diff --git a/scripts/release-mlpack.sh b/scripts/release-mlpack.sh
new file mode 100755
index 0000000000..5997ace895
--- /dev/null
+++ b/scripts/release-mlpack.sh
@@ -0,0 +1,221 @@
+#!/usr/bin/env bash
+#
+# Release a new version of mlpack.
+#
+# Usage: release-mlpack.sh X Y Z
+#
+# where X is the major version, Y is the minor version, and Z is the patch
+# version.  Run this from the root of the repository.
+#
+# Make sure HISTORY.md is updated first!
+set +e
+
+if [ "$#" -ne "4" ];
+then
+  echo "Usage: mlpack-release.sh <github username> <major> <minor> <patch>";
+  exit 1;
+fi
+
+# First, check for any unlicensed files.
+output=$(
+    for i in $(find src/ -iname '*.[hc]pp');
+    do
+        echo -n $i": ";
+        cat $i | grep 'mlpack is free software;' | wc -l;
+    done |\
+        grep -v ': 1' |\
+        grep -v 'arma_extend' |\
+        grep -v 'boost_backport' |\
+        grep -v 'arma_config.hpp' |\
+        grep -v 'gitversion.hpp' |\
+        grep -v 'CLI11.hpp' |\
+        grep -v 'bindings/R/mlpack/src/boost/serialization' |\
+        grep -v 'tests/catch.hpp');
+lines=`echo $output | grep -v '^[ ]*$' | wc -l`;
+
+if [ "0$lines" -gt "0" ];
+then
+  echo "Unlicensed files found!  Aborting release.";
+  echo "$output";
+  exit 1;
+fi
+
+# Now, check that there are no local changes.
+lines=`git diff | wc -l | sed -e 's/^\s*//g'`;
+if [ "$lines" != "0" ]; then
+  echo "git diff returned a nonzero result!";
+  echo "";
+  git diff;
+  exit 1;
+fi
+
+# Next, make sure the origin is right.
+dest_remote_name=`git remote -v |\
+                  grep "mlpack/mlpack (fetch)" |\
+                  head -1 |\
+                  awk -F' ' '{ print $1 }'`;
+
+if [ "a$dest_remote_name" == "a" ]; then
+  echo "No git remote found for https://github.com/mlpack/mlpack!";
+  echo "Make sure that you've got the ensmallen repository as a remote, and" \
+      "that the master branch from that remote is checked out.";
+  echo "You can do this with a fresh repository via \`git clone" \
+      "https://github.com/mlpack/mlpack\`.";
+  exit 1;
+fi
+
+# Also check that we're on the master branch, from the correct origin.
+current_branch=`git branch --no-color | grep '^\* ' | awk -F' ' '{ print $2 }'`;
+current_origin=`git rev-parse --abbrev-ref --symbolic-full-name @{u} |\
+                awk -F'/' '{ print $1 }'`;
+if [ "a$current_branch" != "amaster" ]; then
+  echo "Current branch is $current_branch.";
+  echo "This script has to be run from the master branch.";
+  exit 1;
+elif [ "a$current_origin" != "a$dest_remote_name" ]; then
+  echo "Current branch does not track from remote mlpack repository!";
+  echo "Instead, it tracks from $current_origin/master.";
+  echo "Make sure to check out a branch that tracks $dest_remote_name/master.";
+  exit 1;
+fi
+
+# Make sure `hub` is installed.
+hub_output="`which hub`" || true;
+if [ "a$hub_output" == "a" ]; then
+  echo "The Hub command-line tool must be installed for this script to run" \
+      "successfully.";
+  echo "See https://hub.github.com for more details and installation" \
+      "instructions.";
+  echo "";
+  echo "(apt-get install hub on Debian and Ubuntu)";
+  echo "(brew install hub via Homebrew)";
+  exit 1;
+fi
+
+# Check git remotes: we need to make sure we have a fork to push to.
+github_user=$1;
+remote_name`git remote -v |\
+            grep "$github_user/mlpack (push)" |\
+            head -1 |\
+            awk -F' ' '{ print $1 }'`;
+if [ "a$remote_name" == "a" ]; then
+  echo "No git remote found for $github_user/mlpack!";
+  echo "Adding remote '$github_user'.";
+  git remote add $github_user https://github.com/$github_user/mlpack;
+  remote_name="$github_user";
+fi
+git fetch $github_user;
+
+# Make sure everything is up to date.
+git pull;
+
+# Make updates to files that will be needed for the release.
+MAJOR="$2";
+MINOR="$3";
+PATCH="$4";
+
+# Update version.
+sed --in-place -E 's/PROJECT_NUMBER([ \t]*)= .*$/PROJECT_NUMBER\1= '$MAJOR'.'$MINOR'.'$PATCH'/' \
+    Doxyfile;
+sed --in-place 's/MLPACK_VERSION_MAJOR [0-9]*$/MLPACK_VERSION_MAJOR '$MAJOR'/' \
+    src/mlpack/core/util/version.hpp;
+sed --in-place 's/MLPACK_VERSION_MINOR [0-9]*$/MLPACK_VERSION_MINOR '$MINOR'/' \
+    src/mlpack/core/util/version.hpp;
+sed --in-place 's/MLPACK_VERSION_PATCH [0-9]*$/MLPACK_VERSION_PATCH '$PATCH'/' \
+    src/mlpack/core/util/version.hpp;
+sed --in-place 's/  VERSION [0-9]*\.[0-9]*/  VERSION '$MAJOR'.'$MINOR'/' \
+    src/mlpack/CMakeLists.txt;
+
+sed --in-place 's/mlpack-[0-9]\.[0-9]\.[0-9]/mlpack-'$MAJOR'.'$MINOR'.'$PATCH'/g' \
+    doc/guide/build.hpp;
+sed --in-place 's/mlpack-[0-9]\.[0-9]\.[0-9]/mlpack-'$MAJOR'.'$MINOR'.'$PATCH'/g' \
+    doc/guide/python_quickstart.hpp;
+sed --in-place 's/mlpack-[0-9]\.[0-9]\.[0-9]/mlpack-'$MAJOR'.'$MINOR'.'$PATCH'/g' \
+    doc/guide/sample_ml_app.hpp;
+sed --in-place 's/mlpack-[0-9]\.[0-9]\.[0-9]/mlpack-'$MAJOR'.'$MINOR'.'$PATCH'/g' \
+    doc/examples/sample-ml-app/sample-ml-app/sample-ml-app.vcxproj;
+
+sed --in-place 's/mlpack-[0-9]\.[0-9]\.[0-9]/mlpack-'$MAJOR'.'$MINOR'.'$PATCH'/g' \
+    README.md;
+sed --in-place 's/([0-9]\.[0-9]\.[0-9])/('$MAJOR'.'$MINOR'.'$PATCH')/g' \
+    README.md;
+sed --in-place 's/mlpack [0-9]\.[0-9]\.[0-9]/mlpack '$MAJOR'.'$MINOR'.'$PATCH'/g' \
+    README.md;
+
+sed --in-place 's/### mlpack ?[.]?[.]?/### mlpack '$MAJOR'.'$MINOR'.'$PATCH'/g' HISTORY.md;
+year=`date +%Y`;
+month=`date +%m`;
+day=`date +%d`;
+sed --in-place 's/###### ????-??-??/###### '$year'-'$month'-'$day'/g' \
+    HISTORY.md;
+
+# Get the latest release of ensmallen.
+git clone https://github.com/mlpack/ensmallen /tmp/ensmallen;
+cd /tmp/ensmallen;
