalSplitType,
@@ -205,7 +205,7 @@ DecisionTreeRegressor<FitnessFunction,
     const DecisionTreeRegressor& other,
     MatType data,
     const data::DatasetInfo& datasetInfo,
-    LabelsType labels,
+    ResponsesType responses,
     WeightsType weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -215,16 +215,16 @@ DecisionTreeRegressor<FitnessFunction,
         CategoricalAuxiliarySplitInfo(other)
 {
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, 0,
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
               tmpWeights, minimumLeafSize, minimumGainSplit);
 }
 
@@ -234,7 +234,7 @@ template<typename FitnessFunction,
         template<typename> class CategoricalSplitType,
         typename DimensionSelectionType,
         bool NoRecursion>
-template<typename MatType, typename LabelsType, typename WeightsType>
+template<typename MatType, typename ResponsesType, typename WeightsType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,
                       CategoricalSplitType,
@@ -242,7 +242,7 @@ DecisionTreeRegressor<FitnessFunction,
                       NoRecursion>::DecisionTreeRegressor(
     const DecisionTreeRegressor& other,
     MatType data,
-    LabelsType labels,
+    ResponsesType responses,
     WeightsType weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -255,19 +255,19 @@ DecisionTreeRegressor<FitnessFunction,
         CategoricalAuxiliarySplitInfo(other)  // other info does need to copy
 {
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0, tmpWeights,
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, 0, tmpWeights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -421,7 +421,7 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType>
+template<typename MatType, typename ResponsesType>
 double DecisionTreeRegressor<FitnessFunction,
                              NumericSplitType,
                              CategoricalSplitType,
@@ -429,28 +429,28 @@ double DecisionTreeRegressor<FitnessFunction,
                              NoRecursion>::Train(
     MatType data,
     const data::DatasetInfo& datasetInfo,
-    LabelsType labels,
+    ResponsesType responses,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
     DimensionSelectionType dimensionSelector)
 {
   // Sanity check on data.
-  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+  util::CheckSameSizes(data, responses, "DecisionTreeRegressor::Train()");
 
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
+  return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses,
       0, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -461,35 +461,35 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType>
+template<typename MatType, typename ResponsesType>
 double DecisionTreeRegressor<FitnessFunction,
                              NumericSplitType,
                              CategoricalSplitType,
                              DimensionSelectionType,
                              NoRecursion>::Train(
     MatType data,
-    LabelsType labels,
+    ResponsesType responses,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     const size_t maximumDepth,
     DimensionSelectionType dimensionSelector)
 {
   // Sanity check on data.
-  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+  util::CheckSameSizes(data, responses, "DecisionTreeRegressor::Train()");
 
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  return Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
+  return Train<false>(tmpData, 0, tmpData.n_cols, responses, 0,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -500,7 +500,7 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType, typename WeightsType>
+template<typename MatType, typename ResponsesType, typename WeightsType>
 double DecisionTreeRegressor<FitnessFunction,
                              NumericSplitType,
                              CategoricalSplitType,
@@ -508,7 +508,7 @@ double DecisionTreeRegressor<FitnessFunction,
                              NoRecursion>::Train(
     MatType data,
     const data::DatasetInfo& datasetInfo,
-    LabelsType labels,
+    ResponsesType responses,
     WeightsType weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -520,22 +520,22 @@ double DecisionTreeRegressor<FitnessFunction,
         WeightsType>::type>::value>*)
 {
   // Sanity check on data.
-  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+  util::CheckSameSizes(data, responses, "DecisionTreeRegressor::Train()");
 
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
-  return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels,
+  return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses,
       0, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -546,14 +546,14 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<typename MatType, typename LabelsType, typename WeightsType>
+template<typename MatType, typename ResponsesType, typename WeightsType>
 double DecisionTreeRegressor<FitnessFunction,
                              NumericSplitType,
                              CategoricalSplitType,
                              DimensionSelectionType,
                              NoRecursion>::Train(
     MatType data,
-    LabelsType labels,
+    ResponsesType responses,
     WeightsType weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -565,22 +565,22 @@ double DecisionTreeRegressor<FitnessFunction,
         WeightsType>::type>::value>*)
 {
   // Sanity check on data.
-  util::CheckSameSizes(data, labels, "DecisionTreeRegressor::Train()");
+  util::CheckSameSizes(data, responses, "DecisionTreeRegressor::Train()");
 
   using TrueMatType = typename std::decay<MatType>::type;
-  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
-  TrueLabelsType tmpLabels(std::move(labels));
+  TrueResponsesType tmpResponses(std::move(responses));
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
-  return Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, 0,
+  return Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, 0,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -591,7 +591,7 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<bool UseWeights, typename MatType, typename LabelsType>
+template<bool UseWeights, typename MatType, typename ResponsesType>
 double DecisionTreeRegressor<FitnessFunction,
                              NumericSplitType,
                              CategoricalSplitType,
@@ -601,7 +601,7 @@ double DecisionTreeRegressor<FitnessFunction,
     const size_t begin,
     const size_t count,
     const data::DatasetInfo& datasetInfo,
-    LabelsType& labels,
+    ResponsesType& responses,
     const size_t numClasses,
     arma::rowvec& weights,
     const size_t minimumLeafSize,
@@ -618,7 +618,7 @@ double DecisionTreeRegressor<FitnessFunction,
   // We'll cache the best numeric and categorical split auxiliary information in
   // numericAux and categoricalAux (and clear them later if we make no split),
   double bestGain = FitnessFunction::template Evaluate<UseWeights>(
-      labels.subvec(begin, begin + count - 1),
+      responses.subvec(begin, begin + count - 1),
       numClasses,
       UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
   size_t bestDim = datasetInfo.Dimensionality(); // This means "no split".
@@ -635,7 +635,7 @@ double DecisionTreeRegressor<FitnessFunction,
         dimGain = CategoricalSplit::template SplitIfBetter<UseWeights>(bestGain,
             data.cols(begin, begin + count - 1).row(i),
             datasetInfo.NumMappings(i),
-            labels.subvec(begin, begin + count - 1),
+            responses.subvec(begin, begin + count - 1),
             numClasses,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
@@ -647,7 +647,7 @@ double DecisionTreeRegressor<FitnessFunction,
       {
         dimGain = NumericSplit::template SplitIfBetter<UseWeights>(bestGain,
             data.cols(begin, begin + count - 1).row(i),
-            labels.subvec(begin, begin + count - 1),
+            responses.subvec(begin, begin + count - 1),
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
             minimumGainSplit,
@@ -722,7 +722,7 @@ double DecisionTreeRegressor<FitnessFunction,
         {
           childAssignments.swap_cols(currentCol - begin, j - begin);
           data.swap_cols(currentCol, j);
-          labels.swap_cols(currentCol, j);
+          responses.swap_cols(currentCol, j);
           if (UseWeights)
             weights.swap_cols(currentCol, j);
           ++currentCol;
@@ -734,7 +734,7 @@ double DecisionTreeRegressor<FitnessFunction,
       if (NoRecursion)
       {
         child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, datasetInfo, labels, numClasses,
+            currentCol - currentChildBegin, datasetInfo, responses, numClasses,
             weights, currentCol - currentChildBegin, minimumGainSplit,
             maximumDepth - 1, dimensionSelector);
       }
@@ -742,7 +742,7 @@ double DecisionTreeRegressor<FitnessFunction,
       {
         // During recursion entropy of child node may change.
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, datasetInfo, labels, numClasses,
+            currentCol - currentChildBegin, datasetInfo, responses, numClasses,
             weights, minimumLeafSize, minimumGainSplit, maximumDepth - 1,
             dimensionSelector);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
@@ -758,7 +758,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
     // Calculate prediction label because we are a leaf.
     CalculatePrediction<UseWeights>(
-        labels.subvec(begin, begin + count - 1),
+        responses.subvec(begin, begin + count - 1),
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);   
     std::cout << "Number of points in leaf: " << count << 
         "  Prediction: " << splitPointOrPrediction << std::endl; 
@@ -773,7 +773,7 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<bool UseWeights, typename MatType, typename LabelsType>
+template<bool UseWeights, typename MatType, typename ResponsesType>
 double DecisionTreeRegressor<FitnessFunction,
                              NumericSplitType,
                              CategoricalSplitType,
@@ -782,7 +782,7 @@ double DecisionTreeRegressor<FitnessFunction,
     MatType& data,
     const size_t begin,
     const size_t count,
-    LabelsType& labels,
+    ResponsesType& responses,
     const size_t numClasses,
     arma::rowvec& weights,
     const size_t minimumLeafSize,
@@ -804,7 +804,7 @@ double DecisionTreeRegressor<FitnessFunction,
   // information.  Later we'll overwrite classProbabilities to the empirical
   // class probabilities if we do not split.
   double bestGain = FitnessFunction::template Evaluate<UseWeights>(
-      labels.subvec(begin, begin + count - 1),
+      responses.subvec(begin, begin + count - 1),
       numClasses,
       UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
   size_t bestDim = data.n_rows; // This means "no split".
@@ -817,7 +817,7 @@ double DecisionTreeRegressor<FitnessFunction,
       const double dimGain = NumericSplitType<FitnessFunction>::template
           SplitIfBetter<UseWeights>(bestGain,
                                     data.cols(begin, begin + count - 1).row(i),
-                                    labels.cols(begin, begin + count - 1),
+                                    responses.cols(begin, begin + count - 1),
                                     UseWeights ?
                                         weights.cols(begin, begin + count - 1) :
                                         weights,
@@ -882,7 +882,7 @@ double DecisionTreeRegressor<FitnessFunction,
         {
           childAssignments.swap_cols(currentCol - begin, j - begin);
           data.swap_cols(currentCol, j);
-          labels.swap_cols(currentCol, j);
+          responses.swap_cols(currentCol, j);
           if (UseWeights)
             weights.swap_cols(currentCol, j);
           ++currentCol;
@@ -894,7 +894,7 @@ double DecisionTreeRegressor<FitnessFunction,
       if (NoRecursion)
       {
         child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, labels, numClasses, weights,
+            currentCol - currentChildBegin, responses, numClasses, weights,
             currentCol - currentChildBegin, minimumGainSplit, maximumDepth - 1,
             dimensionSelector);
       }
@@ -902,7 +902,7 @@ double DecisionTreeRegressor<FitnessFunction,
       {
         // During recursion entropy of child node may change.
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, labels, numClasses, weights,
+            currentCol - currentChildBegin, responses, numClasses, weights,
             minimumLeafSize, minimumGainSplit, maximumDepth - 1,
             dimensionSelector);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
@@ -917,7 +917,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
     // Calculate prediction label because we are a leaf.
     CalculatePrediction<UseWeights>(
-        labels.subvec(begin, begin + count - 1),
+        responses.subvec(begin, begin + count - 1),
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
     std::cout << "Number of points in leaf: " << count << 
         "  Prediction: " << splitPointOrPrediction << std::endl;
@@ -980,25 +980,27 @@ template<typename FitnessFunction,
          template<typename> class CategoricalSplitType,
          typename DimensionSelectionType,
          bool NoRecursion>
-template<bool UseWeights, typename LabelsType, typename WeightsType>
+template<bool UseWeights, typename ResponsesType, typename WeightsType>
 void DecisionTreeRegressor<FitnessFunction,
                              NumericSplitType,
                              CategoricalSplitType,
                              DimensionSelectionType,
                              NoRecursion
->::CalculatePrediction(const LabelsType& labels, const WeightsType& weights)
+>::CalculatePrediction(const ResponsesType& responses,
+                       const WeightsType& weights)
 {
   if (UseWeights)
   {
     double accWeights, weightedSum;
-    WeightedSum(labels, weights, 0, labels.n_elem, accWeights, weightedSum);
+    WeightedSum(responses, weights, 0, responses.n_elem, accWeights,
+        weightedSum);
     splitPointOrPrediction = weightedSum / accWeights;
   }
   else
   {
     double sum;
-    Sum(labels, 0, labels.n_elem, sum);
-    splitPointOrPrediction = sum / labels.n_elem;
+    Sum(responses, 0, responses.n_elem, sum);
+    splitPointOrPrediction = sum / responses.n_elem;
   }
 }
 
diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index d64d566eff..0c84c36684 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -34,16 +34,16 @@ class MADGain
    * Evaluate the mean absolute deviation gain from begin to end index. Note
    * that gain can be slightly greater than 0 due to floating-point
    * representation issues. Thus if you are checking for perfect fit, be sure
-   * to use 'gain >= 0.0'. Not 'gain == 0.0'. The labels should always be of
+   * to use 'gain >= 0.0'. Not 'gain == 0.0'. The values should always be of
    * type arma::Row<double> or arma::rowvec.
    *
-   * @param labels Set of labels to evaluate MAD gain on.
-   * @param weights Weight of labels.
+   * @param values Set of values to evaluate MAD gain on.
+   * @param weights Weights associated to each value.
    * @param begin Start index.
    * @param end End index.
    */
   template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& labels,
+  static double Evaluate(const arma::rowvec& values,
                          const WeightVecType& weights,
                          const size_t begin,
                          const size_t end)
@@ -55,7 +55,7 @@ class MADGain
       double accWeights = 0.0;
       double weightedMean = 0.0;
 
-      WeightedSum(labels, weights, begin, end, accWeights, weightedMean);
+      WeightedSum(values, weights, begin, end, accWeights, weightedMean);
 
       // Catch edge case: if there are no weights, the impurity is zero.
       if (accWeights == 0.0)
@@ -65,18 +65,18 @@ class MADGain
 
       for (size_t i = begin; i < end; ++i)
       {
-        mad += weights[i] * (std::abs(labels[i] - weightedMean));
+        mad += weights[i] * (std::abs(values[i] - weightedMean));
       }
       mad /= accWeights;
       }
     else
     {
       double mean = 0.0;
-      Sum(labels, begin, end, mean);
+      Sum(values, begin, end, mean);
       mean /= (double) (end - begin);
 
       for (size_t i = begin; i < end; ++i)
-        mad += std::abs(labels[i] - mean);
+        mad += std::abs(values[i] - mean);
 
       mad /= (double) (end - begin);
     }
@@ -87,19 +87,19 @@ class MADGain
   /**
    * Evaluate the MAD gain on the complete vector.
    *
-   * @param labels Set of labels to evaluate MAD gain on.
-   * @param weights Weights associated to each label.
+   * @param values Set of values to evaluate MAD gain on.
+   * @param weights Weights associated to each value.
    */
   template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& labels,
+  static double Evaluate(const arma::rowvec& values,
                          const size_t /* numClasses */,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.
-    if (labels.n_elem == 0)
+    if (values.n_elem == 0)
       return 0.0;
 
-    return Evaluate<UseWeights>(labels, weights, 0, labels.n_elem);
+    return Evaluate<UseWeights>(values, weights, 0, values.n_elem);
   }
 
 };
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index e12ae34ae4..b42e694945 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -29,19 +29,19 @@ class MSEGain
 {
  public:
   /**
-   * Evaluate the mean squared error gain of labls from begin to end index.
+   * Evaluate the mean squared error gain of values from begin to end index.
    * Note that gain can be slightly greater than 0 due to floating-point
    * representation issues. Thus if you are checking for perfect fit, be
-   * sure to use 'gain >= 0.0' and not 'gain == 0.0'. The labels vector should
-   * always be of type arma::Row<double> or arma::rowvec.
+   * sure to use 'gain >= 0.0' and not 'gain == 0.0'. The values vector
+   * should always be of type arma::Row<double> or arma::rowvec.
    *
-   * @param labels Set of labels to evaluate MAD gain on.
-   * @param weights Weight of labels.
+   * @param values Set of values to evaluate MAD gain on.
+   * @param weights Weights associated to each value.
    * @param begin Start index.
    * @param end End index.
    */
   template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& labels,
+  static double Evaluate(const arma::rowvec& values,
                          const WeightVecType& weights,
                          const size_t begin,
                          const size_t end)
@@ -52,7 +52,7 @@ class MSEGain
     {
       double accWeights = 0.0;
       double weightedMean = 0.0;
-      WeightedSum(labels, weights, begin, end, accWeights, weightedMean);
+      WeightedSum(values, weights, begin, end, accWeights, weightedMean);
 
       // Catch edge case: if there are no weights, the impurity is zero.
       if (accWeights == 0.0)
@@ -61,18 +61,18 @@ class MSEGain
       weightedMean /= accWeights;
 
       for (size_t i = begin; i < end; ++i)
-        mse += weights[i] * std::pow(labels[i] - weightedMean, 2);
+        mse += weights[i] * std::pow(values[i] - weightedMean, 2);
 
       mse /= accWeights;
     }
     else
     {
       double mean = 0.0;
-      Sum(labels, begin, end, mean);
+      Sum(values, begin, end, mean);
       mean /= (double) (end - begin);
 
       for (size_t i = begin; i < end; ++i)
-        mse += std::pow(labels[i] - mean, 2);
+        mse += std::pow(values[i] - mean, 2);
 
       mse /= (double) (end - begin);
     }
@@ -83,19 +83,19 @@ class MSEGain
   /**
    * Evaluate the MSE gain on the complete vector.
    *
-   * @param labels Set of labels to evaluate MAD gain on.
-   * @param weights Weights associated to each label.
+   * @param values Set of values to evaluate MSE gain on.
+   * @param weights Weights associated to each value.
    */
   template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& labels,
+  static double Evaluate(const arma::rowvec& values,
                          const size_t /* numClasses */,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.
-    if (labels.n_elem == 0)
+    if (values.n_elem == 0)
       return 0.0;
 
-    return Evaluate<UseWeights>(labels, weights, 0, labels.n_elem);
+    return Evaluate<UseWeights>(values, weights, 0, values.n_elem);
   }
 };
 
diff --git a/src/mlpack/tests/data/boston_housing_price_labels.csv b/src/mlpack/tests/data/boston_housing_price_responses.csv
similarity index 100%
rename from src/mlpack/tests/data/boston_housing_price_labels.csv
rename to src/mlpack/tests/data/boston_housing_price_responses.csv
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 511c0078fb..38f61ee7ca 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -28,56 +28,56 @@ using namespace mlpack::distribution;
 
 /**
  * Creates dataset with 5 groups with all the points in same group have exactly
- * same label.
+ * same responses.
  */
-void CreateMultiSplitData(arma::mat& d, arma::rowvec& l, const size_t count,
+void CreateMultiSplitData(arma::mat& d, arma::rowvec& r, const size_t count,
     arma::rowvec& values)
 {
   d = arma::mat(10, count, arma::fill::randu);
-  l = arma::rowvec(count);
+  r = arma::rowvec(count);
 
   // Group 1.
   for (size_t i = 0; i < count / 5; i++)
   {
     d(3, i) = i;
-    l(i) = values[0];
+    r(i) = values[0];
   }
   // Group 2.
   for (size_t i = count / 5; i < (count / 5) * 2; i++)
   {
     d(3, i) = i;
-    l(i) = values[1];
+    r(i) = values[1];
   }
   // Group 3.
   for (size_t i = (count / 5) * 2; i < (count / 5) * 3; i++)
   {
     d(3, i) = i;
-    l(i) = values[2];
+    r(i) = values[2];
   }
   // Group 4.
   for (size_t i = (count / 5) * 3; i < (count / 5) * 4; i++)
   {
     d(3, i) = i;
-    l(i) = values[3];
+    r(i) = values[3];
   }
   // Group 5.
   for (size_t i = (count / 5) * 4; i < count; i++)
   {
     d(3, i) = i;
-    l(i) = values[4];
+    r(i) = values[4];
   }
 }
 
 /**
- * Make sure the MSE gain is zero when the labels are perfect.
+ * Make sure the MSE gain is zero when the responses are perfect.
  */
 TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights(10, arma::fill::ones);
-  arma::rowvec labels;
-  labels.ones(10);
+  arma::rowvec responses;
+  responses.ones(10);
 
-  REQUIRE(MSEGain::Evaluate<false>(labels, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<false>(responses, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -87,11 +87,11 @@ TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressorTest]")
 TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights = arma::ones<arma::rowvec>(10);
-  arma::rowvec labels;
-  REQUIRE(MSEGain::Evaluate<false>(labels, 0, weights) ==
+  arma::rowvec responses;
+  REQUIRE(MSEGain::Evaluate<false>(responses, 0, weights) ==
           Approx(0.0).margin(1e-5));
 
-  REQUIRE(MSEGain::Evaluate<true>(labels, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<true>(responses, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -101,48 +101,49 @@ TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressorTest]")
  */
 TEST_CASE("MSEGainHandCalculation", "[DecisionTreeRegressorTest]")
 {
-  arma::rowvec labels = {4., 2., 3., 4., 13., 6., 20., 8., 9., 10.};
+  arma::rowvec responses = {4., 2., 3., 4., 13., 6., 20., 8., 9., 10.};
   arma::rowvec weights = {0.3, 0.3, 0.3, 0.3, 0.3, 0.7, 0.7, 0.7, 0.7, 0.7};
 
   // Hand calculated gain values.
   const double gain = -27.08999;
   const double weightedGain = -27.53960;
-  REQUIRE(MSEGain::Evaluate<false>(labels, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<false>(responses, 0, weights) ==
           Approx(gain).margin(1e-5));
-  REQUIRE(MSEGain::Evaluate<true>(labels, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<true>(responses, 0, weights) ==
           Approx(weightedGain).margin(1e-5));
 }
 
 /**
- * Make sure the MAD gain is zero when the labels are perfect.
+ * Make sure the MAD gain is zero when the responses are perfect.
  */
 TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights(10, arma::fill::ones);
-  arma::rowvec labels;
-  labels.ones(10);
+  arma::rowvec responses;
+  responses.ones(10);
 
-  REQUIRE(MADGain::Evaluate<false>(labels, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<false>(responses, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
 /**
- * Make sure that when mean of labels is zero, MAD_gain = mean of
+ * Make sure that when mean of responses is zero, MAD_gain = mean of
  * absolute values of the distribution.
  */
 TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressorTest")
 {
   arma::rowvec weights(10, arma::fill::ones);
-  arma::rowvec labels = { 1, 2, 3, 4, 5, -1, -2, -3, -4, -5 }; // Mean = 0.
+  arma::rowvec responses = { 1, 2, 3, 4, 5, -1, -2, -3, -4, -5 }; // Mean = 0.
 
   // Theoretical gain.
   double theoreticalGain = 0.0;
-  for (size_t i = 0; i < labels.n_elem; ++i)
-    theoreticalGain -= std::abs(labels[i]);
-  theoreticalGain /= (double) labels.n_elem;
+  for (size_t i = 0; i < responses.n_elem; ++i)
+    theoreticalGain -= std::abs(responses[i]);
+  theoreticalGain /= (double) responses.n_elem;
 
   // Calculated gain.
-  const double calculatedGain = MADGain::Evaluate<false>(labels, 0, weights);
+  const double calculatedGain =
+      MADGain::Evaluate<false>(responses, 0, weights);
 
   REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-5));
 }
@@ -153,11 +154,11 @@ TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressorTest")
 TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights = arma::ones<arma::rowvec>(10);
-  arma::rowvec labels;
-  REQUIRE(MADGain::Evaluate<false>(labels, 0, weights) ==
+  arma::rowvec responses;
+  REQUIRE(MADGain::Evaluate<false>(responses, 0, weights) ==
           Approx(0.0).margin(1e-5));
 
-  REQUIRE(MADGain::Evaluate<true>(labels, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<true>(responses, 0, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -167,15 +168,15 @@ TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressorTest]")
  */
 TEST_CASE("MADGainHandCalculation", "[DecisionTreeRegressorTest]")
 {
-  arma::rowvec labels = {4., 2., 3., 4., 13., 6., 20., 8., 9., 10.};
+  arma::rowvec responses = {4., 2., 3., 4., 13., 6., 20., 8., 9., 10.};
   arma::rowvec weights = {0.3, 0.3, 0.3, 0.3, 0.3, 0.7, 0.7, 0.7, 0.7, 0.7};
 
   // Hand calculated gain values.
   const double gain = -4.1;
   const double weightedGain = -3.8592;
-  REQUIRE(MADGain::Evaluate<false>(labels, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<false>(responses, 0, weights) ==
           Approx(gain).margin(1e-5));
-  REQUIRE(MADGain::Evaluate<true>(labels, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<true>(responses, 0, weights) ==
           Approx(weightedGain).margin(1e-5));
 }
 
@@ -186,28 +187,28 @@ TEST_CASE("MADGainHandCalculation", "[DecisionTreeRegressorTest]")
 TEST_CASE("AllCategoricalSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]")
 {
   arma::vec predictor(100);
-  arma::rowvec labels(100);
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec responses(100);
+  arma::rowvec weights(responses.n_elem);
   weights.ones();
 
   for (size_t i = 0; i < 100; i+=2)
   {
     predictor[i] = 0;
-    labels[i] = 5.0;
+    responses[i] = 5.0;
     predictor[i + 1] = 1;
-    labels[i + 1] = 100;
+    responses[i + 1] = 100;
   }
 
   double splitInfo;
   AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictor, 2, labels, 0, weights, 3, 1e-7, splitInfo, aux);
+      bestGain, predictor, 2, responses, 0, weights, 3, 1e-7, splitInfo, aux);
   const double weightedGain =
       AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictor, 2,
-      labels, 0, weights, 3, 1e-7, splitInfo, aux);
+      responses, 0, weights, 3, 1e-7, splitInfo, aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -225,17 +226,17 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]")
 TEST_CASE("AllCategoricalSplitMinSamplesTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3};
-  arma::rowvec labels = {0, 0, 0, 2, 2, 2, 1, 1, 1, 2, 2, 2};
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec responses = {0, 0, 0, 2, 2, 2, 1, 1, 1, 2, 2, 2};
+  arma::rowvec weights(responses.n_elem);
   weights.ones();
 
   double splitInfo;
   AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, 4, labels, 0, weights, 4, 1e-7, splitInfo, aux);
+      bestGain, predictors, 4, responses, 0, weights, 4, 1e-7, splitInfo, aux);
 
   // Make sure it's not split.
   REQUIRE(gain == DBL_MAX);
@@ -247,30 +248,30 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest_", "[DecisionTreeRegressorTest]")
 TEST_CASE("AllCategoricalSplitNoGainTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors(300);
-  arma::rowvec labels(300);
+  arma::rowvec responses(300);
   arma::rowvec weights = arma::ones<arma::rowvec>(300);
 
   for (size_t i = 0; i < 300; i += 3)
   {
     predictors[i] = int(i / 3) % 10;
-    labels[i] = -0.5;
+    responses[i] = -0.5;
     predictors[i + 1] = int(i / 3) % 10;
-    labels[i + 1] = 0;
+    responses[i + 1] = 0;
     predictors[i + 2] = int(i / 3) % 10;
-    labels[i + 2] = 0.5;
+    responses[i + 2] = 0.5;
   }
 
   double splitInfo;
   AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, 10, labels, 0, weights, 10, 1e-7,
+      bestGain, predictors, 10, responses, 0, weights, 10, 1e-7,
       splitInfo, aux);
   const double weightedGain =
       AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
-      10, labels, 0, predictors, 10, 1e-7, splitInfo, aux);
+      10, responses, 0, predictors, 10, 1e-7, splitInfo, aux);
 
   // Make sure that there was no split.
   REQUIRE(gain == DBL_MAX);
@@ -281,23 +282,26 @@ TEST_CASE("AllCategoricalSplitNoGainTest_", "[DecisionTreeRegressorTest]")
  * Check that the BestBinaryNumericSplit will split on an obviously splittable
  * dimension.
  */
-TEST_CASE("BestBinaryNumericSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]")
+TEST_CASE("BestBinaryNumericSplitSimpleSplitTest_",
+    "[DecisionTreeRegressorTest]")
 {
-  arma::rowvec predictors = { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
-  arma::rowvec labels = { 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec predictors =
+      { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
+  arma::rowvec responses =
+      { 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+  arma::rowvec weights(responses.n_elem);
   weights.ones();
 
   double splitInfo;
   BestBinaryNumericSplit<MADGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MADGain::Evaluate<false>(labels, 0, weights);
+  const double bestGain = MADGain::Evaluate<false>(responses, 0, weights);
   const double gain = BestBinaryNumericSplit<MADGain>::SplitIfBetter<false>(
-      bestGain, predictors, labels, weights, 3, 1e-7, splitInfo, aux);
+      bestGain, predictors, responses, weights, 3, 1e-7, splitInfo, aux);
   const double weightedGain =
       BestBinaryNumericSplit<MADGain>::SplitIfBetter<true>(bestGain, predictors,
-      labels, weights, 3, 1e-7, splitInfo, aux);
+      responses, weights, 3, 1e-7, splitInfo, aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -315,23 +319,26 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]
  * Check that the BestBinaryNumericSplit won't split if not enough points are
  * given.
  */
-TEST_CASE("BestBinaryNumericSplitMinSamplesTest_", "[DecisionTreeRegressorTest]")
+TEST_CASE("BestBinaryNumericSplitMinSamplesTest_",
+    "[DecisionTreeRegressorTest]")
 {
-  arma::rowvec predictors = { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
-  arma::rowvec labels = { 0.5, 0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec predictors =
+      { 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 };
+  arma::rowvec responses =
+      { 0.5, 0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+  arma::rowvec weights(responses.n_elem);
 
   double splitInfo;
   BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
   const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, labels, weights, 8, 1e-7, splitInfo, aux);
+      bestGain, predictors, responses, weights, 8, 1e-7, splitInfo, aux);
   // This should make no difference because it won't split at all.
   const double weightedGain =
-      BestBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
-      labels, weights, 8, 1e-7, splitInfo, aux);
+      BestBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain,
+      predictors, responses, weights, 8, 1e-7, splitInfo, aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
@@ -339,30 +346,29 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest_", "[DecisionTreeRegressorTest]"
 }
 
 /**
- * Check that the BestBinaryNumericSplit doesn't split a dimension that gives no
- * gain.
+ * Check that the BestBinaryNumericSplit doesn't split a dimension that gives
+ * no gain.
  */
 TEST_CASE("BestBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec predictors(100);
-  arma::rowvec labels(100);
+  arma::rowvec responses(100);
   arma::rowvec weights;
   for (size_t i = 0; i < 100; i += 2)
   {
     predictors[i] = i;
-    labels[i] = 0.0;
+    responses[i] = 0.0;
     predictors[i + 1] = i;
-    labels[i + 1] = 1.0;
+    responses[i + 1] = 1.0;
   }
 
   double splitInfo;
   BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
   const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, labels, weights, 10, 1e-7, splitInfo,
-      aux);
+      bestGain, predictors, responses, weights, 10, 1e-7, splitInfo, aux);
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
@@ -376,20 +382,20 @@ TEST_CASE("RandomBinaryNumericSplitAlwaysSplit_",
     "[DecisionTreeRegressorTest]")
 {
   arma::vec values("0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0");
-  arma::rowvec labels("0 0 0 0 0 1 1 1 1 1 1");
+  arma::rowvec responses("0 0 0 0 0 1 1 1 1 1 1");
   arma::rowvec weights;
-  weights.ones(labels.n_elem);
+  weights.ones(responses.n_elem);
 
   double splitInfo;
   RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 2, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 2, weights);
   const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, values, labels, weights, 1, 1e-7, splitInfo, aux);
+      bestGain, values, responses, weights, 1, 1e-7, splitInfo, aux);
   const double weightedGain =
       RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, values,
-      labels, weights, 1, 1e-7, splitInfo, aux);
+      responses, weights, 1, 1e-7, splitInfo, aux);
 
   // Make sure that split was made.
   REQUIRE(gain != DBL_MAX);
@@ -404,20 +410,20 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest_",
     "[DecisionTreeRegressorTest]")
 {
   arma::vec values("0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0");
-  arma::rowvec labels("0 0 0 0 0 1 1 1 1 1 1");
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec responses("0 0 0 0 0 1 1 1 1 1 1");
+  arma::rowvec weights(responses.n_elem);
 
   double splitInfo;
   RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 2, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 2, weights);
   const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, values, labels, weights, 8, 1e-7, splitInfo, aux);
+      bestGain, values, responses, weights, 8, 1e-7, splitInfo, aux);
   // This should make no difference because it won't split at all.
   const double weightedGain =
       RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, values,
-      labels, weights, 8, 1e-7, splitInfo, aux);
+      responses, weights, 8, 1e-7, splitInfo, aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
@@ -431,23 +437,23 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest_",
 TEST_CASE("RandomBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
 {
   arma::vec values(100);
-  arma::Row<double> labels(100);
+  arma::rowvec responses(100);
   arma::rowvec weights;
   for (size_t i = 0; i < 100; i += 2)
   {
     values[i] = i;
-    labels[i] = 0.0;
+    responses[i] = 0.0;
     values[i + 1] = i;
-    labels[i + 1] = 1.0;
+    responses[i + 1] = 1.0;
   }
 
   double splitInfo;
   RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(labels, 2, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, 2, weights);
   const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, values, labels, weights, 10, 1e-7, splitInfo, aux, true);
+      bestGain, values, responses, weights, 10, 1e-7, splitInfo, aux, true);
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
@@ -460,48 +466,48 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
 TEST_CASE("BasicConstructionTest_", "[DecisionTreeRegressorTest]")
 {
   arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
+  arma::rowvec responses(100);
   for (size_t i = 0; i < 50; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 0.0;
+    responses[i] = 0.0;
   }
   for (size_t i = 50; i < 100; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 1.0;
+    responses[i] = 1.0;
   }
 
   // Use default parameters.
-  DecisionTreeRegressor<> d(dataset, labels);
+  DecisionTreeRegressor<> d(dataset, responses);
 
   // Now require that we have some children.
   REQUIRE(d.NumChildren() > 0);
 }
 
 /**
- * Construct a tree with weighted labels.
+ * Construct a tree with weighted responses.
  */
 TEST_CASE("BasicConstructionTestWithWeight_", "[DecisionTreeRegressorTest]")
 {
   arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
+  arma::rowvec responses(100);
   for (size_t i = 0; i < 50; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 0.0;
+    responses[i] = 0.0;
   }
   for (size_t i = 50; i < 100; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 1.0;
+    responses[i] = 1.0;
   }
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec weights(responses.n_elem);
   weights.ones();
 
   // Use default parameters.
-  DecisionTreeRegressor<> wd(dataset, labels, weights);
-  DecisionTreeRegressor<> d(dataset, labels);
+  DecisionTreeRegressor<> wd(dataset, responses, weights);
+  DecisionTreeRegressor<> d(dataset, responses);
 
   // Now require that we have some children.
   REQUIRE(wd.NumChildren() > 0);
@@ -515,53 +521,53 @@ TEST_CASE("BasicConstructionTestWithWeight_", "[DecisionTreeRegressorTest]")
 TEST_CASE("PerfectTrainingSet_", "[DecisionTreeRegressorTest]")
 {
   arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
+  arma::rowvec responses(100);
   for (size_t i = 0; i < 50; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 0.0;
+    responses[i] = 0.0;
   }
   for (size_t i = 50; i < 100; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 1.0;
+    responses[i] = 1.0;
   }
 
-  DecisionTreeRegressor<> d(dataset, labels, 1, 0.0); // Minimum leaf size of 1.
+  // Minimum leaf size of 1.
+  DecisionTreeRegressor<> d(dataset, responses, 1, 0.0);
 
-  // Make sure that we can get perfect accuracy on the training set.
+  // Make sure that we can get perfect fit on the training set.
   for (size_t i = 0; i < 100; ++i)
   {
     double prediction;
     prediction = d.Predict(dataset.col(i));
 
-    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+    REQUIRE(prediction == Approx(responses[i]).epsilon(1e-7));
   }
 }
 
 /**
- * Construct the decision tree with weighted labels
+ * Construct the decision tree with weighted responses.
  */
 TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
 {
   // Completely random dataset with no structure.
   arma::mat dataset(10, 100, arma::fill::randu);
-  arma::Row<double> labels(100);
+  arma::rowvec responses(100);
   for (size_t i = 0; i < 50; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 0.0;
+    responses[i] = 0.0;
   }
   for (size_t i = 50; i < 100; ++i)
   {
     dataset(3, i) = i;
-    labels[i] = 1.0;
+    responses[i] = 1.0;
   }
-  arma::rowvec weights(labels.n_elem);
-  weights.ones();
+  arma::rowvec weights = arma::ones<arma::rowvec>(responses.n_elem);
 
   // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, labels, weights, 1, 0.0);
+  DecisionTreeRegressor<> d(dataset, responses, weights, 1, 0.0);
 
   // This part of code is dupliacte with no weighted one.
   for (size_t i = 0; i < 100; ++i)
@@ -569,7 +575,7 @@ TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
     size_t prediction;
     prediction = d.Predict(dataset.col(i));
 
-    REQUIRE(prediction == Approx(labels[i]).epsilon(1e-7));
+    REQUIRE(prediction == Approx(responses[i]).epsilon(1e-7));
   }
 }
 
@@ -579,18 +585,18 @@ TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
 TEST_CASE("CategoricalBuildTest_", "[DecisionTreeRegressorTest]")
 {
   arma::mat d;
-  arma::rowvec l;
+  arma::rowvec r;
   data::DatasetInfo di;
-  MockCategoricalData(d, l, di);
+  MockCategoricalData(d, r, di);
 
   // Split into a training set and a test set.
   arma::mat trainingData = d.cols(0, 1999);
   arma::mat testData = d.cols(2000, 3999);
-  arma::rowvec trainingLabels = l.subvec(0, 1999);
-  arma::rowvec testLabels = l.subvec(2000, 3999);
+  arma::rowvec trainingResponses = r.subvec(0, 1999);
+  arma::rowvec testResponses = r.subvec(2000, 3999);
 
   // Build the tree.
-  DecisionTreeRegressor<> tree(trainingData, di, trainingLabels, 10);
+  DecisionTreeRegressor<> tree(trainingData, di, trainingResponses, 10);
 
   // Now evaluate the quality of predictions.
   arma::rowvec predictions;
@@ -599,7 +605,7 @@ TEST_CASE("CategoricalBuildTest_", "[DecisionTreeRegressorTest]")
   REQUIRE(predictions.n_elem == testData.n_cols);
 
   // Make sure we get reasonable rmse.
-  const double rmse = RMSE(predictions, testLabels);
+  const double rmse = RMSE(predictions, testResponses);
   REQUIRE(rmse < 1.0);
 }
 
@@ -610,21 +616,21 @@ TEST_CASE("CategoricalBuildTest_", "[DecisionTreeRegressorTest]")
 TEST_CASE("CategoricalBuildTestWithWeight_", "[DecisionTreeRegressorTest]")
 {
   arma::mat d;
-  arma::rowvec l;
+  arma::rowvec r;
   data::DatasetInfo di;
-  MockCategoricalData(d, l, di);
+  MockCategoricalData(d, r, di);
 
   // Split into a training set and a test set.
   arma::mat trainingData = d.cols(0, 1999);
   arma::mat testData = d.cols(2000, 3999);
-  arma::rowvec trainingLabels = l.subvec(0, 1999);
-  arma::rowvec testLabels = l.subvec(2000, 3999);
+  arma::rowvec trainingResponses = r.subvec(0, 1999);
+  arma::rowvec testResponses = r.subvec(2000, 3999);
 
-  arma::rowvec weights = arma::ones<arma::rowvec>(
-      trainingLabels.n_elem);
+  arma::rowvec weights = arma::ones<arma::rowvec>(trainingResponses.n_elem);
 
   // Build the tree.
-  DecisionTreeRegressor<> tree(trainingData, di, trainingLabels, weights, 10);
+  DecisionTreeRegressor<> tree(trainingData, di, trainingResponses, weights,
+      10);
 
   // Now evaluate the quality of predictions.
   arma::rowvec predictions;
@@ -633,7 +639,7 @@ TEST_CASE("CategoricalBuildTestWithWeight_", "[DecisionTreeRegressorTest]")
   REQUIRE(predictions.n_elem == testData.n_cols);
 
   // Make sure we get reasonable rmse.
-  const double rmse = RMSE(predictions, testLabels);
+  const double rmse = RMSE(predictions, testResponses);
   REQUIRE(rmse < 1.0);
 }
 
@@ -689,19 +695,19 @@ TEST_CASE("CategoricalBuildTestWithWeight_", "[DecisionTreeRegressorTest]")
 TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
 {
   arma::mat d;
-  arma::rowvec l;
+  arma::rowvec r;
   data::DatasetInfo di;
-  MockCategoricalData(d, l, di);
+  MockCategoricalData(d, r, di);
 
   // Split into a training set and a test set.
   arma::mat trainingData = d.cols(0, 1999);
   arma::mat testData = d.cols(2000, 3999);
-  arma::rowvec trainingLabels = l.subvec(0, 1999);
-  arma::rowvec testLabels = l.subvec(2000, 3999);
+  arma::rowvec trainingResponses = r.subvec(0, 1999);
+  arma::rowvec testResponses = r.subvec(2000, 3999);
 
   // Now create random points.
   arma::mat randomNoise(5, 2000);
-  arma::rowvec randomLabels(2000);
+  arma::rowvec randomResponses(2000);
   for (size_t i = 0; i < 2000; ++i)
   {
     randomNoise(0, i) = math::Random();
@@ -709,7 +715,7 @@ TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
     randomNoise(2, i) = math::Random();
     randomNoise(3, i) = math::RandInt(0, 2);
     randomNoise(4, i) = math::RandInt(0, 5);
-    randomLabels[i] = math::Random(-10, 18);
+    randomResponses[i] = math::Random(-10, 18);
   }
 
   // Generate weights.
@@ -720,10 +726,11 @@ TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
     weights[i] = math::Random(0.0, 0.001);
 
   arma::mat fullData = arma::join_rows(trainingData, randomNoise);
-  arma::rowvec fullLabels = arma::join_rows(trainingLabels, randomLabels);
+  arma::rowvec fullResponses = arma::join_rows(trainingResponses,
+      randomResponses);
 
   // Build the tree.
-  DecisionTreeRegressor<> tree(fullData, di, fullLabels, weights, 10);
+  DecisionTreeRegressor<> tree(fullData, di, fullResponses, weights, 10);
 
   // Now evaluate the quality of predictions.
   arma::rowvec predictions;
@@ -732,7 +739,7 @@ TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
   REQUIRE(predictions.n_elem == testData.n_cols);
 
   // Make sure we get reasonable rmse.
-  const double rmse = RMSE(predictions, testLabels);
+  const double rmse = RMSE(predictions, testResponses);
   REQUIRE(rmse < 1.5);
 }
 
@@ -856,29 +863,30 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
   // Loading data.
   data::DatasetInfo info;
   arma::mat trainData, testData;
-  arma::Row<double> trainLabels, testLabels;
+  arma::rowvec trainResponses, testResponses;
   arma::rowvec weights = arma::ones<arma::rowvec>(355);
-  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+  LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
+      info);
 
   // Build decision tree.
-  DecisionTreeRegressor<MSEGain> d(trainData, info, trainLabels);
+  DecisionTreeRegressor<MSEGain> d(trainData, info, trainResponses);
 
-  // Get the predicted test labels.
+  // Get the predicted test responses.
   arma::Row<double> predictions;
   d.Predict(testData, predictions);
 
   REQUIRE(predictions.n_elem == testData.n_cols);
 
   // Figure out rmse.
-  double rmse = RMSE(predictions, testLabels);
+  double rmse = RMSE(predictions, testResponses);
 
   // REQUIRE(rmse < 9.21);
-  // std::cout << predictions << std::endl << testLabels;
+  // std::cout << predictions << std::endl << testResponses;
   arma::Row<double> trainPred;
   d.Predict(trainData, trainPred);
   // std::cout << trainPred;
 
-  std::cout << "Train RMSE: " << RMSE(trainLabels, trainPred) << std::endl;
+  std::cout << "Train RMSE: " << RMSE(trainResponses, trainPred) << std::endl;
 }
 
 /**
@@ -959,21 +967,21 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 TEST_CASE("MultiSplitTest1", "[DecisionTreeRegressorTest]")
 {
   arma::mat dataset;
-  arma::rowvec labels;
+  arma::rowvec responses;
   arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
 
-  CreateMultiSplitData(dataset, labels, 1000, values);
+  CreateMultiSplitData(dataset, responses, 1000, values);
 
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec weights(responses.n_elem);
   weights.ones();
 
   // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0);
+  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
   arma::rowvec preds;
   d.Predict(dataset, preds);
 
-  for (size_t i = 0; i < labels.n_elem; ++i)
-    REQUIRE(preds[i] == labels[i]);
+  for (size_t i = 0; i < responses.n_elem; ++i)
+    REQUIRE(preds[i] == responses[i]);
 
   REQUIRE(d.NumLeaves() == 5);
 }
@@ -985,21 +993,21 @@ TEST_CASE("MultiSplitTest1", "[DecisionTreeRegressorTest]")
 TEST_CASE("MultiSplitTest2", "[DecisionTreeRegressorTest]")
 {
   arma::mat dataset;
-  arma::rowvec labels;
+  arma::rowvec responses;
   arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
 
-  CreateMultiSplitData(dataset, labels, 100, values);
+  CreateMultiSplitData(dataset, responses, 100, values);
 
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec weights(responses.n_elem);
   weights.ones();
 
   // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0);
+  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
   arma::rowvec preds;
   d.Predict(dataset, preds);
 
-  for (size_t i = 0; i < labels.n_elem; ++i)
-    REQUIRE(preds[i] == labels[i]);
+  for (size_t i = 0; i < responses.n_elem; ++i)
+    REQUIRE(preds[i] == responses[i]);
 
   REQUIRE(d.NumLeaves() == 5);
 }
@@ -1027,21 +1035,21 @@ TEST_CASE("MultiSplitTest2", "[DecisionTreeRegressorTest]")
 TEST_CASE("MultiSplitTest3", "[DecisionTreeRegressorTest]")
 {
   arma::mat dataset;
-  arma::Row<double> labels;
+  arma::Row<double> responses;
   arma::rowvec values = {0.0, 5.0, 10.0, 15.0, 20.0};
 
-  CreateMultiSplitData(dataset, labels, 500, values);
+  CreateMultiSplitData(dataset, responses, 500, values);
 
-  arma::rowvec weights(labels.n_elem);
+  arma::rowvec weights(responses.n_elem);
   weights.ones();
 
   // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, labels, weights, 2, 0.0);
+  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
   arma::rowvec preds;
   d.Predict(dataset, preds);
 
-  for (size_t i = 0; i < labels.n_elem; ++i)
-    REQUIRE(preds[i] == labels[i]);
+  for (size_t i = 0; i < responses.n_elem; ++i)
+    REQUIRE(preds[i] == responses[i]);
 
   REQUIRE(d.NumLeaves() == 5);
 }
diff --git a/src/mlpack/tests/test_function_tools.hpp b/src/mlpack/tests/test_function_tools.hpp
index 990cb07747..0233402506 100644
--- a/src/mlpack/tests/test_function_tools.hpp
+++ b/src/mlpack/tests/test_function_tools.hpp
@@ -83,20 +83,20 @@ inline void LogisticRegressionTestData(arma::mat& data,
 
 inline void LoadBostonHousingDataset(arma::mat& trainData,
                               arma::mat& testData,
-                              arma::Row<double>& trainLabels,
-                              arma::Row<double>& testLabels,
+                              arma::rowvec& trainResponses,
+                              arma::rowvec& testResponses,
                               data::DatasetInfo& info)
 {
   arma::mat dataset;
-  arma::Row<double> labels;
+  arma::rowvec responses;
 
   if (!data::Load("boston_housing_price.csv", dataset, info))
     FAIL("Cannot load test dataset boston_housing_price.csv!");
-  if (!data::Load("boston_housing_price_labels.csv", labels))
-    FAIL("Cannot load test dataset boston_housing_price_labels.csv!");
+  if (!data::Load("boston_housing_price_responses.csv", responses))
+    FAIL("Cannot load test dataset boston_housing_price_responses.csv!");
 
-  data::Split(dataset, labels, trainData, testData,
-      trainLabels, testLabels, 0.3);
+  data::Split(dataset, responses, trainData, testData,
+      trainResponses, testResponses, 0.3);
   // info.Type(3) = data::Datatype::categorical;
   // info.Type(8) = data::Datatype::categorical;
 
@@ -115,9 +115,9 @@ inline void LoadBostonHousingDataset(arma::mat& trainData,
 }
 
 inline double RMSE(const arma::Row<double>& predictions,
-                   const arma::Row<double>& trueLabels)
+                   const arma::Row<double>& trueResponses)
 {
-  double mse = arma::accu(arma::square(predictions - trueLabels)) /
+  double mse = arma::accu(arma::square(predictions - trueResponses)) /
       predictions.n_elem;
   return sqrt(mse);
 }

From ac0b08ead8a065082ecd81bf38db0474c1c4b7d7 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 16 Jun 2021 19:20:33 +0530
Subject: [PATCH 080/118] This was missed while changing labels to responses

---
 .../random_binary_numeric_split.hpp           |  7 +++----
 .../random_binary_numeric_split_impl.hpp      | 20 +++++++++----------
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
index 98d1f13787..60f51ce5d1 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
@@ -99,9 +99,8 @@ class RandomBinaryNumericSplit
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
    * @param data The dimension of data points to check for a split in.
-   * @param labels Labels for each point.
-   * @param numClasses Number of classes in the dataset.
-   * @param weights Weights associated with labels.
+   * @param responses Responses for each point.
+   * @param weights Weights associated with responses.
    * @param minimumLeafSize Minimum number of points in a leaf node for
    *      splitting.
    * @param minimumGainSplit Minimum gain split.
@@ -116,7 +115,7 @@ class RandomBinaryNumericSplit
   static double SplitIfBetter(
       const double bestGain,
       const VecType& data,
-      const arma::Row<double>& labels,
+      const arma::rowvec& responses,
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
index 2bd09bc53b..cb33c837ba 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
@@ -141,7 +141,7 @@ template<bool UseWeights, typename VecType, typename WeightVecType>
 double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
-    const arma::Row<double>& labels,
+    const arma::rowvec& responses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -203,7 +203,7 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   }
 
   // Splitting data to compute gain.
-  arma::rowvec leftLabels(leftLeafSize), rightLabels(rightLeafSize);
+  arma::rowvec leftResponses(leftLeafSize), rightResponses(rightLeafSize);
   arma::rowvec leftWeights, rightWeights;
   if (UseWeights)
   {
@@ -222,18 +222,16 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
         rightWeights[r] = weights[i];
     }
     if (data[i] < randomPivot)
-      leftLabels[l++] = labels[i];
+      leftResponses[l++] = responses[i];
     else
-      rightLabels[r++] = labels[i];
+      rightResponses[r++] = responses[i];
   }
 
   // Calculate the gain for the left and right child.
-  const double leftGain =
-      FitnessFunction::template Evaluate<UseWeights>(leftLabels, leftWeights,
-          0, leftLeafSize);
-  const double rightGain =
-      FitnessFunction::template Evaluate<UseWeights>(rightLabels, rightWeights,
-          0, rightLeafSize);
+  const double leftGain = FitnessFunction::template
+      Evaluate<UseWeights>(leftResponses, leftWeights, 0, leftLeafSize);
+  const double rightGain = FitnessFunction::template
+      Evaluate<UseWeights>(rightResponses, rightWeights, 0, rightLeafSize);
 
   // Calculate the gain at this split point.
   double gain;
@@ -250,7 +248,7 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   if (UseWeights)
     gain /= totalWeight;
   else
-    gain /= labels.n_elem;
+    gain /= responses.n_elem;
 
   return gain;
 }

From b1f5a836d99a050b8ad444cb069223aabbdd6611 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 16 Jun 2021 19:33:11 +0530
Subject: [PATCH 081/118] Minor style fixes and documentation updates from code
 review

---
 .../decision_tree/best_binary_numeric_split.hpp     |  4 ++--
 .../decision_tree/decision_tree_regressor_impl.hpp  | 13 +++++++------
 src/mlpack/methods/decision_tree/mad_gain.hpp       |  2 +-
 .../decision_tree/random_binary_numeric_split.hpp   |  4 ++--
 .../random_binary_numeric_split_impl.hpp            |  6 +++---
 5 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index f1be1a426a..6907edb943 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -36,7 +36,7 @@ class BestBinaryNumericSplit
    * return the value 'bestGain'.  If a split is made, then splitInfo and aux
    * may be modified.
    *
-   * It is used only for classification tasks.
+   * This overload is used only for classification tasks.
    *
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
@@ -69,7 +69,7 @@ class BestBinaryNumericSplit
    * return the value 'bestGain'.  If a split is made, then splitInfo and aux
    * may be modified.
    *
-   * It is used only for regression tasks.
+   * This overload is used only for regression tasks.
    *
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index be8a4c137c..d74c7e4f7a 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -615,8 +615,10 @@ double DecisionTreeRegressor<FitnessFunction,
   children.clear();
 
   // Look through the list of dimensions and obtain the gain of the best split.
-  // We'll cache the best numeric and categorical split auxiliary information in
-  // numericAux and categoricalAux (and clear them later if we make no split),
+  // We'll cache the best numeric and categorical split auxiliary information
+  // in numericAux and categoricalAux (and clear them later if we make no
+  // split). The split point is stored in splitPointOrPrediction for all
+  // internal nodes of the tree.
   double bestGain = FitnessFunction::template Evaluate<UseWeights>(
       responses.subvec(begin, begin + count - 1),
       numClasses,
@@ -798,11 +800,10 @@ double DecisionTreeRegressor<FitnessFunction,
   // We won't be using these members, so reset them.
   CategoricalAuxiliarySplitInfo::operator=(CategoricalAuxiliarySplitInfo());
 
-  // Look through the list of dimensions and obtain the best split.  We'll cache
+  // Look through the list of dimensions and obtain the best split. We'll cache
   // the best numeric split auxiliary information in numericAux (and clear it
-  // later if we don't make a split), and use classProbabilities as auxiliary
-  // information.  Later we'll overwrite classProbabilities to the empirical
-  // class probabilities if we do not split.
+  // later if we don't make a split). The split point is stored in
+  // splitPointOrPrediction for all internal nodes of the tree.
   double bestGain = FitnessFunction::template Evaluate<UseWeights>(
       responses.subvec(begin, begin + count - 1),
       numClasses,
diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index 0c84c36684..e962ec77a7 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -2,7 +2,7 @@
  * @file methods/decision_tree/mad_gain.hpp
  * @author Rishabh Garg
  *
- * The mean absolute deviation gain class, a fitness funtion for regression
+ * The mean absolute deviation gain class, a fitness function for regression
  * based decision trees.
  *
  * mlpack is free software; you may redistribute it and/or modify it under the
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
index 60f51ce5d1..9d86c69e12 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split.hpp
@@ -37,7 +37,7 @@ class RandomBinaryNumericSplit
    * return the value 'bestGain'.  If a split is made, then splitInfo
    * and aux may be modified.
    *
-   * It is used only for classification tasks.
+   * This overload is used only for classification tasks.
    *
    * @code
    * @article{10.1007/s10994-006-6226-1,
@@ -94,7 +94,7 @@ class RandomBinaryNumericSplit
    * return the value 'bestGain'.  If a split is made, then splitInfo
    * and aux may be modified.
    *
-   * It is used only for regression tasks.
+   * This overload is used only for regression tasks.
    *
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
index cb33c837ba..6a9db54a05 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
@@ -207,12 +207,12 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   arma::rowvec leftWeights, rightWeights;
   if (UseWeights)
   {
-      leftWeights.set_size(leftLeafSize);
-      rightWeights.set_size(rightLeafSize);
+    leftWeights.set_size(leftLeafSize);
+    rightWeights.set_size(rightLeafSize);
   }
 
   size_t l = 0, r = 0;
-  for(size_t i = 0; i < data.n_elem; ++i)
+  for (size_t i = 0; i < data.n_elem; ++i)
   {
     if (UseWeights)
     {

From 34b5ac7badcb6481b8a0b4f85ece42067fc95ec2 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 17 Jun 2021 09:32:40 +0530
Subject: [PATCH 082/118] Use armadillo's functions to evaluate mse and mad
 gains

---
 src/mlpack/methods/decision_tree/mad_gain.hpp | 4 +---
 src/mlpack/methods/decision_tree/mse_gain.hpp | 4 +---
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index e962ec77a7..10016045a0 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -75,9 +75,7 @@ class MADGain
       Sum(values, begin, end, mean);
       mean /= (double) (end - begin);
 
-      for (size_t i = begin; i < end; ++i)
-        mad += std::abs(values[i] - mean);
-
+      mad = arma::accu(arma::abs(values - mean));
       mad /= (double) (end - begin);
     }
 
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index b42e694945..44dadb574e 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -71,9 +71,7 @@ class MSEGain
       Sum(values, begin, end, mean);
       mean /= (double) (end - begin);
 
-      for (size_t i = begin; i < end; ++i)
-        mse += std::pow(values[i] - mean, 2);
-
+      mse = arma::accu(arma::square(values.subvec(begin, end - 1) - mean));
       mse /= (double) (end - begin);
     }
 

From 917885b609adc486d0207d9486310ad16d14454b Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 17 Jun 2021 10:14:40 +0530
Subject: [PATCH 083/118] Fix bug in MAD gain

---
 src/mlpack/methods/decision_tree/mad_gain.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index 10016045a0..aae0439091 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -75,7 +75,7 @@ class MADGain
       Sum(values, begin, end, mean);
       mean /= (double) (end - begin);
 
-      mad = arma::accu(arma::abs(values - mean));
+      mad = arma::accu(arma::abs(values.subvec(begin, end - 1) - mean));
       mad /= (double) (end - begin);
     }
 

From 0717855241c43f2e0a405d2897265061f3e8b463 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Thu, 17 Jun 2021 10:58:54 +0530
Subject: [PATCH 084/118] Added tests on LARS dataset and a test where tree is
 trained on MAD gain

---
 .../tests/decision_tree_regressor_test.cpp    | 151 ++++++++++++------
 1 file changed, 103 insertions(+), 48 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 38f61ee7ca..9d9ac9d2c1 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -797,62 +797,61 @@ TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
 //   REQUIRE(accuracy > 0.75);
 // }
 
-// /**
-//  * Test that we can build a decision tree using information gain on a simple
-//  * categorical dataset using weights, with low-weight noise added.
-//  */
-// TEST_CASE("CategoricalInformationGainWeightedBuildTest", "[DecisionTreeTest]")
-// {
-//   arma::mat d;
-//   arma::Row<size_t> l;
-//   data::DatasetInfo di;
-//   MockCategoricalData(d, l, di);
+/**
+ * Test that we can build a decision tree using MAD gain on a simple
+ * categorical dataset using weights, with low-weight noise added.
+ */
+TEST_CASE("CategoricalInformationGainWeightedBuildTest_", "[DecisionTreeTest]")
+{
+  arma::mat d;
+  arma::rowvec r;
+  data::DatasetInfo di;
+  MockCategoricalData(d, r, di);
 
-//   // Split into a training set and a test set.
-//   arma::mat trainingData = d.cols(0, 1999);
-//   arma::mat testData = d.cols(2000, 3999);
-//   arma::Row<size_t> trainingLabels = l.subvec(0, 1999);
-//   arma::Row<size_t> testLabels = l.subvec(2000, 3999);
+  // Split into a training set and a test set.
+  arma::mat trainingData = d.cols(0, 1999);
+  arma::mat testData = d.cols(2000, 3999);
+  arma::rowvec trainingResponses = r.subvec(0, 1999);
+  arma::rowvec testResponses = r.subvec(2000, 3999);
 
-//   // Now create random points.
-//   arma::mat randomNoise(4, 2000);
-//   arma::Row<size_t> randomLabels(2000);
-//   for (size_t i = 0; i < 2000; ++i)
-//   {
-//     randomNoise(0, i) = math::Random();
-//     randomNoise(1, i) = math::Random();
-//     randomNoise(2, i) = math::RandInt(4);
-//     randomNoise(3, i) = math::RandInt(2);
-//     randomLabels[i] = math::RandInt(5);
-//   }
+  // Now create random points.
+  arma::mat randomNoise(5, 2000);
+  arma::rowvec randomResponses(2000);
+  for (size_t i = 0; i < 2000; ++i)
+  {
+    randomNoise(0, i) = math::Random();
+    randomNoise(1, i) = math::Random(-1, 1);
+    randomNoise(2, i) = math::Random();
+    randomNoise(3, i) = math::RandInt(0, 2);
+    randomNoise(4, i) = math::RandInt(0, 5);
+    randomResponses[i] = math::Random(-10, 18);
+  }
 
-//   // Generate weights.
-//   arma::rowvec weights(4000);
-//   for (size_t i = 0; i < 2000; ++i)
-//     weights[i] = math::Random(0.9, 1.0);
-//   for (size_t i = 2000; i < 4000; ++i)
-//     weights[i] = math::Random(0.0, 0.001);
+  // Generate weights.
+  arma::rowvec weights(4000);
+  for (size_t i = 0; i < 2000; ++i)
+    weights[i] = math::Random(0.9, 1.0);
+  for (size_t i = 2000; i < 4000; ++i)
+    weights[i] = math::Random(0.0, 0.001);
 
-//   arma::mat fullData = arma::join_rows(trainingData, randomNoise);
-//   arma::Row<size_t> fullLabels = arma::join_rows(trainingLabels, randomLabels);
+  arma::mat fullData = arma::join_rows(trainingData, randomNoise);
+  arma::rowvec fullResponses = arma::join_rows(trainingResponses,
+      randomResponses);
 
-//   // Build the tree.
-//   DecisionTree<InformationGain> tree(fullData, di, fullLabels, 5, weights, 10);
+  // Build the tree.
+  DecisionTreeRegressor<MADGain> tree(fullData, di, fullResponses, weights,
+      10);
 
-//   // Now evaluate the accuracy of the tree.
-//   arma::Row<size_t> predictions;
-//   tree.Classify(testData, predictions);
+  // Now evaluate the quality of predictions.
+  arma::rowvec predictions;
+  tree.Predict(testData, predictions);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-//   size_t correct = 0;
-//   for (size_t i = 0; i < testData.n_cols; ++i)
-//     if (testLabels[i] == predictions[i])
-//       ++correct;
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   // Make sure we got at least 70% accuracy.
-//   const double correctPct = double(correct) / double(testData.n_cols);
-//   REQUIRE(correctPct > 0.70);
-// }
+  // Make sure we get reasonable rmse.
+  const double rmse = RMSE(predictions, testResponses);
+  REQUIRE(rmse < 1.0);
+}
 
 /**
  * Test that the decision tree generalizes reasonably.
@@ -1053,3 +1052,59 @@ TEST_CASE("MultiSplitTest3", "[DecisionTreeRegressorTest]")
 
   REQUIRE(d.NumLeaves() == 5);
 }
+
+/**
+ * Test that the tree builds correctly on unweighted numerical dataset.
+ */
+TEST_CASE("LARSDatasetTest", "[DecisionTreeRegressorTest]")
+{
+  arma::mat X;
+  arma::rowvec Y;
+
+  if (!data::Load("lars_dependent_x.csv", X))
+    FAIL("Cannot load dataset lars_dependent_x.csv");
+  if (!data::Load("lars_dependent_y.csv", Y))
+    FAIL("Cannot load dataset lars_dependent_y.csv");
+
+  arma::mat XTrain, XTest;
+  arma::rowvec YTrain, YTest;
+  data::Split(X, Y, XTrain, XTest, YTrain, YTest, 0.3);
+
+  DecisionTreeRegressor<> tree(XTrain, YTrain, 5);
+
+  arma::rowvec predictions;
+  tree.Predict(XTest, predictions);
+
+  const double rmse = RMSE(predictions, YTest);
+
+  REQUIRE(rmse < 1.0);
+}
+
+/**
+ * Test that the tree builds correctly on weighted numerical dataset.
+ */
+TEST_CASE("LARSDatasetWeightedTest", "[DecisionTreeRegressorTest]")
+{
+  arma::mat X;
+  arma::rowvec Y;
+
+  if (!data::Load("lars_dependent_x.csv", X))
+    FAIL("Cannot load dataset lars_dependent_x.csv");
+  if (!data::Load("lars_dependent_y.csv", Y))
+    FAIL("Cannot load dataset lars_dependent_y.csv");
+
+  arma::mat XTrain, XTest;
+  arma::rowvec YTrain, YTest;
+  data::Split(X, Y, XTrain, XTest, YTrain, YTest, 0.3);
+
+  arma::rowvec weights = arma::ones<arma::rowvec>(XTrain.n_elem);
+
+  DecisionTreeRegressor<> tree(XTrain, YTrain, weights, 5);
+
+  arma::rowvec predictions;
+  tree.Predict(XTest, predictions);
+
+  const double rmse = RMSE(predictions, YTest);
+
+  REQUIRE(rmse < 1.0);
+}

From 0ff17398a50e88362a795420d8797b147ead9c1f Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 18 Jun 2021 17:59:18 +0530
Subject: [PATCH 085/118] Making non template functions as inline to avoid
 duplicate definition error

---
 src/mlpack/methods/decision_tree/utils.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/utils.hpp b/src/mlpack/methods/decision_tree/utils.hpp
index 1928803e88..37c6453c56 100644
--- a/src/mlpack/methods/decision_tree/utils.hpp
+++ b/src/mlpack/methods/decision_tree/utils.hpp
@@ -15,7 +15,7 @@
 /**
  * Calculates the weighted sum and total weight of labels.
  */
-void WeightedSum(const arma::rowvec& labels,
+inline void WeightedSum(const arma::rowvec& labels,
                  const arma::rowvec& weights,
                  const size_t begin,
                  const size_t end,
@@ -88,7 +88,7 @@ void WeightedSum(const arma::rowvec& labels,
 /**
  * Sums up the labels vector.
  */
-void Sum(const arma::rowvec& labels,
+inline void Sum(const arma::rowvec& labels,
         const size_t begin,
         const size_t end,
         double& mean)

From 17ae3872fddab67c63554c54373022ee3f888a25 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 19 Jun 2021 22:47:23 +0530
Subject: [PATCH 086/118] =?UTF-8?q?Optimised=20MSEGain=20computation=20usi?=
 =?UTF-8?q?ng=20prefix=20sum=20of=20squares=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../best_binary_numeric_split.hpp             |  33 +++
 .../best_binary_numeric_split_impl.hpp        | 235 +++++++++++++++++-
 .../decision_tree_regressor_impl.hpp          |   9 +-
 src/mlpack/methods/decision_tree/mse_gain.hpp |  50 ++++
 4 files changed, 313 insertions(+), 14 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 6907edb943..9a888b3341 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -13,6 +13,7 @@
 #define MLPACK_METHODS_DECISION_TREE_BEST_BINARY_NUMERIC_SPLIT_HPP
 
 #include <mlpack/prereqs.hpp>
+#include "mse_gain.hpp"
 
 namespace mlpack {
 namespace tree {
@@ -117,6 +118,38 @@ class BestBinaryNumericSplit
       const AuxiliarySplitInfo& /* aux */);
 };
 
+/**
+* Check if we can split a node.  If we can split a node in a way that
+* improves on 'bestGain', then we return the improved gain.  Otherwise we
+* return the value 'bestGain'.  If a split is made, then splitInfo and aux
+* may be modified.
+*
+* This overload is specialized only for MSEGain fitness function.
+*
+* @param bestGain Best gain seen so far (we'll only split if we find gain
+*      better than this).
+* @param data The dimension of data points to check for a split in.
+* @param responses Responses for each point.
+* @param weights Weights associated with responses.
+* @param minimumLeafSize Minimum number of points in a leaf node for
+*      splitting.
+* @param minimumGainSplit Minimum gain split.
+* @param splitInfo Stores split information on a successful split.
+* @param aux Auxiliary split information, which may be modified on a
+*      successful split.
+*/
+template<>
+template<bool UseWeights, typename VecType, typename WeightVecType>
+double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
+    const double bestGain,
+    const VecType& data,
+    const arma::rowvec& responses,
+    const WeightVecType& weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    double& splitInfo,
+    AuxiliarySplitInfo& /* aux */);
+
 } // namespace tree
 } // namespace mlpack
 
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 9a2f3cc3a8..bd94d5f064 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -260,12 +260,6 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     if (data[sortedIndices[index]] == data[sortedIndices[index - 1]])
       continue;
 
-    /* TODO: The following function calculates the gain for each split each
-             time from scratch. This can be greatly improved using advanced
-             techniques like prefix sum and prefix sum of squares etc. This
-             will have drastic effects on runtime and is definitely something
-             we would want in future.
-     */
     // Calculate the gain for the left and right child.
     const double leftGain = FitnessFunction::template
         Evaluate<UseWeights>(sortedResponses, sortedWeights, 0, index);
@@ -319,6 +313,235 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   return bestFoundGain;
 }
 
+// Optimized version when fitness function is MSEGain.
+template<>
+template<bool UseWeights, typename VecType, typename WeightVecType>
+double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
+    const double bestGain,
+    const VecType& data,
+    const arma::rowvec& responses,
+    const WeightVecType& weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    double& splitInfo,
+    AuxiliarySplitInfo& /* aux */)
+{
+  // First sanity check: if we don't have enough points, we can't split.
+  if (data.n_elem < (minimumLeafSize * 2))
+    return DBL_MAX;
+  if (bestGain == 0.0)
+    return DBL_MAX; // It can't be outperformed.
+
+  // Next, sort the data.
+  arma::uvec sortedIndices = arma::sort_index(data);
+  arma::rowvec sortedResponses(responses.n_elem);
+  arma::rowvec sortedWeights;
+  for (size_t i = 0; i < sortedResponses.n_elem; ++i)
+    sortedResponses[i] = responses[sortedIndices[i]];
+
+  // Sanity check: if the first element is the same as the last, we can't split
+  // in this dimension.
+  if (data[sortedIndices[0]] == data[sortedIndices[sortedIndices.n_elem - 1]])
+    return DBL_MAX;
+
+  // Only initialize if we are using weights.
+  if (UseWeights)
+  {
+    sortedWeights.set_size(sortedResponses.n_elem);
+    // The weights must keep the same order as the responses.
+    for (size_t i = 0; i < sortedResponses.n_elem; ++i)
+      sortedWeights[i] = weights[sortedIndices[i]];
+  }
+
+  double bestFoundGain = std::min(bestGain + minimumGainSplit, 0.0);
+  bool improved = false;
+  // Force a minimum leaf size of 1 (empty children don't make sense).
+  const size_t minimum = std::max(minimumLeafSize, (size_t) 1);
+
+  double totalWeight = 0.0;
+  double leftChildWeight = 0.0;
+  double rightChildWeight = 0.0;
+  double leftWeightedMean = 0.0;
+  double rightWeightedMean = 0.0;
+  double totalWeightedSumSquares = 0.0;
+  arma::rowvec weightedSumSquares;
+
+  double leftMean = 0.0;
+  double rightMean = 0.0;
+  size_t leftChildSize = 0;
+  size_t rightChildSize = 0;
+  double totalSumSquares = 0.0;
+  arma::rowvec sumSquares;
+
+  // Precomputing prefix sum of squares and prefix weighted sum of squares.
+  // This will be used by MSEGain::Evaluate to efficiently compute gain
+  // values for all possible splits.
+  if (UseWeights)
+  {
+    totalWeight = arma::accu(sortedWeights);
+    bestFoundGain *= totalWeight;
+
+    weightedSumSquares.set_size(data.n_elem);
+    // Stores the weighted sum of squares till the previous index.
+    double prevWeightedSumSquares = 0.0;
+
+    for (size_t i = 0; i < minimum - 1; ++i)
+    {
+      const double w = sortedWeights[i];
+      const double x = sortedResponses[i];
+
+      // Calculating initial weighted mean of responses for the left child.
+      leftChildWeight += w;
+      leftWeightedMean += w * x;
+      weightedSumSquares[i] = prevWeightedSumSquares + w * x * x;
+      prevWeightedSumSquares += w * x * x;
+    }
+    if (leftChildWeight > 1e-9)
+      leftWeightedMean /= leftChildWeight;
+
+    for (size_t i = minimum - 1; i < data.n_elem; ++i)
+    {
+      const double w = sortedWeights[i];
+      const double x = sortedResponses[i];
+
+      // Calculating initial weighted mean of responses for the right child.
+      rightChildWeight += w;
+      rightWeightedMean += w * x;
+      weightedSumSquares[i] = prevWeightedSumSquares + w * x * x;
+      prevWeightedSumSquares += w * x * x;
+    }
+    if (rightChildWeight > 1e-9)
+      rightWeightedMean /= rightChildWeight;
+
+    totalWeightedSumSquares = prevWeightedSumSquares;
+  }
+  else
+  {
+    bestFoundGain *= data.n_elem;
+
+    sumSquares.set_size(data.n_elem);
+    // Stores the sum of squares till the previous index.
+    double prevSumSquares = 0.0;
+
+    for (size_t i = 0; i < minimum - 1; ++i)
+    {
+      const double x = sortedResponses[i];
+
+      // Calculating the initial mean of responses for the left child.
+      ++leftChildSize;
+      leftMean += x;
+      sumSquares[i] = prevSumSquares + x * x;
+      prevSumSquares += x * x;
+    }
+    if (leftChildSize)
+      leftMean /= (double) leftChildSize;
+
+    for (size_t i = minimum - 1; i < data.n_elem; ++i)
+    {
+      const double x = sortedResponses[i];
+
+      // Calculating the initial mean of responses for the right child.
+      rightChildSize++;
+      rightMean += x;
+      sumSquares[i] = prevSumSquares + x * x;
+      prevSumSquares += x * x;
+    }
+    if (rightChildSize)
+      rightMean /= (double) rightChildSize;
+
+    totalSumSquares = prevSumSquares;
+  }
+
+  // Loop through all possible split points, choosing the best one.
+  for (size_t index = minimum; index < data.n_elem - minimum + 1; ++index)
+  {
+    if (UseWeights)
+    {
+      // Updating the weighted mean for both childs for each index.
+      const double w = sortedWeights[index - 1];
+      const double x = sortedResponses[index - 1];
+      leftWeightedMean = (leftWeightedMean * leftChildWeight + w * x)
+          / (leftChildWeight + w);
+      leftChildWeight += w;
+
+      rightWeightedMean = (rightWeightedMean * rightChildWeight - w * x)
+          / (rightChildWeight - w);
+      rightChildWeight -= w;
+    }
+    else
+    {
+      // Updating the mean for both childs for each index.
+      const double x = sortedResponses[index - 1];
+      leftMean = (leftMean * (double) leftChildSize + x) /
+          (double) (leftChildSize + 1);
+      ++leftChildSize;
+
+      rightMean = (rightMean * (double) rightChildSize - x) /
+          (double) (rightChildSize - 1);
+      --rightChildSize;
+    }
+
+    // Make sure that the value has changed.
+    if (data[sortedIndices[index]] == data[sortedIndices[index - 1]])
+      continue;
+
+    // Calculate the gain for the left and right child.
+    const double leftGain = UseWeights ?
+        MSEGain::Evaluate(weightedSumSquares[index - 1],
+            leftWeightedMean, leftChildWeight) :
+        MSEGain::Evaluate(sumSquares[index - 1], leftMean, leftChildSize);
+    const double rightGain = UseWeights ?
+        MSEGain::Evaluate(
+            totalWeightedSumSquares - weightedSumSquares[index - 1],
+            rightWeightedMean, rightChildWeight) :
+        MSEGain::Evaluate(totalSumSquares - sumSquares[index - 1],
+            rightMean, rightChildSize);
+
+    double gain;
+    if (UseWeights)
+    {
+      gain = leftChildWeight * leftGain + rightChildWeight * rightGain;
+    }
+    else
+    {
+      // Calculate the gain at this split point.
+      gain = double(leftChildSize) * leftGain +
+          double(rightChildSize) * rightGain;
+    }
+
+    // Corner case: is this the best possible split?
+    if (gain >= 0.0)
+    {
+      // We can take a shortcut: no split will be better than this, so just
+      // take this one. The actual split value will be halfway between the
+      // value at index - 1 and index.
+      splitInfo = (data[sortedIndices[index - 1]] +
+          data[sortedIndices[index]]) / 2.0;
+
+      return gain;
+    }
+     if (gain > bestFoundGain)
+    {
+      // We still have a better split.
+      bestFoundGain = gain;
+      splitInfo = (data[sortedIndices[index - 1]] +
+          data[sortedIndices[index]]) / 2.0;
+      improved = true;
+    }
+  }
+  // If we didn't improve, return the original gain exactly as we got it
+  // (without introducing floating point errors).
+  if (!improved)
+    return DBL_MAX;
+
+  if (UseWeights)
+    bestFoundGain /= totalWeight;
+  else
+    bestFoundGain /= data.n_elem;
+
+  return bestFoundGain;
+}
+
 template<typename FitnessFunction>
 template<typename ElemType>
 size_t BestBinaryNumericSplit<FitnessFunction>::CalculateDirection(
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index d74c7e4f7a..d10c83e395 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -761,9 +761,7 @@ double DecisionTreeRegressor<FitnessFunction,
     // Calculate prediction label because we are a leaf.
     CalculatePrediction<UseWeights>(
         responses.subvec(begin, begin + count - 1),
-        UseWeights ? weights.subvec(begin, begin + count - 1) : weights);   
-    std::cout << "Number of points in leaf: " << count << 
-        "  Prediction: " << splitPointOrPrediction << std::endl; 
+        UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
   }
 
   return -bestGain;
@@ -829,7 +827,6 @@ double DecisionTreeRegressor<FitnessFunction,
 
       // If the splitter did not report that it improved, then move to the next
       // dimension.
-      // std::cout << "dim: " << i << "   gain: " << dimGain << std::endl;
       if (dimGain == DBL_MAX)
         continue;
 
@@ -845,8 +842,6 @@ double DecisionTreeRegressor<FitnessFunction,
   // Did we split or not?  If so, then split the data and create the children.
   if (bestDim != data.n_rows)
   {
-    // std::cout << "splitpoint: " << splitPointOrPrediction << std::endl;
-    // std::cout << "bestDim: " << bestDim << std::endl;
     // We know that the split is numeric.
     size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
     splitDimension = bestDim;
@@ -920,8 +915,6 @@ double DecisionTreeRegressor<FitnessFunction,
     CalculatePrediction<UseWeights>(
         responses.subvec(begin, begin + count - 1),
         UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
-    std::cout << "Number of points in leaf: " << count << 
-        "  Prediction: " << splitPointOrPrediction << std::endl;
   }
 
   return -bestGain;
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 44dadb574e..9f70fccb21 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -95,6 +95,56 @@ class MSEGain
 
     return Evaluate<UseWeights>(values, weights, 0, values.n_elem);
   }
+
+  /**
+   * Calculates the weighted mean squared error gain given the sum of squares
+   * and mean.
+   *
+   * X = array of values of size n.
+   * W = array of weights of size n.
+   *
+   * @f{eqnarray*}{
+   *    MSE = \sum\limits_{i=1}^n {W_i * {X_i}^2} -
+   *        {\dfrac{\sum\limits_{j=1}^n W_j * X_j}
+   *        {\sum\limits_{j=1}^n W_i}}^2
+   * @f}
+   *
+   * @param weightedSumSquares Precomputed weighted sum of square
+   *    (sum(Wi * Xi^2)) of values.
+   * @param weightedMean Precomputed weighted mean (sum(Wi * Xi) / sum(Wi)) of
+   *    values.
+   * @param totalChildWeight Total weight of all the samples in that child.
+   */
+  static double Evaluate(const double weightedSumSquares,
+                         const double weightedMean,
+                         const double totalChildWeight)
+  {
+    double mse = weightedSumSquares / totalChildWeight -
+        weightedMean * weightedMean;
+    return -mse;
+  }
+
+  /**
+   * Calculates the  mean squared error gain given the sum of squares and mean.
+   *
+   * X = array of values of size n.
+   *
+   * @f{eqnarray*}{
+   *   MSE = \sum\limits_{i=1}^n {X_i}^2 -
+   *       {\dfrac{\sum\limits_{j=1}^n X_j}{n}}^2
+   * @f}
+   *
+   * @param sumSquares Precomputed sum of square (sum(Xi^2)) of values.
+   * @param mean Precomputed mean (sum(Xi) / n) of values.
+   * @param childSize The total number of samples in that child.
+   */
+  static double Evaluate(const double sumSquares,
+                         const double mean,
+                         const size_t childSize)
+  {
+    double mse = sumSquares / (double) childSize - mean * mean;
+    return -mse;
+  }
 };
 
 } // namespace tree

From db1fcdc26b6363cd7608b57b36c387fe1ab86918 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 10 May 2021 14:30:37 +0530
Subject: [PATCH 087/118] Amend RandomBinaryNumericSplit signature to support
 regression

---
 src/mlpack/tests/decision_tree_test.cpp | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 1412b70495..e5ad32a03b 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -385,7 +385,7 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   arma::Row<size_t> labels("0 0 0 0 0 1 1 1 1 1 1");
   arma::rowvec weights(labels.n_elem);
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
@@ -395,12 +395,11 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   // This should make no difference because it won't split at all.
   const double weightedGain =
       RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 8, 1e-7, classProbabilities, aux);
+      labels, 2, weights, 8, 1e-7, classProbabilities[0], aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
   REQUIRE(gain == weightedGain);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -420,18 +419,17 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities;
+  arma::vec classProbabilities(1);
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities,
+      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities[0],
       aux, true);
 
   // Make sure there was no split.
   REQUIRE(gain == DBL_MAX);
-  REQUIRE(classProbabilities.n_elem == 0);
 }
 
 /**
@@ -451,7 +449,7 @@ TEST_CASE("RandomBinaryNumericSplitDiffSplitTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities, classProbabilities1;
+  arma::vec classProbabilities(1), classProbabilities1(1);
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux1;
 

From 442cb4284dc8454dd3a1193d48013aacf8df6c82 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 21 Jun 2021 21:46:08 +0530
Subject: [PATCH 088/118] Removed headers from csv and fixed loading of boston
 dataset

---
 .../tests/data/boston_housing_price.csv       |  1 -
 .../data/boston_housing_price_responses.csv   |  1 -
 src/mlpack/tests/test_function_tools.hpp      | 31 ++++++++++---------
 3 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/src/mlpack/tests/data/boston_housing_price.csv b/src/mlpack/tests/data/boston_housing_price.csv
index 5c0d211062..50aadfc932 100644
--- a/src/mlpack/tests/data/boston_housing_price.csv
+++ b/src/mlpack/tests/data/boston_housing_price.csv
@@ -1,4 +1,3 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12
 0.00632,18.0,2.31,0,0.538,6.575,65.2,4.09,1,296.0,15.3,396.9,4.98
 0.02731,0.0,7.07,0,0.469,6.421,78.9,4.9671,2,242.0,17.8,396.9,9.14
 0.02729,0.0,7.07,0,0.469,7.185,61.1,4.9671,2,242.0,17.8,392.83,4.03
diff --git a/src/mlpack/tests/data/boston_housing_price_responses.csv b/src/mlpack/tests/data/boston_housing_price_responses.csv
index fd7ad517aa..2a6908a56b 100644
--- a/src/mlpack/tests/data/boston_housing_price_responses.csv
+++ b/src/mlpack/tests/data/boston_housing_price_responses.csv
@@ -1,4 +1,3 @@
-0
 24.0
 21.6
 34.7
diff --git a/src/mlpack/tests/test_function_tools.hpp b/src/mlpack/tests/test_function_tools.hpp
index 0233402506..7b9eec84d4 100644
--- a/src/mlpack/tests/test_function_tools.hpp
+++ b/src/mlpack/tests/test_function_tools.hpp
@@ -97,21 +97,24 @@ inline void LoadBostonHousingDataset(arma::mat& trainData,
 
   data::Split(dataset, responses, trainData, testData,
       trainResponses, testResponses, 0.3);
-  // info.Type(3) = data::Datatype::categorical;
-  // info.Type(8) = data::Datatype::categorical;
 
-  // info.MapString<double>("0", 3);
-  // info.MapString<double>("1", 3);
-  // info.MapString<double>("1", 8);
-  // info.MapString<double>("2", 8);
-  // info.MapString<double>("3", 8);
-  // info.MapString<double>("4", 8);
-  // info.MapString<double>("5", 8);
-  // info.MapString<double>("6", 8);
-  // info.MapString<double>("7", 8);
-  // info.MapString<double>("8", 8);
-  // info.MapString<double>("24", 8);
-  // std::cout << arma::unique(trainData.row(8));
+  // Defining categorical deimensions.
+  info.Type(3) = data::Datatype::categorical;
+  info.Type(8) = data::Datatype::categorical;
+
+  // Creating mappings for categorical dimensions.
+  info.MapString<double>("0", 3);
+  info.MapString<double>("1", 3);
+
+  info.MapString<double>("1", 8);
+  info.MapString<double>("2", 8);
+  info.MapString<double>("3", 8);
+  info.MapString<double>("4", 8);
+  info.MapString<double>("5", 8);
+  info.MapString<double>("6", 8);
+  info.MapString<double>("7", 8);
+  info.MapString<double>("8", 8);
+  info.MapString<double>("24", 8);
 }
 
 inline double RMSE(const arma::Row<double>& predictions,

From ffb68e0f7ce9c44e49430bc1e530a6da79c453df Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 21 Jun 2021 21:46:26 +0530
Subject: [PATCH 089/118] Debugging code

---
 .../all_categorical_split_impl.hpp            | 10 +++++++
 .../decision_tree_regressor_impl.hpp          |  3 ++
 .../tests/decision_tree_regressor_test.cpp    | 30 +++++++------------
 3 files changed, 24 insertions(+), 19 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
index 0fbeeeec25..4f4e60ac68 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
@@ -49,6 +49,7 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     SplitInfoType& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
+  std::cout << "Calling All categorical split " << numCategories << std::endl;
   // Count the number of elements in each potential child.
   const double epsilon = 1e-7; // Tolerance for floating-point errors.
   arma::Col<size_t> counts(numCategories, arma::fill::zeros);
@@ -73,20 +74,29 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
   // If each child will have the minimum number of points in it, we can split.
   // Otherwise we can't.
   if (arma::min(counts) < minimumLeafSize)
+  {
+    std::cout << counts << std::endl;
     return DBL_MAX;
+  }
 
   // Calculate the gain of the split.  First we have to calculate the labels
   // that would be assigned to each child.
   arma::uvec childPositions(numCategories, arma::fill::zeros);
   std::vector<arma::Row<typename LabelsType::elem_type>> childLabels(numCategories);
   std::vector<arma::Row<double>> childWeights(numCategories);
+  
+  std::cout << counts << std::endl;
+  std::cout << "Num categories: " << numCategories << std::endl;
   for (size_t i = 0; i < numCategories; ++i)
   {
+    std::cout << i;
     // Labels and weights should have same length.
     childLabels[i].zeros(counts[i]);
+    if (numCategories == 9) std::cout << "Labels initialized\n";
     if (UseWeights)
       childWeights[i].zeros(counts[i]);
   }
+  std::cout << "\n";
 
   // Extract labels for each child.
   for (size_t i = 0; i < data.n_elem; ++i)
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index d10c83e395..8f4ebb830e 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -135,6 +135,7 @@ DecisionTreeRegressor<FitnessFunction,
   using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
+  std::cout << "Data copying begin\n";
   // Copy or move data.
   TrueMatType tmpData(std::move(data));
   TrueResponsesType tmpResponses(std::move(responses));
@@ -142,6 +143,7 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
+  std::cout << "Pre training!\n";
 
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
@@ -631,6 +633,7 @@ double DecisionTreeRegressor<FitnessFunction,
     for (size_t i = dimensionSelector.Begin(); i != end;
          i = dimensionSelector.Next())
     {
+      if (i == 8 || i == 3) std::cout << "Dimension:" << i << std::endl;
       double dimGain = -DBL_MAX;
       if (datasetInfo.Type(i) == data::Datatype::categorical)
       {
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 9d9ac9d2c1..5c66a79343 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -858,34 +858,26 @@ TEST_CASE("CategoricalInformationGainWeightedBuildTest_", "[DecisionTreeTest]")
  */
 TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 {
-
+  std::cout << "***********************************\n";
   // Loading data.
   data::DatasetInfo info;
   arma::mat trainData, testData;
   arma::rowvec trainResponses, testResponses;
-  arma::rowvec weights = arma::ones<arma::rowvec>(355);
   LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
       info);
 
+  arma::rowvec weights(trainResponses.n_elem, arma::fill::ones);
+  std::cout << weights.n_elem << " " << trainResponses.n_elem << std::endl;
+
+  std::cout << "NumMappings: " << info.NumMappings(8) << std::endl;
+  std::cout << info.Type(8) << std::endl;
+
   // Build decision tree.
-  DecisionTreeRegressor<MSEGain> d(trainData, info, trainResponses);
+  // DecisionTreeRegressor<> d(trainData, info, trainResponses, 1);
+  std::cout << "***********************************\n";
+  DecisionTreeRegressor<> wd(trainData, info, trainResponses, weights);
 
-  // Get the predicted test responses.
-  arma::Row<double> predictions;
-  d.Predict(testData, predictions);
-
-  REQUIRE(predictions.n_elem == testData.n_cols);
-
-  // Figure out rmse.
-  double rmse = RMSE(predictions, testResponses);
-
-  // REQUIRE(rmse < 9.21);
-  // std::cout << predictions << std::endl << testResponses;
-  arma::Row<double> trainPred;
-  d.Predict(trainData, trainPred);
-  // std::cout << trainPred;
-
-  std::cout << "Train RMSE: " << RMSE(trainResponses, trainPred) << std::endl;
+  std::cout << "training done\n";
 }
 
 /**

From 60c2239210343ecd7f0c7ddb8dc4b370e2bbff43 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 01:09:55 +0530
Subject: [PATCH 090/118] Fixed tests from the crazy merge that unexpectedly
 happened

---
 src/mlpack/tests/decision_tree_test.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index e5ad32a03b..1785e05c41 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -395,7 +395,7 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest", "[DecisionTreeTest]")
   // This should make no difference because it won't split at all.
   const double weightedGain =
       RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<true>(bestGain, values,
-      labels, 2, weights, 8, 1e-7, classProbabilities[0], aux);
+      labels, 2, weights, 8, 1e-7, classProbabilities, aux);
 
   // Make sure that no split was made.
   REQUIRE(gain == DBL_MAX);
@@ -419,13 +419,13 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 2, weights);
   const double gain = RandomBinaryNumericSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities[0],
+      bestGain, values, labels, 2, weights, 10, 1e-7, classProbabilities,
       aux, true);
 
   // Make sure there was no split.
@@ -449,7 +449,7 @@ TEST_CASE("RandomBinaryNumericSplitDiffSplitTest", "[DecisionTreeTest]")
     labels[i + 1] = 1;
   }
 
-  arma::vec classProbabilities(1), classProbabilities1(1);
+  arma::vec classProbabilities, classProbabilities1;
   BestBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux;
   RandomBinaryNumericSplit<GiniGain>::AuxiliarySplitInfo aux1;
 

From 8d1c188883c3561efb37055131e83286873743fc Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 01:11:13 +0530
Subject: [PATCH 091/118] Fixed loading the boston housing data (finally) :)

---
 src/mlpack/tests/test_function_tools.hpp | 23 +++++------------------
 1 file changed, 5 insertions(+), 18 deletions(-)

diff --git a/src/mlpack/tests/test_function_tools.hpp b/src/mlpack/tests/test_function_tools.hpp
index 7b9eec84d4..0210b7973d 100644
--- a/src/mlpack/tests/test_function_tools.hpp
+++ b/src/mlpack/tests/test_function_tools.hpp
@@ -90,6 +90,11 @@ inline void LoadBostonHousingDataset(arma::mat& trainData,
   arma::mat dataset;
   arma::rowvec responses;
 
+  // Defining categorical deimensions.
+  info.SetDimensionality(13);
+  info.Type(3) = data::Datatype::categorical;
+  info.Type(8) = data::Datatype::categorical;
+
   if (!data::Load("boston_housing_price.csv", dataset, info))
     FAIL("Cannot load test dataset boston_housing_price.csv!");
   if (!data::Load("boston_housing_price_responses.csv", responses))
@@ -97,24 +102,6 @@ inline void LoadBostonHousingDataset(arma::mat& trainData,
 
   data::Split(dataset, responses, trainData, testData,
       trainResponses, testResponses, 0.3);
-
-  // Defining categorical deimensions.
-  info.Type(3) = data::Datatype::categorical;
-  info.Type(8) = data::Datatype::categorical;
-
-  // Creating mappings for categorical dimensions.
-  info.MapString<double>("0", 3);
-  info.MapString<double>("1", 3);
-
-  info.MapString<double>("1", 8);
-  info.MapString<double>("2", 8);
-  info.MapString<double>("3", 8);
-  info.MapString<double>("4", 8);
-  info.MapString<double>("5", 8);
-  info.MapString<double>("6", 8);
-  info.MapString<double>("7", 8);
-  info.MapString<double>("8", 8);
-  info.MapString<double>("24", 8);
 }
 
 inline double RMSE(const arma::Row<double>& predictions,

From 9539af8caef3691765b1897ba56896b7406de73b Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 01:14:55 +0530
Subject: [PATCH 092/118] Removed the debugging code and reverted back to the
 previous version.