+ens_ver=`git describe --tags $(git rev-list --tags --max-count=1)`;
+echo "Latest version of ensmallen: $ens_ver"
+cd -;
+sed --in-place "s/ensmallen-latest.tar.gz/ensmallen-$ens_ver.tar.gz/" CMakeLists.txt;
+rm -rf /tmp/ensmallen;
+
+# Make these changes on a release branch.
+git checkout -b release-$MAJOR.$MINOR.$PATCH;
+
+git add Doxyfile src/mlpack/core/util/version.hpp src/mlpack/CMakeLists.txt \
+    doc/guide/build.hpp doc/guide/python_quickstart.hpp \
+    doc/guide/sample_ml_app.hpp \
+    doc/examples/sample-ml-app/sample-ml-app/sample-ml-app.vcxproj \
+    CMakeLists.txt \
+    README.md \
+    HISTORY.md;
+
+git commit -m "Update and release version $MAJOR.$MINOR.$PATCH.";
+
+changelog_str=`cat HISTORY.md |\
+    awk '/^### /{f=0} /^### mlpack '"$MAJOR"'.'"$MINOR"'.'"$PATCH"'/{f=1} f{print}' |\
+    grep -v '^#' |\
+    tr '\n' '!' |\
+    sed -e 's/!  [ ]*/ /g' |\
+    tr '!' '\n'`;
+echo "Changelog string:"
+echo "$changelog_str"
+
+# Update version again and add a new block for HISTORY.md.
+sed --in-place 's/MLPACK_VERSION_PATCH [0-9]*$/MLPACK_VERSION_PATCH '$(($PATCH + 1))'/' \
+    src/mlpack/core/util/version.hpp;
+sed --in-place 's/ensmallen-'$ens_ver'.tar.gz/ensmallen-latest.tar.gz/' CMakeLists.txt;
+
+echo "### mlpack ?.?.?" > HISTORY.md.new;
+echo "###### ????-??-??" >> HISTORY.md.new;
+echo "" >> HISTORY.md.new;
+cat HISTORY.md >> HISTORY.md.new;
+mv HISTORY.md.new HISTORY.md;
+
+git add HISTORY.md;
+git add src/mlpack/core/util/version.hpp CMakeLists.txt;
+
+git commit -m "Add new block for next release to HISTORY.md.";
+
+# Push to new branch.
+git push --set-upstream $github_user release-$MAJOR.$MINOR.$PATCH;
+
+# Next, we have to actually open the PR for the release.
+hub pull-request \
+    -b mlpack:master \
+    -h $github_user:release-$MAJOR.$MINOR.$PATCH \
+    -m "Release version $MAJOR.$MINOR.$PATCH" \
+    -m "This automatically-generated pull request adds the commits necessary to
+make the $MAJOR.$MINOR.$PATCH release." \
+    -m "Once the PR is merged, mlpack-bot will tag the release as HEAD~1 (so
+that it doesn't include the new HISTORY block) and publish it." \
+    -m "Or, well, hopefully that will happen someday." \
+    -m "When you merge this PR, be sure to merge it using a *rebase*." \
+    -m "### Changelog" \
+    -m "$changelog_str" \
+    -l "t: release"
+
+echo "";
+echo "Switching back to 'master' branch.";
+echo "If you want to access the release branch again, use \`git checkout " \
+    "release-$MAJOR.$MINOR.$PATCH\`.";
+echo 0;
diff --git a/scripts/update-website-after-release.sh b/scripts/update-website-after-release.sh
new file mode 100755
index 0000000000..e80ad8694f
--- /dev/null
+++ b/scripts/update-website-after-release.sh
@@ -0,0 +1,65 @@
+#!/usr/bin/env bash
+#
+# This script is used to update the website after an mlpack release is made.
+# Push access to the mlpack.org website repository is needed.  Generally, this
+# script will be run by mlpack-bot, so it never needs to be run by hand.
+#
+# Usage: update-website-after-release.sh <major> <minor> <patch>
+
+MAJOR=$1;
+MINOR=$2;
+PATCH=$3;
+
+# Make sure that the mlpack repository exists.
+dest_remote_name=`git remote -v |\
+                  grep "mlpack/mlpack (fetch)" |\
+                  head -1 |\
+                  awk -F' ' '{ print $1 }'`;
+
+if [ "a$dest_remote_name" == "a" ]; then
+  echo "No git remote found for mlpack/mlpack!";
+  echo "Make sure that you've got the mlpack repository as a remote, and" \
+      "that the master branch from that remote is checked out.";
+  echo "You can do this with a fresh repository via \`git clone" \
+      "https://github.com/mlpack/mlpack\`.";
+  exit 1;
+fi
+
+# Update the checked out repository, so that we can get the tags.
+git fetch $dest_remote_name;
+
+# Check out a copy of the ensmallen.org repository.
+git clone git@github.com:mlpack/mlpack.org /tmp/mlpack.org/;
+
+# Create the release file.
+git archive --prefix=mlpack-$MAJOR.$MINOR.$PATCH/ $MAJOR.$MINOR.$PATCH |\
+    gzip > /tmp/mlpack.org/files/mlpack-$MAJOR.$MINOR.$PATCH.tar.gz;
+
+# Now update the website.
+wd=`pwd`;
+cd /tmp/mlpack.org/;
+
+# These may be specific to the old website.
+sed --in-place 's/[0-9]\.[0-9]\.[0-9]/'$MAJOR'.'$MINOR'.'$PATCH'/g' index.md;
+sed --in-place 's/[0-9]\.[0-9]\.[0-9]/'$MAJOR'.'$MINOR'.'$PATCH'/g' docs.md;
+sed --in-place 's/[0-9]\.[0-9]\.[0-9]/'$MAJOR'.'$MINOR'.'$PATCH'/g' getstarted.md;
+sed --in-place 's/[0-9]\.[0-9]\.[0-9]/'$MAJOR'.'$MINOR'.'$PATCH'/g' community.md;
+git add index.md docs.md getstarted.md community.md;
+
+# These may be specific to the new website.
+sed --in-place 's/mlpack-[0-9]\.[0-9]\.[0-9]/mlpack-'$MAJOR'.'$MINOR'.'$PATCH'/g' html/index.html;
+sed --in-place 's/Version [0-9]\.[0-9]\.[0-9]/Version '$MAJOR'.'$MINOR'.'$PATCH'/g' html/index.html;
+sed --in-place 's/[0-9]\.[0-9]\.[0-9]/'$MAJOR'.'$MINOR'.'$PATCH'/g' html/getstarted.html;
+sed --in-place 's/[0-9]\.[0-9]\.[0-9]/'$MAJOR'.'$MINOR'.'$PATCH'/g' html/config/install.md;
+git add html/index.html html/getstarted.html html/config/install.md;
+
+git commit -m "Update links to latest stable version.";
+
+git add files/mlpack-$MAJOR.$MINOR.$PATCH.tar.gz;
+git commit -m "Release version $MAJOR.$MINOR.$PATCH.";
+
+# Finally, push, and we're done.
+git push origin;
+cd $wd;
+
+rm -rf /tmp/mlpack.org;
diff --git a/src/mlpack/bindings/markdown/print_docs.cpp b/src/mlpack/bindings/markdown/print_docs.cpp
index 10805e8209..45b9807afa 100644
--- a/src/mlpack/bindings/markdown/print_docs.cpp
+++ b/src/mlpack/bindings/markdown/print_docs.cpp
@@ -180,7 +180,7 @@ void PrintDocs(const std::string& bindingName,
     }
 
     if (hasOutputOptions)
-    {    
+    {
       // Next, iterate through the list of output options.
       cout << "### Output options" << endl;
       cout << endl;
diff --git a/src/mlpack/core/cereal/array_wrapper.hpp b/src/mlpack/core/cereal/array_wrapper.hpp
index 05a675277f..c696f5b727 100644
--- a/src/mlpack/core/cereal/array_wrapper.hpp
+++ b/src/mlpack/core/cereal/array_wrapper.hpp
@@ -21,7 +21,7 @@
 
 namespace cereal {
 
-/** 
+/**
  * This class is used as a shim for cereal to be able to serialize a raw pointer array.
  */
 template<class T>
@@ -84,7 +84,7 @@ ArrayWrapper<T> make_array(T*& t, S& s)
  * @param T C Style array.
  * @param S Size of the array.
  */
-#define CEREAL_POINTER_ARRAY(T,S) cereal::make_array(T, S)
+#define CEREAL_POINTER_ARRAY(T, S) cereal::make_array(T, S)
 
 } // namespace cereal
 
diff --git a/src/mlpack/core/cereal/is_loading.hpp b/src/mlpack/core/cereal/is_loading.hpp
index d075c3d303..085358b2f4 100644
--- a/src/mlpack/core/cereal/is_loading.hpp
+++ b/src/mlpack/core/cereal/is_loading.hpp
@@ -5,7 +5,7 @@
  * Implementation of is_loading function.
  *
  * This implementation provides backward compatibilty with older
- * version of cereal that does not have Archive::is_loading struct. 
+ * version of cereal that does not have Archive::is_loading struct.
  *
  * 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
@@ -28,11 +28,12 @@ struct is_cereal_archive
 {
   // Archive::is_loading is not implemented yet, so we can use std::is_same<>
   // to check if it is a loading archive.
-  constexpr static bool value = std::is_same<Archive, cereal::BinaryInputArchive>::value ||
-//#if (BINDING_TYPE != BINDING_TYPE_R)
-    std::is_same<Archive, cereal::JSONInputArchive>::value ||
-//#endif
-    std::is_same<Archive, cereal::XMLInputArchive>::value;
+  constexpr static bool value = std::is_same<Archive,
+      cereal::BinaryInputArchive>::value ||
+// #if (BINDING_TYPE != BINDING_TYPE_R)
+      std::is_same<Archive, cereal::JSONInputArchive>::value ||
+// #endif
+      std::is_same<Archive, cereal::XMLInputArchive>::value;
 };
 
 template<typename Archive>
@@ -40,7 +41,7 @@ bool is_loading(
     const typename std::enable_if<
         is_cereal_archive<Archive>::value, Archive>::type* = 0)
 {
-  return true; 
+  return true;
 }
 
 template<typename Archive>
diff --git a/src/mlpack/core/cereal/is_saving.hpp b/src/mlpack/core/cereal/is_saving.hpp
index 147668853f..e54362913f 100644
--- a/src/mlpack/core/cereal/is_saving.hpp
+++ b/src/mlpack/core/cereal/is_saving.hpp
@@ -6,7 +6,7 @@
  * Implementation of is_saving function.
  *
  * This implementation provides backward compatibilty with older
- * version of cereal that does not have Archive::is_saving struct. 
+ * version of cereal that does not have Archive::is_saving struct.
  *
  * 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
@@ -29,11 +29,12 @@ struct is_cereal_archive_saving
 {
   // Archive::is_saving is not implemented yet, so we can use std::is_same<>
   // to check if it is a loading archive.
-  constexpr static bool value = std::is_same<Archive, cereal::BinaryOutputArchive>::value ||
-//#if (BINDING_TYPE != BINDING_TYPE_R)
-    std::is_same<Archive, cereal::JSONOutputArchive>::value ||
-//#endif
-    std::is_same<Archive, cereal::XMLOutputArchive>::value;
+  constexpr static bool value = std::is_same<Archive,
+      cereal::BinaryOutputArchive>::value ||
+// #if (BINDING_TYPE != BINDING_TYPE_R)
+      std::is_same<Archive, cereal::JSONOutputArchive>::value ||
+// #endif
+      std::is_same<Archive, cereal::XMLOutputArchive>::value;
 };
 
 template<typename Archive>
@@ -41,7 +42,7 @@ bool is_saving(
     const typename std::enable_if<
         is_cereal_archive_saving<Archive>::value, Archive>::type* = 0)
 {
-  return true; 
+  return true;
 }
 
 template<typename Archive>
diff --git a/src/mlpack/core/data/has_serialize.hpp b/src/mlpack/core/data/has_serialize.hpp
index 0c3b92c73b..7c2c3d114a 100644
--- a/src/mlpack/core/data/has_serialize.hpp
+++ b/src/mlpack/core/data/has_serialize.hpp
@@ -32,9 +32,12 @@ template<typename T>
 struct HasSerializeFunction
 {
   template<typename C>
-  using NonStaticSerialize = void(C::*)(cereal::XMLOutputArchive&, const uint32_t version);
+  using NonStaticSerialize = void(C::*)(cereal::XMLOutputArchive&,
+      const uint32_t version);
+
   template<typename /* C */>
-  using StaticSerialize = void(*)(cereal::XMLOutputArchive&, const uint32_t version);
+  using StaticSerialize = void(*)(cereal::XMLOutputArchive&,
+      const uint32_t version);
 
   static const bool value = HasSerializeCheck<T, NonStaticSerialize>::value ||
                             HasSerializeCheck<T, StaticSerialize>::value;
diff --git a/src/mlpack/core/metrics/bleu_impl.hpp b/src/mlpack/core/metrics/bleu_impl.hpp
index 66fdd626a5..38e9b29437 100644
--- a/src/mlpack/core/metrics/bleu_impl.hpp
+++ b/src/mlpack/core/metrics/bleu_impl.hpp
@@ -187,7 +187,7 @@ ElemType BLEU<ElemType, PrecisionType>::Evaluate(
 
 template <typename ElemType, typename PrecisionType>
 template <typename Archive>
-void BLEU<ElemType, PrecisionType>::serialize(Archive& ar, 
+void BLEU<ElemType, PrecisionType>::serialize(Archive& ar,
     const uint32_t version)
 {
   ar(CEREAL_NVP(maxOrder));
diff --git a/src/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp b/src/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp
index cc1b86be18..6e2a934004 100644
--- a/src/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp
+++ b/src/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp
@@ -1131,7 +1131,7 @@ void BinarySpaceTree<MetricType, StatisticType, MatType, BoundType, SplitType>::
       if (node->left)
         stack.push(node->left);
       if (node->right)
-       stack.push(node->right); 
+       stack.push(node->right);
     }
   }
 }
diff --git a/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp b/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
index 30d09c1300..03acfb0e74 100644
--- a/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
+++ b/src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
@@ -1431,7 +1431,7 @@ void RectangleTree<MetricType, StatisticType, MatType, SplitType, DescentType,
   {
     MatType*& datasetTemp = const_cast<MatType*&>(dataset);
     ar(CEREAL_POINTER(datasetTemp));
-  } 
+  }
 
   ar(CEREAL_NVP(points));
   ar(CEREAL_NVP(auxiliaryInfo));
diff --git a/src/mlpack/methods/ann/layer/add_impl.hpp b/src/mlpack/methods/ann/layer/add_impl.hpp
index e9903981f6..a268956fe8 100644
--- a/src/mlpack/methods/ann/layer/add_impl.hpp
+++ b/src/mlpack/methods/ann/layer/add_impl.hpp
@@ -23,7 +23,7 @@ template<typename InputDataType, typename OutputDataType>
 Add<InputDataType, OutputDataType>::Add(const size_t outSize) :
     outSize(outSize)
 {
-  weights.set_size(outSize, 1);
+  weights.set_size(WeightSize(), 1);
 }
 
 template<typename InputDataType, typename OutputDataType>
diff --git a/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp b/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
index e71bb2cf5e..cfb200e3ec 100644
--- a/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
+++ b/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
@@ -122,8 +122,7 @@ AtrousConvolution<
     dilationWidth(dilationWidth),
     dilationHeight(dilationHeight)
 {
-  weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize,
-      1);
+  weights.set_size(WeightSize(), 1);
 
   // Transform paddingType to lowercase.
   std::string paddingTypeLow = paddingType;
diff --git a/src/mlpack/methods/ann/layer/concat.hpp b/src/mlpack/methods/ann/layer/concat.hpp
index 81df70a1b2..13d29458cb 100644
--- a/src/mlpack/methods/ann/layer/concat.hpp
+++ b/src/mlpack/methods/ann/layer/concat.hpp
@@ -165,9 +165,9 @@ class Concat
   }
 
   //! Return the initial point for the optimization.
-  const arma::mat& Parameters() const { return parameters; }
+  const arma::mat& Parameters() const { return weights; }
   //! Modify the initial point for the optimization.
-  arma::mat& Parameters() { return parameters; }
+  arma::mat& Parameters() { return weights; }
 
   //! Get the value of run parameter.
   bool Run() const { return run; }
@@ -196,6 +196,9 @@ class Concat
   //! Get the axis of concatenation.
   size_t const& ConcatAxis() const { return axis; }
 
+  //! Get the size of the weight matrix.
+  size_t WeightSize() const { return 0; }
+
   /**
    * Serialize the layer
    */
@@ -225,8 +228,8 @@ class Concat
   //! Locally-stored network modules.
   std::vector<LayerTypes<CustomLayers...> > network;
 
-  //! Locally-stored model parameters.
-  arma::mat parameters;
+  //! Locally-stored model weights.
+  OutputDataType weights;
 