---
 .../all_categorical_split_impl.hpp            | 11 +------
 .../decision_tree_regressor_impl.hpp          |  4 ---
 .../tests/decision_tree_regressor_test.cpp    | 30 ++++++++++++-------
 3 files changed, 20 insertions(+), 25 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
index 4f4e60ac68..32650df507 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
@@ -49,7 +49,6 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     SplitInfoType& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
-  std::cout << "Calling All categorical split " << numCategories << std::endl;
   // Count the number of elements in each potential child.
   const double epsilon = 1e-7; // Tolerance for floating-point errors.
   arma::Col<size_t> counts(numCategories, arma::fill::zeros);
@@ -74,29 +73,21 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
   // If each child will have the minimum number of points in it, we can split.
   // Otherwise we can't.
   if (arma::min(counts) < minimumLeafSize)
-  {
-    std::cout << counts << std::endl;
     return DBL_MAX;
-  }
 
   // Calculate the gain of the split.  First we have to calculate the labels
   // that would be assigned to each child.
   arma::uvec childPositions(numCategories, arma::fill::zeros);
   std::vector<arma::Row<typename LabelsType::elem_type>> childLabels(numCategories);
   std::vector<arma::Row<double>> childWeights(numCategories);
-  
-  std::cout << counts << std::endl;
-  std::cout << "Num categories: " << numCategories << std::endl;
+
   for (size_t i = 0; i < numCategories; ++i)
   {
-    std::cout << i;
     // Labels and weights should have same length.
     childLabels[i].zeros(counts[i]);
-    if (numCategories == 9) std::cout << "Labels initialized\n";
     if (UseWeights)
       childWeights[i].zeros(counts[i]);
   }
-  std::cout << "\n";
 
   // Extract labels for each child.
   for (size_t i = 0; i < data.n_elem; ++i)
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 8f4ebb830e..599b9a0978 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -135,15 +135,12 @@ DecisionTreeRegressor<FitnessFunction,
   using TrueResponsesType = typename std::decay<ResponsesType>::type;
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
-  std::cout << "Data copying begin\n";
-  // Copy or move data.
   TrueMatType tmpData(std::move(data));
   TrueResponsesType tmpResponses(std::move(responses));
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Set the correct dimensionality for the dimension selector.
   dimensionSelector.Dimensions() = tmpData.n_rows;
-  std::cout << "Pre training!\n";
 
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
@@ -633,7 +630,6 @@ double DecisionTreeRegressor<FitnessFunction,
     for (size_t i = dimensionSelector.Begin(); i != end;
          i = dimensionSelector.Next())
     {
-      if (i == 8 || i == 3) std::cout << "Dimension:" << i << std::endl;
       double dimGain = -DBL_MAX;
       if (datasetInfo.Type(i) == data::Datatype::categorical)
       {
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 5c66a79343..9d9ac9d2c1 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -858,26 +858,34 @@ TEST_CASE("CategoricalInformationGainWeightedBuildTest_", "[DecisionTreeTest]")
  */
 TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 {
-  std::cout << "***********************************\n";
+
   // Loading data.
   data::DatasetInfo info;
   arma::mat trainData, testData;
   arma::rowvec trainResponses, testResponses;
+  arma::rowvec weights = arma::ones<arma::rowvec>(355);
   LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
       info);
 
-  arma::rowvec weights(trainResponses.n_elem, arma::fill::ones);
-  std::cout << weights.n_elem << " " << trainResponses.n_elem << std::endl;
-
-  std::cout << "NumMappings: " << info.NumMappings(8) << std::endl;
-  std::cout << info.Type(8) << std::endl;
-
   // Build decision tree.
-  // DecisionTreeRegressor<> d(trainData, info, trainResponses, 1);
-  std::cout << "***********************************\n";
-  DecisionTreeRegressor<> wd(trainData, info, trainResponses, weights);
+  DecisionTreeRegressor<MSEGain> d(trainData, info, trainResponses);
 
-  std::cout << "training done\n";
+  // Get the predicted test responses.
+  arma::Row<double> predictions;
+  d.Predict(testData, predictions);
+
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out rmse.
+  double rmse = RMSE(predictions, testResponses);
+
+  // REQUIRE(rmse < 9.21);
+  // std::cout << predictions << std::endl << testResponses;
+  arma::Row<double> trainPred;
+  d.Predict(trainData, trainPred);
+  // std::cout << trainPred;
+
+  std::cout << "Train RMSE: " << RMSE(trainResponses, trainPred) << std::endl;
 }
 
 /**

From 561c14062d3f8a164d8b66f944e0f64143d23ddc Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 10:50:05 +0530
Subject: [PATCH 093/118] Removed numClasses from regression tree codebase

---
 .../decision_tree/all_categorical_split.hpp   |  41 +++++-
 .../all_categorical_split_impl.hpp            | 126 ++++++++++++++----
 .../best_binary_numeric_split_impl.hpp        |   2 +
 .../decision_tree/decision_tree_regressor.hpp |   4 -
 .../decision_tree_regressor_impl.hpp          |  33 ++---
 src/mlpack/methods/decision_tree/mad_gain.hpp |   1 -
 src/mlpack/methods/decision_tree/mse_gain.hpp |   1 -
 .../random_binary_numeric_split_impl.hpp      |   2 +
 .../tests/decision_tree_regressor_test.cpp    |  50 +++----
 src/mlpack/tests/decision_tree_test.cpp       |   6 +-
 10 files changed, 187 insertions(+), 79 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/all_categorical_split.hpp b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
index ed265bba89..13911887a9 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split.hpp
@@ -40,6 +40,8 @@ class AllCategoricalSplit
    * aux may be modified.  For this particular split type, aux will be empty
    * and splitInfo will store the number of children of the node.
    *
+   * This overload is used only for classification.
+   *
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).
    * @param data The dimension of data points to check for a split in.
@@ -55,7 +57,7 @@ class AllCategoricalSplit
    *      successful split.
    */
   template<bool UseWeights, typename VecType, typename LabelsType,
-           typename WeightVecType, typename SplitInfoType>
+           typename WeightVecType>
   static double SplitIfBetter(
       const double bestGain,
       const VecType& data,
@@ -65,7 +67,42 @@ class AllCategoricalSplit
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
-      SplitInfoType& splitInfo,
+      arma::vec& splitInfo,
+      AuxiliarySplitInfo& aux);
+
+  /**
+   * Check if we can split a node.  If we can split a node in a way that
+   * improves on 'bestGain', then we return the improved gain.  Otherwise we
+   * return the value 'bestGain'.  If a split is made, then splitInfo and
+   * aux may be modified.  For this particular split type, aux will be empty
+   * and splitInfo will store the number of children of the node.
+   *
+   * This overload is used only for regression.
+   *
+   * @param bestGain Best gain seen so far (we'll only split if we find gain
+   *      better than this).
+   * @param data The dimension of data points to check for a split in.
+   * @param numCategories Number of categories in the categorical data.
+   * @param responses Responses for each point.
+   * @param weights Weights associated with responses.
+   * @param minimumLeafSize Minimum number of points in a leaf node for
+   *      splitting.
+   * @param splitInfo Stores split information on a successful split.
+   * @param minimumGainSplit Minimum  gain split.
+   * @param aux Auxiliary split information, which may be modified on a
+   *      successful split.
+   */
+  template<bool UseWeights, typename VecType, typename ResponsesType,
+           typename WeightVecType>
+  static double SplitIfBetter(
+      const double bestGain,
+      const VecType& data,
+      const size_t numCategories,
+      const ResponsesType& responses,
+      const WeightVecType& weights,
+      const size_t minimumLeafSize,
+      const double minimumGainSplit,
+      double& splitInfo,
       AuxiliarySplitInfo& aux);
 
   /**
diff --git a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
index 32650df507..cda19da51f 100644
--- a/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp
@@ -15,28 +15,10 @@
 namespace mlpack {
 namespace tree {
 
-/**
- * Helper function to store split information. This is used for regression.
- * payload contains the information to be stored in splitInfo.
- */
-static void StoreSplitInfo(double& splitInfo, const double& payload)
-{
-  splitInfo = payload;
-}
-
-/**
- * Helper function to store split information. This is used for classification.
- * payload contains the information to be stored in splitInfo.
- */
-static void StoreSplitInfo(arma::vec& splitInfo, const double& payload)
-{
-  splitInfo.set_size(1);
-  splitInfo[0] = payload;
-}
-
+// Overload used in classification.
 template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename LabelsType,
-         typename WeightVecType, typename SplitInfoType>
+         typename WeightVecType>
 double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
@@ -46,14 +28,14 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
-    SplitInfoType& splitInfo,
+    arma::vec& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
   // Count the number of elements in each potential child.
   const double epsilon = 1e-7; // Tolerance for floating-point errors.
   arma::Col<size_t> counts(numCategories, arma::fill::zeros);
 
-  // If we are using weighted training, learn the weights for each child too.
+  // If we are using weighted training, split the weights for each child too.
   arma::vec childWeightSums;
   double sumWeight = 0.0;
   if (UseWeights)
@@ -78,7 +60,7 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
   // Calculate the gain of the split.  First we have to calculate the labels
   // that would be assigned to each child.
   arma::uvec childPositions(numCategories, arma::fill::zeros);
-  std::vector<arma::Row<typename LabelsType::elem_type>> childLabels(numCategories);
+  std::vector<arma::Row<size_t>> childLabels(numCategories);
   std::vector<arma::Row<double>> childWeights(numCategories);
 
   for (size_t i = 0; i < numCategories; ++i)
@@ -121,7 +103,103 @@ double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
   if (overallGain > bestGain + minimumGainSplit + epsilon)
   {
     // This is better, so store it in splitInfo and return.
-    StoreSplitInfo(splitInfo, numCategories);
+    splitInfo.set_size(1);
+    splitInfo[0] = numCategories;
+    return overallGain;
+  }
+
+  // Otherwise there was no improvement.
+  return DBL_MAX;
+}
+
+// Overload used in regression.
+template<typename FitnessFunction>
+template<bool UseWeights, typename VecType, typename ResponsesType,
+         typename WeightVecType>
+double AllCategoricalSplit<FitnessFunction>::SplitIfBetter(
+    const double bestGain,
+    const VecType& data,
+    const size_t numCategories,
+    const ResponsesType& responses,
+    const WeightVecType& weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    double& splitInfo,
+    AuxiliarySplitInfo& /* aux */)
+{
+  // Count the number of elements in each potential child.
+  const double epsilon = 1e-7; // Tolerance for floating-point errors.
+  arma::Col<size_t> counts(numCategories, arma::fill::zeros);
+
+  // If we are using weighted training, split the weights for each child too.
+  arma::vec childWeightSums;
+  double sumWeight = 0.0;
+  if (UseWeights)
+    childWeightSums.zeros(numCategories);
+
+  for (size_t i = 0; i < data.n_elem; ++i)
+  {
+    counts[(size_t) data[i]]++;
+
+    if (UseWeights)
+    {
+      childWeightSums[(size_t) data[i]] += weights[i];
+      sumWeight += weights[i];
+    }
+  }
+
+  // If each child will have the minimum number of points in it, we can split.
+  // Otherwise we can't.
+  if (arma::min(counts) < minimumLeafSize)
+    return DBL_MAX;
+
+  // Calculate the gain of the split.  First we have to calculate the labels
+  // that would be assigned to each child.
+  arma::uvec childPositions(numCategories, arma::fill::zeros);
+  std::vector<arma::rowvec> childResponses(numCategories);
+  std::vector<arma::rowvec> childWeights(numCategories);
+
+  for (size_t i = 0; i < numCategories; ++i)
+  {
+    // Responses and weights should have same length.
+    childResponses[i].zeros(counts[i]);
+    if (UseWeights)
+      childWeights[i].zeros(counts[i]);
+  }
+
+  // Extract labels for each child.
+  for (size_t i = 0; i < data.n_elem; ++i)
+  {
+    const size_t category = (size_t) data[i];
+
+    if (UseWeights)
+    {
+      childResponses[category][childPositions[category]] = responses[i];
+      childWeights[category][childPositions[category]++] = weights[i];
+    }
+    else
+    {
+      childResponses[category][childPositions[category]++] = responses[i];
+    }
+  }
+
+  double overallGain = 0.0;
+  for (size_t i = 0; i < counts.n_elem; ++i)
+  {
+    // Calculate the gain of this child.
+    const double childPct = UseWeights ?
+        double(childWeightSums[i]) / sumWeight :
+        double(counts[i]) / double(data.n_elem);
+    const double childGain = FitnessFunction::template Evaluate<UseWeights>(
+        childResponses[i], childWeights[i]);
+
+    overallGain += childPct * childGain;
+  }
+
+  if (overallGain > bestGain + minimumGainSplit + epsilon)
+  {
+    // This is better, so store it in splitInfo and return.
+    splitInfo = numCategories;
     return overallGain;
   }
 
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index bd94d5f064..f16c25521b 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -15,6 +15,7 @@
 namespace mlpack {
 namespace tree {
 
+// Overload used for classification.
 template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename WeightVecType>
 double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
@@ -184,6 +185,7 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   return bestFoundGain;
 }
 
+// Overload used for regression.
 template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename WeightVecType>
 double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index d9996e727f..aed4529921 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -470,7 +470,6 @@ class DecisionTreeRegressor :
    * @param count Number of points in this node.
    * @param datasetInfo Type information for each dimension.
    * @param responses Responses for each training point.
-   * @param numClasses Number of classes in the dataset.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
@@ -482,7 +481,6 @@ class DecisionTreeRegressor :
                const size_t count,
                const data::DatasetInfo& datasetInfo,
                ResponsesType& responses,
-               const size_t numClasses,
                arma::rowvec& weights,
                const size_t minimumLeafSize,
                const double minimumGainSplit,
@@ -499,7 +497,6 @@ class DecisionTreeRegressor :
    *      this node.
    * @param count Number of points in this node.
    * @param responses Responses for each training point.
-   * @param numClasses Number of classes in the dataset.
    * @param minimumLeafSize Minimum number of points in each leaf node.
    * @param minimumGainSplit Minimum gain for the node to split.
    * @param maximumDepth Maximum depth for the tree.
@@ -510,7 +507,6 @@ class DecisionTreeRegressor :
                const size_t begin,
                const size_t count,
                ResponsesType& responses,
-               const size_t numClasses,
                arma::rowvec& weights,
                const size_t minimumLeafSize,
                const double minimumGainSplit,
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 599b9a0978..d29bae904b 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -68,7 +68,7 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
+  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -104,7 +104,7 @@ DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  Train<false>(tmpData, 0, tmpData.n_cols, tmpResponses, 0, weights,
+  Train<false>(tmpData, 0, tmpData.n_cols, tmpResponses, weights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -143,7 +143,7 @@ DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -185,7 +185,7 @@ DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, 0, tmpWeights,
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, tmpWeights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -223,7 +223,7 @@ DecisionTreeRegressor<FitnessFunction,
   TrueWeightsType tmpWeights(std::move(weights));
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses, 0,
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses,
               tmpWeights, minimumLeafSize, minimumGainSplit);
 }
 
@@ -266,7 +266,7 @@ DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the weighted Train() method.
-  Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, 0, tmpWeights,
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, tmpWeights,
       minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
 }
 
@@ -450,7 +450,7 @@ double DecisionTreeRegressor<FitnessFunction,
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
   return Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses,
-      0, weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
 
@@ -488,7 +488,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
-  return Train<false>(tmpData, 0, tmpData.n_cols, responses, 0,
+  return Train<false>(tmpData, 0, tmpData.n_cols, tmpResponses,
       weights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -535,7 +535,7 @@ double DecisionTreeRegressor<FitnessFunction,
 
   // Pass off work to the Train() method.
   return Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpResponses,
-      0, tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
 
@@ -579,7 +579,7 @@ double DecisionTreeRegressor<FitnessFunction,
   dimensionSelector.Dimensions() = tmpData.n_rows;
 
   // Pass off work to the Train() method.
-  return Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses, 0,
+  return Train<true>(tmpData, 0, tmpData.n_cols, tmpResponses,
       tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
       dimensionSelector);
 }
@@ -601,7 +601,6 @@ double DecisionTreeRegressor<FitnessFunction,
     const size_t count,
     const data::DatasetInfo& datasetInfo,
     ResponsesType& responses,
-    const size_t numClasses,
     arma::rowvec& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -620,7 +619,6 @@ double DecisionTreeRegressor<FitnessFunction,
   // internal nodes of the tree.
   double bestGain = FitnessFunction::template Evaluate<UseWeights>(
       responses.subvec(begin, begin + count - 1),
-      numClasses,
       UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
   size_t bestDim = datasetInfo.Dimensionality(); // This means "no split".
   const size_t end = dimensionSelector.End();
@@ -637,7 +635,6 @@ double DecisionTreeRegressor<FitnessFunction,
             data.cols(begin, begin + count - 1).row(i),
             datasetInfo.NumMappings(i),
             responses.subvec(begin, begin + count - 1),
-            numClasses,
             UseWeights ? weights.subvec(begin, begin + count - 1) : weights,
             minimumLeafSize,
             minimumGainSplit,
@@ -735,7 +732,7 @@ double DecisionTreeRegressor<FitnessFunction,
       if (NoRecursion)
       {
         child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, datasetInfo, responses, numClasses,
+            currentCol - currentChildBegin, datasetInfo, responses,
             weights, currentCol - currentChildBegin, minimumGainSplit,
             maximumDepth - 1, dimensionSelector);
       }
@@ -743,7 +740,7 @@ double DecisionTreeRegressor<FitnessFunction,
       {
         // During recursion entropy of child node may change.
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, datasetInfo, responses, numClasses,
+            currentCol - currentChildBegin, datasetInfo, responses,
             weights, minimumLeafSize, minimumGainSplit, maximumDepth - 1,
             dimensionSelector);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
@@ -782,7 +779,6 @@ double DecisionTreeRegressor<FitnessFunction,
     const size_t begin,
     const size_t count,
     ResponsesType& responses,
-    const size_t numClasses,
     arma::rowvec& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
@@ -803,7 +799,6 @@ double DecisionTreeRegressor<FitnessFunction,
   // splitPointOrPrediction for all internal nodes of the tree.
   double bestGain = FitnessFunction::template Evaluate<UseWeights>(
       responses.subvec(begin, begin + count - 1),
-      numClasses,
       UseWeights ? weights.subvec(begin, begin + count - 1) : weights);
   size_t bestDim = data.n_rows; // This means "no split".
 
@@ -889,7 +884,7 @@ double DecisionTreeRegressor<FitnessFunction,
       if (NoRecursion)
       {
         child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, responses, numClasses, weights,
+            currentCol - currentChildBegin, responses, weights,
             currentCol - currentChildBegin, minimumGainSplit, maximumDepth - 1,
             dimensionSelector);
       }
@@ -897,7 +892,7 @@ double DecisionTreeRegressor<FitnessFunction,
       {
         // During recursion entropy of child node may change.
         double childGain = child->Train<UseWeights>(data, currentChildBegin,
-            currentCol - currentChildBegin, responses, numClasses, weights,
+            currentCol - currentChildBegin, responses, weights,
             minimumLeafSize, minimumGainSplit, maximumDepth - 1,
             dimensionSelector);
         bestGain += double(childCounts[i]) / double(count) * (-childGain);
diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index aae0439091..8a2bf968b9 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -90,7 +90,6 @@ class MADGain
    */
   template<bool UseWeights, typename WeightVecType>
   static double Evaluate(const arma::rowvec& values,
-                         const size_t /* numClasses */,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 9f70fccb21..7d013cd9aa 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -86,7 +86,6 @@ class MSEGain
    */
   template<bool UseWeights, typename WeightVecType>
   static double Evaluate(const arma::rowvec& values,
-                         const size_t /* numClasses */,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.
diff --git a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
index 6a9db54a05..4d459798b1 100644
--- a/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/random_binary_numeric_split_impl.hpp
@@ -17,6 +17,7 @@
 namespace mlpack {
 namespace tree {
 
+// Overload used for classification.
 template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename WeightVecType>
 double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
@@ -136,6 +137,7 @@ double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   return gain;
 }
 
+// Overload used for regression.
 template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename WeightVecType>
 double RandomBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 9d9ac9d2c1..b2bbc37674 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -77,7 +77,7 @@ TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressorTest]")
   arma::rowvec responses;
   responses.ones(10);
 
-  REQUIRE(MSEGain::Evaluate<false>(responses, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<false>(responses, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -88,10 +88,10 @@ TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights = arma::ones<arma::rowvec>(10);
   arma::rowvec responses;
-  REQUIRE(MSEGain::Evaluate<false>(responses, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<false>(responses, weights) ==
           Approx(0.0).margin(1e-5));
 
-  REQUIRE(MSEGain::Evaluate<true>(responses, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<true>(responses, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -107,9 +107,9 @@ TEST_CASE("MSEGainHandCalculation", "[DecisionTreeRegressorTest]")
   // Hand calculated gain values.
   const double gain = -27.08999;
   const double weightedGain = -27.53960;
-  REQUIRE(MSEGain::Evaluate<false>(responses, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<false>(responses, weights) ==
           Approx(gain).margin(1e-5));
-  REQUIRE(MSEGain::Evaluate<true>(responses, 0, weights) ==
+  REQUIRE(MSEGain::Evaluate<true>(responses, weights) ==
           Approx(weightedGain).margin(1e-5));
 }
 
@@ -122,7 +122,7 @@ TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressorTest]")
   arma::rowvec responses;
   responses.ones(10);
 
-  REQUIRE(MADGain::Evaluate<false>(responses, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<false>(responses, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -143,7 +143,7 @@ TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressorTest")
 
   // Calculated gain.
   const double calculatedGain =
-      MADGain::Evaluate<false>(responses, 0, weights);
+      MADGain::Evaluate<false>(responses, weights);
 
   REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-5));
 }
@@ -155,10 +155,10 @@ TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressorTest]")
 {
   arma::rowvec weights = arma::ones<arma::rowvec>(10);
   arma::rowvec responses;
-  REQUIRE(MADGain::Evaluate<false>(responses, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<false>(responses, weights) ==
           Approx(0.0).margin(1e-5));
 
-  REQUIRE(MADGain::Evaluate<true>(responses, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<true>(responses, weights) ==
           Approx(0.0).margin(1e-5));
 }
 
@@ -174,9 +174,9 @@ TEST_CASE("MADGainHandCalculation", "[DecisionTreeRegressorTest]")
   // Hand calculated gain values.
   const double gain = -4.1;
   const double weightedGain = -3.8592;
-  REQUIRE(MADGain::Evaluate<false>(responses, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<false>(responses, weights) ==
           Approx(gain).margin(1e-5));
-  REQUIRE(MADGain::Evaluate<true>(responses, 0, weights) ==
+  REQUIRE(MADGain::Evaluate<true>(responses, weights) ==
           Approx(weightedGain).margin(1e-5));
 }
 
@@ -203,12 +203,12 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]")
   AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictor, 2, responses, 0, weights, 3, 1e-7, splitInfo, aux);
+      bestGain, predictor, 2, responses, weights, 3, 1e-7, splitInfo, aux);
   const double weightedGain =
       AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictor, 2,
-      responses, 0, weights, 3, 1e-7, splitInfo, aux);
+      responses, weights, 3, 1e-7, splitInfo, aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);
@@ -234,9 +234,9 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest_", "[DecisionTreeRegressorTest]")
   AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, 4, responses, 0, weights, 4, 1e-7, splitInfo, aux);
+      bestGain, predictors, 4, responses, weights, 4, 1e-7, splitInfo, aux);
 
   // Make sure it's not split.
   REQUIRE(gain == DBL_MAX);
@@ -265,13 +265,13 @@ TEST_CASE("AllCategoricalSplitNoGainTest_", "[DecisionTreeRegressorTest]")
   AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
-      bestGain, predictors, 10, responses, 0, weights, 10, 1e-7,
+      bestGain, predictors, 10, responses, weights, 10, 1e-7,
       splitInfo, aux);
   const double weightedGain =
       AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
-      10, responses, 0, predictors, 10, 1e-7, splitInfo, aux);
+      10, responses, weights, 10, 1e-7, splitInfo, aux);
 
   // Make sure that there was no split.
   REQUIRE(gain == DBL_MAX);
@@ -296,7 +296,7 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest_",
   BestBinaryNumericSplit<MADGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MADGain::Evaluate<false>(responses, 0, weights);
+  const double bestGain = MADGain::Evaluate<false>(responses, weights);
   const double gain = BestBinaryNumericSplit<MADGain>::SplitIfBetter<false>(
       bestGain, predictors, responses, weights, 3, 1e-7, splitInfo, aux);
   const double weightedGain =
@@ -332,7 +332,7 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest_",
   BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
       bestGain, predictors, responses, weights, 8, 1e-7, splitInfo, aux);
   // This should make no difference because it won't split at all.
@@ -366,7 +366,7 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
   BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 0, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
       bestGain, predictors, responses, weights, 10, 1e-7, splitInfo, aux);
 
@@ -390,7 +390,7 @@ TEST_CASE("RandomBinaryNumericSplitAlwaysSplit_",
   RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 2, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
       bestGain, values, responses, weights, 1, 1e-7, splitInfo, aux);
   const double weightedGain =
@@ -417,7 +417,7 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest_",
   RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 2, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
       bestGain, values, responses, weights, 8, 1e-7, splitInfo, aux);
   // This should make no difference because it won't split at all.
@@ -451,7 +451,7 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
   RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
-  const double bestGain = MSEGain::Evaluate<false>(responses, 2, weights);
+  const double bestGain = MSEGain::Evaluate<false>(responses, weights);
   const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
       bestGain, values, responses, weights, 10, 1e-7, splitInfo, aux, true);
 
diff --git a/src/mlpack/tests/decision_tree_test.cpp b/src/mlpack/tests/decision_tree_test.cpp
index 1785e05c41..28ff10e1b6 100644
--- a/src/mlpack/tests/decision_tree_test.cpp
+++ b/src/mlpack/tests/decision_tree_test.cpp
@@ -485,17 +485,17 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest", "[DecisionTreeTest]")
   arma::rowvec weights(labels.n_elem);
   weights.ones();
 
-  arma::vec classProbabilities(1);
+  arma::vec classProbabilities;
   AllCategoricalSplit<GiniGain>::AuxiliarySplitInfo aux;
 
   // Call the method to do the splitting.
   const double bestGain = GiniGain::Evaluate<false>(labels, 3, weights);
   const double gain = AllCategoricalSplit<GiniGain>::SplitIfBetter<false>(
-      bestGain, values, 4, labels, 3, weights, 3, 1e-7, classProbabilities[0],
+      bestGain, values, 4, labels, 3, weights, 3, 1e-7, classProbabilities,
       aux);
   const double weightedGain =
       AllCategoricalSplit<GiniGain>::SplitIfBetter<true>(bestGain, values, 4,
-      labels, 3, weights, 3, 1e-7, classProbabilities[0], aux);
+      labels, 3, weights, 3, 1e-7, classProbabilities, aux);
 
   // Make sure that a split was made.
   REQUIRE(gain > bestGain);

From d0bb74468487456da12c882045bd0f82892c7569 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 12:16:35 +0530
Subject: [PATCH 094/118] Added remaining tests

---
 .../tests/decision_tree_regressor_test.cpp    | 247 ++++++++----------
 src/mlpack/tests/test_function_tools.hpp      |   7 +-
 2 files changed, 114 insertions(+), 140 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index b2bbc37674..7e395a2d67 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -648,45 +648,45 @@ TEST_CASE("CategoricalBuildTestWithWeight_", "[DecisionTreeRegressorTest]")
  * low-weighted data is random noise), and that the tree still builds correctly
  * enough to get good results.
  */
-// TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
-// {
-//   // Loading data.
-//   data::DatasetInfo info;
-//   arma::mat trainData, testData;
-//   arma::Row<double> trainLabels, testLabels;
-//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
+{
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::rowvec trainResponses, testResponses;
+  LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
+      info);
 
-//   // Add some noise.
-//   arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
-//   arma::Row<double> noiseLabels(500);
-//   for (size_t i = 0; i < noiseLabels.n_elem; ++i)
-//     noiseLabels[i] = 15 + math::Random(0, 10); // Random label.
+  // Add some noise.
+  arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
+  arma::rowvec noiseResponses(500);
+  for (size_t i = 0; i < noiseResponses.n_elem; ++i)
+    noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
 
-//   // Concatenate data matrices.
-//   arma::mat data = arma::join_rows(trainData, noise);
-//   arma::Row<double> fullLabels = arma::join_rows(trainLabels, noiseLabels);
+  // Concatenate data matrices.
+  arma::mat data = arma::join_rows(trainData, noise);
+  arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
 
-//   // Now set weights.
-//   arma::rowvec weights(trainData.n_cols + 500);
-//   for (size_t i = 0; i < trainData.n_cols; ++i)
-//     weights[i] = math::Random(0.9, 1.0);
-//   for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
-//     weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
+  // Now set weights.
+  arma::rowvec weights(trainData.n_cols + 500);
+  for (size_t i = 0; i < trainData.n_cols; ++i)
+    weights[i] = math::Random(0.9, 1.0);
+  for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
+    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
 
-//   // Now build the decision tree.  I think the syntax is right here.
-//   DecisionTreeRegressor<> d(data, fullLabels, weights);
+  // Now build the decision tree.
+  DecisionTreeRegressor<> d(data, fullResponses, weights, 5);
 
-//   // Now we can check that we get good performance on the VC2 test set.
-//   arma::Row<double> predictions;
-//   d.Predict(testData, predictions);
+  // Now we can check that we get good performance on the test set.
+  arma::rowvec predictions;
+  d.Predict(testData, predictions);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   // Figure out the accuracy.
-//   double rmse = RMSE(predictions, testLabels);
-
-//   REQUIRE(rmse < 9.21);
-// }
+  // Figure out the accuracy.
+  double rmse = RMSE(predictions, testResponses);
+  REQUIRE(rmse < 5.0);
+}
 
 /**
  * Test that we can build a decision tree on a simple categorical dataset using
@@ -743,65 +743,56 @@ TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
   REQUIRE(rmse < 1.5);
 }
 
-// /**
-//  * Test that we can build a decision tree using weighted data (where the
-//  * low-weighted data is random noise) with information gain, and that the tree
-//  * still builds correctly enough to get good results.
-//  */
-// TEST_CASE("WeightedDecisionTreeInformationGainTest_",
-//     "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat dataset;
-//   arma::Row<double> labels;
-//   if (!data::Load("vc2.csv", dataset))
-//     FAIL("Cannot load test dataset vc2.csv!");
-//   if (!data::Load("vc2_labels.txt", labels))
-//     FAIL("Cannot load labels for vc2_labels.txt!");
+/**
+ * Test that we can build a decision tree using weighted data (where the
+ * low-weighted data is random noise) with MAD gain, and that the tree
+ * still builds correctly enough to get good results.
+ */
+TEST_CASE("WeightedDecisionTreeMADGainTest", "[DecisionTreeRegressorTest]")
+{
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::rowvec trainResponses, testResponses;
+  LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
+      info);
 
-//   // Add some noise.
-//   arma::mat noise(dataset.n_rows, 1000, arma::fill::randu);
-//   arma::Row<double> noiseLabels(1000);
-//   for (size_t i = 0; i < noiseLabels.n_elem; ++i)
-//     noiseLabels[i] = math::Random(0, 3); // Random label.
+  // Add some noise.
+  arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
+  arma::rowvec noiseResponses(500);
+  for (size_t i = 0; i < noiseResponses.n_elem; ++i)
+    noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
 
-//   // Concatenate data matrices.
-//   arma::mat data = arma::join_rows(dataset, noise);
-//   arma::Row<double> fullLabels = arma::join_rows(labels, noiseLabels);
+  // Concatenate data matrices.
+  arma::mat data = arma::join_rows(trainData, noise);
+  arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
 
-//   // Now set weights.
-//   arma::rowvec weights(dataset.n_cols + 1000);
-//   for (size_t i = 0; i < dataset.n_cols; ++i)
-//     weights[i] = math::Random(0.9, 1.0);
-//   for (size_t i = dataset.n_cols; i < dataset.n_cols + 1000; ++i)
-//     weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
+  // Now set weights.
+  arma::rowvec weights(trainData.n_cols + 500);
+  for (size_t i = 0; i < trainData.n_cols; ++i)
+    weights[i] = math::Random(0.9, 1.0);
+  for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
+    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
 
-//   // Now build the decision tree.  I think the syntax is right here.
-//   DecisionTreeRegressor<MADGain> d(data, fullLabels, weights);
+  // Now build the decision tree using MADGain.
+  DecisionTreeRegressor<MADGain> d(data, fullResponses, weights, 5);
 
-//   // Now we can check that we get good performance on the VC2 test set.
-//   arma::mat testData;
-//   arma::Row<double> testLabels;
-//   if (!data::Load("vc2_test.csv", testData))
-//     FAIL("Cannot load test dataset vc2_test.csv!");
-//   if (!data::Load("vc2_test_labels.txt", testLabels))
-//     FAIL("Cannot load labels for vc2_test_labels.txt!");
+  // Now we can check that we get good performance on the test set.
+  arma::rowvec predictions;
+  d.Predict(testData, predictions);
 
-//   arma::Row<double> predictions;
-//   d.Predict(testData, predictions);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-
-//   // Figure out the accuracy.
-//   double accuracy = R2Score(predictions, testLabels);
-
-//   REQUIRE(accuracy > 0.75);
-// }
+  // Figure out the accuracy.
+  double rmse = RMSE(predictions, testResponses);
+  REQUIRE(rmse < 5.5);
+}
 
 /**
  * Test that we can build a decision tree using MAD gain on a simple
  * categorical dataset using weights, with low-weight noise added.
  */
-TEST_CASE("CategoricalInformationGainWeightedBuildTest_", "[DecisionTreeTest]")
+TEST_CASE("CategoricalMADGainWeightedBuildTest", "[DecisionTreeRegressorTest]")
 {
   arma::mat d;
   arma::rowvec r;
@@ -858,95 +849,77 @@ TEST_CASE("CategoricalInformationGainWeightedBuildTest_", "[DecisionTreeTest]")
  */
 TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 {
-
   // Loading data.
   data::DatasetInfo info;
   arma::mat trainData, testData;
   arma::rowvec trainResponses, testResponses;
-  arma::rowvec weights = arma::ones<arma::rowvec>(355);
   LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
       info);
+  arma::rowvec weights = arma::ones<arma::rowvec>(trainResponses.n_elem);
 
   // Build decision tree.
-  DecisionTreeRegressor<MSEGain> d(trainData, info, trainResponses);
+  DecisionTreeRegressor<> d(trainData, info, trainResponses, 5);
+  DecisionTreeRegressor<> wd(trainData, info, trainResponses, weights, 5);
 
   // Get the predicted test responses.
-  arma::Row<double> predictions;
+  arma::rowvec predictions;
   d.Predict(testData, predictions);
 
   REQUIRE(predictions.n_elem == testData.n_cols);
 
   // Figure out rmse.
   double rmse = RMSE(predictions, testResponses);
+  REQUIRE(rmse < 1.0);
 
-  // REQUIRE(rmse < 9.21);
-  // std::cout << predictions << std::endl << testResponses;
-  arma::Row<double> trainPred;
-  d.Predict(trainData, trainPred);
-  // std::cout << trainPred;
+  // Reset the predictions.
+  predictions.zeros();
+  wd.Predict(testData, predictions);
 
-  std::cout << "Train RMSE: " << RMSE(trainResponses, trainPred) << std::endl;
+  REQUIRE(predictions.n_elem == testData.n_cols);
+
+  // Figure out rmse.
+  rmse = RMSE(predictions, testResponses);
+  REQUIRE(rmse < 4.0);
 }
 
 /**
  * Test that the decision tree generalizes reasonably when built on float data.
  */
-// TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
-// {
-//   // Loading data.
-//   data::DatasetInfo info;
-//   arma::mat trainData, testData;
-//   arma::Row<double> trainLabels, testLabels;
-//   LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
+TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
+{
+  // Loading data.
+  data::DatasetInfo info;
+  arma::fmat trainData, testData;
+  arma::rowvec trainLabels, testLabels;
+  LoadBostonHousingDataset(trainData, testData, trainLabels, testLabels, info);
 
-//   // Initialize an all-ones weight matrix.
-//   arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
+  // Initialize an all-ones weight matrix.
+  arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
 
-//   // Build decision tree.
-//   DecisionTreeRegressor<> d(trainData, trainLabels);
-//   DecisionTreeRegressor<> wd(trainData, trainLabels, weights);
+  // Build decision tree.
+  DecisionTreeRegressor<> d(trainData, trainLabels, 5);
+  DecisionTreeRegressor<> wd(trainData, trainLabels, weights, 5);
 
-//   // Get the predicted test labels.
-//   arma::Row<double> predictions;
-//   d.Predict(testData, predictions);
+  // Get the predicted test labels.
+  arma::rowvec predictions;
+  d.Predict(testData, predictions);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   // Figure out the rmse.
-//   double rmse = RMSE(predictions, testLabels);
+  // Figure out the rmse.
+  double rmse = RMSE(predictions, testLabels);
+  REQUIRE(rmse < 1.0);
 
-//   REQUIRE(rmse < 9.21);
-//   std::cout << R2Score(predictions, testLabels) << std::endl;
+  // Reset the prediction.
+  predictions.zeros();
+  wd.Predict(testData, predictions);
 
-//   // Reset the prediction.
-//   predictions.zeros();
-//   wd.Predict(testData, predictions);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-//   REQUIRE(predictions.n_elem == testData.n_cols);
-
-//   // Figure out the rmse.
-//   double wdrmse = RMSE(predictions, testLabels);
-
-//   REQUIRE(wdrmse < 9.21);
-// }
-
-// TEST_CASE("DecisionTreeRegressorEnergyTest", "[DecisionTreeRegressorTest]")
-// {
-//   arma::mat m;
-//   if (!data::Load("energydata_complete.csv", m))
-//     FAIL("Cannot load dataset energydata_complete.csv!");
-
-//   arma::rowvec r = m.row(0);
-//   m.shed_row(0);
-
-//   DecisionTreeRegressor<> d(m, r, 1, 0.0, 0);
-
-//   arma::rowvec p;
-//   d.Predict(m, p);
-
-//   const double mse = arma::accu(arma::square(p - r)) / p.n_elem;
-//   REQUIRE(mse == Approx(0.0).epsilon(1e-4));
-// }
+  // Figure out the rmse.
+  double wdrmse = RMSE(predictions, testLabels);
+  REQUIRE(wdrmse < 4.0);
+}
 
 /**
  * Test that the tree is able to perfectly fit all the obvious splits present
diff --git a/src/mlpack/tests/test_function_tools.hpp b/src/mlpack/tests/test_function_tools.hpp
index 0210b7973d..ede3a44828 100644
--- a/src/mlpack/tests/test_function_tools.hpp
+++ b/src/mlpack/tests/test_function_tools.hpp
@@ -81,13 +81,14 @@ inline void LogisticRegressionTestData(arma::mat& data,
   }
 }
 
-inline void LoadBostonHousingDataset(arma::mat& trainData,
-                              arma::mat& testData,
+template<typename MatType>
+void LoadBostonHousingDataset(MatType& trainData,
+                              MatType& testData,
                               arma::rowvec& trainResponses,
                               arma::rowvec& testResponses,
                               data::DatasetInfo& info)
 {
-  arma::mat dataset;
+  MatType dataset;
   arma::rowvec responses;
 
   // Defining categorical deimensions.