   //! Locally-stored delta visitor.
   DeltaVisitor deltaVisitor;
diff --git a/src/mlpack/methods/ann/layer/concat_impl.hpp b/src/mlpack/methods/ann/layer/concat_impl.hpp
index 694784c7a5..b410361295 100644
--- a/src/mlpack/methods/ann/layer/concat_impl.hpp
+++ b/src/mlpack/methods/ann/layer/concat_impl.hpp
@@ -33,7 +33,7 @@ Concat<InputDataType, OutputDataType, CustomLayers...>::Concat(
     run(run),
     channels(1)
 {
-  parameters.set_size(0, 0);
+  weights.set_size(0, 0);
 }
 
 template<typename InputDataType, typename OutputDataType,
@@ -49,7 +49,7 @@ Concat<InputDataType, OutputDataType, CustomLayers...>::Concat(
     model(model),
     run(run)
 {
-  parameters.set_size(0, 0);
+  weights.set_size(0, 0);
 
   // Parameters to help calculate the number of channels.
   size_t oldColSize = 1, newColSize = 1;
diff --git a/src/mlpack/methods/ann/layer/dropconnect.hpp b/src/mlpack/methods/ann/layer/dropconnect.hpp
index 7451705974..db8d76aa4b 100644
--- a/src/mlpack/methods/ann/layer/dropconnect.hpp
+++ b/src/mlpack/methods/ann/layer/dropconnect.hpp
@@ -115,9 +115,9 @@ class DropConnect
   std::vector<LayerTypes<> >& Model() { return network; }
 
   //! Get the parameters.
-  OutputDataType const& Parameters() const { return parameters; }
+  OutputDataType const& Parameters() const { return weights; }
   //! Modify the parameters.
-  OutputDataType& Parameters() { return parameters; }
+  OutputDataType& Parameters() { return weights; }
 
   //! Get the output parameter.
   OutputDataType const& OutputParameter() const { return outputParameter; }
@@ -150,6 +150,9 @@ class DropConnect
     scale = 1.0 / (1.0 - ratio);
   }
 
+  //! Return the size of the weight matrix.
+  size_t WeightSize() const { return 0; }
+
   /**
    * Serialize the layer.
    */
@@ -164,7 +167,7 @@ class DropConnect
   double scale;
 
   //! Locally-stored weight object.
-  OutputDataType parameters;
+  OutputDataType weights;
 
   //! Locally-stored delta object.
   OutputDataType delta;
diff --git a/src/mlpack/methods/ann/layer/dropconnect_impl.hpp b/src/mlpack/methods/ann/layer/dropconnect_impl.hpp
index ccbda1c888..9b22c91951 100644
--- a/src/mlpack/methods/ann/layer/dropconnect_impl.hpp
+++ b/src/mlpack/methods/ann/layer/dropconnect_impl.hpp
@@ -108,7 +108,7 @@ template<typename Archive>
 void DropConnect<InputDataType, OutputDataType>::serialize(
     Archive& ar, const uint32_t /* version */)
 {
-   // Delete the old network first, if needed.
+  // Delete the old network first, if needed.
   if (cereal::is_loading<Archive>())
   {
     boost::apply_visitor(DeleteVisitor(), baseLayer);
diff --git a/src/mlpack/methods/ann/layer/fast_lstm.hpp b/src/mlpack/methods/ann/layer/fast_lstm.hpp
index a0400772fd..121c97e176 100644
--- a/src/mlpack/methods/ann/layer/fast_lstm.hpp
+++ b/src/mlpack/methods/ann/layer/fast_lstm.hpp
@@ -164,6 +164,12 @@ class FastLSTM
   //! Get the number of output units.
   size_t OutSize() const { return outSize; }
 
+  //! Get the size of the weight matrix.
+  size_t WeightSize() const
+  {
+    return 4 * outSize * inSize + 4 * outSize + 4 * outSize * outSize;
+  }
+
   /**
    * Serialize the layer
    */
diff --git a/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp b/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp
index b283dbc5ad..5f5502cf9a 100644
--- a/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp
+++ b/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp
@@ -42,8 +42,7 @@ FastLSTM<InputDataType, OutputDataType>::FastLSTM(
 {
   // Weights for: input to gate layer (4 * outsize * inSize + 4 * outsize)
   // and output to gate (4 * outSize).
-  weights.set_size(
-      4 * outSize * inSize + 4 * outSize + 4 * outSize * outSize, 1);
+  weights.set_size(WeightSize(), 1);
 }
 
 template<typename InputDataType, typename OutputDataType>
diff --git a/src/mlpack/methods/ann/layer/linear_impl.hpp b/src/mlpack/methods/ann/layer/linear_impl.hpp
index 83a7b1f157..2edfb4802c 100644
--- a/src/mlpack/methods/ann/layer/linear_impl.hpp
+++ b/src/mlpack/methods/ann/layer/linear_impl.hpp
@@ -38,7 +38,7 @@ Linear<InputDataType, OutputDataType, RegularizerType>::Linear(
     outSize(outSize),
     regularizer(regularizer)
 {
-  weights.set_size(outSize * inSize + outSize, 1);
+  weights.set_size(WeightSize(), 1);
 }
 
 template<typename InputDataType, typename OutputDataType,
@@ -70,7 +70,7 @@ template<typename InputDataType, typename OutputDataType,
 Linear<InputDataType, OutputDataType, RegularizerType>&
 Linear<InputDataType, OutputDataType, RegularizerType>::
 operator=(const Linear& layer)
-{ 
+{
   if (this != &layer)
   {
     inSize = layer.inSize;
@@ -86,7 +86,7 @@ template<typename InputDataType, typename OutputDataType,
 Linear<InputDataType, OutputDataType, RegularizerType>&
 Linear<InputDataType, OutputDataType, RegularizerType>::
 operator=(Linear&& layer)
-{ 
+{
   if (this != &layer)
   {
     inSize = layer.inSize;
diff --git a/src/mlpack/methods/ann/layer/softmin_impl.hpp b/src/mlpack/methods/ann/layer/softmin_impl.hpp
index e2389f5856..6945256b44 100644
--- a/src/mlpack/methods/ann/layer/softmin_impl.hpp
+++ b/src/mlpack/methods/ann/layer/softmin_impl.hpp
@@ -29,7 +29,7 @@ template<typename InputType, typename OutputType>
 void Softmin<InputDataType, OutputDataType>::Forward(
     const InputType& input,
     OutputType& output)
-{ 
+{
   InputType softminInput = arma::exp(-(input.each_row() -
       arma::min(input, 0)));
   output = softminInput.each_row() / sum(softminInput, 0);
@@ -53,7 +53,7 @@ void Softmin<InputDataType, OutputDataType>::serialize(
 {
   // Nothing to do here.
 }
-                                             
+
 } // namespace ann
 } // namespace mlpack
 
diff --git a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp
index c17badf733..52b6281a18 100644
--- a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp
+++ b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp
@@ -32,7 +32,7 @@ MeanAbsolutePercentageError<InputDataType, OutputDataType>::Forward(
     const InputType& input,
     const TargetType& target)
 {
-  InputType loss = arma::abs((input - target) / target); 
+  InputType loss = arma::abs((input - target) / target);
   return arma::accu(loss) * (100 / target.n_cols);
 }
 
@@ -43,9 +43,9 @@ void MeanAbsolutePercentageError<InputDataType, OutputDataType>::Backward(
     const TargetType& target,
     OutputType& output)
 
-{ 
+{
   output = (((arma::conv_to<arma::mat>::from(input < target) * -2) + 1) /
-      target) * (100 / target.n_cols) ;
+      target) * (100 / target.n_cols);
 }
 
 template<typename InputDataType, typename OutputDataType>
diff --git a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp
index 8842cc82e5..d0881dd06a 100644
--- a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp
+++ b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp
@@ -21,7 +21,8 @@ namespace regression {
  * Serialize the Bayesian linear regression model.
  */
 template<typename Archive>
-void BayesianLinearRegression::serialize(Archive& ar, const uint32_t /* version */)
+void BayesianLinearRegression::serialize(Archive& ar,
+                                         const uint32_t /* version */)
 {
   ar(CEREAL_NVP(centerData));
   ar(CEREAL_NVP(scaleData));
diff --git a/src/mlpack/methods/hmm/hmm_util_impl.hpp b/src/mlpack/methods/hmm/hmm_util_impl.hpp
index 24ee4adc6b..017cc65bcd 100644
--- a/src/mlpack/methods/hmm/hmm_util_impl.hpp
+++ b/src/mlpack/methods/hmm/hmm_util_impl.hpp
@@ -41,14 +41,25 @@ void LoadHMMAndPerformAction(const std::string& modelFile,
 {
   const std::string extension = data::Extension(modelFile);
   if (extension == "xml")
-    LoadHMMAndPerformActionHelper<ActionType, cereal::XMLInputArchive>(modelFile, x);
+  {
+    LoadHMMAndPerformActionHelper<ActionType, cereal::XMLInputArchive>(
+        modelFile, x);
+  }
   else if (extension == "bin")
-    LoadHMMAndPerformActionHelper<ActionType, cereal::BinaryInputArchive>(modelFile, x);
+  {
+    LoadHMMAndPerformActionHelper<ActionType, cereal::BinaryInputArchive>(
+        modelFile, x);
+  }
   else if (extension == "json")
-    LoadHMMAndPerformActionHelper<ActionType, cereal::JSONInputArchive>(modelFile, x);
+  {
+    LoadHMMAndPerformActionHelper<ActionType, cereal::JSONInputArchive>(
+        modelFile, x);
+  }
   else
+  {
     Log::Fatal << "Unknown extension '" << extension << "' for HMM model file "
         << "(known: 'xml', 'json', 'bin')." << std::endl;
+  }
 }
 
 template<typename ActionType,
diff --git a/src/mlpack/tests/activation_functions_test.cpp b/src/mlpack/tests/activation_functions_test.cpp
index 9ee1ebcaf9..d88c0a5109 100644
--- a/src/mlpack/tests/activation_functions_test.cpp
+++ b/src/mlpack/tests/activation_functions_test.cpp
@@ -573,7 +573,7 @@ void CheckSoftminActivationCorrect(const arma::colvec input,
 
   // Test the activation function using the entire vector as input.
   arma::colvec activations;
-  softmin.Forward(input,activations);
+  softmin.Forward(input, activations);
   for (size_t i = 0; i < activations.n_elem; ++i)
   {
     REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5));
@@ -606,8 +606,7 @@ void CheckSoftminDerivativeCorrect(const arma::colvec input,
   for (size_t i = 0; i < derivatives.n_elem; ++i)
   {
     REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5));
-  } 
-
+  }
 }
 
 /**
diff --git a/src/mlpack/tests/ann_visitor_test.cpp b/src/mlpack/tests/ann_visitor_test.cpp
index c53b23dd44..f42a0a367e 100644
--- a/src/mlpack/tests/ann_visitor_test.cpp
+++ b/src/mlpack/tests/ann_visitor_test.cpp
@@ -86,15 +86,78 @@ TEST_CASE("WeightSetVisitorTest", "[ANNVisitorTest]")
 }
 
 /**
- * Test that WeightSizeVisitor works properly.
+ * Test that WeightSizeVisitor works properly for linear layer.
  */
-TEST_CASE("WeightSizeVisitorTest", "[ANNVisitorTest]")
+TEST_CASE("WeightSizeVisitorTestForLinearLayer", "[ANNVisitorTest]")
 {
-  size_t randomSize = arma::randi(arma::distr_param(1, 100));
+  size_t randomInSize = arma::randi(arma::distr_param(1, 100));
+  size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
 
-  LayerTypes<> linear = new Linear<>(randomSize, randomSize);
+  LayerTypes<> linearLayer = new Linear<>(randomInSize, randomOutSize);
 
-  CheckCorrectnessOfWeightSize(linear);
+  size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), linearLayer);
+
+  CheckCorrectnessOfWeightSize(linearLayer);
+}
+
+/**
+ * Test that WeightSizeVisitor works properly for concat layer.
+ */
+TEST_CASE("WeightSizeVisitorTestForConcatLayer", "[ANNVisitorTest]")
+{
+  LayerTypes<> concatLayer = new Concat<>();
+
+  size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), concatLayer);
+
+  CheckCorrectnessOfWeightSize(concatLayer);
+}
+
+/**
+ * Test that WeightSizeVisitor works properly for fast lstm layer.
+ */
+TEST_CASE("WeightSizeVisitorTestForFastLSTMLayer", "[ANNVisitorTest]")
+{
+  size_t randomInSize = arma::randi(arma::distr_param(1, 100));
+  size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
+
+  LayerTypes<> fastLSTMLayer = new FastLSTM<>(randomInSize, randomOutSize);
+
+  size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), fastLSTMLayer);
+
+  CheckCorrectnessOfWeightSize(fastLSTMLayer);
+}
+
+/**
+ * Test that WeightSizeVisitor works properly for Add layer.
+ */
+TEST_CASE("WeightSizeVisitorTestForAddLayer", "[ANNVisitorTest]")
+{
+  size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
+
+  LayerTypes<> addLayer = new Add<>(randomOutSize);
+
+  size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), addLayer);
+
+  CheckCorrectnessOfWeightSize(addLayer);
+}
+
+/**
+ * Test that WeightSizeVisitor works properly for Atrous Convolution Layer.
+ */
+TEST_CASE("WeightSizeVisitorTestForAtrousConvolutionLayer", "[ANNVisitorTest]")
+{
+  size_t randomInSize = arma::randi(arma::distr_param(1, 100));
+  size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
+  size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100));
+  size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100));
+
+  LayerTypes<> atrousConvLayer = new AtrousConvolution<>(randomInSize,
+      randomOutSize, randomKernelWidth, randomKernelHeight);
+
+  size_t weightSize = boost::apply_visitor(WeightSizeVisitor(),
+      atrousConvLayer);
+
+  CheckCorrectnessOfWeightSize(atrousConvLayer);
 }
 
 
diff --git a/src/mlpack/tests/distribution_test.cpp b/src/mlpack/tests/distribution_test.cpp
index 0ec7fd3cb6..7346098d43 100644
--- a/src/mlpack/tests/distribution_test.cpp
+++ b/src/mlpack/tests/distribution_test.cpp
@@ -1518,7 +1518,8 @@ TEST_CASE("DiagonalGaussianUnbiasedEstimatorTest", "[DistributionTest]")
  * the weighted mean and covariance reduce to the unweighted sample mean and
  * covariance.
  */
-TEST_CASE("DiagonalGaussianWeightedParametersReductionTest", "[DistributionTest]")
+TEST_CASE("DiagonalGaussianWeightedParametersReductionTest",
+          "[DistributionTest]")
 {
   arma::vec mean("2.5 1.5 8.2 3.1");
   arma::vec cov("1.2 3.1 8.3 4.3");
diff --git a/src/mlpack/tests/hoeffding_tree_test.cpp b/src/mlpack/tests/hoeffding_tree_test.cpp
index 8787dfe28f..49c60b0cea 100644
--- a/src/mlpack/tests/hoeffding_tree_test.cpp
+++ b/src/mlpack/tests/hoeffding_tree_test.cpp
@@ -173,7 +173,8 @@ TEST_CASE("HoeffdingInformationGainBadSplitTest", "[HoeffdingTreeTest]")
   counts(1, 0) = 5;
   counts(1, 1) = 5;
 
-  REQUIRE(HoeffdingInformationGain::Evaluate(counts) == Approx(0.0).margin(1e-10));
+  REQUIRE(HoeffdingInformationGain::Evaluate(counts) ==
+      Approx(0.0).margin(1e-10));
 }
 
 /**
@@ -216,7 +217,8 @@ TEST_CASE("HoeffdingInformationGainZeroTest", "[HoeffdingTreeTest]")
   // When nothing has been seen, the information gain should be zero.
   arma::Mat<size_t> counts = arma::zeros<arma::Mat<size_t>>(10, 10);
 
-  REQUIRE(HoeffdingInformationGain::Evaluate(counts) == Approx(0.0).margin(1e-10));
+  REQUIRE(HoeffdingInformationGain::Evaluate(counts) ==
+      Approx(0.0).margin(1e-10));
 }
 