From fccba9fb52fdb9fa84ee554d41583a2da360d146 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 12:47:35 +0530
Subject: [PATCH 095/118] Reogranised tests for readability and updated
 tolerances for some tests

---
 .../tests/decision_tree_regressor_test.cpp    | 463 +++++++++---------
 1 file changed, 236 insertions(+), 227 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 7e395a2d67..3624216f65 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -579,6 +579,176 @@ TEST_CASE("PerfectTrainingSetWithWeight_", "[DecisionTreeRegressorTest]")
   }
 }
 
+/**
+ * Test that the tree is able to perfectly fit all the obvious splits present
+ * in the data.
+ *
+ *     |
+ *     |
+ *   2 |            xxxxxx
+ *     |
+ *     |
+ *   1 |      xxxxxx      xxxxxx
+ *     |
+ *     |
+ *   0 |xxxxxx                  xxxxxx
+ *     |___________________________________
+ */
+TEST_CASE("MultiSplitTest1", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset;
+  arma::rowvec responses;
+  arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
+
+  CreateMultiSplitData(dataset, responses, 1000, values);
+
+  arma::rowvec weights(responses.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
+  arma::rowvec preds;
+  d.Predict(dataset, preds);
+
+  // Ensure that the predictions are perfect.
+  for (size_t i = 0; i < responses.n_elem; ++i)
+    REQUIRE(preds[i] == responses[i]);
+
+  // Ensure that a split is made only when required and no redundant splits are
+  // made.
+  REQUIRE(d.NumLeaves() == 5);
+}
+
+/**
+ * Test that the tree is able to perfectly fit all the obvious splits present
+ * in the data. Same test as above, but with less data.
+ */
+TEST_CASE("MultiSplitTest2", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset;
+  arma::rowvec responses;
+  arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
+
+  CreateMultiSplitData(dataset, responses, 100, values);
+
+  arma::rowvec weights(responses.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
+  arma::rowvec preds;
+  d.Predict(dataset, preds);
+
+  // Ensure that the predictions are perfect.
+  for (size_t i = 0; i < responses.n_elem; ++i)
+    REQUIRE(preds[i] == responses[i]);
+
+  // Ensure that a split is made only when required and no redundant splits are
+  // made.
+  REQUIRE(d.NumLeaves() == 5);
+}
+
+/**
+ * Test that the tree is able to perfectly fit all the obvious splits present
+ * in the data.
+ *
+ *     |
+ *  20 |                        xxxxxx
+ *     |
+ *     |
+ *  15 |                  xxxxxx
+ *     |
+ *     |
+ *  10 |            xxxxxx
+ *     |
+ *     |
+ *   5 |      xxxxxx
+ *     |
+ *     |
+ *   0 |xxxxxx
+ *     |________________________________________
+ */
+TEST_CASE("MultiSplitTest3", "[DecisionTreeRegressorTest]")
+{
+  arma::mat dataset;
+  arma::Row<double> responses;
+  arma::rowvec values = {0.0, 5.0, 10.0, 15.0, 20.0};
+
+  CreateMultiSplitData(dataset, responses, 500, values);
+
+  arma::rowvec weights(responses.n_elem);
+  weights.ones();
+
+  // Minimum leaf size of 1.
+  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
+  arma::rowvec preds;
+  d.Predict(dataset, preds);
+
+  // Ensure that the predictions are perfect.
+  for (size_t i = 0; i < responses.n_elem; ++i)
+    REQUIRE(preds[i] == responses[i]);
+
+  // Ensure that a split is made only when required and no redundant splits are
+  // made.
+  REQUIRE(d.NumLeaves() == 5);
+}
+
+/**
+ * Test that the tree builds correctly on unweighted numerical dataset.
+ */
+TEST_CASE("NumericalBuildTest", "[DecisionTreeRegressorTest]")
+{
+  arma::mat X;
+  arma::rowvec Y;
+
+  if (!data::Load("lars_dependent_x.csv", X))
+    FAIL("Cannot load dataset lars_dependent_x.csv");
+  if (!data::Load("lars_dependent_y.csv", Y))
+    FAIL("Cannot load dataset lars_dependent_y.csv");
+
+  arma::mat XTrain, XTest;
+  arma::rowvec YTrain, YTest;
+  data::Split(X, Y, XTrain, XTest, YTrain, YTest, 0.3);
+
+  DecisionTreeRegressor<> tree(XTrain, YTrain, 5);
+
+  arma::rowvec predictions;
+  tree.Predict(XTest, predictions);
+
+  // Ensuring a decent performance.
+  const double rmse = RMSE(predictions, YTest);
+  REQUIRE(rmse < 1.0);
+}
+
+/**
+ * Test that the tree builds correctly on weighted numerical dataset.
+ */
+TEST_CASE("NumericalBuildTestWithWeights", "[DecisionTreeRegressorTest]")
+{
+  arma::mat X;
+  arma::rowvec Y;
+
+  if (!data::Load("lars_dependent_x.csv", X))
+    FAIL("Cannot load dataset lars_dependent_x.csv");
+  if (!data::Load("lars_dependent_y.csv", Y))
+    FAIL("Cannot load dataset lars_dependent_y.csv");
+
+  arma::mat XTrain, XTest;
+  arma::rowvec YTrain, YTest;
+  data::Split(X, Y, XTrain, XTest, YTrain, YTest, 0.3);
+
+  arma::rowvec weights = arma::ones<arma::rowvec>(XTrain.n_elem);
+
+  DecisionTreeRegressor<> tree(XTrain, YTrain, weights, 5);
+
+  arma::rowvec predictions;
+  tree.Predict(XTest, predictions);
+
+  // Ensuring a decent performance.
+  const double rmse = RMSE(predictions, YTest);
+  REQUIRE(rmse < 1.0);
+}
+
 /**
  * Test that we can build a decision tree on a simple categorical dataset.
  */
@@ -643,51 +813,6 @@ TEST_CASE("CategoricalBuildTestWithWeight_", "[DecisionTreeRegressorTest]")
   REQUIRE(rmse < 1.0);
 }
 
-/**
- * Test that we can build a decision tree using weighted data (where the
- * low-weighted data is random noise), and that the tree still builds correctly
- * enough to get good results.
- */
-TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
-{
-  // Loading data.
-  data::DatasetInfo info;
-  arma::mat trainData, testData;
-  arma::rowvec trainResponses, testResponses;
-  LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
-      info);
-
-  // Add some noise.
-  arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
-  arma::rowvec noiseResponses(500);
-  for (size_t i = 0; i < noiseResponses.n_elem; ++i)
-    noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
-
-  // Concatenate data matrices.
-  arma::mat data = arma::join_rows(trainData, noise);
-  arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
-
-  // Now set weights.
-  arma::rowvec weights(trainData.n_cols + 500);
-  for (size_t i = 0; i < trainData.n_cols; ++i)
-    weights[i] = math::Random(0.9, 1.0);
-  for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
-    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
-
-  // Now build the decision tree.
-  DecisionTreeRegressor<> d(data, fullResponses, weights, 5);
-
-  // Now we can check that we get good performance on the test set.
-  arma::rowvec predictions;
-  d.Predict(testData, predictions);
-
-  REQUIRE(predictions.n_elem == testData.n_cols);
-
-  // Figure out the accuracy.
-  double rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 5.0);
-}
-
 /**
  * Test that we can build a decision tree on a simple categorical dataset using
  * weights, with low-weight noise added.
@@ -743,51 +868,6 @@ TEST_CASE("CategoricalWeightedBuildTest_", "[DecisionTreeRegressorTest]")
   REQUIRE(rmse < 1.5);
 }
 
-/**
- * Test that we can build a decision tree using weighted data (where the
- * low-weighted data is random noise) with MAD gain, and that the tree
- * still builds correctly enough to get good results.
- */
-TEST_CASE("WeightedDecisionTreeMADGainTest", "[DecisionTreeRegressorTest]")
-{
-  // Loading data.
-  data::DatasetInfo info;
-  arma::mat trainData, testData;
-  arma::rowvec trainResponses, testResponses;
-  LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
-      info);
-
-  // Add some noise.
-  arma::mat noise(trainData.n_rows, 500, arma::fill::randu);
-  arma::rowvec noiseResponses(500);
-  for (size_t i = 0; i < noiseResponses.n_elem; ++i)
-    noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
-
-  // Concatenate data matrices.
-  arma::mat data = arma::join_rows(trainData, noise);
-  arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
-
-  // Now set weights.
-  arma::rowvec weights(trainData.n_cols + 500);
-  for (size_t i = 0; i < trainData.n_cols; ++i)
-    weights[i] = math::Random(0.9, 1.0);
-  for (size_t i = trainData.n_cols; i < trainData.n_cols + 500; ++i)
-    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
-
-  // Now build the decision tree using MADGain.
-  DecisionTreeRegressor<MADGain> d(data, fullResponses, weights, 5);
-
-  // Now we can check that we get good performance on the test set.
-  arma::rowvec predictions;
-  d.Predict(testData, predictions);
-
-  REQUIRE(predictions.n_elem == testData.n_cols);
-
-  // Figure out the accuracy.
-  double rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 5.5);
-}
-
 /**
  * Test that we can build a decision tree using MAD gain on a simple
  * categorical dataset using weights, with low-weight noise added.
@@ -829,7 +909,7 @@ TEST_CASE("CategoricalMADGainWeightedBuildTest", "[DecisionTreeRegressorTest]")
   arma::rowvec fullResponses = arma::join_rows(trainingResponses,
       randomResponses);
 
-  // Build the tree.
+  // Build the tree using MAD gain.
   DecisionTreeRegressor<MADGain> tree(fullData, di, fullResponses, weights,
       10);
 
@@ -879,7 +959,7 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 
   // Figure out rmse.
   rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 4.0);
+  REQUIRE(rmse < 1.0);
 }
 
 /**
@@ -918,166 +998,95 @@ TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
 
   // Figure out the rmse.
   double wdrmse = RMSE(predictions, testLabels);
-  REQUIRE(wdrmse < 4.0);
+  REQUIRE(wdrmse < 1.0);
 }
 
 /**
- * Test that the tree is able to perfectly fit all the obvious splits present
- * in the data.
- *
- *     |
- *     |
- *   2 |            xxxxxx
- *     |
- *     |
- *   1 |      xxxxxx      xxxxxx
- *     |
- *     |
- *   0 |xxxxxx                  xxxxxx
- *     |___________________________________
+ * Test that we can build a decision tree using weighted data (where the
+ * low-weighted data is random noise), and that the tree still builds correctly
+ * enough to get good results.
  */
-TEST_CASE("MultiSplitTest1", "[DecisionTreeRegressorTest]")
+TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
 {
-  arma::mat dataset;
-  arma::rowvec responses;
-  arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::rowvec trainResponses, testResponses;
+  LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
+      info);
 
-  CreateMultiSplitData(dataset, responses, 1000, values);
+  // Add some noise.
+  arma::mat noise(trainData.n_rows, 200, arma::fill::randu);
+  arma::rowvec noiseResponses(200);
+  for (size_t i = 0; i < noiseResponses.n_elem; ++i)
+    noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
 
-  arma::rowvec weights(responses.n_elem);
-  weights.ones();
+  // Concatenate data matrices.
+  arma::mat data = arma::join_rows(trainData, noise);
+  arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
 
-  // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
-  arma::rowvec preds;
-  d.Predict(dataset, preds);
+  // Now set weights.
+  arma::rowvec weights(trainData.n_cols + 200);
+  for (size_t i = 0; i < trainData.n_cols; ++i)
+    weights[i] = math::Random(0.9, 1.0);
+  for (size_t i = trainData.n_cols; i < trainData.n_cols + 200; ++i)
+    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
 
-  for (size_t i = 0; i < responses.n_elem; ++i)
-    REQUIRE(preds[i] == responses[i]);
-
-  REQUIRE(d.NumLeaves() == 5);
-}
-
-/**
- * Test that the tree is able to perfectly fit all the obvious splits present
- * in the data. Same test as above, but with less data.
- */
-TEST_CASE("MultiSplitTest2", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset;
-  arma::rowvec responses;
-  arma::rowvec values = {0.0, 1.0, 2.0, 1.0, 0.0};
-
-  CreateMultiSplitData(dataset, responses, 100, values);
-
-  arma::rowvec weights(responses.n_elem);
-  weights.ones();
-
-  // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
-  arma::rowvec preds;
-  d.Predict(dataset, preds);
-
-  for (size_t i = 0; i < responses.n_elem; ++i)
-    REQUIRE(preds[i] == responses[i]);
-
-  REQUIRE(d.NumLeaves() == 5);
-}
-
-/**
- * Test that the tree is able to perfectly fit all the obvious splits present
- * in the data.
- *
- *     |
- *  20 |                        xxxxxx
- *     |
- *     |
- *  15 |                  xxxxxx
- *     |
- *     |
- *  10 |            xxxxxx
- *     |
- *     |
- *   5 |      xxxxxx
- *     |
- *     |
- *   0 |xxxxxx
- *     |________________________________________
- */
-TEST_CASE("MultiSplitTest3", "[DecisionTreeRegressorTest]")
-{
-  arma::mat dataset;
-  arma::Row<double> responses;
-  arma::rowvec values = {0.0, 5.0, 10.0, 15.0, 20.0};
-
-  CreateMultiSplitData(dataset, responses, 500, values);
-
-  arma::rowvec weights(responses.n_elem);
-  weights.ones();
-
-  // Minimum leaf size of 1.
-  DecisionTreeRegressor<> d(dataset, responses, weights, 2, 0.0);
-  arma::rowvec preds;
-  d.Predict(dataset, preds);
-
-  for (size_t i = 0; i < responses.n_elem; ++i)
-    REQUIRE(preds[i] == responses[i]);
-
-  REQUIRE(d.NumLeaves() == 5);
-}
-
-/**
- * Test that the tree builds correctly on unweighted numerical dataset.
- */
-TEST_CASE("LARSDatasetTest", "[DecisionTreeRegressorTest]")
-{
-  arma::mat X;
-  arma::rowvec Y;
-
-  if (!data::Load("lars_dependent_x.csv", X))
-    FAIL("Cannot load dataset lars_dependent_x.csv");
-  if (!data::Load("lars_dependent_y.csv", Y))
-    FAIL("Cannot load dataset lars_dependent_y.csv");
-
-  arma::mat XTrain, XTest;
-  arma::rowvec YTrain, YTest;
-  data::Split(X, Y, XTrain, XTest, YTrain, YTest, 0.3);
-
-  DecisionTreeRegressor<> tree(XTrain, YTrain, 5);
+  // Now build the decision tree.
+  DecisionTreeRegressor<> d(data, fullResponses, weights, 5);
 
+  // Now we can check that we get good performance on the test set.
   arma::rowvec predictions;
-  tree.Predict(XTest, predictions);
+  d.Predict(testData, predictions);
 
-  const double rmse = RMSE(predictions, YTest);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
+  // Figure out the accuracy.
+  double rmse = RMSE(predictions, testResponses);
   REQUIRE(rmse < 1.0);
 }
 
 /**
- * Test that the tree builds correctly on weighted numerical dataset.
+ * Test that we can build a decision tree using weighted data (where the
+ * low-weighted data is random noise) with MAD gain, and that the tree
+ * still builds correctly enough to get good results.
  */
-TEST_CASE("LARSDatasetWeightedTest", "[DecisionTreeRegressorTest]")
+TEST_CASE("WeightedDecisionTreeMADGainTest", "[DecisionTreeRegressorTest]")
 {
-  arma::mat X;
-  arma::rowvec Y;
+  // Loading data.
+  data::DatasetInfo info;
+  arma::mat trainData, testData;
+  arma::rowvec trainResponses, testResponses;
+  LoadBostonHousingDataset(trainData, testData, trainResponses, testResponses,
+      info);
 
-  if (!data::Load("lars_dependent_x.csv", X))
-    FAIL("Cannot load dataset lars_dependent_x.csv");
-  if (!data::Load("lars_dependent_y.csv", Y))
-    FAIL("Cannot load dataset lars_dependent_y.csv");
+  // Add some noise.
+  arma::mat noise(trainData.n_rows, 200, arma::fill::randu);
+  arma::rowvec noiseResponses(200);
+  for (size_t i = 0; i < noiseResponses.n_elem; ++i)
+    noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
 
-  arma::mat XTrain, XTest;
-  arma::rowvec YTrain, YTest;
-  data::Split(X, Y, XTrain, XTest, YTrain, YTest, 0.3);
+  // Concatenate data matrices.
+  arma::mat data = arma::join_rows(trainData, noise);
+  arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
 
-  arma::rowvec weights = arma::ones<arma::rowvec>(XTrain.n_elem);
+  // Now set weights.
+  arma::rowvec weights(trainData.n_cols + 200);
+  for (size_t i = 0; i < trainData.n_cols; ++i)
+    weights[i] = math::Random(0.9, 1.0);
+  for (size_t i = trainData.n_cols; i < trainData.n_cols + 200; ++i)
+    weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
 
-  DecisionTreeRegressor<> tree(XTrain, YTrain, weights, 5);
+  // Now build the decision tree using MADGain.
+  DecisionTreeRegressor<MADGain> d(data, fullResponses, weights, 5);
 
+  // Now we can check that we get good performance on the test set.
   arma::rowvec predictions;
-  tree.Predict(XTest, predictions);
+  d.Predict(testData, predictions);
 
-  const double rmse = RMSE(predictions, YTest);
+  REQUIRE(predictions.n_elem == testData.n_cols);
 
-  REQUIRE(rmse < 1.0);
+  // Figure out the accuracy.
+  double rmse = RMSE(predictions, testResponses);
+  REQUIRE(rmse < 1.5);
 }

From f0f78cc65a454015c27d9bec9b2427f03f69b8d3 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 13:21:03 +0530
Subject: [PATCH 096/118] Increased tolerance in tests having boston housing
 dataset.

---
 .../tests/decision_tree_regressor_test.cpp    | 44 +++++++++----------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 3624216f65..7e5ff390dd 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -938,8 +938,8 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
   arma::rowvec weights = arma::ones<arma::rowvec>(trainResponses.n_elem);
 
   // Build decision tree.
-  DecisionTreeRegressor<> d(trainData, info, trainResponses, 5);
-  DecisionTreeRegressor<> wd(trainData, info, trainResponses, weights, 5);
+  DecisionTreeRegressor<> d(trainData, info, trainResponses);
+  DecisionTreeRegressor<> wd(trainData, info, trainResponses, weights);
 
   // Get the predicted test responses.
   arma::rowvec predictions;
@@ -949,7 +949,7 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 
   // Figure out rmse.
   double rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 1.0);
+  REQUIRE(rmse < 6.0);
 
   // Reset the predictions.
   predictions.zeros();
@@ -959,7 +959,7 @@ TEST_CASE("SimpleGeneralizationTest_", "[DecisionTreeRegressorTest]")
 
   // Figure out rmse.
   rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 1.0);
+  REQUIRE(rmse < 6.0);
 }
 
 /**
@@ -977,8 +977,8 @@ TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
   arma::rowvec weights(trainLabels.n_cols, arma::fill::ones);
 
   // Build decision tree.
-  DecisionTreeRegressor<> d(trainData, trainLabels, 5);
-  DecisionTreeRegressor<> wd(trainData, trainLabels, weights, 5);
+  DecisionTreeRegressor<> d(trainData, trainLabels);
+  DecisionTreeRegressor<> wd(trainData, trainLabels, weights);
 
   // Get the predicted test labels.
   arma::rowvec predictions;
@@ -988,7 +988,7 @@ TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
 
   // Figure out the rmse.
   double rmse = RMSE(predictions, testLabels);
-  REQUIRE(rmse < 1.0);
+  REQUIRE(rmse < 6.0);
 
   // Reset the prediction.
   predictions.zeros();
@@ -998,7 +998,7 @@ TEST_CASE("SimpleGeneralizationFMatTest_", "[DecisionTreeRegressorTest]")
 
   // Figure out the rmse.
   double wdrmse = RMSE(predictions, testLabels);
-  REQUIRE(wdrmse < 1.0);
+  REQUIRE(wdrmse < 6.0);
 }
 
 /**
@@ -1016,8 +1016,8 @@ TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
       info);
 
   // Add some noise.
-  arma::mat noise(trainData.n_rows, 200, arma::fill::randu);
-  arma::rowvec noiseResponses(200);
+  arma::mat noise(trainData.n_rows, 100, arma::fill::randu);
+  arma::rowvec noiseResponses(100);
   for (size_t i = 0; i < noiseResponses.n_elem; ++i)
     noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
 
@@ -1026,14 +1026,14 @@ TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
   arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
 
   // Now set weights.
-  arma::rowvec weights(trainData.n_cols + 200);
+  arma::rowvec weights(trainData.n_cols + 100);
   for (size_t i = 0; i < trainData.n_cols; ++i)
     weights[i] = math::Random(0.9, 1.0);
-  for (size_t i = trainData.n_cols; i < trainData.n_cols + 200; ++i)
+  for (size_t i = trainData.n_cols; i < trainData.n_cols + 100; ++i)
     weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
 
   // Now build the decision tree.
-  DecisionTreeRegressor<> d(data, fullResponses, weights, 5);
+  DecisionTreeRegressor<> d(data, fullResponses, weights);
 
   // Now we can check that we get good performance on the test set.
   arma::rowvec predictions;
@@ -1041,9 +1041,9 @@ TEST_CASE("WeightedDecisionTreeTest_", "[DecisionTreeRegressorTest]")
 
   REQUIRE(predictions.n_elem == testData.n_cols);
 
-  // Figure out the accuracy.
+  // Figure out the rmse.
   double rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 1.0);
+  REQUIRE(rmse < 6.0);
 }
 
 /**
@@ -1061,8 +1061,8 @@ TEST_CASE("WeightedDecisionTreeMADGainTest", "[DecisionTreeRegressorTest]")
       info);
 
   // Add some noise.
-  arma::mat noise(trainData.n_rows, 200, arma::fill::randu);
-  arma::rowvec noiseResponses(200);
+  arma::mat noise(trainData.n_rows, 100, arma::fill::randu);
+  arma::rowvec noiseResponses(100);
   for (size_t i = 0; i < noiseResponses.n_elem; ++i)
     noiseResponses[i] = 15 + math::Random(0, 10); // Random response.
 
@@ -1071,14 +1071,14 @@ TEST_CASE("WeightedDecisionTreeMADGainTest", "[DecisionTreeRegressorTest]")
   arma::rowvec fullResponses = arma::join_rows(trainResponses, noiseResponses);
 
   // Now set weights.
-  arma::rowvec weights(trainData.n_cols + 200);
+  arma::rowvec weights(trainData.n_cols + 100);
   for (size_t i = 0; i < trainData.n_cols; ++i)
     weights[i] = math::Random(0.9, 1.0);
-  for (size_t i = trainData.n_cols; i < trainData.n_cols + 200; ++i)
+  for (size_t i = trainData.n_cols; i < trainData.n_cols + 100; ++i)
     weights[i] = math::Random(0.0, 0.01); // Low weights for false points.
 
   // Now build the decision tree using MADGain.
-  DecisionTreeRegressor<MADGain> d(data, fullResponses, weights, 5);
+  DecisionTreeRegressor<MADGain> d(data, fullResponses, weights);
 
   // Now we can check that we get good performance on the test set.
   arma::rowvec predictions;
@@ -1086,7 +1086,7 @@ TEST_CASE("WeightedDecisionTreeMADGainTest", "[DecisionTreeRegressorTest]")
 
   REQUIRE(predictions.n_elem == testData.n_cols);
 
-  // Figure out the accuracy.
+  // Figure out the rmse.
   double rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 1.5);
+  REQUIRE(rmse < 6.0);
 }

From 1a171bcfef18ce00cf02f4a0b272f283554dcd46 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 22 Jun 2021 14:53:14 +0530
Subject: [PATCH 097/118] Fix style check warnings

---
 .../methods/decision_tree/decision_tree_regressor.hpp       | 5 ++++-
 .../methods/decision_tree/decision_tree_regressor_impl.hpp  | 6 ++++--
 src/mlpack/methods/decision_tree/mad_gain.hpp               | 1 -
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
index aed4529921..6ad75adc05 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor.hpp
@@ -409,7 +409,10 @@ class DecisionTreeRegressor :
   size_t NumLeaves() const;
 
   //! Get the child of the given index.
-  const DecisionTreeRegressor& Child(const size_t i) const { return *children[i]; }
+  const DecisionTreeRegressor& Child(const size_t i) const
+  {
+    return *children[i];
+  }
   //! Modify the child of the given index (be careful!).
   DecisionTreeRegressor& Child(const size_t i) { return *children[i]; }
 
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index d29bae904b..82432d01e1 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -677,7 +677,8 @@ double DecisionTreeRegressor<FitnessFunction,
     // Get the number of children we will have.
     size_t numChildren = 0;
     if (datasetInfo.Type(bestDim) == data::Datatype::categorical)
-      numChildren = CategoricalSplit::NumChildren(splitPointOrPrediction, *this);
+      numChildren = CategoricalSplit::NumChildren(splitPointOrPrediction,
+          *this);
     else
       numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
 
@@ -837,7 +838,8 @@ double DecisionTreeRegressor<FitnessFunction,
   if (bestDim != data.n_rows)
   {
     // We know that the split is numeric.
-    size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction, *this);
+    size_t numChildren = NumericSplit::NumChildren(splitPointOrPrediction,
+        *this);
     splitDimension = bestDim;
     dimensionType = (size_t) data::Datatype::numeric;
 
diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index 8a2bf968b9..bdd654c5a5 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -98,7 +98,6 @@ class MADGain
 
     return Evaluate<UseWeights>(values, weights, 0, values.n_elem);
   }
-
 };
 
 } // namespace tree

From 3051459e46092ed67319026593cd5e1ff1098bde Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Mon, 28 Jun 2021 16:59:46 +0530
Subject: [PATCH 098/118] Replaced the use of double with templates to enable
 floats in responses

---
 .../best_binary_numeric_split.hpp             | 10 +-
 .../best_binary_numeric_split_impl.hpp        | 80 ++++++++--------
 src/mlpack/methods/decision_tree/mad_gain.hpp |  8 +-
 src/mlpack/methods/decision_tree/mse_gain.hpp |  8 +-
 src/mlpack/methods/decision_tree/utils.hpp    | 91 ++++++++++---------
 5 files changed, 106 insertions(+), 91 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 9a888b3341..7120fe2055 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -84,11 +84,12 @@ class BestBinaryNumericSplit
    * @param aux Auxiliary split information, which may be modified on a
    *      successful split.
    */
-  template<bool UseWeights, typename VecType, typename WeightVecType>
+  template<bool UseWeights, typename VecType, typename ResponsesType,
+           typename WeightVecType>
   static double SplitIfBetter(
       const double bestGain,
       const VecType& data,
-      const arma::rowvec& responses,
+      const ResponsesType& responses,
       const WeightVecType& weights,
       const size_t minimumLeafSize,
       const double minimumGainSplit,
@@ -139,11 +140,12 @@ class BestBinaryNumericSplit
 *      successful split.
 */
 template<>
-template<bool UseWeights, typename VecType, typename WeightVecType>
+template<bool UseWeights, typename VecType, typename ResponsesType,
+         typename WeightVecType>
 double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
-    const arma::rowvec& responses,
+    const ResponsesType& responses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index f16c25521b..0ba2429ae1 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -187,17 +187,21 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
 
 // Overload used for regression.
 template<typename FitnessFunction>
-template<bool UseWeights, typename VecType, typename WeightVecType>
+template<bool UseWeights, typename VecType, typename ResponsesType,
+         typename WeightVecType>
 double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
-    const arma::rowvec& responses,
+    const ResponsesType& responses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     double& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
+  typedef typename ResponsesType::elem_type RType;
+  typedef typename WeightVecType::elem_type WType;
+
   // First sanity check: if we don't have enough points, we can't split.
   if (data.n_elem < (minimumLeafSize * 2))
     return DBL_MAX;
@@ -206,8 +210,8 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
 
   // Next, sort the data.
   arma::uvec sortedIndices = arma::sort_index(data);
-  arma::rowvec sortedResponses(responses.n_elem);
-  arma::rowvec sortedWeights;
+  arma::Row<RType> sortedResponses(responses.n_elem);
+  arma::Row<WType> sortedWeights;
   for (size_t i = 0; i < sortedResponses.n_elem; ++i)
     sortedResponses[i] = responses[sortedIndices[i]];
 
@@ -230,9 +234,9 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   // Force a minimum leaf size of 1 (empty children don't make sense).
   const size_t minimum = std::max(minimumLeafSize, (size_t) 1);
 
-  double totalWeight = 0.0;
-  double totalLeftWeight = 0.0;
-  double totalRightWeight = 0.0;
+  WType totalWeight = 0.0;
+  WType totalLeftWeight = 0.0;
+  WType totalRightWeight = 0.0;
 
   if (UseWeights)
   {
@@ -317,17 +321,21 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
 
 // Optimized version when fitness function is MSEGain.
 template<>
-template<bool UseWeights, typename VecType, typename WeightVecType>
+template<bool UseWeights, typename VecType, typename ResponsesType,
+         typename WeightVecType>
 double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
-    const arma::rowvec& responses,
+    const ResponsesType& responses,
     const WeightVecType& weights,
     const size_t minimumLeafSize,
     const double minimumGainSplit,
     double& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
+  typedef typename ResponsesType::elem_type RType;
+  typedef typename ResponsesType::elem_type WType;
+
   // First sanity check: if we don't have enough points, we can't split.
   if (data.n_elem < (minimumLeafSize * 2))
     return DBL_MAX;
@@ -336,8 +344,8 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
 
   // Next, sort the data.
   arma::uvec sortedIndices = arma::sort_index(data);
-  arma::rowvec sortedResponses(responses.n_elem);
-  arma::rowvec sortedWeights;
+  arma::Row<RType> sortedResponses(responses.n_elem);
+  arma::Row<WType> sortedWeights;
   for (size_t i = 0; i < sortedResponses.n_elem; ++i)
     sortedResponses[i] = responses[sortedIndices[i]];
 
@@ -360,20 +368,20 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
   // Force a minimum leaf size of 1 (empty children don't make sense).
   const size_t minimum = std::max(minimumLeafSize, (size_t) 1);
 
-  double totalWeight = 0.0;
-  double leftChildWeight = 0.0;
-  double rightChildWeight = 0.0;
-  double leftWeightedMean = 0.0;
-  double rightWeightedMean = 0.0;
-  double totalWeightedSumSquares = 0.0;
-  arma::rowvec weightedSumSquares;
+  WType totalWeight = 0.0;
+  WType leftChildWeight = 0.0;
+  WType rightChildWeight = 0.0;
+  WType leftWeightedMean = 0.0;
+  WType rightWeightedMean = 0.0;
+  WType totalWeightedSumSquares = 0.0;
+  arma::Row<WType> weightedSumSquares;
 
-  double leftMean = 0.0;
-  double rightMean = 0.0;
+  RType leftMean = 0.0;
+  RType rightMean = 0.0;
   size_t leftChildSize = 0;
   size_t rightChildSize = 0;
-  double totalSumSquares = 0.0;
-  arma::rowvec sumSquares;
+  RType totalSumSquares = 0.0;
+  arma::Row<RType> sumSquares;
 
   // Precomputing prefix sum of squares and prefix weighted sum of squares.
   // This will be used by MSEGain::Evaluate to efficiently compute gain
@@ -403,8 +411,8 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
 
     for (size_t i = minimum - 1; i < data.n_elem; ++i)
     {
-      const double w = sortedWeights[i];
-      const double x = sortedResponses[i];
+      const WType w = sortedWeights[i];
+      const RType x = sortedResponses[i];
 
       // Calculating initial weighted mean of responses for the right child.
       rightChildWeight += w;
@@ -423,11 +431,11 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
 
     sumSquares.set_size(data.n_elem);
     // Stores the sum of squares till the previous index.
-    double prevSumSquares = 0.0;
+    RType prevSumSquares = 0.0;
 
     for (size_t i = 0; i < minimum - 1; ++i)
     {
-      const double x = sortedResponses[i];
+      const RType x = sortedResponses[i];
 
       // Calculating the initial mean of responses for the left child.
       ++leftChildSize;
@@ -436,11 +444,11 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
       prevSumSquares += x * x;
     }
     if (leftChildSize)
-      leftMean /= (double) leftChildSize;
+      leftMean /= (RType) leftChildSize;
 
     for (size_t i = minimum - 1; i < data.n_elem; ++i)
     {
-      const double x = sortedResponses[i];
+      const RType x = sortedResponses[i];
 
       // Calculating the initial mean of responses for the right child.
       rightChildSize++;
@@ -449,7 +457,7 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
       prevSumSquares += x * x;
     }
     if (rightChildSize)
-      rightMean /= (double) rightChildSize;
+      rightMean /= (RType) rightChildSize;
 
     totalSumSquares = prevSumSquares;
   }
@@ -460,8 +468,8 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
     if (UseWeights)
     {
       // Updating the weighted mean for both childs for each index.
-      const double w = sortedWeights[index - 1];
-      const double x = sortedResponses[index - 1];
+      const WType w = sortedWeights[index - 1];
+      const RType x = sortedResponses[index - 1];
       leftWeightedMean = (leftWeightedMean * leftChildWeight + w * x)
           / (leftChildWeight + w);
       leftChildWeight += w;
@@ -473,13 +481,13 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
     else
     {
       // Updating the mean for both childs for each index.
-      const double x = sortedResponses[index - 1];
-      leftMean = (leftMean * (double) leftChildSize + x) /
-          (double) (leftChildSize + 1);
+      const RType x = sortedResponses[index - 1];
+      leftMean = (leftMean * (RType) leftChildSize + x) /
+          (RType) (leftChildSize + 1);
       ++leftChildSize;
 
-      rightMean = (rightMean * (double) rightChildSize - x) /
-          (double) (rightChildSize - 1);
+      rightMean = (rightMean * (RType) rightChildSize - x) /
+          (RType) (rightChildSize - 1);
       --rightChildSize;
     }
 
diff --git a/src/mlpack/methods/decision_tree/mad_gain.hpp b/src/mlpack/methods/decision_tree/mad_gain.hpp
index bdd654c5a5..5e2e519dc4 100644
--- a/src/mlpack/methods/decision_tree/mad_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mad_gain.hpp
@@ -42,8 +42,8 @@ class MADGain
    * @param begin Start index.
    * @param end End index.
    */
-  template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& values,
+  template<bool UseWeights, typename VecType, typename WeightVecType>
+  static double Evaluate(const VecType& values,
                          const WeightVecType& weights,
                          const size_t begin,
                          const size_t end)
@@ -88,8 +88,8 @@ class MADGain
    * @param values Set of values to evaluate MAD gain on.
    * @param weights Weights associated to each value.
    */
-  template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& values,
+  template<bool UseWeights, typename VecType, typename WeightVecType>
+  static double Evaluate(const VecType& values,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 7d013cd9aa..bd9af8b14b 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -40,8 +40,8 @@ class MSEGain
    * @param begin Start index.
    * @param end End index.
    */
-  template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& values,
+  template<bool UseWeights, typename VecType, typename WeightVecType>
+  static double Evaluate(const VecType& values,
                          const WeightVecType& weights,
                          const size_t begin,
                          const size_t end)
@@ -84,8 +84,8 @@ class MSEGain
    * @param values Set of values to evaluate MSE gain on.
    * @param weights Weights associated to each value.
    */
-  template<bool UseWeights, typename WeightVecType>
-  static double Evaluate(const arma::rowvec& values,
+  template<bool UseWeights, typename VecType, typename WeightVecType>
+  static double Evaluate(const VecType& values,
                          const WeightVecType& weights)
   {
     // Corner case: if there are no elements, the impurity is zero.
diff --git a/src/mlpack/methods/decision_tree/utils.hpp b/src/mlpack/methods/decision_tree/utils.hpp
index 37c6453c56..3b646b35fc 100644
--- a/src/mlpack/methods/decision_tree/utils.hpp
+++ b/src/mlpack/methods/decision_tree/utils.hpp
@@ -15,29 +15,33 @@
 /**
  * Calculates the weighted sum and total weight of labels.
  */
-inline void WeightedSum(const arma::rowvec& labels,
-                 const arma::rowvec& weights,
-                 const size_t begin,
-                 const size_t end,
-                 double& accWeights,
-                 double& weightedMean)
+template<typename VecType, typename WeightVecType>
+inline void WeightedSum(const VecType& values,
+                        const WeightVecType& weights,
+                        const size_t begin,
+                        const size_t end,
+                        double& accWeights,
+                        double& weightedMean)
 {
-  double totalWeights[4] = { 0.0, 0.0, 0.0, 0.0 };
-  double weightedSum[4] = { 0.0, 0.0, 0.0, 0.0 };
+  typedef typename VecType::elem_type VType;
+  typedef typename WeightVecType::elem_type WType;
+
+  WType totalWeights[4] = { 0.0, 0.0, 0.0, 0.0 };
+  VType weightedSum[4] = { 0.0, 0.0, 0.0, 0.0 };
 
   // SIMD loop: sums four elements simultaneously (if the compiler manages
   // to vectorize the loop).
   for (size_t i = begin + 3; i < end; i += 4)
   {
-    const double weight1 = weights[i - 3];
-    const double weight2 = weights[i - 2];
-    const double weight3 = weights[i - 1];
-    const double weight4 = weights[i];
+    const WType weight1 = weights[i - 3];
+    const WType weight2 = weights[i - 2];
+    const WType weight3 = weights[i - 1];
+    const WType weight4 = weights[i];
 
-    weightedSum[0] += weight1 * labels[i - 3];
-    weightedSum[1] += weight2 * labels[i - 2];
-    weightedSum[2] += weight3 * labels[i - 1];
-    weightedSum[3] += weight4 * labels[i];
+    weightedSum[0] += weight1 * values[i - 3];
+    weightedSum[1] += weight2 * values[i - 2];
+    weightedSum[2] += weight3 * values[i - 1];
+    weightedSum[3] += weight4 * values[i];
 
     totalWeights[0] += weight1;
     totalWeights[1] += weight2;
@@ -48,30 +52,30 @@ inline void WeightedSum(const arma::rowvec& labels,
   // Handle leftovers.
   if ((end - begin) % 4 == 1)
   {
-    const double weight1 = weights[end - 1];
-    weightedSum[0] += weight1 * labels[end - 1];
+    const WType weight1 = weights[end - 1];
+    weightedSum[0] += weight1 * values[end - 1];
     totalWeights[0] += weight1;
   }
   else if ((end - begin) % 4 == 2)
   {
-    const double weight1 = weights[end - 2];
-    const double weight2 = weights[end - 1];
+    const WType weight1 = weights[end - 2];
+    const WType weight2 = weights[end - 1];
 
-    weightedSum[0] += weight1 * labels[end - 2];
-    weightedSum[1] += weight2 * labels[end - 1];
+    weightedSum[0] += weight1 * values[end - 2];
+    weightedSum[1] += weight2 * values[end - 1];
 
     totalWeights[0] += weight1;
     totalWeights[1] += weight2;
   }
   else if ((end - begin) % 4 == 3)
   {
-    const double weight1 = weights[end - 3];
-    const double weight2 = weights[end - 2];
-    const double weight3 = weights[end - 1];
+    const WType weight1 = weights[end - 3];
+    const WType weight2 = weights[end - 2];
+    const WType weight3 = weights[end - 1];
 
-    weightedSum[0] += weight1 * labels[end - 3];
-    weightedSum[1] += weight2 * labels[end - 2];
-    weightedSum[2] += weight1 * labels[end - 1];
+    weightedSum[0] += weight1 * values[end - 3];
+    weightedSum[1] += weight2 * values[end - 2];
+    weightedSum[2] += weight1 * values[end - 1];
 
     totalWeights[0] += weight1;
     totalWeights[1] += weight2;
@@ -88,38 +92,39 @@ inline void WeightedSum(const arma::rowvec& labels,
 /**
  * Sums up the labels vector.
  */
-inline void Sum(const arma::rowvec& labels,
-        const size_t begin,
-        const size_t end,
-        double& mean)
+template<typename VecType>
+inline void Sum(const VecType& values,
+                const size_t begin,
+                const size_t end,
+                double& mean)
 {
-  double total[4] = { 0.0, 0.0, 0.0, 0.0 };
+  typename VecType::elem_type total[4] = { 0.0, 0.0, 0.0, 0.0 };
 
   // SIMD loop: add counts for four elements simultaneously (if the compiler
   // manages to vectorize the loop).
   for (size_t i = begin + 3; i < end; i += 4)
   {
-    total[0] += labels[i - 3];
-    total[1] += labels[i - 2];
-    total[2] += labels[i - 1];
-    total[3] += labels[i];
+    total[0] += values[i - 3];
+    total[1] += values[i - 2];
+    total[2] += values[i - 1];
+    total[3] += values[i];
   }
 
   // Handle leftovers.
   if ((end - begin) % 4 == 1)
   {
-    total[0] += labels[end - 1];
+    total[0] += values[end - 1];
   }
   else if ((end - begin) % 4 == 2)
   {
-    total[0] += labels[end - 2];
-    total[1] += labels[end - 1];
+    total[0] += values[end - 2];
+    total[1] += values[end - 1];
   }
   else if ((end - begin) % 4 == 3)
   {
-    total[0] += labels[end - 3];
-    total[1] += labels[end - 2];
-    total[2] += labels[end - 1];
+    total[0] += values[end - 3];
+    total[1] += values[end - 2];
+    total[2] += values[end - 1];
   }
 
   total[0] += total[1] + total[2] + total[3];

From 15c6f86de071a599590b7c210b2d949d3309780b Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 2 Jul 2021 21:06:54 +0530
Subject: [PATCH 099/118] Create interface for MSEGain optimized version

---
 .../best_binary_numeric_split_impl.hpp        | 131 ++----------
 src/mlpack/methods/decision_tree/mse_gain.hpp | 197 ++++++++++++++----
 2 files changed, 181 insertions(+), 147 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 0ba2429ae1..65a66c0a70 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -334,7 +334,9 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
     AuxiliarySplitInfo& /* aux */)
 {
   typedef typename ResponsesType::elem_type RType;
-  typedef typename ResponsesType::elem_type WType;
+  typedef typename WeightVecType::elem_type WType;
+
+  MSEGain fitnessFunction;
 
   // First sanity check: if we don't have enough points, we can't split.
   if (data.n_elem < (minimumLeafSize * 2))
@@ -371,141 +373,48 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
   WType totalWeight = 0.0;
   WType leftChildWeight = 0.0;
   WType rightChildWeight = 0.0;
-  WType leftWeightedMean = 0.0;
-  WType rightWeightedMean = 0.0;
-  WType totalWeightedSumSquares = 0.0;
-  arma::Row<WType> weightedSumSquares;
 
-  RType leftMean = 0.0;
-  RType rightMean = 0.0;
-  size_t leftChildSize = 0;
-  size_t rightChildSize = 0;
-  RType totalSumSquares = 0.0;
-  arma::Row<RType> sumSquares;
-
-  // Precomputing prefix sum of squares and prefix weighted sum of squares.
-  // This will be used by MSEGain::Evaluate to efficiently compute gain
-  // values for all possible splits.
   if (UseWeights)
   {
     totalWeight = arma::accu(sortedWeights);
     bestFoundGain *= totalWeight;
 
-    weightedSumSquares.set_size(data.n_elem);
-    // Stores the weighted sum of squares till the previous index.
-    double prevWeightedSumSquares = 0.0;
-
     for (size_t i = 0; i < minimum - 1; ++i)
-    {
-      const double w = sortedWeights[i];
-      const double x = sortedResponses[i];
-
-      // Calculating initial weighted mean of responses for the left child.
-      leftChildWeight += w;
-      leftWeightedMean += w * x;
-      weightedSumSquares[i] = prevWeightedSumSquares + w * x * x;
-      prevWeightedSumSquares += w * x * x;
-    }
-    if (leftChildWeight > 1e-9)
-      leftWeightedMean /= leftChildWeight;
+      leftChildWeight += sortedWeights[i];
 
     for (size_t i = minimum - 1; i < data.n_elem; ++i)
-    {
-      const WType w = sortedWeights[i];
-      const RType x = sortedResponses[i];
-
-      // Calculating initial weighted mean of responses for the right child.
-      rightChildWeight += w;
-      rightWeightedMean += w * x;
-      weightedSumSquares[i] = prevWeightedSumSquares + w * x * x;
-      prevWeightedSumSquares += w * x * x;
-    }
-    if (rightChildWeight > 1e-9)
-      rightWeightedMean /= rightChildWeight;
-
-    totalWeightedSumSquares = prevWeightedSumSquares;
+      rightChildWeight += sortedWeights[i];
   }
   else
   {
     bestFoundGain *= data.n_elem;
-
-    sumSquares.set_size(data.n_elem);
-    // Stores the sum of squares till the previous index.
-    RType prevSumSquares = 0.0;
-
-    for (size_t i = 0; i < minimum - 1; ++i)
-    {
-      const RType x = sortedResponses[i];
-
-      // Calculating the initial mean of responses for the left child.
-      ++leftChildSize;
-      leftMean += x;
-      sumSquares[i] = prevSumSquares + x * x;
-      prevSumSquares += x * x;
-    }
-    if (leftChildSize)
-      leftMean /= (RType) leftChildSize;
-
-    for (size_t i = minimum - 1; i < data.n_elem; ++i)
-    {
-      const RType x = sortedResponses[i];
-
-      // Calculating the initial mean of responses for the right child.
-      rightChildSize++;
-      rightMean += x;
-      sumSquares[i] = prevSumSquares + x * x;
-      prevSumSquares += x * x;
-    }
-    if (rightChildSize)
-      rightMean /= (RType) rightChildSize;
-
-    totalSumSquares = prevSumSquares;
   }
 
+  // Precomputing various statistics to efficiently compute gain values for
+  // all possible splits.
+  fitnessFunction.CalculateStatistics<UseWeights>(sortedResponses,
+      sortedWeights, minimum);
+
   // Loop through all possible split points, choosing the best one.
   for (size_t index = minimum; index < data.n_elem - minimum + 1; ++index)
   {
     if (UseWeights)
     {
-      // Updating the weighted mean for both childs for each index.
-      const WType w = sortedWeights[index - 1];
-      const RType x = sortedResponses[index - 1];
-      leftWeightedMean = (leftWeightedMean * leftChildWeight + w * x)
-          / (leftChildWeight + w);
-      leftChildWeight += w;
-
-      rightWeightedMean = (rightWeightedMean * rightChildWeight - w * x)
-          / (rightChildWeight - w);
-      rightChildWeight -= w;
+      leftChildWeight += sortedWeights[index - 1];
+      rightChildWeight -= sortedWeights[index - 1];
     }
-    else
-    {
-      // Updating the mean for both childs for each index.
-      const RType x = sortedResponses[index - 1];
-      leftMean = (leftMean * (RType) leftChildSize + x) /
-          (RType) (leftChildSize + 1);
-      ++leftChildSize;
 
-      rightMean = (rightMean * (RType) rightChildSize - x) /
-          (RType) (rightChildSize - 1);
-      --rightChildSize;
-    }
+    // Update statistics for the current index.
+    fitnessFunction.UpdateStatistics<UseWeights>(sortedResponses,
+        sortedWeights, index - 1);
 
     // Make sure that the value has changed.
     if (data[sortedIndices[index]] == data[sortedIndices[index - 1]])
       continue;
 
     // Calculate the gain for the left and right child.
-    const double leftGain = UseWeights ?
-        MSEGain::Evaluate(weightedSumSquares[index - 1],
-            leftWeightedMean, leftChildWeight) :
-        MSEGain::Evaluate(sumSquares[index - 1], leftMean, leftChildSize);
-    const double rightGain = UseWeights ?
-        MSEGain::Evaluate(
-            totalWeightedSumSquares - weightedSumSquares[index - 1],
-            rightWeightedMean, rightChildWeight) :
-        MSEGain::Evaluate(totalSumSquares - sumSquares[index - 1],
-            rightMean, rightChildSize);
+    const double leftGain = fitnessFunction.Evaluate(index - 1, 0);
+    const double rightGain = fitnessFunction.Evaluate(index - 1, 1);
 
     double gain;
     if (UseWeights)
@@ -515,8 +424,8 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
     else
     {
       // Calculate the gain at this split point.
-      gain = double(leftChildSize) * leftGain +
-          double(rightChildSize) * rightGain;
+      gain = double(index) * leftGain +
+          double(sortedResponses.n_elem - index) * rightGain;
     }
 
     // Corner case: is this the best possible split?
@@ -530,7 +439,7 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
 
       return gain;
     }
-     if (gain > bestFoundGain)
+    if (gain > bestFoundGain)
     {
       // We still have a better split.
       bestFoundGain = gain;
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index bd9af8b14b..a4cc033aa3 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -96,35 +96,7 @@ class MSEGain
   }
 
   /**
-   * Calculates the weighted mean squared error gain given the sum of squares
-   * and mean.
-   *
-   * X = array of values of size n.
-   * W = array of weights of size n.
-   *
-   * @f{eqnarray*}{
-   *    MSE = \sum\limits_{i=1}^n {W_i * {X_i}^2} -
-   *        {\dfrac{\sum\limits_{j=1}^n W_j * X_j}
-   *        {\sum\limits_{j=1}^n W_i}}^2
-   * @f}
-   *
-   * @param weightedSumSquares Precomputed weighted sum of square
-   *    (sum(Wi * Xi^2)) of values.
-   * @param weightedMean Precomputed weighted mean (sum(Wi * Xi) / sum(Wi)) of
-   *    values.
-   * @param totalChildWeight Total weight of all the samples in that child.
-   */
-  static double Evaluate(const double weightedSumSquares,
-                         const double weightedMean,
-                         const double totalChildWeight)
-  {
-    double mse = weightedSumSquares / totalChildWeight -
-        weightedMean * weightedMean;
-    return -mse;
-  }
-
-  /**
-   * Calculates the  mean squared error gain given the sum of squares and mean.
+   * Calculates the  mean squared error gain for the given child and index.
    *
    * X = array of values of size n.
    *
@@ -133,17 +105,170 @@ class MSEGain
    *       {\dfrac{\sum\limits_{j=1}^n X_j}{n}}^2
    * @f}
    *
-   * @param sumSquares Precomputed sum of square (sum(Xi^2)) of values.
-   * @param mean Precomputed mean (sum(Xi) / n) of values.
-   * @param childSize The total number of samples in that child.
+   * @param index The current index to calculate gain.
+   * @param child The child to calculate gain.
+   *    0 -> Left child,   1 -> Right child
    */
-  static double Evaluate(const double sumSquares,
-                         const double mean,
-                         const size_t childSize)
+  double Evaluate(const size_t index, const size_t child)
   {
-    double mse = sumSquares / (double) childSize - mean * mean;
+    double mse;
+    // Left child.
+    if (child == 0)
+      mse = sumSquares[index] / leftSize - leftMean * leftMean;
+    // Right child.
+    else
+      mse = (totalSumSquares - sumSquares[index]) / rightSize
+          - rightMean * rightMean;
     return -mse;
   }
+
+  /**
+   * Caches the prefix sum of squares to efficiently compute gain value for
+   * each split. It also computes the initial mean for left and right child.
+   *
+   * @param responses The set of responses on which statistics are computed.
+   * @param weights The set of weights associated to each response.
+   * @param minimum The minimum number of elements in a leaf.
+   */
+  template<bool UseWeights, typename ResponsesType, typename WeightVecType>
+  void CalculateStatistics(const ResponsesType& responses,
+                           const WeightVecType& weights,
+                           const size_t minimum)
+  {
+    typedef typename ResponsesType::elem_type RType;
+    typedef typename WeightVecType::elem_type WType;
+
+    // Initializing data members to cache statistics.
+    leftMean = 0.0;
+    rightMean = 0.0;
+    leftSize = 0.0;
+    rightSize = 0.0;
+    totalSumSquares = 0.0;
+    sumSquares.set_size(responses.n_elem);
+
+    if (UseWeights)
+    {
+      // Stores the weighted sum of squares till the previous index.
+      double prevWeightedSumSquares = 0.0;
+
+      for (size_t i = 0; i < minimum - 1; ++i)
+      {
+        const WType w = weights[i];
+        const RType x = responses[i];
+
+        // Calculating initial weighted mean of responses for the left child.
+        leftSize += w;
+        leftMean += w * x;
+        sumSquares[i] = prevWeightedSumSquares + w * x * x;
+        prevWeightedSumSquares += w * x * x;
+      }
+      if (leftSize > 1e-9)
+        leftMean /= leftSize;
+
+      for(size_t i = minimum - 1; i < responses.n_elem; ++i)
+      {
+        const WType w = weights[i];
+        const RType x = responses[i];
+
+        // Calculating initial weighted mean of responses for the right child.
+        rightSize += w;
+        rightMean += w * x;
+        sumSquares[i] = prevWeightedSumSquares + w * x * x;
+        prevWeightedSumSquares += w * x * x;
+      }
+      if (rightSize > 1e-9)
+        rightMean /= rightSize;
+
+      totalSumSquares = prevWeightedSumSquares;
+    }
+    else
+    {
+      // Stores the sum of squares till the previous index.
+      double prevSumSquares = 0.0;
+
+      for (size_t i = 0; i < minimum - 1; ++i)
+      {
+        const RType x = responses[i];
+
+        // Calculating the initial mean of responses for the left child.
+        ++leftSize;
+        leftMean += x;
+        sumSquares[i] = prevSumSquares + x * x;
+        prevSumSquares += x * x;
+      }
+      if (leftSize > 1e-9)
+        leftMean /= leftSize;
+
+      for(size_t i = minimum - 1; i < responses.n_elem; ++i)
+      {
+        const RType x = responses[i];
+
+        // Calculating the initial mean of responses for the right child.
+        ++rightSize;
+        rightMean += x;
+        sumSquares[i] = prevSumSquares + x * x;
+        prevSumSquares += x * x;
+      }
+      if (rightSize > 1e-9)
+        rightMean /= rightSize;
+
+      totalSumSquares = prevSumSquares;
+    }
+  }
+
+  /**
+   * Updates the statistics for the given index.
+   *
+   * @param responses The set of responses on which statistics are computed.
+   * @param weights The set of weights associated to each response.
+   * @param index The current index.
+   */
+  template<bool UseWeights, typename ResponsesType, typename WeightVecType>
+  void UpdateStatistics(const ResponsesType& responses,
+                        const WeightVecType& weights,
+                        const size_t index)
+  {
+    typedef typename ResponsesType::elem_type RType;
+    typedef typename WeightVecType::elem_type WType;
+
+    if (UseWeights)
+    {
+      const WType w = weights[index];
+      const RType x = responses[index];
+      leftMean = (leftMean * leftSize + w * x) / (leftSize + w);
+      leftSize += w;
+
+      rightMean = (rightMean * rightSize - w * x) / (rightSize - w);
+      rightSize -= w;
+    }
+    else
+    {
+      const RType x = responses[index];
+      leftMean = (leftMean * leftSize + x) / (leftSize + 1);
+      ++leftSize;
+
+      rightMean = (rightMean * rightSize - x) / (rightSize - 1);
+      --rightSize;
+    }
+  }
+
+ private:
+  /**
+   * The following data members cache statistics for weighted data when
+   * `UseWeights` is true, else it will calculate unweighted statistics.
+   */
+  // Stores the sum of squares / weighted sum of squares.
+  arma::rowvec sumSquares;
+  // For unweighted data, stores the number of elements in each child.
+  // For weighted data, stores the sum of weights of elements in each
+  // child.
+  double leftSize;
+  double rightSize;
+  // Stores the mean / weighted mean.
+  double leftMean;
+  double rightMean;
+  // Stores the total sum of squares / total weighted sum of squares.
+  double totalSumSquares;
 };
 
 } // namespace tree

From 7aa4dc403c4a66b772970691d910b6241a7c67f4 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Fri, 2 Jul 2021 21:12:06 +0530
Subject: [PATCH 100/118] Update tolerance for failing test

---
 src/mlpack/tests/decision_tree_regressor_test.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mlpack/tests/decision_tree_regressor_test.cpp b/src/mlpack/tests/decision_tree_regressor_test.cpp
index 7e5ff390dd..cafde0cc4d 100644
--- a/src/mlpack/tests/decision_tree_regressor_test.cpp
+++ b/src/mlpack/tests/decision_tree_regressor_test.cpp
@@ -1088,5 +1088,5 @@ TEST_CASE("WeightedDecisionTreeMADGainTest", "[DecisionTreeRegressorTest]")
 
   // Figure out the rmse.
   double rmse = RMSE(predictions, testResponses);
-  REQUIRE(rmse < 6.0);
+  REQUIRE(rmse < 6.5);
 }

From d25657755b694d6ca2a04c1c85911f64b0ce0a40 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 3 Jul 2021 10:01:49 +0530
Subject: [PATCH 101/118] Removed sumSquared vector to use O(1) space

---
 src/mlpack/methods/decision_tree/mse_gain.hpp | 45 +++++++++----------
 1 file changed, 22 insertions(+), 23 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index a4cc033aa3..f4973ed48d 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -114,10 +114,10 @@ class MSEGain
     double mse;
     // Left child.
     if (child == 0)
-      mse = sumSquares[index] / leftSize - leftMean * leftMean;
+      mse = leftSumSquares / leftSize - leftMean * leftMean;
     // Right child.
     else
-      mse = (totalSumSquares - sumSquares[index]) / rightSize
+      mse = (totalSumSquares - leftSumSquares) / rightSize
           - rightMean * rightMean;
     return -mse;
   }
@@ -143,14 +143,15 @@ class MSEGain
     rightMean = 0.0;
     leftSize = 0.0;
     rightSize = 0.0;
+    leftSumSquares = 0.0;
     totalSumSquares = 0.0;
-    sumSquares.set_size(responses.n_elem);
 
     if (UseWeights)
     {
-      // Stores the weighted sum of squares till the previous index.
-      double prevWeightedSumSquares = 0.0;
-
+      // Do I need to document that the % symbol does the elementwise multiplication?
+      // It might be misleading to general developers who might confuse it with modulo
+      // operator.
+      totalSumSquares = arma::accu(weights % arma::square(responses));
       for (size_t i = 0; i < minimum - 1; ++i)
       {
         const WType w = weights[i];
@@ -159,8 +160,7 @@ class MSEGain
         // Calculating initial weighted mean of responses for the left child.
         leftSize += w;
         leftMean += w * x;
-        sumSquares[i] = prevWeightedSumSquares + w * x * x;
-        prevWeightedSumSquares += w * x * x;
+        leftSumSquares += w * x * x;
       }
       if (leftSize > 1e-9)
         leftMean /= leftSize;
@@ -173,19 +173,13 @@ class MSEGain
         // Calculating initial weighted mean of responses for the right child.
         rightSize += w;
         rightMean += w * x;
-        sumSquares[i] = prevWeightedSumSquares + w * x * x;
-        prevWeightedSumSquares += w * x * x;
       }
       if (rightSize > 1e-9)
         rightMean /= rightSize;
-
-      totalSumSquares = prevWeightedSumSquares;
     }
     else
     {
-      // Stores the sum of squares till the previous index.
-      double prevSumSquares = 0.0;
-
+      totalSumSquares = arma::accu(arma::square(responses));
       for (size_t i = 0; i < minimum - 1; ++i)
       {
         const RType x = responses[i];
@@ -193,8 +187,7 @@ class MSEGain
         // Calculating the initial mean of responses for the left child.
         ++leftSize;
         leftMean += x;
-        sumSquares[i] = prevSumSquares + x * x;
-        prevSumSquares += x * x;
+        leftSumSquares += x * x;
       }
       if (leftSize > 1e-9)
         leftMean /= leftSize;
@@ -206,13 +199,9 @@ class MSEGain
         // Calculating the initial mean of responses for the right child.
         ++rightSize;
         rightMean += x;
-        sumSquares[i] = prevSumSquares + x * x;
-        prevSumSquares += x * x;
       }
       if (rightSize > 1e-9)
         rightMean /= rightSize;
-
-      totalSumSquares = prevSumSquares;
     }
   }
 
@@ -235,6 +224,11 @@ class MSEGain
     {
       const WType w = weights[index];
       const RType x = responses[index];
+
+      // Update weighted sum of squares for left child.
+      leftSumSquares += w * x * x;
+
+      // Update weighted mean for both childs.
       leftMean = (leftMean * leftSize + w * x) / (leftSize + w);
       leftSize += w;
 
@@ -244,6 +238,11 @@ class MSEGain
     else
     {
       const RType x = responses[index];
+
+      // Update sum of squares for left child.
+      leftSumSquares += x * x;
+
+      // Update mean for both childs.
       leftMean = (leftMean * leftSize + x) / (leftSize + 1);
       ++leftSize;
 
@@ -257,8 +256,8 @@ class MSEGain
    * The following data members cache statistics for weighted data when
    * `UseWeights` is true, else it will calculate unweighted statistics.
    */
-  // Stores the sum of squares / weighted sum of squares.
-  arma::rowvec sumSquares;
+  // Stores the sum of squares / weighted sum of squares for the left child.
+  double leftSumSquares;
   // For unweighted data, stores the number of elements in each child.
   // For weighted data, stores the sum of weights of elements in each
   // child.

From 16f5fc1a22827d5ce321bae9cc7283738a26e778 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 3 Jul 2021 10:56:03 +0530
Subject: [PATCH 102/118] Return tuple from Evaluate method.

---
 .../best_binary_numeric_split_impl.hpp        |  5 +++--
 src/mlpack/methods/decision_tree/mse_gain.hpp | 21 +++++++------------
 2 files changed, 10 insertions(+), 16 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 65a66c0a70..2aacd38439 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -413,8 +413,9 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
       continue;
 
     // Calculate the gain for the left and right child.
-    const double leftGain = fitnessFunction.Evaluate(index - 1, 0);
-    const double rightGain = fitnessFunction.Evaluate(index - 1, 1);
+    auto value = fitnessFunction.Evaluate();
+    const double leftGain = std::get<0>(value);
+    const double rightGain = std::get<1>(value);
 
     double gain;
     if (UseWeights)
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index f4973ed48d..4226e9699f 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -96,7 +96,8 @@ class MSEGain
   }
 
   /**
-   * Calculates the  mean squared error gain for the given child and index.
+   * Calculates the  mean squared error gain for the left and right children
+   * for the current index.
    *
    * X = array of values of size n.
    *
@@ -104,22 +105,14 @@ class MSEGain
    *   MSE = \sum\limits_{i=1}^n {X_i}^2 -
    *       {\dfrac{\sum\limits_{j=1}^n X_j}{n}}^2
    * @f}
-   *
-   * @param index The current index to calculate gain.
-   * @param child The child to calculate gain.
-   *    0 -> Left child,   1 -> Right child
    */
-  double Evaluate(const size_t index, const size_t child)
+  std::tuple<double, double> Evaluate()
   {
-    double mse;
-    // Left child.
-    if (child == 0)
-      mse = leftSumSquares / leftSize - leftMean * leftMean;
-    // Right child.
-    else
-      mse = (totalSumSquares - leftSumSquares) / rightSize
+    double mseLeft = leftSumSquares / leftSize - leftMean * leftMean;
+    double mseRight = (totalSumSquares - leftSumSquares) / rightSize
           - rightMean * rightMean;
-    return -mse;
+
+    return {-mseLeft, -mseRight};
   }
 
   /**

From 451fa167ea35f70e7b32486bb16e5d3318a533e8 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 7 Jul 2021 18:08:54 +0530
Subject: [PATCH 103/118] Change names of functions

---
 .../best_binary_numeric_split_impl.hpp          | 16 ++++++++--------
 src/mlpack/methods/decision_tree/mse_gain.hpp   | 17 +++++++----------
 2 files changed, 15 insertions(+), 18 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 2aacd38439..8d8031293c 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -390,9 +390,9 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
     bestFoundGain *= data.n_elem;
   }
 
-  // Precomputing various statistics to efficiently compute gain values for
-  // all possible splits.
-  fitnessFunction.CalculateStatistics<UseWeights>(sortedResponses,
+  // Initialize and precompute various statistics to efficiently compute gain
+  // values for all possible splits.
+  fitnessFunction.BinaryScanInitialize<UseWeights>(sortedResponses,
       sortedWeights, minimum);
 
   // Loop through all possible split points, choosing the best one.
@@ -404,8 +404,8 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
       rightChildWeight -= sortedWeights[index - 1];
     }
 
-    // Update statistics for the current index.
-    fitnessFunction.UpdateStatistics<UseWeights>(sortedResponses,
+    // Steps through the current index and updates the cached data.
+    fitnessFunction.BinaryStep<UseWeights>(sortedResponses,
         sortedWeights, index - 1);
 
     // Make sure that the value has changed.
@@ -413,9 +413,9 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
       continue;
 
     // Calculate the gain for the left and right child.
-    auto value = fitnessFunction.Evaluate();
-    const double leftGain = std::get<0>(value);
-    const double rightGain = std::get<1>(value);
+    auto binaryGains = fitnessFunction.BinaryGains();
+    const double leftGain = std::get<0>(binaryGains);
+    const double rightGain = std::get<1>(binaryGains);
 
     double gain;
     if (UseWeights)
diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 4226e9699f..2b4e058eb2 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -106,7 +106,7 @@ class MSEGain
    *       {\dfrac{\sum\limits_{j=1}^n X_j}{n}}^2
    * @f}
    */
-  std::tuple<double, double> Evaluate()
+  std::tuple<double, double> BinaryGains()
   {
     double mseLeft = leftSumSquares / leftSize - leftMean * leftMean;
     double mseRight = (totalSumSquares - leftSumSquares) / rightSize
@@ -124,9 +124,9 @@ class MSEGain
    * @param minimum The minimum number of elements in a leaf.
    */
   template<bool UseWeights, typename ResponsesType, typename WeightVecType>
-  void CalculateStatistics(const ResponsesType& responses,
-                           const WeightVecType& weights,
-                           const size_t minimum)
+  void BinaryScanInitialize(const ResponsesType& responses,
+                            const WeightVecType& weights,
+                            const size_t minimum)
   {
     typedef typename ResponsesType::elem_type RType;
     typedef typename WeightVecType::elem_type WType;
@@ -141,9 +141,6 @@ class MSEGain
 
     if (UseWeights)
     {
-      // Do I need to document that the % symbol does the elementwise multiplication?
-      // It might be misleading to general developers who might confuse it with modulo
-      // operator.
       totalSumSquares = arma::accu(weights % arma::square(responses));
       for (size_t i = 0; i < minimum - 1; ++i)
       {
@@ -206,9 +203,9 @@ class MSEGain
    * @param index The current index.
    */
   template<bool UseWeights, typename ResponsesType, typename WeightVecType>
-  void UpdateStatistics(const ResponsesType& responses,
-                        const WeightVecType& weights,
-                        const size_t index)
+  void BinaryStep(const ResponsesType& responses,
+                  const WeightVecType& weights,
+                  const size_t index)
   {
     typedef typename ResponsesType::elem_type RType;
     typedef typename WeightVecType::elem_type WType;

From 8174764fe1552817d5b696fdf29cf76a858e7162 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 7 Jul 2021 18:45:04 +0530
Subject: [PATCH 104/118] Finally after so many errors... Add SFINAE to resolve
 optimized and unoptimized overloads of SplitIfBetter

---
 .../best_binary_numeric_split.hpp             | 82 +++++++++++--------
 .../best_binary_numeric_split_impl.hpp        | 12 ++-
 2 files changed, 58 insertions(+), 36 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 7120fe2055..043c66f212 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -15,9 +15,20 @@
 #include <mlpack/prereqs.hpp>
 #include "mse_gain.hpp"
 
+#include <mlpack/core/util/sfinae_utility.hpp>
+
 namespace mlpack {
 namespace tree {
 
+// This gives us a HasBinaryScanInitialize<T, U> type (where U is a function
+// pointer) we can use with SFINAE to catch when a type has a
+// BinaryScanInitialize(...) function.
+HAS_MEM_FUNC(BinaryScanInitialize, HasBinaryScanInitialize);
+
+// This gives us a HasBinaryStep<T, U> type (where U is a function pointer)
+// we can use with SFINAE to catch when a type has a BinaryStep(...) function.
+HAS_MEM_FUNC(BinaryStep, HasBinaryStep);
+
 /**
  * The BestBinaryNumericSplit is a splitting function for decision trees that
  * will exhaustively search a numeric dimension for the best binary split.
@@ -96,6 +107,44 @@ class BestBinaryNumericSplit
       double& splitInfo,
       AuxiliarySplitInfo& aux);
 
+  /**
+  * Check if we can split a node.  If we can split a node in a way that
+  * improves on 'bestGain', then we return the improved gain.  Otherwise we
+  * return the value 'bestGain'.  If a split is made, then splitInfo and aux
+  * may be modified.
+  *
+  * This overload is specialized only for MSEGain fitness function.
+  *
+  * @param bestGain Best gain seen so far (we'll only split if we find gain
+  *      better than this).
+  * @param data The dimension of data points to check for a split in.
+  * @param responses Responses for each point.
+  * @param weights Weights associated with responses.
+  * @param minimumLeafSize Minimum number of points in a leaf node for
+  *      splitting.
+  * @param minimumGainSplit Minimum gain split.
+  * @param splitInfo Stores split information on a successful split.
+  * @param aux Auxiliary split information, which may be modified on a
+  *      successful split.
+  */
+  template<bool UseWeights, typename VecType, typename ResponsesType,
+          typename WeightVecType>
+  typename std::enable_if<
+      HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
+          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+      HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
+          (const ResponsesType&, const WeightVecType&, const size_t)>::value,
+      double>::type
+  SplitIfBetter(
+      const double bestGain,
+      const VecType& data,
+      const ResponsesType& responses,
+      const WeightVecType& weights,
+      const size_t minimumLeafSize,
+      const double minimumGainSplit,
+      double& splitInfo,
+      AuxiliarySplitInfo& /* aux */);
+
   /**
    * Returns 2, since the binary split always has two children.
    */
@@ -119,39 +168,6 @@ class BestBinaryNumericSplit
       const AuxiliarySplitInfo& /* aux */);
 };
 
-/**
-* Check if we can split a node.  If we can split a node in a way that
-* improves on 'bestGain', then we return the improved gain.  Otherwise we
-* return the value 'bestGain'.  If a split is made, then splitInfo and aux
-* may be modified.
-*
-* This overload is specialized only for MSEGain fitness function.
-*
-* @param bestGain Best gain seen so far (we'll only split if we find gain
-*      better than this).
-* @param data The dimension of data points to check for a split in.
-* @param responses Responses for each point.
-* @param weights Weights associated with responses.
-* @param minimumLeafSize Minimum number of points in a leaf node for
-*      splitting.
-* @param minimumGainSplit Minimum gain split.
-* @param splitInfo Stores split information on a successful split.
-* @param aux Auxiliary split information, which may be modified on a
-*      successful split.
-*/
-template<>
-template<bool UseWeights, typename VecType, typename ResponsesType,
-         typename WeightVecType>
-double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
-    const double bestGain,
-    const VecType& data,
-    const ResponsesType& responses,
-    const WeightVecType& weights,
-    const size_t minimumLeafSize,
-    const double minimumGainSplit,
-    double& splitInfo,
-    AuxiliarySplitInfo& /* aux */);
-
 } // namespace tree
 } // namespace mlpack
 
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 8d8031293c..1913fc0868 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -320,10 +320,16 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
 }
 
 // Optimized version when fitness function is MSEGain.
-template<>
+template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename ResponsesType,
          typename WeightVecType>
-double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
+typename std::enable_if<
+    HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
+        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+    HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
+        (const ResponsesType&, const WeightVecType&, const size_t)>::value,
+    double>::type
+BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
     const ResponsesType& responses,
@@ -336,7 +342,7 @@ double BestBinaryNumericSplit<MSEGain>::SplitIfBetter(
   typedef typename ResponsesType::elem_type RType;
   typedef typename WeightVecType::elem_type WType;
 
-  MSEGain fitnessFunction;
+  FitnessFunction fitnessFunction;
 
   // First sanity check: if we don't have enough points, we can't split.
   if (data.n_elem < (minimumLeafSize * 2))

From d204474e8d50aeb820af1ec722726d9f0128a991 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 7 Jul 2021 18:45:45 +0530
Subject: [PATCH 105/118] Fix style warning

---
 src/mlpack/methods/decision_tree/mse_gain.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 2b4e058eb2..3752319e4f 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -155,7 +155,7 @@ class MSEGain
       if (leftSize > 1e-9)
         leftMean /= leftSize;
 
-      for(size_t i = minimum - 1; i < responses.n_elem; ++i)
+      for (size_t i = minimum - 1; i < responses.n_elem; ++i)
       {
         const WType w = weights[i];
         const RType x = responses[i];
@@ -182,7 +182,7 @@ class MSEGain
       if (leftSize > 1e-9)
         leftMean /= leftSize;
 
-      for(size_t i = minimum - 1; i < responses.n_elem; ++i)
+      for (size_t i = minimum - 1; i < responses.n_elem; ++i)
       {
         const RType x = responses[i];
 

From f9f43d4957aa5050aa4ab0217d57b98aeea5348d Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 7 Jul 2021 18:54:10 +0530
Subject: [PATCH 106/118] Add a log to HISTORY.md

---
 HISTORY.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/HISTORY.md b/HISTORY.md
index 1c1c157cf8..08909cde5f 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,5 +1,7 @@
 ### mlpack ?.?.?
 ###### ????-??-??
+  * Added Decision Tree Regressor (#2905). It can be used using the class
+    `mlpack::tree::DecisionTreeRegressor`. It is accessible only though C++.
   * Added dict-style inspection of mlpack models in python bindings (#2868).
 
   * Added Extra Trees Algorithm (#2883). Currently, it can be used using the

From af3e985d784d469e6f3eb75d258a9b2107415367 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Jul 2021 07:31:32 +0530
Subject: [PATCH 107/118] Fix style issues from code review

---
 .../best_binary_numeric_split.hpp             | 38 +++++++++----------
 .../decision_tree_regressor_impl.hpp          | 16 ++++----
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 043c66f212..77dfa29569 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -108,25 +108,25 @@ class BestBinaryNumericSplit
       AuxiliarySplitInfo& aux);
 
   /**
-  * Check if we can split a node.  If we can split a node in a way that
-  * improves on 'bestGain', then we return the improved gain.  Otherwise we
-  * return the value 'bestGain'.  If a split is made, then splitInfo and aux
-  * may be modified.
-  *
-  * This overload is specialized only for MSEGain fitness function.
-  *
-  * @param bestGain Best gain seen so far (we'll only split if we find gain
-  *      better than this).
-  * @param data The dimension of data points to check for a split in.
-  * @param responses Responses for each point.
-  * @param weights Weights associated with responses.
-  * @param minimumLeafSize Minimum number of points in a leaf node for
-  *      splitting.
-  * @param minimumGainSplit Minimum gain split.
-  * @param splitInfo Stores split information on a successful split.
-  * @param aux Auxiliary split information, which may be modified on a
-  *      successful split.
-  */
+   * Check if we can split a node.  If we can split a node in a way that
+   * improves on 'bestGain', then we return the improved gain.  Otherwise we
+   * return the value 'bestGain'.  If a split is made, then splitInfo and aux
+   * may be modified.
+   *
+   * This overload is specialized only for MSEGain fitness function.
+   *
+   * @param bestGain Best gain seen so far (we'll only split if we find gain
+   *      better than this).
+   * @param data The dimension of data points to check for a split in.
+   * @param responses Responses for each point.
+   * @param weights Weights associated with responses.
+   * @param minimumLeafSize Minimum number of points in a leaf node for
+   *      splitting.
+   * @param minimumGainSplit Minimum gain split.
+   * @param splitInfo Stores split information on a successful split.
+   * @param aux Auxiliary split information, which may be modified on a
+   *      successful split.
+   */
   template<bool UseWeights, typename VecType, typename ResponsesType,
           typename WeightVecType>
   typename std::enable_if<
diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
index 82432d01e1..31a63fedd0 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
@@ -191,10 +191,10 @@ DecisionTreeRegressor<FitnessFunction,
 
 //! Take ownership of another tree and train with weights.
 template<typename FitnessFunction,
-        template<typename> class NumericSplitType,
-        template<typename> class CategoricalSplitType,
-        typename DimensionSelectionType,
-        bool NoRecursion>
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
 template<typename MatType, typename ResponsesType, typename WeightsType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,
@@ -229,10 +229,10 @@ DecisionTreeRegressor<FitnessFunction,
 
 //! Take ownership of another tree and train with weights.
 template<typename FitnessFunction,
-        template<typename> class NumericSplitType,
-        template<typename> class CategoricalSplitType,
-        typename DimensionSelectionType,
-        bool NoRecursion>
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
 template<typename MatType, typename ResponsesType, typename WeightsType>
 DecisionTreeRegressor<FitnessFunction,
                       NumericSplitType,

From ed21bb6a89bbeda6ca2bf715827505c1bee780af Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Jul 2021 07:35:29 +0530
Subject: [PATCH 108/118] Update documentation.

---
 src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 77dfa29569..657df344d9 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -113,7 +113,8 @@ class BestBinaryNumericSplit
    * return the value 'bestGain'.  If a split is made, then splitInfo and aux
    * may be modified.
    *
-   * This overload is specialized only for MSEGain fitness function.
+   * This overload is specialized for any fitness function that implements
+   * BinaryScanInitialize(), BinaryStep() and BinaryGains() functions.
    *
    * @param bestGain Best gain seen so far (we'll only split if we find gain
    *      better than this).

From d0ecbf5f85ca75a25b1ea828beab6ec329611bb6 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Jul 2021 07:49:22 +0530
Subject: [PATCH 109/118] Add BinaryGains() to SFINAE check

---
 .../methods/decision_tree/best_binary_numeric_split.hpp   | 8 +++++++-
 .../decision_tree/best_binary_numeric_split_impl.hpp      | 4 +++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 657df344d9..3f5cd27e65 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -29,6 +29,10 @@ HAS_MEM_FUNC(BinaryScanInitialize, HasBinaryScanInitialize);
 // we can use with SFINAE to catch when a type has a BinaryStep(...) function.
 HAS_MEM_FUNC(BinaryStep, HasBinaryStep);
 
+// This gives us a HasBinaryGains<T, U> type (where U is a function pointer)
+// we can use with SFINAE to catch when a type has a BinaryGains(...) function.
+HAS_MEM_FUNC(BinaryGains, HasBinaryGains);
+
 /**
  * The BestBinaryNumericSplit is a splitting function for decision trees that
  * will exhaustively search a numeric dimension for the best binary split.
@@ -134,7 +138,9 @@ class BestBinaryNumericSplit
       HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
           (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
       HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
-          (const ResponsesType&, const WeightVecType&, const size_t)>::value,
+          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+      HasBinaryGains<FitnessFunction,
+          std::tuple<double, double>(FitnessFunction::*)()>::value,
       double>::type
   SplitIfBetter(
       const double bestGain,
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 1913fc0868..3d0ebdc078 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -327,7 +327,9 @@ typename std::enable_if<
     HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
         (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
     HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
-        (const ResponsesType&, const WeightVecType&, const size_t)>::value,
+        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+    HasBinaryGains<FitnessFunction,
+        std::tuple<double, double>(FitnessFunction::*)()>::value,
     double>::type
 BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,

From f6c8e1b3eb79da0b03af8b6afbbdb4afad6710f2 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Jul 2021 08:15:59 +0530
Subject: [PATCH 110/118] Add static keyword to function... Doesn't compile
 yet.

---
 .../decision_tree/best_binary_numeric_split.hpp    | 14 +++++++++++---
 .../best_binary_numeric_split_impl.hpp             | 12 ++++++++++--
 2 files changed, 21 insertions(+), 5 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 3f5cd27e65..6114b7e1b9 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -101,7 +101,15 @@ class BestBinaryNumericSplit
    */
   template<bool UseWeights, typename VecType, typename ResponsesType,
            typename WeightVecType>
-  static double SplitIfBetter(
+  static typename std::enable_if<
+      !HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
+          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+      !HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
+          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+      !HasBinaryGains<FitnessFunction,
+          std::tuple<double, double>(FitnessFunction::*)(void)>::value,
+      double>::type
+  SplitIfBetter(
       const double bestGain,
       const VecType& data,
       const ResponsesType& responses,
@@ -134,13 +142,13 @@ class BestBinaryNumericSplit
    */
   template<bool UseWeights, typename VecType, typename ResponsesType,
           typename WeightVecType>
-  typename std::enable_if<
+  static typename std::enable_if<
       HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
           (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
       HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
           (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
       HasBinaryGains<FitnessFunction,
-          std::tuple<double, double>(FitnessFunction::*)()>::value,
+          std::tuple<double, double>(FitnessFunction::*)(void)>::value,
       double>::type
   SplitIfBetter(
       const double bestGain,
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 3d0ebdc078..76fb90df95 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -189,7 +189,15 @@ double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
 template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename ResponsesType,
          typename WeightVecType>
-double BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
+typename std::enable_if<
+    !HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
+        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+    !HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
+        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
+    !HasBinaryGains<FitnessFunction,
+        std::tuple<double, double>(FitnessFunction::*)(void)>::value,
+    double>::type
+BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
     const VecType& data,
     const ResponsesType& responses,
@@ -329,7 +337,7 @@ typename std::enable_if<
     HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
         (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
     HasBinaryGains<FitnessFunction,
-        std::tuple<double, double>(FitnessFunction::*)()>::value,
+        std::tuple<double, double>(FitnessFunction::*)(void)>::value,
     double>::type
 BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,

From 167667785056fe1d2b6a8d79b8ad6717f6c9a09c Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Jul 2021 08:17:29 +0530
Subject: [PATCH 111/118] Change auto to std::tuple<double, double>

---
 .../methods/decision_tree/best_binary_numeric_split_impl.hpp    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 76fb90df95..486a1ac370 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -429,7 +429,7 @@ BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
       continue;
 
     // Calculate the gain for the left and right child.
-    auto binaryGains = fitnessFunction.BinaryGains();
+    std::tuple<double, double> binaryGains = fitnessFunction.BinaryGains();
     const double leftGain = std::get<0>(binaryGains);
     const double rightGain = std::get<1>(binaryGains);
 

From edb9879f2a185eff4f8b4d1ba3945b746137a5cf Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sat, 10 Jul 2021 08:22:03 +0530
Subject: [PATCH 112/118] Update HISTORY.md

Co-authored-by: Ryan Curtin <ryan@ratml.org>
---
 HISTORY.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.md b/HISTORY.md
index 08909cde5f..80d463881a 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -2,6 +2,7 @@
 ###### ????-??-??
   * Added Decision Tree Regressor (#2905). It can be used using the class
     `mlpack::tree::DecisionTreeRegressor`. It is accessible only though C++.
+    
   * Added dict-style inspection of mlpack models in python bindings (#2868).
 