 /**
@@ -225,14 +227,22 @@ TEST_CASE("HoeffdingInformationGainZeroTest", "[HoeffdingTreeTest]")
  */
 TEST_CASE("HoeffdingInformationGainRangeTest", "[HoeffdingTreeTest]")
 {
-  REQUIRE(HoeffdingInformationGain::Range(1) == Approx(0).epsilon(1e-7));
-  REQUIRE(HoeffdingInformationGain::Range(2) == Approx(1.0).epsilon(1e-7));
-  REQUIRE(HoeffdingInformationGain::Range(3) == Approx(1.5849625).epsilon(1e-7));
-  REQUIRE(HoeffdingInformationGain::Range(4) == Approx(2).epsilon(1e-7));
-  REQUIRE(HoeffdingInformationGain::Range(5) == Approx(2.32192809).epsilon(1e-7));
-  REQUIRE(HoeffdingInformationGain::Range(10) == Approx(3.32192809).epsilon(1e-7));
-  REQUIRE(HoeffdingInformationGain::Range(100) == Approx(6.64385619).epsilon(1e-7));
-  REQUIRE(HoeffdingInformationGain::Range(1000) == Approx(9.96578428).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(1) ==
+      Approx(0).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(2) ==
+      Approx(1.0).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(3) ==
+      Approx(1.5849625).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(4) ==
+      Approx(2).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(5) ==
+      Approx(2.32192809).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(10) ==
+      Approx(3.32192809).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(100) ==
+      Approx(6.64385619).epsilon(1e-7));
+  REQUIRE(HoeffdingInformationGain::Range(1000) ==
+      Approx(9.96578428).epsilon(1e-7));
 }
 
 /**
diff --git a/src/mlpack/tests/io_test.cpp b/src/mlpack/tests/io_test.cpp
index d09b7e97a0..fa363face9 100644
--- a/src/mlpack/tests/io_test.cpp
+++ b/src/mlpack/tests/io_test.cpp
@@ -795,7 +795,8 @@ TEST_CASE_METHOD(IOTestDestroyer, "OutputMatrixParamTest",
   remove("test.csv");
 }
 
-TEST_CASE_METHOD(IOTestDestroyer, "OutputMatrixNoTransposeParamTest", "[IOTest]")
+TEST_CASE_METHOD(IOTestDestroyer, "OutputMatrixNoTransposeParamTest",
+                "[IOTest]")
 {
   AddRequiredCLIOptions();
 
diff --git a/src/mlpack/tests/lin_alg_test.cpp b/src/mlpack/tests/lin_alg_test.cpp
index 7454564c20..a43821d75d 100644
--- a/src/mlpack/tests/lin_alg_test.cpp
+++ b/src/mlpack/tests/lin_alg_test.cpp
@@ -190,7 +190,6 @@ TEST_CASE("TestSvecSmat", "[LinAlgTest]")
   for (size_t i = 0; i < 3; ++i)
     for (size_t j = 0; j < 3; ++j)
       REQUIRE(X(i, j) == Approx(Xtest(i, j)).epsilon(1e-9));
-
 }
 
 TEST_CASE("TestSparseSvec", "[LinAlgTest]")
diff --git a/src/mlpack/tests/lmnn_test.cpp b/src/mlpack/tests/lmnn_test.cpp
index 52d014c9aa..b767f4a940 100644
--- a/src/mlpack/tests/lmnn_test.cpp
+++ b/src/mlpack/tests/lmnn_test.cpp
@@ -120,7 +120,7 @@ TEST_CASE("LMNNInitialPointTest", "[LMNNTest]")
     for (int col = 0; col < 5; col++)
     {
       if (row == col)
-        REQUIRE(initialPoint(row, col) == Approx( 1.0).epsilon(1e-7));
+        REQUIRE(initialPoint(row, col) == Approx(1.0).epsilon(1e-7));
       else
         REQUIRE(initialPoint(row, col) == Approx(0.0).margin(1e-5));
     }
@@ -207,8 +207,8 @@ TEST_CASE("LMNNSeparableObjectiveTest", "[LMNNTest]")
 
   // Result calculated by hand.
   arma::mat coordinates = arma::eye<arma::mat>(2, 2);
-  REQUIRE(lmnnfn.Evaluate(coordinates, 0, 1) == Approx( 1.576).epsilon(1e-7));
-  REQUIRE(lmnnfn.Evaluate(coordinates, 1, 1) == Approx( 1.576).epsilon(1e-7));
+  REQUIRE(lmnnfn.Evaluate(coordinates, 0, 1) == Approx(1.576).epsilon(1e-7));
+  REQUIRE(lmnnfn.Evaluate(coordinates, 1, 1) == Approx(1.576).epsilon(1e-7));
   REQUIRE(lmnnfn.Evaluate(coordinates, 2, 1) == Approx(1.576).epsilon(1e-7));
   REQUIRE(lmnnfn.Evaluate(coordinates, 3, 1) == Approx(1.576).epsilon(1e-7));
   REQUIRE(lmnnfn.Evaluate(coordinates, 4, 1) == Approx(1.576).epsilon(1e-7));
@@ -326,21 +326,21 @@ TEST_CASE("LMNNSeparableEvaluateWithGradientTest", "[LMNNTest]")
 
   objective = lmnnfn.EvaluateWithGradient(coordinates, 4, gradient, 1);
 
-  REQUIRE(objective == Approx( 1.576).epsilon(1e-7));
+  REQUIRE(objective == Approx(1.576).epsilon(1e-7));
 
-  REQUIRE(gradient(0, 0) == Approx( -0.048).epsilon(1e-7));
-  REQUIRE(gradient(0, 1) == Approx( 0.0).epsilon(1e-7));
-  REQUIRE(gradient(1, 0) == Approx( 0.0).epsilon(1e-7));
-  REQUIRE(gradient(1, 1) == Approx( 2.0).epsilon(1e-7));
+  REQUIRE(gradient(0, 0) == Approx(-0.048).epsilon(1e-7));
+  REQUIRE(gradient(0, 1) == Approx(0.0).epsilon(1e-7));
+  REQUIRE(gradient(1, 0) == Approx(0.0).epsilon(1e-7));
+  REQUIRE(gradient(1, 1) == Approx(2.0).epsilon(1e-7));
 
   objective = lmnnfn.EvaluateWithGradient(coordinates, 5, gradient, 1);
 
-  REQUIRE(objective == Approx( 1.576).epsilon(1e-7));
+  REQUIRE(objective == Approx(1.576).epsilon(1e-7));
 
-  REQUIRE(gradient(0, 0) == Approx( -0.048).epsilon(1e-7));
-  REQUIRE(gradient(0, 1) == Approx( 0.0).epsilon(1e-7));
-  REQUIRE(gradient(1, 0) == Approx( 0.0).epsilon(1e-7));
-  REQUIRE(gradient(1, 1) == Approx( 2.0).epsilon(1e-7));
+  REQUIRE(gradient(0, 0) == Approx(-0.048).epsilon(1e-7));
+  REQUIRE(gradient(0, 1) == Approx(0.0).epsilon(1e-7));
+  REQUIRE(gradient(1, 0) == Approx(0.0).epsilon(1e-7));
+  REQUIRE(gradient(1, 1) == Approx(2.0).epsilon(1e-7));
 }
 
 // Check that final objective value using SGD optimizer is optimal.
@@ -450,7 +450,7 @@ TEST_CASE("LMNNAccuracyTest", "[LMNNTest]")
   REQUIRE(initAccuracy < finalAccuracy);
 
   // Since this is a very simple dataset final accuracy should be around 100%.
-  REQUIRE(finalAccuracy == Approx( 100.0).epsilon(1e-7));
+  REQUIRE(finalAccuracy == Approx(100.0).epsilon(1e-7));
 }
 
 // Check that accuracy while learning square distance matrix is the same as when
diff --git a/src/mlpack/tests/loss_functions_test.cpp b/src/mlpack/tests/loss_functions_test.cpp
index cb814d021e..42762bd96d 100644
--- a/src/mlpack/tests/loss_functions_test.cpp
+++ b/src/mlpack/tests/loss_functions_test.cpp
@@ -201,7 +201,7 @@ TEST_CASE("KLDivergenceMeanTest", "[LossFunctionsTest]")
   target = arma::exp(arma::mat("2 1 1 1 1 1 1 1 1 1"));
 
   loss = module.Forward(input, target);
-  REQUIRE(loss == Approx(-1.1 ).epsilon(1e-5));
+  REQUIRE(loss == Approx(-1.1).epsilon(1e-5));
 