   * Added Extra Trees Algorithm (#2883). Currently, it can be used using the

From 64c84846f052a3fe1779c1a630ad8cca93fafeee Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Sun, 11 Jul 2021 10:49:49 +0530
Subject: [PATCH 113/118] Removing template function checks is failing too

---
 .../methods/decision_tree/best_binary_numeric_split.hpp  | 8 --------
 .../decision_tree/best_binary_numeric_split_impl.hpp     | 9 +--------
 2 files changed, 1 insertion(+), 16 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 6114b7e1b9..18919900a3 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -102,10 +102,6 @@ class BestBinaryNumericSplit
   template<bool UseWeights, typename VecType, typename ResponsesType,
            typename WeightVecType>
   static typename std::enable_if<
-      !HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
-          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
-      !HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
-          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
       !HasBinaryGains<FitnessFunction,
           std::tuple<double, double>(FitnessFunction::*)(void)>::value,
       double>::type
@@ -143,10 +139,6 @@ class BestBinaryNumericSplit
   template<bool UseWeights, typename VecType, typename ResponsesType,
           typename WeightVecType>
   static typename std::enable_if<
-      HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
-          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
-      HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
-          (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
       HasBinaryGains<FitnessFunction,
           std::tuple<double, double>(FitnessFunction::*)(void)>::value,
       double>::type
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 486a1ac370..9b77cbb29f 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -190,10 +190,6 @@ template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename ResponsesType,
          typename WeightVecType>
 typename std::enable_if<
-    !HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
-        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
-    !HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
-        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
     !HasBinaryGains<FitnessFunction,
         std::tuple<double, double>(FitnessFunction::*)(void)>::value,
     double>::type
@@ -332,10 +328,6 @@ template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename ResponsesType,
          typename WeightVecType>
 typename std::enable_if<
-    HasBinaryScanInitialize<FitnessFunction, void(FitnessFunction::*)
-        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
-    HasBinaryStep<FitnessFunction, void(FitnessFunction::*)
-        (const ResponsesType&, const WeightVecType&, const size_t)>::value &&
     HasBinaryGains<FitnessFunction,
         std::tuple<double, double>(FitnessFunction::*)(void)>::value,
     double>::type
@@ -349,6 +341,7 @@ BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     double& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
+  std::cout << "Optimized\n";
   typedef typename ResponsesType::elem_type RType;
   typedef typename WeightVecType::elem_type WType;
 

From 0f4bd653cfe096f2617481f20c71d717087591ac Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 13 Jul 2021 08:05:34 +0530
Subject: [PATCH 114/118] Fix the SFINAE bug with @rcurtin's patch

---
 .../best_binary_numeric_split.hpp             | 30 +++++++++++--------
 .../best_binary_numeric_split_impl.hpp        | 10 +++----
 2 files changed, 21 insertions(+), 19 deletions(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
index 18919900a3..a39ac91d8f 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split.hpp
@@ -20,19 +20,25 @@
 namespace mlpack {
 namespace tree {
 
-// This gives us a HasBinaryScanInitialize<T, U> type (where U is a function
-// pointer) we can use with SFINAE to catch when a type has a
-// BinaryScanInitialize(...) function.
-HAS_MEM_FUNC(BinaryScanInitialize, HasBinaryScanInitialize);
-
-// This gives us a HasBinaryStep<T, U> type (where U is a function pointer)
-// we can use with SFINAE to catch when a type has a BinaryStep(...) function.
-HAS_MEM_FUNC(BinaryStep, HasBinaryStep);
-
 // This gives us a HasBinaryGains<T, U> type (where U is a function pointer)
 // we can use with SFINAE to catch when a type has a BinaryGains(...) function.
 HAS_MEM_FUNC(BinaryGains, HasBinaryGains);
 
+// This struct will have `value` set to `true` if a BinaryGains() function of
+// the right signature is detected.  We only check for BinaryGains(), and not
+// BinaryScanInitialize() or BinaryStep(), because those two are template
+// members functions and would make this check far more difficult.
+//
+// The unused UseWeights template parameter is necessary to ensure that the
+// compiler thinks the result `value` depends on a parameter specific to the
+// SplitIfBetter() function in BestBinaryNumericSplit().
+template<typename T, bool /* UseWeights */>
+struct HasOptimizedBinarySplitForms
+{
+  const static bool value = HasBinaryGains<T,
+      std::tuple<double, double>(T::*)()>::value;
+};
+
 /**
  * The BestBinaryNumericSplit is a splitting function for decision trees that
  * will exhaustively search a numeric dimension for the best binary split.
@@ -102,8 +108,7 @@ class BestBinaryNumericSplit
   template<bool UseWeights, typename VecType, typename ResponsesType,
            typename WeightVecType>
   static typename std::enable_if<
-      !HasBinaryGains<FitnessFunction,
-          std::tuple<double, double>(FitnessFunction::*)(void)>::value,
+      !HasOptimizedBinarySplitForms<FitnessFunction, UseWeights>::value,
       double>::type
   SplitIfBetter(
       const double bestGain,
@@ -139,8 +144,7 @@ class BestBinaryNumericSplit
   template<bool UseWeights, typename VecType, typename ResponsesType,
           typename WeightVecType>
   static typename std::enable_if<
-      HasBinaryGains<FitnessFunction,
-          std::tuple<double, double>(FitnessFunction::*)(void)>::value,
+      HasOptimizedBinarySplitForms<FitnessFunction, UseWeights>::value,
       double>::type
   SplitIfBetter(
       const double bestGain,
diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 9b77cbb29f..6a69194153 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -190,8 +190,7 @@ template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename ResponsesType,
          typename WeightVecType>
 typename std::enable_if<
-    !HasBinaryGains<FitnessFunction,
-        std::tuple<double, double>(FitnessFunction::*)(void)>::value,
+    !HasOptimizedBinarySplitForms<FitnessFunction, UseWeights>::value,
     double>::type
 BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
@@ -328,8 +327,7 @@ template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename ResponsesType,
          typename WeightVecType>
 typename std::enable_if<
-    HasBinaryGains<FitnessFunction,
-        std::tuple<double, double>(FitnessFunction::*)(void)>::value,
+    HasOptimizedBinarySplitForms<FitnessFunction, UseWeights>::value,
     double>::type
 BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     const double bestGain,
@@ -401,7 +399,7 @@ BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
 
   // Initialize and precompute various statistics to efficiently compute gain
   // values for all possible splits.
-  fitnessFunction.BinaryScanInitialize<UseWeights>(sortedResponses,
+  fitnessFunction.template BinaryScanInitialize<UseWeights>(sortedResponses,
       sortedWeights, minimum);
 
   // Loop through all possible split points, choosing the best one.
@@ -414,7 +412,7 @@ BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     }
 
     // Steps through the current index and updates the cached data.
-    fitnessFunction.BinaryStep<UseWeights>(sortedResponses,
+    fitnessFunction.template BinaryStep<UseWeights>(sortedResponses,
         sortedWeights, index - 1);
 
     // Make sure that the value has changed.

From ff5892cc7ec66b349be7de5b9ccc78179fe7dd10 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 13 Jul 2021 08:06:37 +0530
Subject: [PATCH 115/118] Update comment

---
 .../methods/decision_tree/best_binary_numeric_split_impl.hpp   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 6a69194153..68d02ecb17 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -322,7 +322,8 @@ BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
   return bestFoundGain;
 }
 
-// Optimized version when fitness function is MSEGain.
+// Optimized version for any fitness function that implements
+// BinaryScanInitialize(), BinaryStep() and BinaryGains() functions.
 template<typename FitnessFunction>
 template<bool UseWeights, typename VecType, typename ResponsesType,
          typename WeightVecType>

From 18e609efc0a945a23ff64a2e5edbe1b7bf190743 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 13 Jul 2021 08:07:13 +0530
Subject: [PATCH 116/118] Removed debugging print statement

---
 .../methods/decision_tree/best_binary_numeric_split_impl.hpp     | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
index 68d02ecb17..0f20f97145 100644
--- a/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
+++ b/src/mlpack/methods/decision_tree/best_binary_numeric_split_impl.hpp
@@ -340,7 +340,6 @@ BestBinaryNumericSplit<FitnessFunction>::SplitIfBetter(
     double& splitInfo,
     AuxiliarySplitInfo& /* aux */)
 {
-  std::cout << "Optimized\n";
   typedef typename ResponsesType::elem_type RType;
   typedef typename WeightVecType::elem_type WType;
 

From 9a7290a47d0cf1829e583df831a9c22f751fd9e1 Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Tue, 13 Jul 2021 13:50:08 +0530
Subject: [PATCH 117/118] Attempt to fix tuple error

---
 src/mlpack/methods/decision_tree/mse_gain.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mlpack/methods/decision_tree/mse_gain.hpp b/src/mlpack/methods/decision_tree/mse_gain.hpp
index 3752319e4f..2516351d80 100644
--- a/src/mlpack/methods/decision_tree/mse_gain.hpp
+++ b/src/mlpack/methods/decision_tree/mse_gain.hpp
@@ -112,7 +112,7 @@ class MSEGain
     double mseRight = (totalSumSquares - leftSumSquares) / rightSize
           - rightMean * rightMean;
 
-    return {-mseLeft, -mseRight};
+    return std::make_tuple(-mseLeft, -mseRight);
   }
 
   /**

From 1e16d1194cc7d5c50afc22bab4778f8bd713506e Mon Sep 17 00:00:00 2001
From: Yashwants19 <Yashwants19@users.noreply.github.com>
Date: Sat, 17 Jul 2021 10:00:38 +0000
Subject: [PATCH 118/118] Upgrade CLI11 to 2.0.0

---
 .../bindings/cli/third_party/CLI/CLI11.hpp    | 2034 +++++++++++------
 1 file changed, 1357 insertions(+), 677 deletions(-)

diff --git a/src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp b/src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp
index 68244d3864..ce1d06e3f2 100644
--- a/src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp
+++ b/src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp
@@ -1,20 +1,16 @@
-#pragma once
-
-// CLI11: Version 1.9.1
+// CLI11: Version 2.0.0
 // Originally designed by Henry Schreiner
 // https://github.com/CLIUtils/CLI11
 //
 // This is a standalone header file generated by MakeSingleHeader.py in CLI11/scripts
-// from: v1.9.1
+// from: v2.0.0 (added include gaurd)
 //
-// From LICENSE:
-//
-// CLI11 1.8 Copyright (c) 2017-2019 University of Cincinnati, developed by Henry
+// CLI11 2.0.0 Copyright (c) 2017-2020 University of Cincinnati, developed by Henry
 // Schreiner under NSF AWARD 1414736. All rights reserved.
-// 
+//
 // Redistribution and use in source and binary forms of CLI11, with or without
 // modification, are permitted provided that the following conditions are met:
-// 
+//
 // 1. Redistributions of source code must retain the above copyright notice, this
 //    list of conditions and the following disclaimer.
 // 2. Redistributions in binary form must reproduce the above copyright notice,
@@ -23,7 +19,7 @@
 // 3. Neither the name of the copyright holder nor the names of its contributors
 //    may be used to endorse or promote products derived from this software without
 //    specific prior written permission.
-// 
+//
 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 // ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 // WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
@@ -35,48 +31,42 @@
 // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
+#pragma once
 
 // Standard combined includes:
-
-#include <algorithm>
-#include <cmath>
-#include <cstdint>
-#include <exception>
-#include <fstream>
-#include <functional>
-#include <iomanip>
-#include <iostream>
-#include <iterator>
-#include <limits>
-#include <locale>
-#include <map>
 #include <memory>
-#include <numeric>
-#include <set>
-#include <sstream>
-#include <stdexcept>
 #include <string>
-#include <tuple>
-#include <type_traits>
-#include <utility>
+#include <functional>
 #include <vector>
+#include <exception>
+#include <sstream>
+#include <cstdint>
+#include <utility>
+#include <iomanip>
+#include <iterator>
+#include <algorithm>
+#include <tuple>
+#include <stdexcept>
+#include <iostream>
+#include <fstream>
+#include <locale>
+#include <type_traits>
+#include <map>
+#include <limits>
+#include <numeric>
+#include <cmath>
+#include <set>
 
 
-// Verbatim copy from Version.hpp:
-
-
-#define CLI11_VERSION_MAJOR 1
-#define CLI11_VERSION_MINOR 9
-#define CLI11_VERSION_PATCH 1
-#define CLI11_VERSION "1.9.1"
+#define CLI11_VERSION_MAJOR 2
+#define CLI11_VERSION_MINOR 0
+#define CLI11_VERSION_PATCH 0
+#define CLI11_VERSION "2.0.0"
 
 
 
 
-// Verbatim copy from Macros.hpp:
-
-
-// The following version macro is very similar to the one in PyBind11
+// The following version macro is very similar to the one in pybind11
 #if !(defined(_MSC_VER) && __cplusplus == 199711L) && !defined(__INTEL_COMPILER)
 #if __cplusplus >= 201402L
 #define CLI11_CPP14
@@ -112,9 +102,6 @@
 
 
 
-// Verbatim copy from Validators.hpp:
-
-
 // C standard library
 // Only needed for existence checking
 #if defined CLI11_CPP17 && defined __has_include && !defined CLI11_HAS_FILESYSTEM
@@ -149,18 +136,9 @@
 
 
 
-// From Version.hpp:
-
-
-
-// From Macros.hpp:
-
-
-
-// From StringTools.hpp:
-
 namespace CLI {
 
+
 /// Include the items in this namespace to get free conversion of enums to/from streams.
 /// (This is available inside CLI as well, so CLI11 will use this without a using statement).
 namespace enums {
@@ -220,10 +198,14 @@ std::string join(const T &v, Callable func, std::string delim = ",") {
     std::ostringstream s;
     auto beg = std::begin(v);
     auto end = std::end(v);
-    if(beg != end)
-        s << func(*beg++);
+    auto loc = s.tellp();
     while(beg != end) {
-        s << delim << func(*beg++);
+        auto nloc = s.tellp();
+        if(nloc > loc) {
+            s << delim;
+            loc = nloc;
+        }
+        s << func(*beg++);
     }
     return s.str();
 }
@@ -299,7 +281,7 @@ inline std::string trim_copy(const std::string &str, const std::string &filter)
     return trim(s, filter);
 }
 /// Print a two part "help" string
-inline std::ostream &format_help(std::ostream &out, std::string name, std::string description, std::size_t wid) {
+inline std::ostream &format_help(std::ostream &out, std::string name, const std::string &description, std::size_t wid) {
     name = "  " + name;
     out << std::setw(static_cast<int>(wid)) << std::left << name;
     if(!description.empty()) {
@@ -316,6 +298,24 @@ inline std::ostream &format_help(std::ostream &out, std::string name, std::strin
     return out;
 }
 
+/// Print subcommand aliases
+inline std::ostream &format_aliases(std::ostream &out, const std::vector<std::string> &aliases, std::size_t wid) {
+    if(!aliases.empty()) {
+        out << std::setw(static_cast<int>(wid)) << "     aliases: ";
+        bool front = true;
+        for(const auto &alias : aliases) {
+            if(!front) {
+                out << ", ";
+            } else {
+                front = false;
+            }
+            out << alias;
+        }
+        out << "\n";
+    }
+    return out;
+}
+
 /// Verify the first character of an option
 template <typename T> bool valid_first_char(T c) {
     return std::isalnum(c, std::locale()) || c == '_' || c == '?' || c == '@';
@@ -334,6 +334,12 @@ inline bool valid_name_string(const std::string &str) {
     return true;
 }
 
+/// check if a string is a container segment separator (empty or "%%")
+inline bool is_separator(const std::string &str) {
+    static const std::string sep("%%");
+    return (str.empty() || str == sep);
+}
+
 /// Verify that str consists of letters only
 inline bool isalpha(const std::string &str) {
     return std::all_of(str.begin(), str.end(), [](char c) { return std::isalpha(c, std::locale()); });
@@ -448,7 +454,12 @@ inline std::vector<std::string> split_up(std::string str, char delimiter = '\0')
             }
             if(end != std::string::npos) {
                 output.push_back(str.substr(1, end - 1));
-                str = str.substr(end + 1);
+                if(end + 2 < str.size()) {
+                    str = str.substr(end + 2);
+                } else {
+                    str.clear();
+                }
+
             } else {
                 output.push_back(str.substr(1));
                 str = "";
@@ -520,11 +531,8 @@ inline std::string &add_quotes_if_needed(std::string &str) {
 
 }  // namespace detail
 
-}  // namespace CLI
 
-// From Error.hpp:
 
-namespace CLI {
 
 // Use one of these on all error classes.
 // These are temporary and are undef'd at the end of this file.
@@ -666,19 +674,26 @@ class Success : public ParseError {
 };
 
 /// -h or --help on command line
-class CallForHelp : public ParseError {
-    CLI11_ERROR_DEF(ParseError, CallForHelp)
+class CallForHelp : public Success {
+    CLI11_ERROR_DEF(Success, CallForHelp)
     CallForHelp() : CallForHelp("This should be caught in your main function, see examples", ExitCodes::Success) {}
 };
 
 /// Usually something like --help-all on command line
-class CallForAllHelp : public ParseError {
-    CLI11_ERROR_DEF(ParseError, CallForAllHelp)
+class CallForAllHelp : public Success {
+    CLI11_ERROR_DEF(Success, CallForAllHelp)
     CallForAllHelp()
         : CallForAllHelp("This should be caught in your main function, see examples", ExitCodes::Success) {}
 };
 
-/// Does not output a diagnostic in CLI11_PARSE, but allows to return from main() with a specific error code.
+/// -v or --version on command line
+class CallForVersion : public Success {
+    CLI11_ERROR_DEF(Success, CallForVersion)
+    CallForVersion()
+        : CallForVersion("This should be caught in your main function, see examples", ExitCodes::Success) {}
+};
+
+/// Does not output a diagnostic in CLI11_PARSE, but allows main() to return with a specific error code.
 class RuntimeError : public ParseError {
     CLI11_ERROR_DEF(ParseError, RuntimeError)
     explicit RuntimeError(int exit_code = 1) : RuntimeError("Runtime error", exit_code) {}
@@ -846,11 +861,8 @@ class OptionNotFound : public Error {
 
 /// @}
 
-}  // namespace CLI
 
-// From TypeTools.hpp:
 
-namespace CLI {
 
 // Type tools
 
@@ -880,15 +892,6 @@ template <typename... Ts> using void_t = typename make_void<Ts...>::type;
 /// A copy of std::conditional_t from C++14 - same reasoning as enable_if_t, it does not hurt to redefine
 template <bool B, class T, class F> using conditional_t = typename std::conditional<B, T, F>::type;
 
-/// Check to see if something is a vector (fail check by default)
-template <typename T> struct is_vector : std::false_type {};
-
-/// Check to see if something is a vector (true if actually a vector)
-template <class T, class A> struct is_vector<std::vector<T, A>> : std::true_type {};
-
-/// Check to see if something is a vector (true if actually a const vector)
-template <class T, class A> struct is_vector<const std::vector<T, A>> : std::true_type {};
-
 /// Check to see if something is bool (fail check by default)
 template <typename T> struct is_bool : std::false_type {};
 
@@ -1030,6 +1033,17 @@ template <typename T, typename S = std::istringstream> class is_istreamable {
     static constexpr bool value = decltype(test<T, S>(0))::value;
 };
 
+/// Check for complex
+template <typename T> class is_complex {
+    template <typename TT>
+    static auto test(int) -> decltype(std::declval<TT>().real(), std::declval<TT>().imag(), std::true_type());
+
+    template <typename> static auto test(...) -> std::false_type;
+
+  public:
+    static constexpr bool value = decltype(test<T>(0))::value;
+};
+
 /// Templated operation to get a value from a stream
 template <typename T, enable_if_t<is_istreamable<T>::value, detail::enabler> = detail::dummy>
 bool from_stream(const std::string &istring, T &obj) {
@@ -1044,12 +1058,49 @@ bool from_stream(const std::string & /*istring*/, T & /*obj*/) {
     return false;
 }
 
+// check to see if an object is a mutable container (fail by default)
+template <typename T, typename _ = void> struct is_mutable_container : std::false_type {};
+
+/// type trait to test if a type is a mutable container meaning it has a value_type, it has an iterator, a clear, and
+/// end methods and an insert function.  And for our purposes we exclude std::string and types that can be constructed
+/// from a std::string
+template <typename T>
+struct is_mutable_container<
+    T,
+    conditional_t<false,
+                  void_t<typename T::value_type,
+                         decltype(std::declval<T>().end()),
+                         decltype(std::declval<T>().clear()),
+                         decltype(std::declval<T>().insert(std::declval<decltype(std::declval<T>().end())>(),
+                                                           std::declval<const typename T::value_type &>()))>,
+                  void>>
+    : public conditional_t<std::is_constructible<T, std::string>::value, std::false_type, std::true_type> {};
+
+// check to see if an object is a mutable container (fail by default)
+template <typename T, typename _ = void> struct is_readable_container : std::false_type {};
+
+/// type trait to test if a type is a container meaning it has a value_type, it has an iterator, a clear, and an end
+/// methods and an insert function.  And for our purposes we exclude std::string and types that can be constructed from
+/// a std::string
+template <typename T>
+struct is_readable_container<
+    T,
+    conditional_t<false, void_t<decltype(std::declval<T>().end()), decltype(std::declval<T>().begin())>, void>>
+    : public std::true_type {};
+
+// check to see if an object is a wrapper (fail by default)
+template <typename T, typename _ = void> struct is_wrapper : std::false_type {};
+
+// check if an object is a wrapper (it has a value_type defined)
+template <typename T>
+struct is_wrapper<T, conditional_t<false, void_t<typename T::value_type>, void>> : public std::true_type {};
+
 // Check for tuple like types, as in classes with a tuple_size type trait
 template <typename S> class is_tuple_like {
     template <typename SS>
     // static auto test(int)
     //     -> decltype(std::conditional<(std::tuple_size<SS>::value > 0), std::true_type, std::false_type>::type());
-    static auto test(int) -> decltype(std::tuple_size<SS>::value, std::true_type{});
+    static auto test(int) -> decltype(std::tuple_size<typename std::decay<SS>::type>::value, std::true_type{});
     template <typename> static auto test(...) -> std::false_type;
 
   public:
@@ -1084,20 +1135,19 @@ std::string to_string(T &&value) {
 /// If conversion is not supported, return an empty string (streaming is not supported for that type)
 template <typename T,
           enable_if_t<!std::is_constructible<std::string, T>::value && !is_ostreamable<T>::value &&
-                          !is_vector<typename std::remove_reference<typename std::remove_const<T>::type>::type>::value,
+                          !is_readable_container<typename std::remove_const<T>::type>::value,
                       detail::enabler> = detail::dummy>
 std::string to_string(T &&) {
     return std::string{};
 }
 
-/// convert a vector to a string
+/// convert a readable container to a string
 template <typename T,
           enable_if_t<!std::is_constructible<std::string, T>::value && !is_ostreamable<T>::value &&
-                          is_vector<typename std::remove_reference<typename std::remove_const<T>::type>::type>::value,
+                          is_readable_container<T>::value,
                       detail::enabler> = detail::dummy>
 std::string to_string(T &&variable) {
     std::vector<std::string> defaults;
-    defaults.reserve(variable.size());
     auto cval = variable.begin();
     auto end = variable.end();
     while(cval != end) {
@@ -1141,25 +1191,142 @@ auto value_string(const T &value) -> decltype(to_string(value)) {
     return to_string(value);
 }
 
-/// This will only trigger for actual void type
-template <typename T, typename Enable = void> struct type_count { static const int value{0}; };
+/// template to get the underlying value type if it exists or use a default
+template <typename T, typename def, typename Enable = void> struct wrapped_type { using type = def; };
 
-/// Set of overloads to get the type size of an object
-template <typename T> struct type_count<T, typename std::enable_if<is_tuple_like<T>::value>::type> {
-    static constexpr int value{std::tuple_size<T>::value};
+/// Type size for regular object types that do not look like a tuple
+template <typename T, typename def> struct wrapped_type<T, def, typename std::enable_if<is_wrapper<T>::value>::type> {
+    using type = typename T::value_type;
 };
+
+/// This will only trigger for actual void type
+template <typename T, typename Enable = void> struct type_count_base { static const int value{0}; };
+
 /// Type size for regular object types that do not look like a tuple
 template <typename T>
-struct type_count<
-    T,
-    typename std::enable_if<!is_vector<T>::value && !is_tuple_like<T>::value && !std::is_void<T>::value>::type> {
+struct type_count_base<T,
+                       typename std::enable_if<!is_tuple_like<T>::value && !is_mutable_container<T>::value &&
+                                               !std::is_void<T>::value>::type> {
     static constexpr int value{1};
 };
 
-/// Type size of types that look like a vector
-template <typename T> struct type_count<T, typename std::enable_if<is_vector<T>::value>::type> {
-    static constexpr int value{is_vector<typename T::value_type>::value ? expected_max_vector_size
-                                                                        : type_count<typename T::value_type>::value};
+/// the base tuple size
+template <typename T>
+struct type_count_base<T, typename std::enable_if<is_tuple_like<T>::value && !is_mutable_container<T>::value>::type> {
+    static constexpr int value{std::tuple_size<T>::value};
+};
+
+/// Type count base for containers is the type_count_base of the individual element
+template <typename T> struct type_count_base<T, typename std::enable_if<is_mutable_container<T>::value>::type> {
+    static constexpr int value{type_count_base<typename T::value_type>::value};
+};
+
+/// Set of overloads to get the type size of an object
+
+/// forward declare the subtype_count structure
+template <typename T> struct subtype_count;
+
+/// forward declare the subtype_count_min structure
+template <typename T> struct subtype_count_min;
+
+/// This will only trigger for actual void type
+template <typename T, typename Enable = void> struct type_count { static const int value{0}; };
+
+/// Type size for regular object types that do not look like a tuple
+template <typename T>
+struct type_count<T,
+                  typename std::enable_if<!is_wrapper<T>::value && !is_tuple_like<T>::value && !is_complex<T>::value &&
+                                          !std::is_void<T>::value>::type> {
+    static constexpr int value{1};
+};
+
+/// Type size for complex since it sometimes looks like a wrapper
+template <typename T> struct type_count<T, typename std::enable_if<is_complex<T>::value>::type> {
+    static constexpr int value{2};
+};
+
+/// Type size of types that are wrappers,except complex and tuples(which can also be wrappers sometimes)
+template <typename T> struct type_count<T, typename std::enable_if<is_mutable_container<T>::value>::type> {
+    static constexpr int value{subtype_count<typename T::value_type>::value};
+};
+
+/// Type size of types that are wrappers,except containers complex and tuples(which can also be wrappers sometimes)
+template <typename T>
+struct type_count<T,
+                  typename std::enable_if<is_wrapper<T>::value && !is_complex<T>::value && !is_tuple_like<T>::value &&
+                                          !is_mutable_container<T>::value>::type> {
+    static constexpr int value{type_count<typename T::value_type>::value};
+};
+
+/// 0 if the index > tuple size
+template <typename T, std::size_t I>
+constexpr typename std::enable_if<I == type_count_base<T>::value, int>::type tuple_type_size() {
+    return 0;
+}
+
+/// Recursively generate the tuple type name
+template <typename T, std::size_t I>
+    constexpr typename std::enable_if < I<type_count_base<T>::value, int>::type tuple_type_size() {
+    return subtype_count<typename std::tuple_element<I, T>::type>::value + tuple_type_size<T, I + 1>();
+}
+
+/// Get the type size of the sum of type sizes for all the individual tuple types
+template <typename T> struct type_count<T, typename std::enable_if<is_tuple_like<T>::value>::type> {
+    static constexpr int value{tuple_type_size<T, 0>()};
+};
+
+/// definition of subtype count
+template <typename T> struct subtype_count {
+    static constexpr int value{is_mutable_container<T>::value ? expected_max_vector_size : type_count<T>::value};
+};
+
+/// This will only trigger for actual void type
+template <typename T, typename Enable = void> struct type_count_min { static const int value{0}; };
+
+/// Type size for regular object types that do not look like a tuple
+template <typename T>
+struct type_count_min<
+    T,
+    typename std::enable_if<!is_mutable_container<T>::value && !is_tuple_like<T>::value && !is_wrapper<T>::value &&
+                            !is_complex<T>::value && !std::is_void<T>::value>::type> {
+    static constexpr int value{type_count<T>::value};
+};
+
+/// Type size for complex since it sometimes looks like a wrapper
+template <typename T> struct type_count_min<T, typename std::enable_if<is_complex<T>::value>::type> {
+    static constexpr int value{1};
+};
+
+/// Type size min of types that are wrappers,except complex and tuples(which can also be wrappers sometimes)
+template <typename T>
+struct type_count_min<
+    T,
+    typename std::enable_if<is_wrapper<T>::value && !is_complex<T>::value && !is_tuple_like<T>::value>::type> {
+    static constexpr int value{subtype_count_min<typename T::value_type>::value};
+};
+
+/// 0 if the index > tuple size
+template <typename T, std::size_t I>
+constexpr typename std::enable_if<I == type_count_base<T>::value, int>::type tuple_type_size_min() {
+    return 0;
+}
+
+/// Recursively generate the tuple type name
+template <typename T, std::size_t I>
+    constexpr typename std::enable_if < I<type_count_base<T>::value, int>::type tuple_type_size_min() {
+    return subtype_count_min<typename std::tuple_element<I, T>::type>::value + tuple_type_size_min<T, I + 1>();
+}
+
+/// Get the type size of the sum of type sizes for all the individual tuple types
+template <typename T> struct type_count_min<T, typename std::enable_if<is_tuple_like<T>::value>::type> {
+    static constexpr int value{tuple_type_size_min<T, 0>()};
+};
+
+/// definition of subtype count
+template <typename T> struct subtype_count_min {
+    static constexpr int value{is_mutable_container<T>::value
+                                   ? ((type_count<T>::value < expected_max_vector_size) ? type_count<T>::value : 0)
+                                   : type_count_min<T>::value};
 };
 
 /// This will only trigger for actual void type
@@ -1167,16 +1334,25 @@ template <typename T, typename Enable = void> struct expected_count { static con
 
 /// For most types the number of expected items is 1
 template <typename T>
-struct expected_count<T, typename std::enable_if<!is_vector<T>::value && !std::is_void<T>::value>::type> {
+struct expected_count<T,
+                      typename std::enable_if<!is_mutable_container<T>::value && !is_wrapper<T>::value &&
+                                              !std::is_void<T>::value>::type> {
     static constexpr int value{1};
 };
 /// number of expected items in a vector
-template <typename T> struct expected_count<T, typename std::enable_if<is_vector<T>::value>::type> {
+template <typename T> struct expected_count<T, typename std::enable_if<is_mutable_container<T>::value>::type> {
     static constexpr int value{expected_max_vector_size};
 };
 
+/// number of expected items in a vector
+template <typename T>
+struct expected_count<T, typename std::enable_if<!is_mutable_container<T>::value && is_wrapper<T>::value>::type> {
+    static constexpr int value{expected_count<typename T::value_type>::value};
+};
+
 // Enumeration of the different supported categorizations of objects
 enum class object_category : int {
+    char_value = 1,
     integral_value = 2,
     unsigned_integral = 4,
     enumeration = 6,
@@ -1185,36 +1361,48 @@ enum class object_category : int {
     number_constructible = 12,
     double_constructible = 14,
     integer_constructible = 16,
-    vector_value = 30,
-    tuple_value = 35,
-    // string assignable or greater used in a condition so anything string like must come last
-    string_assignable = 50,
-    string_constructible = 60,
-    other = 200,
+    // string like types
+    string_assignable = 23,
+    string_constructible = 24,
+    other = 45,
+    // special wrapper or container types
+    wrapper_value = 50,
+    complex_number = 60,
+    tuple_value = 70,
+    container_value = 80,
 
 };
 
+/// Set of overloads to classify an object according to type
+
 /// some type that is not otherwise recognized
 template <typename T, typename Enable = void> struct classify_object {
     static constexpr object_category value{object_category::other};
 };
 
-/// Set of overloads to classify an object according to type
+/// Signed integers
 template <typename T>
-struct classify_object<T,
-                       typename std::enable_if<std::is_integral<T>::value && std::is_signed<T>::value &&
-                                               !is_bool<T>::value && !std::is_enum<T>::value>::type> {
+struct classify_object<
+    T,
+    typename std::enable_if<std::is_integral<T>::value && !std::is_same<T, char>::value && std::is_signed<T>::value &&
+                            !is_bool<T>::value && !std::is_enum<T>::value>::type> {
     static constexpr object_category value{object_category::integral_value};
 };
 
 /// Unsigned integers
 template <typename T>
-struct classify_object<
-    T,
-    typename std::enable_if<std::is_integral<T>::value && std::is_unsigned<T>::value && !is_bool<T>::value>::type> {
+struct classify_object<T,
+                       typename std::enable_if<std::is_integral<T>::value && std::is_unsigned<T>::value &&
+                                               !std::is_same<T, char>::value && !is_bool<T>::value>::type> {
     static constexpr object_category value{object_category::unsigned_integral};
 };
 
+/// single character values
+template <typename T>
+struct classify_object<T, typename std::enable_if<std::is_same<T, char>::value && !std::is_enum<T>::value>::type> {
+    static constexpr object_category value{object_category::char_value};
+};
+
 /// Boolean values
 template <typename T> struct classify_object<T, typename std::enable_if<is_bool<T>::value>::type> {
     static constexpr object_category value{object_category::boolean_value};
@@ -1227,10 +1415,9 @@ template <typename T> struct classify_object<T, typename std::enable_if<std::is_
 
 /// String and similar direct assignment
 template <typename T>
-struct classify_object<
-    T,
-    typename std::enable_if<!std::is_floating_point<T>::value && !std::is_integral<T>::value &&
-                            std::is_assignable<T &, std::string>::value && !is_vector<T>::value>::type> {
+struct classify_object<T,
+                       typename std::enable_if<!std::is_floating_point<T>::value && !std::is_integral<T>::value &&
+                                               std::is_assignable<T &, std::string>::value>::type> {
     static constexpr object_category value{object_category::string_assignable};
 };
 
@@ -1239,8 +1426,8 @@ template <typename T>
 struct classify_object<
     T,
     typename std::enable_if<!std::is_floating_point<T>::value && !std::is_integral<T>::value &&
-                            !std::is_assignable<T &, std::string>::value &&
-                            std::is_constructible<T, std::string>::value && !is_vector<T>::value>::type> {
+                            !std::is_assignable<T &, std::string>::value && (type_count<T>::value == 1) &&
+                            std::is_constructible<T, std::string>::value>::type> {
     static constexpr object_category value{object_category::string_constructible};
 };
 
@@ -1249,23 +1436,35 @@ template <typename T> struct classify_object<T, typename std::enable_if<std::is_
     static constexpr object_category value{object_category::enumeration};
 };
 
+template <typename T> struct classify_object<T, typename std::enable_if<is_complex<T>::value>::type> {
+    static constexpr object_category value{object_category::complex_number};
+};
+
 /// Handy helper to contain a bunch of checks that rule out many common types (integers, string like, floating point,
 /// vectors, and enumerations
 template <typename T> struct uncommon_type {
     using type = typename std::conditional<!std::is_floating_point<T>::value && !std::is_integral<T>::value &&
                                                !std::is_assignable<T &, std::string>::value &&
-                                               !std::is_constructible<T, std::string>::value && !is_vector<T>::value &&
-                                               !std::is_enum<T>::value,
+                                               !std::is_constructible<T, std::string>::value && !is_complex<T>::value &&
+                                               !is_mutable_container<T>::value && !std::is_enum<T>::value,
                                            std::true_type,
                                            std::false_type>::type;
     static constexpr bool value = type::value;
 };
 
+/// wrapper type
+template <typename T>
+struct classify_object<T,
+                       typename std::enable_if<(!is_mutable_container<T>::value && is_wrapper<T>::value &&
+                                                !is_tuple_like<T>::value && uncommon_type<T>::value)>::type> {
+    static constexpr object_category value{object_category::wrapper_value};
+};
+
 /// Assignable from double or int
 template <typename T>
 struct classify_object<T,
                        typename std::enable_if<uncommon_type<T>::value && type_count<T>::value == 1 &&
-                                               is_direct_constructible<T, double>::value &&
+                                               !is_wrapper<T>::value && is_direct_constructible<T, double>::value &&
                                                is_direct_constructible<T, int>::value>::type> {
     static constexpr object_category value{object_category::number_constructible};
 };
@@ -1274,7 +1473,7 @@ struct classify_object<T,
 template <typename T>
 struct classify_object<T,
                        typename std::enable_if<uncommon_type<T>::value && type_count<T>::value == 1 &&
-                                               !is_direct_constructible<T, double>::value &&
+                                               !is_wrapper<T>::value && !is_direct_constructible<T, double>::value &&
                                                is_direct_constructible<T, int>::value>::type> {
     static constexpr object_category value{object_category::integer_constructible};
 };
@@ -1283,24 +1482,30 @@ struct classify_object<T,
 template <typename T>
 struct classify_object<T,
                        typename std::enable_if<uncommon_type<T>::value && type_count<T>::value == 1 &&
-                                               is_direct_constructible<T, double>::value &&
+                                               !is_wrapper<T>::value && is_direct_constructible<T, double>::value &&
                                                !is_direct_constructible<T, int>::value>::type> {
     static constexpr object_category value{object_category::double_constructible};
 };
 
 /// Tuple type
 template <typename T>
-struct classify_object<T,
-                       typename std::enable_if<(type_count<T>::value >= 2 && !is_vector<T>::value) ||
-                                               (is_tuple_like<T>::value && uncommon_type<T>::value &&
-                                                !is_direct_constructible<T, double>::value &&
-                                                !is_direct_constructible<T, int>::value)>::type> {
+struct classify_object<
+    T,
+    typename std::enable_if<is_tuple_like<T>::value &&
+                            ((type_count<T>::value >= 2 && !is_wrapper<T>::value) ||
+                             (uncommon_type<T>::value && !is_direct_constructible<T, double>::value &&
+                              !is_direct_constructible<T, int>::value))>::type> {
     static constexpr object_category value{object_category::tuple_value};
+    // the condition on this class requires it be like a tuple, but on some compilers (like Xcode) tuples can be
+    // constructed from just the first element so tuples of <string, int,int> can be constructed from a string, which
+    // could lead to issues so there are two variants of the condition, the first isolates things with a type size >=2
+    // mainly to get tuples on Xcode with the exception of wrappers, the second is the main one and just separating out
+    // those cases that are caught by other object classifications
 };
 
-/// Vector type
-template <typename T> struct classify_object<T, typename std::enable_if<is_vector<T>::value>::type> {
-    static constexpr object_category value{object_category::vector_value};
+/// container type
+template <typename T> struct classify_object<T, typename std::enable_if<is_mutable_container<T>::value>::type> {
+    static constexpr object_category value{object_category::container_value};
 };
 
 // Type name print
@@ -1309,6 +1514,12 @@ template <typename T> struct classify_object<T, typename std::enable_if<is_vecto
 ///  http://stackoverflow.com/questions/1055452/c-get-name-of-type-in-template
 /// But this is cleaner and works better in this case
 
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::char_value, detail::enabler> = detail::dummy>
+constexpr const char *type_name() {
+    return "CHAR";
+}
+
 template <typename T,
           enable_if_t<classify_object<T>::value == object_category::integral_value ||
                           classify_object<T>::value == object_category::integer_constructible,
@@ -1346,31 +1557,53 @@ constexpr const char *type_name() {
     return "BOOLEAN";
 }
 
+/// Print name for enumeration types
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::complex_number, detail::enabler> = detail::dummy>
+constexpr const char *type_name() {
+    return "COMPLEX";
+}
+
 /// Print for all other types
 template <typename T,
-          enable_if_t<classify_object<T>::value >= object_category::string_assignable, detail::enabler> = detail::dummy>
+          enable_if_t<classify_object<T>::value >= object_category::string_assignable &&
+                          classify_object<T>::value <= object_category::other,
+                      detail::enabler> = detail::dummy>
 constexpr const char *type_name() {
     return "TEXT";
 }
+/// typename for tuple value
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::tuple_value && type_count_base<T>::value >= 2,
+                      detail::enabler> = detail::dummy>
+std::string type_name();  // forward declaration
+
+/// Generate type name for a wrapper or container value
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::container_value ||
+                          classify_object<T>::value == object_category::wrapper_value,
+                      detail::enabler> = detail::dummy>
+std::string type_name();  // forward declaration
 
 /// Print name for single element tuple types
 template <typename T,
-          enable_if_t<classify_object<T>::value == object_category::tuple_value && type_count<T>::value == 1,
+          enable_if_t<classify_object<T>::value == object_category::tuple_value && type_count_base<T>::value == 1,
                       detail::enabler> = detail::dummy>
 inline std::string type_name() {
-    return type_name<typename std::tuple_element<0, T>::type>();
+    return type_name<typename std::decay<typename std::tuple_element<0, T>::type>::type>();
 }
 
 /// Empty string if the index > tuple size
 template <typename T, std::size_t I>
-inline typename std::enable_if<I == type_count<T>::value, std::string>::type tuple_name() {
+inline typename std::enable_if<I == type_count_base<T>::value, std::string>::type tuple_name() {
     return std::string{};
 }
 
 /// Recursively generate the tuple type name
 template <typename T, std::size_t I>
-    inline typename std::enable_if < I<type_count<T>::value, std::string>::type tuple_name() {
-    std::string str = std::string(type_name<typename std::tuple_element<I, T>::type>()) + ',' + tuple_name<T, I + 1>();
+inline typename std::enable_if<(I < type_count_base<T>::value), std::string>::type tuple_name() {
+    std::string str = std::string(type_name<typename std::decay<typename std::tuple_element<I, T>::type>::type>()) +
+                      ',' + tuple_name<T, I + 1>();
     if(str.back() == ',')
         str.pop_back();
     return str;
@@ -1378,23 +1611,49 @@ template <typename T, std::size_t I>
 
 /// Print type name for tuples with 2 or more elements
 template <typename T,
-          enable_if_t<classify_object<T>::value == object_category::tuple_value && type_count<T>::value >= 2,
-                      detail::enabler> = detail::dummy>
-std::string type_name() {
+          enable_if_t<classify_object<T>::value == object_category::tuple_value && type_count_base<T>::value >= 2,
+                      detail::enabler>>
+inline std::string type_name() {
     auto tname = std::string(1, '[') + tuple_name<T, 0>();
     tname.push_back(']');
     return tname;
 }
 
-/// This one should not be used normally, since vector types print the internal type
+/// get the type name for a type that has a value_type member
 template <typename T,
-          enable_if_t<classify_object<T>::value == object_category::vector_value, detail::enabler> = detail::dummy>
+          enable_if_t<classify_object<T>::value == object_category::container_value ||
+                          classify_object<T>::value == object_category::wrapper_value,
+                      detail::enabler>>
 inline std::string type_name() {
     return type_name<typename T::value_type>();
 }
 
 // Lexical cast
 
+/// Convert to an unsigned integral
+template <typename T, enable_if_t<std::is_unsigned<T>::value, detail::enabler> = detail::dummy>
+bool integral_conversion(const std::string &input, T &output) noexcept {
+    if(input.empty()) {
+        return false;
+    }
+    char *val = nullptr;
+    std::uint64_t output_ll = std::strtoull(input.c_str(), &val, 0);
+    output = static_cast<T>(output_ll);
+    return val == (input.c_str() + input.size()) && static_cast<std::uint64_t>(output) == output_ll;
+}
+
+/// Convert to a signed integral
+template <typename T, enable_if_t<std::is_signed<T>::value, detail::enabler> = detail::dummy>
+bool integral_conversion(const std::string &input, T &output) noexcept {
+    if(input.empty()) {
+        return false;
+    }
+    char *val = nullptr;
+    std::int64_t output_ll = std::strtoll(input.c_str(), &val, 0);
+    output = static_cast<T>(output_ll);
+    return val == (input.c_str() + input.size()) && static_cast<std::int64_t>(output) == output_ll;
+}
+
 /// Convert a flag into an integer value  typically binary flags
 inline std::int64_t to_flag_value(std::string val) {
     static const std::string trueString("true");
@@ -1438,39 +1697,24 @@ inline std::int64_t to_flag_value(std::string val) {
     return ret;
 }
 
-/// Signed integers
+/// Integer conversion
 template <typename T,
-          enable_if_t<classify_object<T>::value == object_category::integral_value, detail::enabler> = detail::dummy>
+          enable_if_t<classify_object<T>::value == object_category::integral_value ||
+                          classify_object<T>::value == object_category::unsigned_integral,
+                      detail::enabler> = detail::dummy>
 bool lexical_cast(const std::string &input, T &output) {
-    try {
-        std::size_t n = 0;
-        std::int64_t output_ll = std::stoll(input, &n, 0);
-        output = static_cast<T>(output_ll);
-        return n == input.size() && static_cast<std::int64_t>(output) == output_ll;
-    } catch(const std::invalid_argument &) {
-        return false;
-    } catch(const std::out_of_range &) {
-        return false;
-    }
+    return integral_conversion(input, output);
 }
 
-/// Unsigned integers
+/// char values
 template <typename T,
-          enable_if_t<classify_object<T>::value == object_category::unsigned_integral, detail::enabler> = detail::dummy>
+          enable_if_t<classify_object<T>::value == object_category::char_value, detail::enabler> = detail::dummy>
 bool lexical_cast(const std::string &input, T &output) {
-    if(!input.empty() && input.front() == '-')
-        return false;  // std::stoull happily converts negative values to junk without any errors.
-
-    try {
-        std::size_t n = 0;
-        std::uint64_t output_ll = std::stoull(input, &n, 0);
-        output = static_cast<T>(output_ll);
-        return n == input.size() && static_cast<std::uint64_t>(output) == output_ll;
-    } catch(const std::invalid_argument &) {
-        return false;
-    } catch(const std::out_of_range &) {
-        return false;
+    if(input.size() == 1) {
+        output = static_cast<T>(input[0]);
+        return true;
     }
+    return integral_conversion(input, output);
 }
 
 /// Boolean values
@@ -1495,15 +1739,45 @@ bool lexical_cast(const std::string &input, T &output) {
 template <typename T,
           enable_if_t<classify_object<T>::value == object_category::floating_point, detail::enabler> = detail::dummy>
 bool lexical_cast(const std::string &input, T &output) {
-    try {
-        std::size_t n = 0;
-        output = static_cast<T>(std::stold(input, &n));
-        return n == input.size();
-    } catch(const std::invalid_argument &) {
-        return false;
-    } catch(const std::out_of_range &) {
+    if(input.empty()) {
         return false;
     }
+    char *val = nullptr;
+    auto output_ld = std::strtold(input.c_str(), &val);
+    output = static_cast<T>(output_ld);
+    return val == (input.c_str() + input.size());
+}
+
+/// complex
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::complex_number, detail::enabler> = detail::dummy>
+bool lexical_cast(const std::string &input, T &output) {
+    using XC = typename wrapped_type<T, double>::type;
+    XC x{0.0}, y{0.0};
+    auto str1 = input;
+    bool worked = false;
+    auto nloc = str1.find_last_of("+-");
+    if(nloc != std::string::npos && nloc > 0) {
+        worked = detail::lexical_cast(str1.substr(0, nloc), x);
+        str1 = str1.substr(nloc);
+        if(str1.back() == 'i' || str1.back() == 'j')
+            str1.pop_back();
+        worked = worked && detail::lexical_cast(str1, y);
+    } else {
+        if(str1.back() == 'i' || str1.back() == 'j') {
+            str1.pop_back();
+            worked = detail::lexical_cast(str1, y);
+            x = XC{0};
+        } else {
+            worked = detail::lexical_cast(str1, x);
+            y = XC{0};
+        }
+    }
+    if(worked) {
+        output = T{x, y};
+        return worked;
+    }
+    return from_stream(input, output);
 }
 
 /// String and similar direct assignment
@@ -1528,21 +1802,47 @@ template <typename T,
           enable_if_t<classify_object<T>::value == object_category::enumeration, detail::enabler> = detail::dummy>
 bool lexical_cast(const std::string &input, T &output) {
     typename std::underlying_type<T>::type val;
-    bool retval = detail::lexical_cast(input, val);
-    if(!retval) {
+    if(!integral_conversion(input, val)) {
         return false;
     }
     output = static_cast<T>(val);
     return true;
 }
 
+/// wrapper types
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::wrapper_value &&
+                          std::is_assignable<T &, typename T::value_type>::value,
+                      detail::enabler> = detail::dummy>
+bool lexical_cast(const std::string &input, T &output) {
+    typename T::value_type val;
+    if(lexical_cast(input, val)) {
+        output = val;
+        return true;
+    }
+    return from_stream(input, output);
+}
+
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::wrapper_value &&
+                          !std::is_assignable<T &, typename T::value_type>::value && std::is_assignable<T &, T>::value,
+                      detail::enabler> = detail::dummy>
+bool lexical_cast(const std::string &input, T &output) {
+    typename T::value_type val;
+    if(lexical_cast(input, val)) {
+        output = T{val};
+        return true;
+    }
+    return from_stream(input, output);
+}
+
 /// Assignable from double or int
 template <
     typename T,
     enable_if_t<classify_object<T>::value == object_category::number_constructible, detail::enabler> = detail::dummy>
 bool lexical_cast(const std::string &input, T &output) {
     int val;
-    if(lexical_cast(input, val)) {
+    if(integral_conversion(input, val)) {
         output = T(val);
         return true;
     } else {
@@ -1561,7 +1861,7 @@ template <
     enable_if_t<classify_object<T>::value == object_category::integer_constructible, detail::enabler> = detail::dummy>
 bool lexical_cast(const std::string &input, T &output) {
     int val;
-    if(lexical_cast(input, val)) {
+    if(integral_conversion(input, val)) {
         output = T(val);
         return true;
     }
@@ -1581,8 +1881,36 @@ bool lexical_cast(const std::string &input, T &output) {
     return from_stream(input, output);
 }
 
+/// Non-string convertible from an int
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::other && std::is_assignable<T &, int>::value,
+                      detail::enabler> = detail::dummy>
+bool lexical_cast(const std::string &input, T &output) {
+    int val;
+    if(integral_conversion(input, val)) {
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable : 4800)
+#endif
+        // with Atomic<XX> this could produce a warning due to the conversion but if atomic gets here it is an old style
+        // so will most likely still work
+        output = val;
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+        return true;
+    }
+    // LCOV_EXCL_START
+    // This version of cast is only used for odd cases in an older compilers the fail over
+    // from_stream is tested elsewhere an not relevant for coverage here
+    return from_stream(input, output);
+    // LCOV_EXCL_STOP
+}
+
 /// Non-string parsable by a stream
-template <typename T, enable_if_t<classify_object<T>::value == object_category::other, detail::enabler> = detail::dummy>
+template <typename T,
+          enable_if_t<classify_object<T>::value == object_category::other && !std::is_assignable<T &, int>::value,
+                      detail::enabler> = detail::dummy>
 bool lexical_cast(const std::string &input, T &output) {
     static_assert(is_istreamable<T>::value,
                   "option object type must have a lexical cast overload or streaming input operator(>>) defined, if it "
@@ -1591,38 +1919,77 @@ bool lexical_cast(const std::string &input, T &output) {
 }
 
 /// Assign a value through lexical cast operations
-template <
-    typename T,
-    typename XC,
-    enable_if_t<std::is_same<T, XC>::value && (classify_object<T>::value == object_category::string_assignable ||
-                                               classify_object<T>::value == object_category::string_constructible),
-                detail::enabler> = detail::dummy>
-bool lexical_assign(const std::string &input, T &output) {
+/// Strings can be empty so we need to do a little different
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<std::is_same<AssignTo, ConvertTo>::value &&
+                          (classify_object<AssignTo>::value == object_category::string_assignable ||
+                           classify_object<AssignTo>::value == object_category::string_constructible),
+                      detail::enabler> = detail::dummy>
+bool lexical_assign(const std::string &input, AssignTo &output) {
     return lexical_cast(input, output);
 }
 
 /// Assign a value through lexical cast operations
-template <typename T,
-          typename XC,
-          enable_if_t<std::is_same<T, XC>::value && classify_object<T>::value != object_category::string_assignable &&
-                          classify_object<T>::value != object_category::string_constructible,
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<std::is_same<AssignTo, ConvertTo>::value && std::is_assignable<AssignTo &, AssignTo>::value &&
+                          classify_object<AssignTo>::value != object_category::string_assignable &&
+                          classify_object<AssignTo>::value != object_category::string_constructible,
                       detail::enabler> = detail::dummy>
-bool lexical_assign(const std::string &input, T &output) {
+bool lexical_assign(const std::string &input, AssignTo &output) {
     if(input.empty()) {
-        output = T{};
+        output = AssignTo{};
+        return true;
+    }
+
+    return lexical_cast(input, output);
+}
+
+/// Assign a value through lexical cast operations
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<std::is_same<AssignTo, ConvertTo>::value && !std::is_assignable<AssignTo &, AssignTo>::value &&
+                          classify_object<AssignTo>::value == object_category::wrapper_value,
+                      detail::enabler> = detail::dummy>
+bool lexical_assign(const std::string &input, AssignTo &output) {
+    if(input.empty()) {
+        typename AssignTo::value_type emptyVal{};
+        output = emptyVal;
         return true;
     }
     return lexical_cast(input, output);
 }
 
+/// Assign a value through lexical cast operations for int compatible values
+/// mainly for atomic operations on some compilers
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<std::is_same<AssignTo, ConvertTo>::value && !std::is_assignable<AssignTo &, AssignTo>::value &&
+                          classify_object<AssignTo>::value != object_category::wrapper_value &&
+                          std::is_assignable<AssignTo &, int>::value,
+                      detail::enabler> = detail::dummy>
+bool lexical_assign(const std::string &input, AssignTo &output) {
+    if(input.empty()) {
+        output = 0;
+        return true;
+    }
+    int val;
+    if(lexical_cast(input, val)) {
+        output = val;
+        return true;
+    }
+    return false;
+}
+
 /// Assign a value converted from a string in lexical cast to the output value directly
-template <
-    typename T,
-    typename XC,
-    enable_if_t<!std::is_same<T, XC>::value && std::is_assignable<T &, XC &>::value, detail::enabler> = detail::dummy>
-bool lexical_assign(const std::string &input, T &output) {
-    XC val{};
-    bool parse_result = (!input.empty()) ? lexical_cast<XC>(input, val) : true;
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<!std::is_same<AssignTo, ConvertTo>::value && std::is_assignable<AssignTo &, ConvertTo &>::value,
+                      detail::enabler> = detail::dummy>
+bool lexical_assign(const std::string &input, AssignTo &output) {
+    ConvertTo val{};
+    bool parse_result = (!input.empty()) ? lexical_cast<ConvertTo>(input, val) : true;
     if(parse_result) {
         output = val;
     }
@@ -1630,84 +1997,242 @@ bool lexical_assign(const std::string &input, T &output) {
 }
 
 /// Assign a value from a lexical cast through constructing a value and move assigning it
-template <typename T,
-          typename XC,
-          enable_if_t<!std::is_same<T, XC>::value && !std::is_assignable<T &, XC &>::value &&
-                          std::is_move_assignable<T>::value,
-                      detail::enabler> = detail::dummy>
-bool lexical_assign(const std::string &input, T &output) {
-    XC val{};
-    bool parse_result = input.empty() ? true : lexical_cast<XC>(input, val);
+template <
+    typename AssignTo,
+    typename ConvertTo,
+    enable_if_t<!std::is_same<AssignTo, ConvertTo>::value && !std::is_assignable<AssignTo &, ConvertTo &>::value &&
+                    std::is_move_assignable<AssignTo>::value,
+                detail::enabler> = detail::dummy>
+bool lexical_assign(const std::string &input, AssignTo &output) {
+    ConvertTo val{};
+    bool parse_result = input.empty() ? true : lexical_cast<ConvertTo>(input, val);
     if(parse_result) {
-        output = T(val);  // use () form of constructor to allow some implicit conversions
+        output = AssignTo(val);  // use () form of constructor to allow some implicit conversions
     }
     return parse_result;
 }
-/// Lexical conversion if there is only one element
-template <
-    typename T,
-    typename XC,
-    enable_if_t<!is_tuple_like<T>::value && !is_tuple_like<XC>::value && !is_vector<T>::value && !is_vector<XC>::value,
-                detail::enabler> = detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
-    return lexical_assign<T, XC>(strings[0], output);
+
+/// primary lexical conversion operation, 1 string to 1 type of some kind
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<classify_object<ConvertTo>::value <= object_category::other &&
+                          classify_object<AssignTo>::value <= object_category::wrapper_value,
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std ::string> &strings, AssignTo &output) {
+    return lexical_assign<AssignTo, ConvertTo>(strings[0], output);
 }
 
-/// Lexical conversion if there is only one element but the conversion type is for two call a two element constructor
-template <typename T,
-          typename XC,
-          enable_if_t<type_count<T>::value == 1 && type_count<XC>::value == 2, detail::enabler> = detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
-    typename std::tuple_element<0, XC>::type v1;
-    typename std::tuple_element<1, XC>::type v2;
+/// Lexical conversion if there is only one element but the conversion type is for two, then call a two element
+/// constructor
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<(type_count<AssignTo>::value <= 2) && expected_count<AssignTo>::value == 1 &&
+                          is_tuple_like<ConvertTo>::value && type_count_base<ConvertTo>::value == 2,
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std ::string> &strings, AssignTo &output) {
+    // the remove const is to handle pair types coming from a container
+    typename std::remove_const<typename std::tuple_element<0, ConvertTo>::type>::type v1;
+    typename std::tuple_element<1, ConvertTo>::type v2;
     bool retval = lexical_assign<decltype(v1), decltype(v1)>(strings[0], v1);
     if(strings.size() > 1) {
         retval = retval && lexical_assign<decltype(v2), decltype(v2)>(strings[1], v2);
     }
     if(retval) {
-        output = T{v1, v2};
+        output = AssignTo{v1, v2};
     }
     return retval;
 }
 
-/// Lexical conversion of a vector types
-template <class T,
-          class XC,
-          enable_if_t<expected_count<T>::value == expected_max_vector_size &&
-                          expected_count<XC>::value == expected_max_vector_size && type_count<XC>::value == 1,
+/// Lexical conversion of a container types of single elements
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_mutable_container<AssignTo>::value && is_mutable_container<ConvertTo>::value &&
+                          type_count<ConvertTo>::value == 1,
                       detail::enabler> = detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
+bool lexical_conversion(const std::vector<std ::string> &strings, AssignTo &output) {
+    output.erase(output.begin(), output.end());
+    for(const auto &elem : strings) {
+        typename AssignTo::value_type out;
+        bool retval = lexical_assign<typename AssignTo::value_type, typename ConvertTo::value_type>(elem, out);
+        if(!retval) {
+            return false;
+        }
+        output.insert(output.end(), std::move(out));
+    }
+    return (!output.empty());
+}
+
+/// Lexical conversion for complex types
+template <class AssignTo, class ConvertTo, enable_if_t<is_complex<ConvertTo>::value, detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std::string> &strings, AssignTo &output) {
+
+    if(strings.size() >= 2 && !strings[1].empty()) {
+        using XC2 = typename wrapped_type<ConvertTo, double>::type;
+        XC2 x{0.0}, y{0.0};
+        auto str1 = strings[1];
+        if(str1.back() == 'i' || str1.back() == 'j') {
+            str1.pop_back();
+        }
+        auto worked = detail::lexical_cast(strings[0], x) && detail::lexical_cast(str1, y);
+        if(worked) {
+            output = ConvertTo{x, y};
+        }
+        return worked;
+    } else {
+        return lexical_assign<AssignTo, ConvertTo>(strings[0], output);
+    }
+}
+
+/// Conversion to a vector type using a particular single type as the conversion type
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_mutable_container<AssignTo>::value && (expected_count<ConvertTo>::value == 1) &&
+                          (type_count<ConvertTo>::value == 1),
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std ::string> &strings, AssignTo &output) {
+    bool retval = true;
     output.clear();
     output.reserve(strings.size());
     for(const auto &elem : strings) {
 
         output.emplace_back();
-        bool retval = lexical_assign<typename T::value_type, typename XC::value_type>(elem, output.back());
-        if(!retval) {
-            return false;
-        }
+        retval = retval && lexical_assign<typename AssignTo::value_type, ConvertTo>(elem, output.back());
     }
-    return (!output.empty());
+    return (!output.empty()) && retval;
 }
 
-/// Lexical conversion of a vector types with type size of two
-template <class T,
-          class XC,
-          enable_if_t<expected_count<T>::value == expected_max_vector_size &&
-                          expected_count<XC>::value == expected_max_vector_size && type_count<XC>::value == 2,
-                      detail::enabler> = detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
-    output.clear();
-    for(std::size_t ii = 0; ii < strings.size(); ii += 2) {
+// forward declaration
 
-        typename std::tuple_element<0, typename XC::value_type>::type v1;
-        typename std::tuple_element<1, typename XC::value_type>::type v2;
-        bool retval = lexical_assign<decltype(v1), decltype(v1)>(strings[ii], v1);
-        if(strings.size() > ii + 1) {
-            retval = retval && lexical_assign<decltype(v2), decltype(v2)>(strings[ii + 1], v2);
+/// Lexical conversion of a container types with conversion type of two elements
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_mutable_container<AssignTo>::value && is_mutable_container<ConvertTo>::value &&
+                          type_count_base<ConvertTo>::value == 2,
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(std::vector<std::string> strings, AssignTo &output);
+
+/// Lexical conversion of a vector types with type_size >2 forward declaration
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_mutable_container<AssignTo>::value && is_mutable_container<ConvertTo>::value &&
+                          type_count_base<ConvertTo>::value != 2 &&
+                          ((type_count<ConvertTo>::value > 2) ||
+                           (type_count<ConvertTo>::value > type_count_base<ConvertTo>::value)),
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std::string> &strings, AssignTo &output);
+
+/// Conversion for tuples
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_tuple_like<AssignTo>::value && is_tuple_like<ConvertTo>::value &&
+                          (type_count_base<ConvertTo>::value != type_count<ConvertTo>::value ||
+                           type_count<ConvertTo>::value > 2),
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std::string> &strings, AssignTo &output);  // forward declaration
+
+/// Conversion for operations where the assigned type is some class but the conversion is a mutable container or large
+/// tuple
+template <typename AssignTo,
+          typename ConvertTo,
+          enable_if_t<!is_tuple_like<AssignTo>::value && !is_mutable_container<AssignTo>::value &&
+                          classify_object<ConvertTo>::value != object_category::wrapper_value &&
+                          (is_mutable_container<ConvertTo>::value || type_count<ConvertTo>::value > 2),
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std ::string> &strings, AssignTo &output) {
+
+    if(strings.size() > 1 || (!strings.empty() && !(strings.front().empty()))) {
+        ConvertTo val;
+        auto retval = lexical_conversion<ConvertTo, ConvertTo>(strings, val);
+        output = AssignTo{val};
+        return retval;
+    }
+    output = AssignTo{};
+    return true;
+}
+
+/// function template for converting tuples if the static Index is greater than the tuple size
+template <class AssignTo, class ConvertTo, std::size_t I>
+inline typename std::enable_if<(I >= type_count_base<AssignTo>::value), bool>::type
+tuple_conversion(const std::vector<std::string> &, AssignTo &) {
+    return true;
+}
+
+/// Conversion of a tuple element where the type size ==1 and not a mutable container
+template <class AssignTo, class ConvertTo>
+inline typename std::enable_if<!is_mutable_container<ConvertTo>::value && type_count<ConvertTo>::value == 1, bool>::type
+tuple_type_conversion(std::vector<std::string> &strings, AssignTo &output) {
+    auto retval = lexical_assign<AssignTo, ConvertTo>(strings[0], output);
+    strings.erase(strings.begin());
+    return retval;
+}
+
+/// Conversion of a tuple element where the type size !=1 but the size is fixed and not a mutable container
+template <class AssignTo, class ConvertTo>
+inline typename std::enable_if<!is_mutable_container<ConvertTo>::value && (type_count<ConvertTo>::value > 1) &&
+                                   type_count<ConvertTo>::value == type_count_min<ConvertTo>::value,
+                               bool>::type
+tuple_type_conversion(std::vector<std::string> &strings, AssignTo &output) {
+    auto retval = lexical_conversion<AssignTo, ConvertTo>(strings, output);
+    strings.erase(strings.begin(), strings.begin() + type_count<ConvertTo>::value);
+    return retval;
+}
+
+/// Conversion of a tuple element where the type is a mutable container or a type with different min and max type sizes
+template <class AssignTo, class ConvertTo>
+inline typename std::enable_if<is_mutable_container<ConvertTo>::value ||
+                                   type_count<ConvertTo>::value != type_count_min<ConvertTo>::value,
+                               bool>::type
+tuple_type_conversion(std::vector<std::string> &strings, AssignTo &output) {
+
+    std::size_t index{subtype_count_min<ConvertTo>::value};
+    const std::size_t mx_count{subtype_count<ConvertTo>::value};
+    const std::size_t mx{(std::max)(mx_count, strings.size())};
+
+    while(index < mx) {
+        if(is_separator(strings[index])) {
+            break;
+        }
+        ++index;
+    }
+    bool retval = lexical_conversion<AssignTo, ConvertTo>(
+        std::vector<std::string>(strings.begin(), strings.begin() + static_cast<std::ptrdiff_t>(index)), output);
+    strings.erase(strings.begin(), strings.begin() + static_cast<std::ptrdiff_t>(index) + 1);
+    return retval;
+}
+
+/// Tuple conversion operation
+template <class AssignTo, class ConvertTo, std::size_t I>
+inline typename std::enable_if<(I < type_count_base<AssignTo>::value), bool>::type
+tuple_conversion(std::vector<std::string> strings, AssignTo &output) {
+    bool retval = true;
+    using ConvertToElement = typename std::
+        conditional<is_tuple_like<ConvertTo>::value, typename std::tuple_element<I, ConvertTo>::type, ConvertTo>::type;
+    if(!strings.empty()) {
+        retval = retval && tuple_type_conversion<typename std::tuple_element<I, AssignTo>::type, ConvertToElement>(
+                               strings, std::get<I>(output));
+    }
+    retval = retval && tuple_conversion<AssignTo, ConvertTo, I + 1>(std::move(strings), output);
+    return retval;
+}
+
+/// Lexical conversion of a container types with tuple elements of size 2
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_mutable_container<AssignTo>::value && is_mutable_container<ConvertTo>::value &&
+                          type_count_base<ConvertTo>::value == 2,
+                      detail::enabler>>
+bool lexical_conversion(std::vector<std::string> strings, AssignTo &output) {
+    output.clear();
+    while(!strings.empty()) {
+
+        typename std::remove_const<typename std::tuple_element<0, typename ConvertTo::value_type>::type>::type v1;
+        typename std::tuple_element<1, typename ConvertTo::value_type>::type v2;
+        bool retval = tuple_type_conversion<decltype(v1), decltype(v1)>(strings, v1);
+        if(!strings.empty()) {
+            retval = retval && tuple_type_conversion<decltype(v2), decltype(v2)>(strings, v2);
         }
         if(retval) {
-            output.emplace_back(v1, v2);
+            output.insert(output.end(), typename AssignTo::value_type{v1, v2});
         } else {
             return false;
         }
@@ -1715,110 +2240,103 @@ bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
     return (!output.empty());
 }
 
-/// Conversion to a vector type using a particular single type as the conversion type
-template <class T,
-          class XC,
-          enable_if_t<(expected_count<T>::value == expected_max_vector_size) && (expected_count<XC>::value == 1) &&
-                          (type_count<XC>::value == 1),
-                      detail::enabler> = detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
-    bool retval = true;
-    output.clear();
-    output.reserve(strings.size());
-    for(const auto &elem : strings) {
-
-        output.emplace_back();
-        retval = retval && lexical_assign<typename T::value_type, XC>(elem, output.back());
-    }
-    return (!output.empty()) && retval;
-}
-// This one is last since it can call other lexical_conversion functions
-/// Lexical conversion if there is only one element but the conversion type is a vector
-template <typename T,
-          typename XC,
-          enable_if_t<!is_tuple_like<T>::value && !is_vector<T>::value && is_vector<XC>::value, detail::enabler> =
-              detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
-
-    if(strings.size() > 1 || (!strings.empty() && !(strings.front().empty()))) {
-        XC val;
-        auto retval = lexical_conversion<XC, XC>(strings, val);
-        output = T{val};
-        return retval;
-    }
-    output = T{};
-    return true;
-}
-
-/// function template for converting tuples if the static Index is greater than the tuple size
-template <class T, class XC, std::size_t I>
-inline typename std::enable_if<I >= type_count<T>::value, bool>::type tuple_conversion(const std::vector<std::string> &,
-                                                                                       T &) {
-    return true;
-}
-/// Tuple conversion operation
-template <class T, class XC, std::size_t I>
-    inline typename std::enable_if <
-    I<type_count<T>::value, bool>::type tuple_conversion(const std::vector<std::string> &strings, T &output) {
-    bool retval = true;
-    if(strings.size() > I) {
-        retval = retval && lexical_assign<typename std::tuple_element<I, T>::type,
-                                          typename std::conditional<is_tuple_like<XC>::value,
-                                                                    typename std::tuple_element<I, XC>::type,
-                                                                    XC>::type>(strings[I], std::get<I>(output));
-    }
-    retval = retval && tuple_conversion<T, XC, I + 1>(strings, output);
-    return retval;
-}
-
-/// Conversion for tuples
-template <class T, class XC, enable_if_t<is_tuple_like<T>::value, detail::enabler> = detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
+/// lexical conversion of tuples with type count>2 or tuples of types of some element with a type size>=2
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_tuple_like<AssignTo>::value && is_tuple_like<ConvertTo>::value &&
+                          (type_count_base<ConvertTo>::value != type_count<ConvertTo>::value ||
+                           type_count<ConvertTo>::value > 2),
+                      detail::enabler>>
+bool lexical_conversion(const std::vector<std ::string> &strings, AssignTo &output) {
     static_assert(
-        !is_tuple_like<XC>::value || type_count<T>::value == type_count<XC>::value,
+        !is_tuple_like<ConvertTo>::value || type_count_base<AssignTo>::value == type_count_base<ConvertTo>::value,
         "if the conversion type is defined as a tuple it must be the same size as the type you are converting to");
-    return tuple_conversion<T, XC, 0>(strings, output);
+    return tuple_conversion<AssignTo, ConvertTo, 0>(strings, output);
 }
 
-/// Lexical conversion of a vector types with type_size >2
-template <class T,
-          class XC,
-          enable_if_t<expected_count<T>::value == expected_max_vector_size &&
-                          expected_count<XC>::value == expected_max_vector_size && (type_count<XC>::value > 2),
-                      detail::enabler> = detail::dummy>
-bool lexical_conversion(const std::vector<std ::string> &strings, T &output) {
+/// Lexical conversion of a vector types for everything but tuples of two elements and types of size 1
+template <class AssignTo,
+          class ConvertTo,
+          enable_if_t<is_mutable_container<AssignTo>::value && is_mutable_container<ConvertTo>::value &&
+                          type_count_base<ConvertTo>::value != 2 &&
+                          ((type_count<ConvertTo>::value > 2) ||
+                           (type_count<ConvertTo>::value > type_count_base<ConvertTo>::value)),
+                      detail::enabler>>
+bool lexical_conversion(const std::vector<std ::string> &strings, AssignTo &output) {
     bool retval = true;
     output.clear();
     std::vector<std::string> temp;
-    std::size_t ii = 0;
-    std::size_t icount = 0;
-    std::size_t xcm = type_count<XC>::value;
-    while(ii < strings.size()) {
+    std::size_t ii{0};
+    std::size_t icount{0};
+    std::size_t xcm{type_count<ConvertTo>::value};
+    auto ii_max = strings.size();
+    while(ii < ii_max) {
         temp.push_back(strings[ii]);
         ++ii;
         ++icount;
-        if(icount == xcm || temp.back().empty()) {
-            if(static_cast<int>(xcm) == expected_max_vector_size) {
+        if(icount == xcm || is_separator(temp.back()) || ii == ii_max) {
+            if(static_cast<int>(xcm) > type_count_min<ConvertTo>::value && is_separator(temp.back())) {
                 temp.pop_back();
             }
-            output.emplace_back();
-            retval = retval && lexical_conversion<typename T::value_type, typename XC::value_type>(temp, output.back());
+            typename AssignTo::value_type temp_out;
+            retval = retval &&
+                     lexical_conversion<typename AssignTo::value_type, typename ConvertTo::value_type>(temp, temp_out);
             temp.clear();
             if(!retval) {
                 return false;
             }
+            output.insert(output.end(), std::move(temp_out));
             icount = 0;
         }
     }
     return retval;
 }
+
+/// conversion for wrapper types
+template <typename AssignTo,
+          class ConvertTo,
+          enable_if_t<classify_object<ConvertTo>::value == object_category::wrapper_value &&
+                          std::is_assignable<ConvertTo &, ConvertTo>::value,
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std::string> &strings, AssignTo &output) {
+    if(strings.empty() || strings.front().empty()) {
+        output = ConvertTo{};
+        return true;
+    }
+    typename ConvertTo::value_type val;
+    if(lexical_conversion<typename ConvertTo::value_type, typename ConvertTo::value_type>(strings, val)) {
+        output = ConvertTo{val};
+        return true;
+    }
+    return false;
+}
+
+/// conversion for wrapper types
+template <typename AssignTo,
+          class ConvertTo,
+          enable_if_t<classify_object<ConvertTo>::value == object_category::wrapper_value &&
+                          !std::is_assignable<AssignTo &, ConvertTo>::value,
+                      detail::enabler> = detail::dummy>
+bool lexical_conversion(const std::vector<std::string> &strings, AssignTo &output) {
+    using ConvertType = typename ConvertTo::value_type;
+    if(strings.empty() || strings.front().empty()) {
+        output = ConvertType{};
+        return true;
+    }
+    ConvertType val;
+    if(lexical_conversion<typename ConvertTo::value_type, typename ConvertTo::value_type>(strings, val)) {
+        output = val;
+        return true;
+    }
+    return false;
+}
+
 /// Sum a vector of flag representations
 /// The flag vector produces a series of strings in a vector,  simple true is represented by a "1",  simple false is
 /// by
 /// "-1" an if numbers are passed by some fashion they are captured as well so the function just checks for the most
 /// common true and false strings then uses stoll to convert the rest for summing
-template <typename T,
-          enable_if_t<std::is_integral<T>::value && std::is_unsigned<T>::value, detail::enabler> = detail::dummy>
+template <typename T, enable_if_t<std::is_unsigned<T>::value, detail::enabler> = detail::dummy>
 void sum_flag_vector(const std::vector<std::string> &flags, T &output) {
     std::int64_t count{0};
     for(auto &flag : flags) {
@@ -1832,8 +2350,7 @@ void sum_flag_vector(const std::vector<std::string> &flags, T &output) {
 /// by
 /// "-1" an if numbers are passed by some fashion they are captured as well so the function just checks for the most
 /// common true and false strings then uses stoll to convert the rest for summing
-template <typename T,
-          enable_if_t<std::is_integral<T>::value && std::is_signed<T>::value, detail::enabler> = detail::dummy>
+template <typename T, enable_if_t<std::is_signed<T>::value, detail::enabler> = detail::dummy>
 void sum_flag_vector(const std::vector<std::string> &flags, T &output) {
     std::int64_t count{0};
     for(auto &flag : flags) {
@@ -1842,12 +2359,37 @@ void sum_flag_vector(const std::vector<std::string> &flags, T &output) {
     output = static_cast<T>(count);
 }
 
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable : 4800)
+#endif
+// with Atomic<XX> this could produce a warning due to the conversion but if atomic gets here it is an old style so will
+// most likely still work
+
+/// Sum a vector of flag representations
+/// The flag vector produces a series of strings in a vector,  simple true is represented by a "1",  simple false is
+/// by
+/// "-1" an if numbers are passed by some fashion they are captured as well so the function just checks for the most
+/// common true and false strings then uses stoll to convert the rest for summing
+template <typename T,
+          enable_if_t<!std::is_signed<T>::value && !std::is_unsigned<T>::value, detail::enabler> = detail::dummy>
+void sum_flag_vector(const std::vector<std::string> &flags, T &output) {
+    std::int64_t count{0};
+    for(auto &flag : flags) {
+        count += detail::to_flag_value(flag);
+    }
+    std::string out = detail::to_string(count);
+    lexical_cast(out, output);
+}
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
 }  // namespace detail
-}  // namespace CLI
 
-// From Split.hpp:
 
-namespace CLI {
+
 namespace detail {
 
 // Returns false if not a short option. Otherwise, sets opt name and rest and returns true
@@ -1968,11 +2510,8 @@ get_names(const std::vector<std::string> &input) {
 }
 
 }  // namespace detail
-}  // namespace CLI
 
-// From ConfigFwd.hpp:
 
-namespace CLI {
 
 class App;
 
@@ -2028,19 +2567,23 @@ class Config {
     virtual ~Config() = default;
 };
 
-/// This converter works with INI/TOML files; to write proper TOML files use ConfigTOML
+/// This converter works with INI/TOML files; to write INI files use ConfigINI
 class ConfigBase : public Config {
   protected:
     /// the character used for comments
-    char commentChar = ';';
+    char commentChar = '#';
     /// the character used to start an array '\0' is a default to not use
-    char arrayStart = '\0';
+    char arrayStart = '[';
     /// the character used to end an array '\0' is a default to not use
-    char arrayEnd = '\0';
+    char arrayEnd = ']';
     /// the character used to separate elements in an array
-    char arraySeparator = ' ';
+    char arraySeparator = ',';
     /// the character used separate the name from the value
     char valueDelimiter = '=';
+    /// the character to use around strings
+    char stringQuote = '"';
+    /// the character to use around single characters
+    char characterQuote = '\'';
 
   public:
     std::string
@@ -2068,28 +2611,31 @@ class ConfigBase : public Config {
         valueDelimiter = vSep;
         return this;
     }
+    /// Specify the quote characters used around strings and characters
+    ConfigBase *quoteCharacter(char qString, char qChar) {
+        stringQuote = qString;
+        characterQuote = qChar;
+        return this;
+    }
 };
 
-/// the default Config is the INI file format
-using ConfigINI = ConfigBase;
+/// the default Config is the TOML file format
+using ConfigTOML = ConfigBase;
 
-/// ConfigTOML generates a TOML compliant output
-class ConfigTOML : public ConfigINI {
+/// ConfigINI generates a "standard" INI compliant output
+class ConfigINI : public ConfigTOML {
 
   public:
-    ConfigTOML() {
-        commentChar = '#';
-        arrayStart = '[';
-        arrayEnd = ']';
-        arraySeparator = ',';
+    ConfigINI() {
+        commentChar = ';';
+        arrayStart = '\0';
+        arrayEnd = '\0';
+        arraySeparator = ' ';
         valueDelimiter = '=';
     }
 };
-}  // namespace CLI
 
-// From Validators.hpp:
 
-namespace CLI {
 
 class Option;
 
@@ -2451,53 +2997,6 @@ class IPV4Validator : public Validator {
     }
 };
 
-/// Validate the argument is a number and greater than 0
-class PositiveNumber : public Validator {
-  public:
-    PositiveNumber() : Validator("POSITIVE") {
-        func_ = [](std::string &number_str) {
-            double number;
-            if(!detail::lexical_cast(number_str, number)) {
-                return std::string("Failed parsing number: (") + number_str + ')';
-            }
-            if(number <= 0) {
-                return std::string("Number less or equal to 0: (") + number_str + ')';
-            }
-            return std::string();
-        };
-    }
-};
-/// Validate the argument is a number and greater than or equal to 0
-class NonNegativeNumber : public Validator {
-  public:
-    NonNegativeNumber() : Validator("NONNEGATIVE") {
-        func_ = [](std::string &number_str) {
-            double number;
-            if(!detail::lexical_cast(number_str, number)) {
-                return std::string("Failed parsing number: (") + number_str + ')';
-            }
-            if(number < 0) {
-                return std::string("Number less than 0: (") + number_str + ')';
-            }
-            return std::string();
-        };
-    }
-};
-
-/// Validate the argument is a number
-class Number : public Validator {
-  public:
-    Number() : Validator("NUMBER") {
-        func_ = [](std::string &number_str) {
-            double number;
-            if(!detail::lexical_cast(number_str, number)) {
-                return std::string("Failed parsing as a number (") + number_str + ')';
-            }
-            return std::string();
-        };
-    }
-};
-
 }  // namespace detail
 
 // Static is not needed here, because global const implies static.
@@ -2517,14 +3016,23 @@ const detail::NonexistentPathValidator NonexistentPath;
 /// Check for an IP4 address
 const detail::IPV4Validator ValidIPV4;
 
-/// Check for a positive number
-const detail::PositiveNumber PositiveNumber;
-
-/// Check for a non-negative number
-const detail::NonNegativeNumber NonNegativeNumber;
+/// Validate the input as a particular type
+template <typename DesiredType> class TypeValidator : public Validator {
+  public:
+    explicit TypeValidator(const std::string &validator_name) : Validator(validator_name) {
+        func_ = [](std::string &input_string) {
+            auto val = DesiredType();
+            if(!detail::lexical_cast(input_string, val)) {
+                return std::string("Failed parsing ") + input_string + " as a " + detail::type_name<DesiredType>();
+            }
+            return std::string();
+        };
+    }
+    TypeValidator() : TypeValidator(detail::type_name<DesiredType>()) {}
+};
 
 /// Check for a number
-const detail::Number Number;
+const TypeValidator<double> Number("NUMBER");
 
 /// Produce a range (factory). Min and max are inclusive.
 class Range : public Validator {
@@ -2533,10 +3041,13 @@ class Range : public Validator {
     ///
     /// Note that the constructor is templated, but the struct is not, so C++17 is not
     /// needed to provide nice syntax for Range(a,b).
-    template <typename T> Range(T min, T max) {
-        std::stringstream out;
-        out << detail::type_name<T>() << " in [" << min << " - " << max << "]";
-        description(out.str());
+    template <typename T>
+    Range(T min, T max, const std::string &validator_name = std::string{}) : Validator(validator_name) {
+        if(validator_name.empty()) {
+            std::stringstream out;
+            out << detail::type_name<T>() << " in [" << min << " - " << max << "]";
+            description(out.str());
+        }
 
         func_ = [min, max](std::string &input) {
             T val;
@@ -2550,9 +3061,17 @@ class Range : public Validator {
     }
 
     /// Range of one value is 0 to value
-    template <typename T> explicit Range(T max) : Range(static_cast<T>(0), max) {}
+    template <typename T>
+    explicit Range(T max, const std::string &validator_name = std::string{})
+        : Range(static_cast<T>(0), max, validator_name) {}
 };
 
+/// Check for a non negative number
+const Range NonNegativeNumber(std::numeric_limits<double>::max(), "NONNEGATIVE");
+
+/// Check for a positive valued number (val>0.0), min() her is the smallest positive number
+const Range PositiveNumber(std::numeric_limits<double>::min(), std::numeric_limits<double>::max(), "POSITIVE");
+
 /// Produce a bounded range (factory). Min and max are inclusive.
 class Bound : public Validator {
   public:
@@ -2778,9 +3297,7 @@ class IsMember : public Validator {
             }
 
             // If you reach this point, the result was not found
-            std::string out(" not in ");
-            out += detail::generate_set(detail::smart_deref(set));
-            return out;
+            return input + " not in " + detail::generate_set(detail::smart_deref(set));
         };
     }
 
@@ -3000,14 +3517,11 @@ class AsNumberWithUnit : public Validator {
             if(opts & CASE_INSENSITIVE) {
                 unit = detail::to_lower(unit);
             }
-
-            bool converted = detail::lexical_cast(input, num);
-            if(!converted) {
-                throw ValidationError(std::string("Value ") + input + " could not be converted to " +
-                                      detail::type_name<Number>());
-            }
-
             if(unit.empty()) {
+                if(!detail::lexical_cast(input, num)) {
+                    throw ValidationError(std::string("Value ") + input + " could not be converted to " +
+                                          detail::type_name<Number>());
+                }
                 // No need to modify input if no unit passed
                 return {};
             }
@@ -3021,12 +3535,22 @@ class AsNumberWithUnit : public Validator {
                                       detail::generate_map(mapping, true));
             }
 
-            // perform safe multiplication
-            bool ok = detail::checked_multiply(num, it->second);
-            if(!ok) {
-                throw ValidationError(detail::to_string(num) + " multiplied by " + unit +
-                                      " factor would cause number overflow. Use smaller value.");
+            if(!input.empty()) {
+                bool converted = detail::lexical_cast(input, num);
+                if(!converted) {
+                    throw ValidationError(std::string("Value ") + input + " could not be converted to " +
+                                          detail::type_name<Number>());
+                }
+                // perform safe multiplication
+                bool ok = detail::checked_multiply(num, it->second);
+                if(!ok) {
+                    throw ValidationError(detail::to_string(num) + " multiplied by " + unit +
+                                          " factor would cause number overflow. Use smaller value.");
+                }
+            } else {
+                num = static_cast<Number>(it->second);
             }
+
             input = detail::to_string(num);
 
             return {};
@@ -3151,12 +3675,36 @@ inline std::pair<std::string, std::string> split_program_name(std::string comman
         if(esp == std::string::npos) {
             // if we have reached the end and haven't found a valid file just assume the first argument is the
             // program name
-            esp = commandline.find_first_of(' ', 1);
+            if(commandline[0] == '"' || commandline[0] == '\'' || commandline[0] == '`') {
+                bool embeddedQuote = false;
+                auto keyChar = commandline[0];
+                auto end = commandline.find_first_of(keyChar, 1);
+                while((end != std::string::npos) && (commandline[end - 1] == '\\')) {  // deal with escaped quotes
+                    end = commandline.find_first_of(keyChar, end + 1);
+                    embeddedQuote = true;
+                }
+                if(end != std::string::npos) {
+                    vals.first = commandline.substr(1, end - 1);
+                    esp = end + 1;
+                    if(embeddedQuote) {
+                        vals.first = find_and_replace(vals.first, std::string("\\") + keyChar, std::string(1, keyChar));
+                        embeddedQuote = false;
+                    }
+                } else {
+                    esp = commandline.find_first_of(' ', 1);
+                }
+            } else {
+                esp = commandline.find_first_of(' ', 1);
+            }
+
             break;
         }
     }
-    vals.first = commandline.substr(0, esp);
-    rtrim(vals.first);
+    if(vals.first.empty()) {
+        vals.first = commandline.substr(0, esp);
+        rtrim(vals.first);
+    }
+
     // strip the program name
     vals.second = (esp != std::string::npos) ? commandline.substr(esp + 1) : std::string{};
     ltrim(vals.second);
@@ -3166,11 +3714,8 @@ inline std::pair<std::string, std::string> split_program_name(std::string comman
 }  // namespace detail
 /// @}
 
-}  // namespace CLI
 
-// From FormatterFwd.hpp:
 
-namespace CLI {
 
 class Option;
 class App;
@@ -3334,11 +3879,8 @@ class Formatter : public FormatterBase {
     ///@}
 };
 
-}  // namespace CLI
 
-// From Option.hpp:
 
-namespace CLI {
 
 using results_t = std::vector<std::string>;
 /// callback function definition
@@ -3581,6 +4123,9 @@ class Option : public OptionBase<Option> {
     /// A human readable default value, either manually set, captured, or captured by default
     std::string default_str_{};
 
+    /// If given, replace the text that describes the option type and usage in the help text
+    std::string option_text_{};
+
     /// A human readable type value, set when App creates this
     ///
     /// This is a lambda function so "types" can be dynamic, such as when a set prints its contents.
@@ -3617,7 +4162,7 @@ class Option : public OptionBase<Option> {
     /// @name Other
     ///@{
 
-    /// Remember the parent app
+    /// link back up to the parent App for fallthrough
     App *parent_{nullptr};
 
     /// Options store a callback to do all the work
@@ -3632,7 +4177,7 @@ class Option : public OptionBase<Option> {
     /// results after reduction
     results_t proc_results_{};
     /// enumeration for the option state machine
-    enum class option_state {
+    enum class option_state : char {
         parsing = 0,       //!< The option is currently collecting parsed results
         validated = 2,     //!< the results have been validated
         reduced = 4,       //!< a subset of results has been generated
@@ -3646,6 +4191,8 @@ class Option : public OptionBase<Option> {
     bool flag_like_{false};
     /// Control option to run the callback to set the default
     bool run_callback_for_default_{false};
+    /// flag indicating a separator needs to be injected after each argument call
+    bool inject_separator_{false};
     ///@}
 
     /// Making an option by hand is not defined, it must be made by the App class
@@ -3975,6 +4522,9 @@ class Option : public OptionBase<Option> {
     /// The maximum number of arguments the option expects
     int get_type_size_max() const { return type_size_max_; }
 
+    /// The number of arguments the option expects
+    int get_inject_separator() const { return inject_separator_; }
+
     /// The environment variable associated to this value
     std::string get_envname() const { return envname_; }
 