   // Test the Backward function.
   module.Backward(input, target, output);
@@ -846,7 +846,8 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]")
 
   // Test the Backward function.
   module1.Backward(input, target, output);
-  REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-1.48227).epsilon(1e-3));
+  REQUIRE(arma::as_scalar(arma::accu(output)) ==
+      Approx(-1.48227).epsilon(1e-3));
   REQUIRE(output.n_rows == input.n_rows);
   REQUIRE(output.n_cols == input.n_cols);
   CheckMatrices(output, expectedOutput, 0.1);
@@ -865,7 +866,8 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]")
 
   // Test the Backward function.
   module2.Backward(input, target, output);
-  REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-0.164697).epsilon(1e-3));
+  REQUIRE(arma::as_scalar(arma::accu(output)) ==
+      Approx(-0.164697).epsilon(1e-3));
   REQUIRE(output.n_rows == input.n_rows);
   REQUIRE(output.n_cols == input.n_cols);
   CheckMatrices(output, expectedOutput, 0.1);
@@ -884,12 +886,13 @@ TEST_CASE("MeanAbsolutePercentageErrorTest", "[LossFunctionsTest]")
 
   // Test the Forward function. Loss should be 95.625.
   // Loss value calculated manually.
-  double loss = module.Forward(input,target);
-  REQUIRE(loss == Approx(95.625).epsilon(1e-1)); 
+  double loss = module.Forward(input, target);
+  REQUIRE(loss == Approx(95.625).epsilon(1e-1));
 
   // Test the Backward function.
   module.Backward(input, target, output);
-  REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-105.625).epsilon(1e-3));
+  REQUIRE(arma::as_scalar(arma::accu(output)) ==
+      Approx(-105.625).epsilon(1e-3));
   REQUIRE(output.n_rows == input.n_rows);
   REQUIRE(output.n_cols == input.n_cols);
   CheckMatrices(output, expectedOutput, 0.1);
diff --git a/src/mlpack/tests/main_tests/kde_test.cpp b/src/mlpack/tests/main_tests/kde_test.cpp
index f4604f9475..325130c928 100644
--- a/src/mlpack/tests/main_tests/kde_test.cpp
+++ b/src/mlpack/tests/main_tests/kde_test.cpp
@@ -87,7 +87,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEGaussianRTreeResultsMain",
 
   // Check whether results are equal.
   for (size_t i = 0; i < query.n_cols; ++i)
-    REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
+    REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
 }
 
 /**
@@ -128,7 +128,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDETriangularBallTreeResultsMain",
 
   // Check whether results are equal.
   for (size_t i = 0; i < query.n_cols; ++i)
-    REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
+    REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
 }
 
 /**
@@ -170,7 +170,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEMonoResultsMain",
 
   // Check whether results are equal.
   for (size_t i = 0; i < reference.n_cols; ++i)
-    REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
+    REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
 }
 
 /**
@@ -239,7 +239,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEModelReuse",
 
   // Check estimations are the same.
   for (size_t i = 0; i < samples; ++i)
-    REQUIRE(oldEstimations[i] == Approx( newEstimations[i]).epsilon(relError));
+    REQUIRE(oldEstimations[i] == Approx(newEstimations[i]).epsilon(relError));
 }
 
 /**
@@ -282,7 +282,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEGaussianSingleKDTreeResultsMain",
 
   // Check whether results are equal.
   for (size_t i = 0; i < query.n_cols; ++i)
-    REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
+    REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
 }
 
 /**
diff --git a/src/mlpack/tests/main_tests/krann_test.cpp b/src/mlpack/tests/main_tests/krann_test.cpp
index 244b88c8c5..b61044f104 100644
--- a/src/mlpack/tests/main_tests/krann_test.cpp
+++ b/src/mlpack/tests/main_tests/krann_test.cpp
@@ -453,7 +453,8 @@ TEST_CASE_METHOD(KRANNTestFixture, "KRANNDifferentTreeType",
 
   // Check that initial output matrices and the output matrices using
   // saved model are equal
-  CHECK(output_model->TreeType() == 0);
+  const bool check = output_model->TreeType() == 0;
+  CHECK(check == true);
   CHECK(IO::GetParam<RANNModel*>("output_model")->TreeType() ==
       8);
   delete output_model;
@@ -491,7 +492,8 @@ TEST_CASE_METHOD(KRANNTestFixture, "KRANNDifferentSingleSampleLimit",
 
   // Check that initial output matrices and the output matrices using
   // saved model are equal.
-  CHECK( IO::GetParam<RANNModel*>("output_model")->SingleSampleLimit() == (int) 15);
+  CHECK(IO::GetParam<RANNModel*>("output_model")->SingleSampleLimit() ==
+      (int) 15);
   CHECK(output_model->SingleSampleLimit() == (int) 20);
   delete output_model;
 }
diff --git a/src/mlpack/tests/nbc_test.cpp b/src/mlpack/tests/nbc_test.cpp
index f487060db4..ddc0487315 100644
--- a/src/mlpack/tests/nbc_test.cpp
+++ b/src/mlpack/tests/nbc_test.cpp
@@ -81,7 +81,6 @@ TEST_CASE("NaiveBayesClassifierTest", "[NBCTest]")
   {
     for (size_t j = 0; j < testResProbs.n_rows; ++j)
     {
-      
       REQUIRE(testResProbs(j, i) + 0.0001 ==
           Approx(calcProbs(j, i) + 0.0001).epsilon(0.0001));
     }
diff --git a/src/mlpack/tests/pca_test.cpp b/src/mlpack/tests/pca_test.cpp
index fff63d0024..6ccefbbaeb 100644
--- a/src/mlpack/tests/pca_test.cpp
+++ b/src/mlpack/tests/pca_test.cpp
@@ -316,7 +316,8 @@ TEST_CASE("PCAScalingTest", "[PCATest]")
   // zero.  There is noise, of course...
   REQUIRE(std::abs(eigvec(0, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
   REQUIRE(std::abs(eigvec(1, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
-  REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1)); // Large tolerance for noise.
+  // Large tolerance for noise.
+  REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1));
 
   // The second component should be focused almost entirely in the third
   // dimension.
@@ -328,7 +329,8 @@ TEST_CASE("PCAScalingTest", "[PCATest]")
   // the first (plus tolerance).
   REQUIRE(std::abs(eigvec(0, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
   REQUIRE(std::abs(eigvec(1, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
-  REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1)); // Large tolerance for noise.
+  // Large tolerance for noise.
+  REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1));
 
   // The eigenvalues should sum to three.
   REQUIRE(accu(eigval) == Approx(3.0).epsilon(0.001));
diff --git a/src/mlpack/tests/range_search_test.cpp b/src/mlpack/tests/range_search_test.cpp
index 3df0247a6b..8e6e79da17 100644
--- a/src/mlpack/tests/range_search_test.cpp
+++ b/src/mlpack/tests/range_search_test.cpp
@@ -206,13 +206,20 @@ TEST_CASE("ExhaustiveSyntheticTest", "[RangeSearchTest]")
     // Neighbors of point 10.
     REQUIRE(sortedOutput[newFromOld[10]].size() == 4);
     REQUIRE(sortedOutput[newFromOld[10]][0].second == newFromOld[9]);
-    REQUIRE(sortedOutput[newFromOld[10]][0].first == Approx(0.10).epsilon(1e-7));
-    REQUIRE(sortedOutput[newFromOld[10]][1].second == newFromOld[3]);
-    REQUIRE(sortedOutput[newFromOld[10]][1].first == Approx(0.25).epsilon(1e-7));
-    REQUIRE(sortedOutput[newFromOld[10]][2].second == newFromOld[8]);
-    REQUIRE(sortedOutput[newFromOld[10]][2].first == Approx(0.55).epsilon(1e-7));
-    REQUIRE(sortedOutput[newFromOld[10]][3].second == newFromOld[1]);
-    REQUIRE(sortedOutput[newFromOld[10]][3].first == Approx(0.65).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][0].first ==
+        Approx(0.10).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][1].second ==
+        newFromOld[3]);
+    REQUIRE(sortedOutput[newFromOld[10]][1].first ==
+        Approx(0.25).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][2].second ==
+        newFromOld[8]);
+    REQUIRE(sortedOutput[newFromOld[10]][2].first ==
+        Approx(0.55).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][3].second ==
+        newFromOld[1]);
+    REQUIRE(sortedOutput[newFromOld[10]][3].first ==
+        Approx(0.65).epsilon(1e-7));
 