@@ -3998,7 +4548,19 @@ class Option : public OptionBase<Option> {
 
     /// Get the flag names with specified default values
     const std::vector<std::string> &get_fnames() const { return fnames_; }
-
+    /// Get a single name for the option, first of lname, pname, sname, envname
+    const std::string &get_single_name() const {
+        if(!lnames_.empty()) {
+            return lnames_[0];
+        }
+        if(!pname_.empty()) {
+            return pname_;
+        }
+        if(!snames_.empty()) {
+            return snames_[0];
+        }
+        return envname_;
+    }
     /// The number of times the option expects to be included
     int get_expected() const { return expected_min_; }
 
@@ -4036,6 +4598,13 @@ class Option : public OptionBase<Option> {
         return this;
     }
 
+    Option *option_text(std::string text) {
+        option_text_ = std::move(text);
+        return this;
+    }
+
+    const std::string &get_option_text() const { return option_text_; }
+
     ///@}
     /// @name Help tools
     ///@{
@@ -4153,23 +4722,33 @@ class Option : public OptionBase<Option> {
     bool operator==(const Option &other) const { return !matching_name(other).empty(); }
 
     /// Check a name. Requires "-" or "--" for short / long, supports positional name
-    bool check_name(std::string name) const {
+    bool check_name(const std::string &name) const {
 
         if(name.length() > 2 && name[0] == '-' && name[1] == '-')
             return check_lname(name.substr(2));
         if(name.length() > 1 && name.front() == '-')
             return check_sname(name.substr(1));
+        if(!pname_.empty()) {
+            std::string local_pname = pname_;
+            std::string local_name = name;
+            if(ignore_underscore_) {
+                local_pname = detail::remove_underscore(local_pname);
+                local_name = detail::remove_underscore(local_name);
+            }
+            if(ignore_case_) {
+                local_pname = detail::to_lower(local_pname);
+                local_name = detail::to_lower(local_name);
+            }
+            if(local_name == local_pname) {
+                return true;
+            }
+        }
 
-        std::string local_pname = pname_;
-        if(ignore_underscore_) {
-            local_pname = detail::remove_underscore(local_pname);
-            name = detail::remove_underscore(name);
+        if(!envname_.empty()) {
+            // this needs to be the original since envname_ shouldn't match on case insensitivity
+            return (name == envname_);
         }
-        if(ignore_case_) {
-            local_pname = detail::to_lower(local_pname);
-            name = detail::to_lower(name);
-        }
-        return name == local_pname;
+        return false;
     }
 
     /// Requires "-" to be removed from string
@@ -4258,8 +4837,8 @@ class Option : public OptionBase<Option> {
         return this;
     }
 
-    /// Get a copy of the results
-    results_t results() const { return results_; }
+    /// Get the current complete results set
+    const results_t &results() const { return results_; }
 
     /// Get a copy of the results
     results_t reduced_results() const {
@@ -4281,8 +4860,7 @@ class Option : public OptionBase<Option> {
     }
 
     /// Get the results as a specified type
-    template <typename T, enable_if_t<!std::is_const<T>::value, detail::enabler> = detail::dummy>
-    void results(T &output) const {
+    template <typename T> void results(T &output) const {
         bool retval;
         if(current_option_state_ >= option_state::reduced || (results_.size() == 1 && validators_.empty())) {
             const results_t &res = (proc_results_.empty()) ? results_ : proc_results_;
@@ -4349,6 +4927,8 @@ class Option : public OptionBase<Option> {
             type_size_max_ = option_type_size;
             if(type_size_max_ < detail::expected_max_vector_size) {
                 type_size_min_ = option_type_size;
+            } else {
+                inject_separator_ = true;
             }
             if(type_size_max_ == 0)
                 required_ = false;
@@ -4374,9 +4954,15 @@ class Option : public OptionBase<Option> {
         if(type_size_max_ == 0) {
             required_ = false;
         }
+        if(type_size_max_ >= detail::expected_max_vector_size) {
+            inject_separator_ = true;
+        }
         return this;
     }
 
+    /// Set the value of the separator injection flag
+    void inject_separator(bool value = true) { inject_separator_ = value; }
+
     /// Set a capture function for the default. Mostly used by App.
     Option *default_function(const std::function<std::string()> &func) {
         default_function_ = func;
@@ -4452,7 +5038,7 @@ class Option : public OptionBase<Option> {
                 }
 
                 for(std::string &result : res) {
-                    if(result.empty() && type_size_max_ != type_size_min_ && index >= 0) {
+                    if(detail::is_separator(result) && type_size_max_ != type_size_min_ && index >= 0) {
                         index = 0;  // reset index for variable size chunks
                         continue;
                     }
@@ -4589,11 +5175,8 @@ class Option : public OptionBase<Option> {
     }
 };  // namespace CLI
 
-}  // namespace CLI
 
-// From App.hpp:
 
-namespace CLI {
 
 #ifndef CLI11_PARSE
 #define CLI11_PARSE(app, argc, argv)                                                                                   \
@@ -4605,7 +5188,7 @@ namespace CLI {
 #endif
 
 namespace detail {
-enum class Classifier { NONE, POSITIONAL_MARK, SHORT, LONG, WINDOWS, SUBCOMMAND, SUBCOMMAND_TERMINATOR };
+enum class Classifier { NONE, POSITIONAL_MARK, SHORT, LONG, WINDOWS_STYLE, SUBCOMMAND, SUBCOMMAND_TERMINATOR };
 struct AppFriend;
 }  // namespace detail
 
@@ -4674,6 +5257,7 @@ class App {
 
     /// This is a function that runs when parsing has finished.
     std::function<void()> parse_complete_callback_{};
+
     /// This is a function that runs when all processing has completed
     std::function<void()> final_callback_{};
 
@@ -4703,6 +5287,9 @@ class App {
     /// A pointer to the help all flag if there is one INHERITABLE
     Option *help_all_ptr_{nullptr};
 
+    /// A pointer to a version flag if there is one
+    Option *version_ptr_{nullptr};
+
     /// This is the formatter for help printing. Default provided. INHERITABLE (same pointer)
     std::shared_ptr<FormatterBase> formatter_{new Formatter()};
 
@@ -4779,11 +5366,12 @@ class App {
     /// If set to true positional options are validated before assigning INHERITABLE
     bool validate_positionals_{false};
 
-    /// A pointer to the parent if this is a subcommand
-    App *parent_{nullptr};
+    /// indicator that the subcommand is silent and won't show up in subcommands list
+    /// This is potentially useful as a modifier subcommand
+    bool silent_{false};
 
     /// Counts the number of times this command/subcommand was parsed
-    std::size_t parsed_{0};
+    std::uint32_t parsed_{0U};
 
     /// Minimum required subcommands (not inheritable!)
     std::size_t require_subcommand_min_{0};
@@ -4797,6 +5385,9 @@ class App {
     /// Max number of options allowed. 0 is unlimited (not inheritable)
     std::size_t require_option_max_{0};
 
+    /// A pointer to the parent if this is a subcommand
+    App *parent_{nullptr};
+
     /// The group membership INHERITABLE
     std::string group_{"Subcommands"};
 
@@ -4811,7 +5402,7 @@ class App {
     Option *config_ptr_{nullptr};
 
     /// This is the formatter for help printing. Default provided. INHERITABLE (same pointer)
-    std::shared_ptr<Config> config_formatter_{new ConfigINI()};
+    std::shared_ptr<Config> config_formatter_{new ConfigTOML()};
 
     ///@}
 
@@ -4922,7 +5513,20 @@ class App {
     /// Set an alias for the app
     App *alias(std::string app_name) {
         if(!detail::valid_name_string(app_name)) {
-            throw(IncorrectConstruction("alias is not a valid name string"));
+            if(app_name.empty()) {
+                throw IncorrectConstruction("Empty aliases are not allowed");
+            }
+            if(!detail::valid_first_char(app_name[0])) {
+                throw IncorrectConstruction(
+                    "Alias starts with invalid character, allowed characters are [a-zA-z0-9]+'_','?','@' ");
+            }
+            for(auto c : app_name) {
+                if(!detail::valid_later_char(c)) {
+                    throw IncorrectConstruction(std::string("Alias contains invalid character ('") + c +
+                                                "'), allowed characters are "
+                                                "[a-zA-z0-9]+'_','?','@','.','-' ");
+                }
+            }
         }
 
         if(parent_ != nullptr) {
@@ -4957,6 +5561,12 @@ class App {
         return this;
     }
 
+    /// silence the subcommand from showing up in the processed list
+    App *silent(bool silence = true) {
+        silent_ = silence;
+        return this;
+    }
+
     /// Set the subcommand to be disabled by default, so on clear(), at the start of each parse it is disabled
     App *disabled_by_default(bool disable = true) {
         if(disable) {
@@ -5159,14 +5769,13 @@ class App {
               enable_if_t<!std::is_const<ConvertTo>::value, detail::enabler> = detail::dummy>
     Option *add_option(std::string option_name,
                        AssignTo &variable,  ///< The variable to set
-                       std::string option_description = "",
-                       bool defaulted = false) {
+                       std::string option_description = "") {
 
         auto fun = [&variable](const CLI::results_t &res) {  // comment for spacing
             return detail::lexical_conversion<AssignTo, ConvertTo>(res, variable);
         };
 
-        Option *opt = add_option(option_name, fun, option_description, defaulted, [&variable]() {
+        Option *opt = add_option(option_name, fun, option_description, false, [&variable]() {
             return CLI::detail::checked_to_string<AssignTo, ConvertTo>(variable);
         });
         opt->type_name(detail::type_name<ConvertTo>());
@@ -5174,21 +5783,39 @@ class App {
         // to structs used in the evaluation can be temporary so that would cause issues.
         auto Tcount = detail::type_count<AssignTo>::value;
         auto XCcount = detail::type_count<ConvertTo>::value;
-        opt->type_size((std::max)(Tcount, XCcount));
+        opt->type_size(detail::type_count_min<ConvertTo>::value, (std::max)(Tcount, XCcount));
         opt->expected(detail::expected_count<ConvertTo>::value);
         opt->run_callback_for_default();
         return opt;
     }
 
+    /// Add option for assigning to a variable
+    template <typename AssignTo, enable_if_t<!std::is_const<AssignTo>::value, detail::enabler> = detail::dummy>
+    Option *add_option_no_stream(std::string option_name,
+                                 AssignTo &variable,  ///< The variable to set
+                                 std::string option_description = "") {
+
+        auto fun = [&variable](const CLI::results_t &res) {  // comment for spacing
+            return detail::lexical_conversion<AssignTo, AssignTo>(res, variable);
+        };
+
+        Option *opt = add_option(option_name, fun, option_description, false, []() { return std::string{}; });
+        opt->type_name(detail::type_name<AssignTo>());
+        opt->type_size(detail::type_count_min<AssignTo>::value, detail::type_count<AssignTo>::value);
+        opt->expected(detail::expected_count<AssignTo>::value);
+        opt->run_callback_for_default();
+        return opt;
+    }
+
     /// Add option for a callback of a specific type
-    template <typename T>
+    template <typename ArgType>
     Option *add_option_function(std::string option_name,
-                                const std::function<void(const T &)> &func,  ///< the callback to execute
+                                const std::function<void(const ArgType &)> &func,  ///< the callback to execute
                                 std::string option_description = "") {
 
         auto fun = [func](const CLI::results_t &res) {
-            T variable;
-            bool result = detail::lexical_conversion<T, T>(res, variable);
+            ArgType variable;
+            bool result = detail::lexical_conversion<ArgType, ArgType>(res, variable);
             if(result) {
                 func(variable);
             }
@@ -5196,15 +5823,15 @@ class App {
         };
 
         Option *opt = add_option(option_name, std::move(fun), option_description, false);
-        opt->type_name(detail::type_name<T>());
-        opt->type_size(detail::type_count<T>::value);
-        opt->expected(detail::expected_count<T>::value);
+        opt->type_name(detail::type_name<ArgType>());
+        opt->type_size(detail::type_count_min<ArgType>::value, detail::type_count<ArgType>::value);
+        opt->expected(detail::expected_count<ArgType>::value);
         return opt;
     }
 
     /// Add option with no description or variable assignment
     Option *add_option(std::string option_name) {
-        return add_option(option_name, CLI::callback_t(), std::string{}, false);
+        return add_option(option_name, CLI::callback_t{}, std::string{}, false);
     }
 
     /// Add option with description but with no variable assignment or callback
@@ -5249,6 +5876,44 @@ class App {
         return help_all_ptr_;
     }
 
+    /// Set a version flag and version display string, replace the existing one if present
+    Option *set_version_flag(std::string flag_name = "",
+                             const std::string &versionString = "",
+                             const std::string &version_help = "Display program version information and exit") {
+        // take flag_description by const reference otherwise add_flag tries to assign to version_description
+        if(version_ptr_ != nullptr) {
+            remove_option(version_ptr_);
+            version_ptr_ = nullptr;
+        }
+
+        // Empty name will simply remove the version flag
+        if(!flag_name.empty()) {
+            version_ptr_ = add_flag_callback(
+                flag_name, [versionString]() { throw(CLI::CallForVersion(versionString, 0)); }, version_help);
+            version_ptr_->configurable(false);
+        }
+
+        return version_ptr_;
+    }
+    /// Generate the version string through a callback function
+    Option *set_version_flag(std::string flag_name,
+                             std::function<std::string()> vfunc,
+                             const std::string &version_help = "Display program version information and exit") {
+        if(version_ptr_ != nullptr) {
+            remove_option(version_ptr_);
+            version_ptr_ = nullptr;
+        }
+
+        // Empty name will simply remove the version flag
+        if(!flag_name.empty()) {
+            version_ptr_ = add_flag_callback(
+                flag_name, [vfunc]() { throw(CLI::CallForVersion(vfunc(), 0)); }, version_help);
+            version_ptr_->configurable(false);
+        }
+
+        return version_ptr_;
+    }
+
   private:
     /// Internal function for adding a flag
     Option *_add_flag_internal(std::string flag_name, CLI::callback_t fun, std::string flag_description) {
@@ -5293,7 +5958,8 @@ class App {
     /// Add option for flag with integer result - defaults to allowing multiple passings, but can be forced to one
     /// if `multi_option_policy(CLI::MultiOptionPolicy::Throw)` is used.
     template <typename T,
-              enable_if_t<std::is_integral<T>::value && !is_bool<T>::value, detail::enabler> = detail::dummy>
+              enable_if_t<std::is_constructible<T, std::int64_t>::value && !is_bool<T>::value, detail::enabler> =
+                  detail::dummy>
     Option *add_flag(std::string flag_name,
                      T &flag_count,  ///< A variable holding the count
                      std::string flag_description = "") {
@@ -5313,8 +5979,8 @@ class App {
     /// Other type version accepts all other types that are not vectors such as bool, enum, string or other classes
     /// that can be converted from a string
     template <typename T,
-              enable_if_t<!is_vector<T>::value && !std::is_const<T>::value &&
-                              (!std::is_integral<T>::value || is_bool<T>::value) &&
+              enable_if_t<!detail::is_mutable_container<T>::value && !std::is_const<T>::value &&
+                              (!std::is_constructible<T, std::int64_t>::value || is_bool<T>::value) &&
                               !std::is_constructible<std::function<void(int)>, T>::value,
                           detail::enabler> = detail::dummy>
     Option *add_flag(std::string flag_name,
@@ -5328,9 +5994,9 @@ class App {
     }
 
     /// Vector version to capture multiple flags.
-    template <
-        typename T,
-        enable_if_t<!std::is_assignable<std::function<void(std::int64_t)>, T>::value, detail::enabler> = detail::dummy>
+    template <typename T,
+              enable_if_t<!std::is_assignable<std::function<void(std::int64_t)> &, T>::value, detail::enabler> =
+                  detail::dummy>
     Option *add_flag(std::string flag_name,
                      std::vector<T> &flag_results,  ///< A vector of values with the flag results
                      std::string flag_description = "") {
@@ -5387,106 +6053,6 @@ class App {
     }
 #endif
 
-    /// Add set of options (No default, temp reference, such as an inline set) DEPRECATED
-    template <typename T>
-    Option *add_set(std::string option_name,
-                    T &member,            ///< The selected member of the set
-                    std::set<T> options,  ///< The set of possibilities
-                    std::string option_description = "") {
-
-        Option *opt = add_option(option_name, member, std::move(option_description));
-        opt->check(IsMember{options});
-        return opt;
-    }
-
-    /// Add set of options (No default, set can be changed afterwards - do not destroy the set) DEPRECATED
-    template <typename T>
-    Option *add_mutable_set(std::string option_name,
-                            T &member,                   ///< The selected member of the set
-                            const std::set<T> &options,  ///< The set of possibilities
-                            std::string option_description = "") {
-
-        Option *opt = add_option(option_name, member, std::move(option_description));
-        opt->check(IsMember{&options});
-        return opt;
-    }
-
-    /// Add set of options (with default, static set, such as an inline set) DEPRECATED
-    template <typename T>
-    Option *add_set(std::string option_name,
-                    T &member,            ///< The selected member of the set
-                    std::set<T> options,  ///< The set of possibilities
-                    std::string option_description,
-                    bool defaulted) {
-
-        Option *opt = add_option(option_name, member, std::move(option_description), defaulted);
-        opt->check(IsMember{options});
-        return opt;
-    }
-
-    /// Add set of options (with default, set can be changed afterwards - do not destroy the set) DEPRECATED
-    template <typename T>
-    Option *add_mutable_set(std::string option_name,
-                            T &member,                   ///< The selected member of the set
-                            const std::set<T> &options,  ///< The set of possibilities
-                            std::string option_description,
-                            bool defaulted) {
-
-        Option *opt = add_option(option_name, member, std::move(option_description), defaulted);
-        opt->check(IsMember{&options});
-        return opt;
-    }
-
-    /// Add a complex number
-    template <typename T, typename XC = double>
-    Option *add_complex(std::string option_name,
-                        T &variable,
-                        std::string option_description = "",
-                        bool defaulted = false,
-                        std::string label = "COMPLEX") {
-
-        CLI::callback_t fun = [&variable](const results_t &res) {
-            XC x, y;
-            bool worked;
-            if(res.size() >= 2 && !res[1].empty()) {
-                auto str1 = res[1];
-                if(str1.back() == 'i' || str1.back() == 'j')
-                    str1.pop_back();
-                worked = detail::lexical_cast(res[0], x) && detail::lexical_cast(str1, y);
-            } else {
-                auto str1 = res.front();
-                auto nloc = str1.find_last_of('-');
-                if(nloc != std::string::npos && nloc > 0) {
-                    worked = detail::lexical_cast(str1.substr(0, nloc), x);
-                    str1 = str1.substr(nloc);
-                    if(str1.back() == 'i' || str1.back() == 'j')
-                        str1.pop_back();
-                    worked = worked && detail::lexical_cast(str1, y);
-                } else {
-                    if(str1.back() == 'i' || str1.back() == 'j') {
-                        str1.pop_back();
-                        worked = detail::lexical_cast(str1, y);
-                        x = XC{0};
-                    } else {
-                        worked = detail::lexical_cast(str1, x);
-                        y = XC{0};
-                    }
-                }
-            }
-            if(worked)
-                variable = T{x, y};
-            return worked;
-        };
-
-        auto default_function = [&variable]() { return CLI::detail::checked_to_string<T, T>(variable); };
-
-        CLI::Option *opt =
-            add_option(option_name, std::move(fun), std::move(option_description), defaulted, default_function);
-
-        opt->type_name(label)->type_size(1, 2)->delimiter('+')->run_callback_for_default();
-        return opt;
-    }
-
     /// Set a configuration ini file option, or clear it if no name passed
     Option *set_config(std::string option_name = "",
                        std::string default_filename = "",
@@ -5554,7 +6120,17 @@ class App {
     /// Add a subcommand. Inherits INHERITABLE and OptionDefaults, and help flag
     App *add_subcommand(std::string subcommand_name = "", std::string subcommand_description = "") {
         if(!subcommand_name.empty() && !detail::valid_name_string(subcommand_name)) {
-            throw IncorrectConstruction("subcommand name is not valid");
+            if(!detail::valid_first_char(subcommand_name[0])) {
+                throw IncorrectConstruction(
+                    "Subcommand name starts with invalid character, allowed characters are [a-zA-z0-9]+'_','?','@' ");
+            }
+            for(auto c : subcommand_name) {
+                if(!detail::valid_later_char(c)) {
+                    throw IncorrectConstruction(std::string("Subcommand name contains invalid character ('") + c +
+                                                "'), allowed characters are "
+                                                "[a-zA-z0-9]+'_','?','@','.','-' ");
+                }
+            }
         }
         CLI::App_p subcom = std::shared_ptr<App>(new App(std::move(subcommand_description), subcommand_name, this));
         return add_subcommand(std::move(subcom));
@@ -5891,6 +6467,11 @@ class App {
             return e.get_exit_code();
         }
 
+        if(e.get_name() == "CallForVersion") {
+            out << e.what() << std::endl;
+            return e.get_exit_code();
+        }
+
         if(e.get_exit_code() != static_cast<int>(ExitCodes::Success)) {
             if(failure_message_)
                 err << failure_message_(this, e) << std::flush;
@@ -6055,7 +6636,7 @@ class App {
         return this;
     }
     /// Produce a string that could be read in as a config of the current values of the App. Set default_also to
-    /// include default arguments. Prefix will add a string to the beginning of each option.
+    /// include default arguments. write_descriptions will print a description for the App and for each option.
     std::string config_to_str(bool default_also = false, bool write_description = false) const {
         return config_formatter_->to_config(this, default_also, write_description, "");
     }
@@ -6076,6 +6657,23 @@ class App {
         return formatter_->make_help(this, prev, mode);
     }
 
+    /// Displays a version string
+    std::string version() const {
+        std::string val;
+        if(version_ptr_ != nullptr) {
+            auto rv = version_ptr_->results();
+            version_ptr_->clear();
+            version_ptr_->add_result("true");
+            try {
+                version_ptr_->run_callback();
+            } catch(const CLI::CallForVersion &cfv) {
+                val = cfv.what();
+            }
+            version_ptr_->clear();
+            version_ptr_->add_result(rv);
+        }
+        return val;
+    }
     ///@}
     /// @name Getters
     ///@{
@@ -6248,6 +6846,9 @@ class App {
     /// Get the status of disabled
     bool get_disabled() const { return disabled_; }
 
+    /// Get the status of silence
+    bool get_silent() const { return silent_; }
+
     /// Get the status of disabled
     bool get_immediate_callback() const { return immediate_callback_; }
 
@@ -6277,6 +6878,12 @@ class App {
     /// Get a pointer to the config option. (const)
     const Option *get_config_ptr() const { return config_ptr_; }
 
+    /// Get a pointer to the version option.
+    Option *get_version_ptr() { return version_ptr_; }
+
+    /// Get a pointer to the version option. (const)
+    const Option *get_version_ptr() const { return version_ptr_; }
+
     /// Get the parent of this subcommand (or nullptr if master app)
     App *get_parent() { return parent_; }
 
@@ -6296,7 +6903,21 @@ class App {
     }
 
     /// Get a display name for an app
-    std::string get_display_name() const { return (!name_.empty()) ? name_ : "[Option Group: " + get_group() + "]"; }
+    std::string get_display_name(bool with_aliases = false) const {
+        if(name_.empty()) {
+            return std::string("[Option Group: ") + get_group() + "]";
+        }
+        if(aliases_.empty() || !with_aliases || aliases_.empty()) {
+            return name_;
+        }
+        std::string dispname = name_;
+        for(const auto &lalias : aliases_) {
+            dispname.push_back(',');
+            dispname.push_back(' ');
+            dispname.append(lalias);
+        }
+        return dispname;
+    }
 
     /// Check the name, case insensitive and underscore insensitive if set
     bool check_name(std::string name_to_check) const {
@@ -6458,8 +7079,9 @@ class App {
             app->_configure();
         }
     }
+
     /// Internal function to run (App) callback, bottom up
-    void run_callback(bool final_mode = false) {
+    void run_callback(bool final_mode = false, bool suppress_final_callback = false) {
         pre_callback();
         // in the main app if immediate_callback_ is set it runs the main callback before the used subcommands
         if(!final_mode && parse_complete_callback_) {
@@ -6467,18 +7089,18 @@ class App {
         }
         // run the callbacks for the received subcommands
         for(App *subc : get_subcommands()) {
-            subc->run_callback(true);
+            subc->run_callback(true, suppress_final_callback);
         }
         // now run callbacks for option_groups
         for(auto &subc : subcommands_) {
             if(subc->name_.empty() && subc->count_all() > 0) {
-                subc->run_callback(true);
+                subc->run_callback(true, suppress_final_callback);
             }
         }
 
         // finally run the main callback
-        if(final_callback_ && (parsed_ > 0)) {
-            if(!name_.empty() || count_all() > 0) {
+        if(final_callback_ && (parsed_ > 0) && (!suppress_final_callback)) {
+            if(!name_.empty() || count_all() > 0 || parent_ == nullptr) {
                 final_callback_();
             }
         }
@@ -6517,7 +7139,7 @@ class App {
             return detail::Classifier::SHORT;
         }
         if((allow_windows_style_options_) && (detail::split_windows_style(current, dummy1, dummy2)))
-            return detail::Classifier::WINDOWS;
+            return detail::Classifier::WINDOWS_STYLE;
         if((current == "++") && !name_.empty() && parent_ != nullptr)
             return detail::Classifier::SUBCOMMAND_TERMINATOR;
         return detail::Classifier::NONE;
@@ -6529,29 +7151,31 @@ class App {
     void _process_config_file() {
         if(config_ptr_ != nullptr) {
             bool config_required = config_ptr_->get_required();
-            bool file_given = config_ptr_->count() > 0;
-            auto config_file = config_ptr_->as<std::string>();
-            if(config_file.empty()) {
+            auto file_given = config_ptr_->count() > 0;
+            auto config_files = config_ptr_->as<std::vector<std::string>>();
+            if(config_files.empty() || config_files.front().empty()) {
                 if(config_required) {
                     throw FileError::Missing("no specified config file");
                 }
                 return;
             }
-
-            auto path_result = detail::check_path(config_file.c_str());
-            if(path_result == detail::path_type::file) {
-                try {
-                    std::vector<ConfigItem> values = config_formatter_->from_file(config_file);
-                    _parse_config(values);
-                    if(!file_given) {
-                        config_ptr_->add_result(config_file);
+            for(auto rit = config_files.rbegin(); rit != config_files.rend(); ++rit) {
+                const auto &config_file = *rit;
+                auto path_result = detail::check_path(config_file.c_str());
+                if(path_result == detail::path_type::file) {
+                    try {
+                        std::vector<ConfigItem> values = config_formatter_->from_file(config_file);
+                        _parse_config(values);
+                        if(!file_given) {
+                            config_ptr_->add_result(config_file);
+                        }
+                    } catch(const FileError &) {
+                        if(config_required || file_given)
+                            throw;
                     }
-                } catch(const FileError &) {
-                    if(config_required || file_given)
-                        throw;
+                } else if(config_required || file_given) {
+                    throw FileError::Missing(config_file);
                 }
-            } else if(config_required || file_given) {
-                throw FileError::Missing(config_file);
             }
         }
     }
@@ -6726,10 +7350,13 @@ class App {
         }
 
         if(require_option_min_ > used_options || (require_option_max_ > 0 && require_option_max_ < used_options)) {
-            auto option_list = detail::join(options_, [](const Option_p &ptr) { return ptr->get_name(false, true); });
-            if(option_list.compare(0, 10, "-h,--help,") == 0) {
-                option_list.erase(0, 10);
-            }
+            auto option_list = detail::join(options_, [this](const Option_p &ptr) {
+                if(ptr.get() == help_ptr_ || ptr.get() == help_all_ptr_) {
+                    return std::string{};
+                }
+                return ptr->get_name(false, true);
+            });
+
             auto subc_list = get_subcommands([](App *app) { return ((app->get_name().empty()) && (!app->disabled_)); });
             if(!subc_list.empty()) {
                 option_list += "," + detail::join(subc_list, [](const App *app) { return app->get_display_name(); });
@@ -6767,10 +7394,27 @@ class App {
 
     /// Process callbacks and such.
     void _process() {
-        _process_config_file();
-        _process_env();
+        CLI::FileError fe("ne");
+        bool caught_error{false};
+        try {
+            // the config file might generate a FileError but that should not be processed until later in the process
+            // to allow for help, version and other errors to generate first.
+            _process_config_file();
+            // process env shouldn't throw but no reason to process it if config generated an error
+            _process_env();
+        } catch(const CLI::FileError &fe2) {
+            fe = fe2;
+            caught_error = true;
+        }
+        // callbacks and help_flags can generate exceptions which should take priority over the config file error if one
+        // exists
         _process_callbacks();
         _process_help_flags();
+
+        if(caught_error) {
+            throw CLI::FileError(std::move(fe));
+        }
+
         _process_requirements();
     }
 
@@ -6839,7 +7483,7 @@ class App {
             _process_callbacks();
             _process_help_flags();
             _process_requirements();
-            run_callback();
+            run_callback(false, true);
         }
     }
 
@@ -6864,8 +7508,8 @@ class App {
     ///
     /// If this has more than one dot.separated.name, go into the subcommand matching it
     /// Returns true if it managed to find the option, if false you'll need to remove the arg manually.
-    void _parse_config(std::vector<ConfigItem> &args) {
-        for(ConfigItem item : args) {
+    void _parse_config(const std::vector<ConfigItem> &args) {
+        for(const ConfigItem &item : args) {
             if(!_parse_single_config(item) && allow_config_extras_ == config_extras_mode::error)
                 throw ConfigError::Extras(item.fullname());
         }
@@ -6904,6 +7548,14 @@ class App {
             return true;
         }
         Option *op = get_option_no_throw("--" + item.name);
+        if(op == nullptr) {
+            if(item.name.size() == 1) {
+                op = get_option_no_throw("-" + item.name);
+            }
+        }
+        if(op == nullptr) {
+            op = get_option_no_throw(item.name);
+        }
         if(op == nullptr) {
             // If the option was not present
             if(get_allow_config_extras() == config_extras_mode::capture)
@@ -6957,7 +7609,7 @@ class App {
             break;
         case detail::Classifier::LONG:
         case detail::Classifier::SHORT:
-        case detail::Classifier::WINDOWS:
+        case detail::Classifier::WINDOWS_STYLE:
             // If already parsed a subcommand, don't accept options_
             _parse_arg(args, classifier);
             break;
@@ -7135,12 +7787,16 @@ class App {
         auto com = _find_subcommand(args.back(), true, true);
         if(com != nullptr) {
             args.pop_back();
-            parsed_subcommands_.push_back(com);
+            if(!com->silent_) {
+                parsed_subcommands_.push_back(com);
+            }
             com->_parse(args);
             auto parent_app = com->parent_;
             while(parent_app != this) {
                 parent_app->_trigger_pre_parse(args.size());
-                parent_app->parsed_subcommands_.push_back(com);
+                if(!com->silent_) {
+                    parent_app->parsed_subcommands_.push_back(com);
+                }
                 parent_app = parent_app->parent_;
             }
             return true;
@@ -7170,7 +7826,7 @@ class App {
             if(!detail::split_short(current, arg_name, rest))
                 throw HorribleError("Short parsed but missing! You should not see this");
             break;
-        case detail::Classifier::WINDOWS:
+        case detail::Classifier::WINDOWS_STYLE:
             if(!detail::split_windows_style(current, arg_name, value))
                 throw HorribleError("windows option parsed but missing! You should not see this");
             break;
@@ -7188,7 +7844,7 @@ class App {
                     return opt->check_lname(arg_name);
                 if(current_type == detail::Classifier::SHORT)
                     return opt->check_sname(arg_name);
-                // this will only get called for detail::Classifier::WINDOWS
+                // this will only get called for detail::Classifier::WINDOWS_STYLE
                 return opt->check_lname(arg_name) || opt->check_sname(arg_name);
             });
 
@@ -7221,10 +7877,20 @@ class App {
 
         // Get a reference to the pointer to make syntax bearable
         Option_p &op = *op_ptr;
-
+        /// if we require a separator add it here
+        if(op->get_inject_separator()) {
+            if(!op->results().empty() && !op->results().back().empty()) {
+                op->add_result(std::string{});
+            }
+        }
         int min_num = (std::min)(op->get_type_size_min(), op->get_items_expected_min());
         int max_num = op->get_items_expected_max();
-
+        // check container like options to limit the argument size to a single type if the allow_extra_flags argument is
+        // set. 16 is somewhat arbitrary (needs to be at least 4)
+        if(max_num >= detail::expected_max_vector_size / 16 && !op->get_allow_extra_args()) {
+            auto tmax = op->get_type_size_max();
+            max_num = detail::checked_multiply(tmax, op->get_expected_min()) ? tmax : detail::expected_max_vector_size;
+        }
         // Make sure we always eat the minimum for unlimited vectors
         int collected = 0;     // total number of arguments collected
         int result_count = 0;  // local variable for number of results in a single arg string
@@ -7286,7 +7952,7 @@ class App {
         }
 
         // if we only partially completed a type then add an empty string for later processing
-        if(min_num > 0 && op->get_type_size_max() != min_num && collected % op->get_type_size_max() != 0) {
+        if(min_num > 0 && op->get_type_size_max() != min_num && (collected % op->get_type_size_max()) != 0) {
             op->add_result(std::string{});
         }
 
@@ -7655,17 +8321,14 @@ struct AppFriend {
 };
 }  // namespace detail
 
-}  // namespace CLI
 
-// From Config.hpp:
 
-namespace CLI {
 
 namespace detail {
 
-inline std::string convert_arg_for_ini(const std::string &arg) {
+inline std::string convert_arg_for_ini(const std::string &arg, char stringQuote = '"', char characterQuote = '\'') {
     if(arg.empty()) {
-        return std::string(2, '"');
+        return std::string(2, stringQuote);
     }
     // some specifically supported strings
     if(arg == "true" || arg == "false" || arg == "nan" || arg == "inf") {
@@ -7680,7 +8343,7 @@ inline std::string convert_arg_for_ini(const std::string &arg) {
     }
     // just quote a single non numeric character
     if(arg.size() == 1) {
-        return std::string("'") + arg + '\'';
+        return std::string(1, characterQuote) + arg + characterQuote;
     }
     // handle hex, binary or octal arguments
     if(arg.front() == '0') {
@@ -7700,16 +8363,20 @@ inline std::string convert_arg_for_ini(const std::string &arg) {
             }
         }
     }
-    if(arg.find_first_of('"') == std::string::npos) {
-        return std::string("\"") + arg + '"';
+    if(arg.find_first_of(stringQuote) == std::string::npos) {
+        return std::string(1, stringQuote) + arg + stringQuote;
     } else {
-        return std::string("'") + arg + '\'';
+        return characterQuote + arg + characterQuote;
     }
 }
 
 /// Comma separated join, adds quotes if needed
-inline std::string
-ini_join(const std::vector<std::string> &args, char sepChar = ',', char arrayStart = '[', char arrayEnd = ']') {
+inline std::string ini_join(const std::vector<std::string> &args,
+                            char sepChar = ',',
+                            char arrayStart = '[',
+                            char arrayEnd = ']',
+                            char stringQuote = '"',
+                            char characterQuote = '\'') {
     std::string joined;
     if(args.size() > 1 && arrayStart != '\0') {
         joined.push_back(arrayStart);
@@ -7722,7 +8389,7 @@ ini_join(const std::vector<std::string> &args, char sepChar = ',', char arraySta
                 joined.push_back(' ');
             }
         }
-        joined.append(convert_arg_for_ini(arg));
+        joined.append(convert_arg_for_ini(arg, stringQuote, characterQuote));
     }
     if(args.size() > 1 && arrayEnd != '\0') {
         joined.push_back(arrayEnd);
@@ -7809,10 +8476,11 @@ inline std::vector<ConfigItem> ConfigBase::from_config(std::istream &input) cons
     std::string section = "default";
 
     std::vector<ConfigItem> output;
-    bool defaultArray = (arrayStart == '\0' || arrayStart == ' ') && arrayStart == arrayEnd;
-    char aStart = (defaultArray) ? '[' : arrayStart;
-    char aEnd = (defaultArray) ? ']' : arrayEnd;
-    char aSep = (defaultArray && arraySeparator == ' ') ? ',' : arraySeparator;
+    bool isDefaultArray = (arrayStart == '[' && arrayEnd == ']' && arraySeparator == ',');
+    bool isINIArray = (arrayStart == '\0' || arrayStart == ' ') && arrayStart == arrayEnd;
+    char aStart = (isINIArray) ? '[' : arrayStart;
+    char aEnd = (isINIArray) ? ']' : arrayEnd;
+    char aSep = (isINIArray && arraySeparator == ' ') ? ',' : arraySeparator;
 
     while(getline(input, line)) {
         std::vector<std::string> items_buffer;
@@ -7852,11 +8520,15 @@ inline std::vector<ConfigItem> ConfigBase::from_config(std::istream &input) cons
         if(pos != std::string::npos) {
             name = detail::trim_copy(line.substr(0, pos));
             std::string item = detail::trim_copy(line.substr(pos + 1));
-            if(item.size() > 1 && item.front() == aStart && item.back() == aEnd) {
+            if(item.size() > 1 && item.front() == aStart) {
+                for(std::string multiline; item.back() != aEnd && std::getline(input, multiline);) {
+                    detail::trim(multiline);
+                    item += multiline;
+                }
                 items_buffer = detail::split_up(item.substr(1, item.length() - 2), aSep);
-            } else if(defaultArray && item.find_first_of(aSep) != std::string::npos) {
+            } else if((isDefaultArray || isINIArray) && item.find_first_of(aSep) != std::string::npos) {
                 items_buffer = detail::split_up(item, aSep);
-            } else if(defaultArray && item.find_first_of(' ') != std::string::npos) {
+            } else if((isDefaultArray || isINIArray) && item.find_first_of(' ') != std::string::npos) {
                 items_buffer = detail::split_up(item);
             } else {
                 items_buffer = {item};
@@ -7908,8 +8580,8 @@ ConfigBase::to_config(const App *app, bool default_also, bool write_description,
     std::vector<std::string> groups = app->get_groups();
     bool defaultUsed = false;
     groups.insert(groups.begin(), std::string("Options"));
-    if(write_description) {
-        out << commentLead << app->get_description() << '\n';
+    if(write_description && (app->get_configurable() || app->get_parent() == nullptr || app->get_name().empty())) {
+        out << commentLead << detail::fix_newlines(commentLead, app->get_description()) << '\n';
     }
     for(auto &group : groups) {
         if(group == "Options" || group.empty()) {
@@ -7923,21 +8595,24 @@ ConfigBase::to_config(const App *app, bool default_also, bool write_description,
         }
         for(const Option *opt : app->get_options({})) {
 
-            // Only process option with a long-name and configurable
-            if(!opt->get_lnames().empty() && opt->get_configurable()) {
+            // Only process options that are configurable
+            if(opt->get_configurable()) {
                 if(opt->get_group() != group) {
                     if(!(group == "Options" && opt->get_group().empty())) {
                         continue;
                     }
                 }
-                std::string name = prefix + opt->get_lnames()[0];
-                std::string value = detail::ini_join(opt->reduced_results(), arraySeparator, arrayStart, arrayEnd);
+                std::string name = prefix + opt->get_single_name();
+                std::string value = detail::ini_join(
+                    opt->reduced_results(), arraySeparator, arrayStart, arrayEnd, stringQuote, characterQuote);
 
                 if(value.empty() && default_also) {
                     if(!opt->get_default_str().empty()) {
-                        value = detail::convert_arg_for_ini(opt->get_default_str());
+                        value = detail::convert_arg_for_ini(opt->get_default_str(), stringQuote, characterQuote);
                     } else if(opt->get_expected_min() == 0) {
                         value = "false";
+                    } else if(opt->get_run_callback_for_default()) {
+                        value = "\"\"";  // empty string default value
                     }
                 }
 
@@ -7985,11 +8660,8 @@ ConfigBase::to_config(const App *app, bool default_also, bool write_description,
     return out.str();
 }
 
-}  // namespace CLI
 
-// From Formatter.hpp:
 
-namespace CLI {
 
 inline std::string
 Formatter::make_group(std::string group, bool is_positional, std::vector<const Option *> opts) const {
@@ -8181,15 +8853,18 @@ inline std::string Formatter::make_subcommands(const App *app, AppFormatMode mod
 
 inline std::string Formatter::make_subcommand(const App *sub) const {
     std::stringstream out;
-    detail::format_help(out, sub->get_name(), sub->get_description(), column_width_);
+    detail::format_help(out, sub->get_display_name(true), sub->get_description(), column_width_);
     return out.str();
 }
 
 inline std::string Formatter::make_expanded(const App *sub) const {
     std::stringstream out;
-    out << sub->get_display_name() << "\n";
+    out << sub->get_display_name(true) << "\n";
 
     out << make_description(sub);
+    if(sub->get_name().empty() && !sub->get_aliases().empty()) {
+        detail::format_aliases(out, sub->get_aliases(), column_width_ + 2);
+    }
     out << make_positionals(sub);
     out << make_groups(sub, AppFormatMode::Sub);
     out << make_subcommands(sub, AppFormatMode::Sub);
@@ -8212,30 +8887,34 @@ inline std::string Formatter::make_option_name(const Option *opt, bool is_positi
 inline std::string Formatter::make_option_opts(const Option *opt) const {
     std::stringstream out;
 
-    if(opt->get_type_size() != 0) {
-        if(!opt->get_type_name().empty())
-            out << " " << get_label(opt->get_type_name());
-        if(!opt->get_default_str().empty())
-            out << "=" << opt->get_default_str();
-        if(opt->get_expected_max() == detail::expected_max_vector_size)
-            out << " ...";
-        else if(opt->get_expected_min() > 1)
-            out << " x " << opt->get_expected();
+    if(!opt->get_option_text().empty()) {
+        out << " " << opt->get_option_text();
+    } else {
+        if(opt->get_type_size() != 0) {
+            if(!opt->get_type_name().empty())
+                out << " " << get_label(opt->get_type_name());
+            if(!opt->get_default_str().empty())
+                out << "=" << opt->get_default_str();
+            if(opt->get_expected_max() == detail::expected_max_vector_size)
+                out << " ...";
+            else if(opt->get_expected_min() > 1)
+                out << " x " << opt->get_expected();
 
-        if(opt->get_required())
-            out << " " << get_label("REQUIRED");
-    }
-    if(!opt->get_envname().empty())
-        out << " (" << get_label("Env") << ":" << opt->get_envname() << ")";
-    if(!opt->get_needs().empty()) {
-        out << " " << get_label("Needs") << ":";
-        for(const Option *op : opt->get_needs())
-            out << " " << op->get_name();
-    }
-    if(!opt->get_excludes().empty()) {
-        out << " " << get_label("Excludes") << ":";
-        for(const Option *op : opt->get_excludes())
-            out << " " << op->get_name();
+            if(opt->get_required())
+                out << " " << get_label("REQUIRED");
+        }
+        if(!opt->get_envname().empty())
+            out << " (" << get_label("Env") << ":" << opt->get_envname() << ")";
+        if(!opt->get_needs().empty()) {
+            out << " " << get_label("Needs") << ":";
+            for(const Option *op : opt->get_needs())
+                out << " " << op->get_name();
+        }
+        if(!opt->get_excludes().empty()) {
+            out << " " << get_label("Excludes") << ":";
+            for(const Option *op : opt->get_excludes())
+                out << " " << op->get_name();
+        }
     }
     return out.str();
 }
@@ -8254,5 +8933,6 @@ inline std::string Formatter::make_option_usage(const Option *opt) const {
     return opt->get_required() ? out.str() : "[" + out.str() + "]";
 }
 
-}  // namespace CLI
 
+
+} // namespace CLI