     // Now do it again with a different range: [sqrt(0.5) 1.0].
     if (rs->ReferenceTree())
@@ -273,9 +280,11 @@ TEST_CASE("ExhaustiveSyntheticTest", "[RangeSearchTest]")
     // Neighbors of point 10.
     REQUIRE(sortedOutput[newFromOld[10]].size() == 2);
     REQUIRE(sortedOutput[newFromOld[10]][0].second == newFromOld[2]);
-    REQUIRE(sortedOutput[newFromOld[10]][0].first == Approx(0.85).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][0].first ==
+        Approx(0.85).epsilon(1e-7));
     REQUIRE(sortedOutput[newFromOld[10]][1].second == newFromOld[0]);
-    REQUIRE(sortedOutput[newFromOld[10]][1].first == Approx(0.95).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][1].first ==
+        Approx(0.95).epsilon(1e-7));
 
     // Now do it again with a different range: [1.0 inf].
     if (rs->ReferenceTree())
@@ -433,13 +442,20 @@ TEST_CASE("ExhaustiveSyntheticTest", "[RangeSearchTest]")
     // Neighbors of point 10.
     REQUIRE(sortedOutput[newFromOld[10]].size() == 4);
     REQUIRE(sortedOutput[newFromOld[10]][0].second == newFromOld[5]);
-    REQUIRE(sortedOutput[newFromOld[10]][0].first == Approx(1.22).epsilon(1e-7));
-    REQUIRE(sortedOutput[newFromOld[10]][1].second == newFromOld[7]);
-    REQUIRE(sortedOutput[newFromOld[10]][1].first == Approx(2.30).epsilon(1e-7));
-    REQUIRE(sortedOutput[newFromOld[10]][2].second == newFromOld[6]);
-    REQUIRE(sortedOutput[newFromOld[10]][2].first == Approx(3.00).epsilon(1e-7));
-    REQUIRE(sortedOutput[newFromOld[10]][3].second == newFromOld[4]);
-    REQUIRE(sortedOutput[newFromOld[10]][3].first == Approx(4.05).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][0].first ==
+        Approx(1.22).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][1].second ==
+        newFromOld[7]);
+    REQUIRE(sortedOutput[newFromOld[10]][1].first ==
+        Approx(2.30).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][2].second ==
+        newFromOld[6]);
+    REQUIRE(sortedOutput[newFromOld[10]][2].first ==
+        Approx(3.00).epsilon(1e-7));
+    REQUIRE(sortedOutput[newFromOld[10]][3].second ==
+        newFromOld[4]);
+    REQUIRE(sortedOutput[newFromOld[10]][3].first ==
+        Approx(4.05).epsilon(1e-7));
 
     // Clean the memory.
     delete rs;
@@ -1042,7 +1058,7 @@ TEST_CASE("DualBallTreeTest2", "[RangeSearchTest]")
       {
         REQUIRE(kdSorted[i][j].second == ballSorted[i][j].second);
         REQUIRE(kdSorted[i][j].first ==
-            Approx(ballSorted[i][j].first).epsilon (1e-7));
+            Approx(ballSorted[i][j].first).epsilon(1e-7));
       }
     }
   }
diff --git a/src/mlpack/tests/rl_components_test.cpp b/src/mlpack/tests/rl_components_test.cpp
index 7eb69cb003..8ce209903e 100644
--- a/src/mlpack/tests/rl_components_test.cpp
+++ b/src/mlpack/tests/rl_components_test.cpp
@@ -196,7 +196,7 @@ TEST_CASE("DoublePoleCartTest", "[RLComponentsTest]")
 }
 
 /**
- * Constructs a ContinuousDoublePoleCart instance and check if the main 
+ * Constructs a ContinuousDoublePoleCart instance and check if the main
  * routine works as it should be.
  */
 TEST_CASE("ContinuousDoublePoleCartTest", "[RLComponentsTest]")
@@ -285,5 +285,4 @@ TEST_CASE("GreedyPolicyTest", "[RLComponentsTest]")
   CartPole::Action action = policy.Sample(actionValue);
   REQUIRE(actionValue[action.action] ==
       Approx(actionValue.max()).epsilon(1e-7));
-
 }
diff --git a/src/mlpack/tests/sort_policy_test.cpp b/src/mlpack/tests/sort_policy_test.cpp
index 6418135f8e..cb73f96cad 100644
--- a/src/mlpack/tests/sort_policy_test.cpp
+++ b/src/mlpack/tests/sort_policy_test.cpp
@@ -107,7 +107,7 @@ TEST_CASE("NnsNodeToNodeDistance", "[SortPolicyTest]")
   utility[0] = 0.5;
   nodeTwo.Bound() |= utility;
 
-  REQUIRE(NearestNeighborSort::BestNodeToNodeDistance(&nodeOne, &nodeTwo) == 
+  REQUIRE(NearestNeighborSort::BestNodeToNodeDistance(&nodeOne, &nodeTwo) ==
       Approx(0.0).margin(1e-5));
 }
 
@@ -146,7 +146,7 @@ TEST_CASE("NnsPointToNodeDistance", "[SortPolicyTest]")
   // And now when the point is inside the bound.
   point[0] = 0.5;
 
-  REQUIRE(NearestNeighborSort::BestPointToNodeDistance(point, &node) == 
+  REQUIRE(NearestNeighborSort::BestPointToNodeDistance(point, &node) ==
       Approx(0.0).margin(1e-5));
 }
 
diff --git a/src/mlpack/tests/string_encoding_test.cpp b/src/mlpack/tests/string_encoding_test.cpp
index 23c72da3a4..8793981fce 100644
--- a/src/mlpack/tests/string_encoding_test.cpp
+++ b/src/mlpack/tests/string_encoding_test.cpp
@@ -268,7 +268,8 @@ TEST_CASE("DictionaryEncodingIndividualCharactersTest", "[StringEncodingTest]")
  * Test the one pass modification of the dictionary encoding algorithm
  * in case of individual character encoding.
  */
-TEST_CASE("OnePassDictionaryEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("OnePassDictionaryEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   std::vector<string> input = {
     "GACCA",
@@ -541,7 +542,7 @@ TEST_CASE("CharExtractDictionaryEncodingSerialization", "[StringEncodingTest]")
 
 /**
  * Test the Bag of Words encoding algorithm.
- */ 
+ */
 TEST_CASE("BagOfWordsEncodingTest", "[StringEncodingTest]")
 {
   using DictionaryType = StringEncodingDictionary<boost::string_view>;
@@ -616,7 +617,7 @@ TEST_CASE("BagOfWordsEncodingTest", "[StringEncodingTest]")
 
 /**
  * Test the Bag of Words encoding algorithm. The output is saved into a vector.
- */ 
+ */
 TEST_CASE("VectorBagOfWordsEncodingTest", "[StringEncodingTest]")
 {
   using DictionaryType = StringEncodingDictionary<boost::string_view>;
@@ -683,7 +684,8 @@ TEST_CASE("BagOfWordsEncodingIndividualCharactersTest", "[StringEncodingTest]")
  * Test the Bag of Words encoding algorithm in case of individual
  * characters encoding. The output type is vector<vector<size_t>>.
  */
-TEST_CASE("VectorBagOfWordsEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("VectorBagOfWordsEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   std::vector<string> input = {
     "GACCA",
@@ -860,7 +862,8 @@ TEST_CASE("VectorRawCountSmoothIdfEncodingTest", "[StringEncodingTest]")
  * raw count term frequency type and the smooth inverse document frequency type.
  * These parameters are the default ones.
  */
-TEST_CASE("RawCountSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("RawCountSmoothIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   vector<string> input = {
     "GACCA",
@@ -941,7 +944,8 @@ TEST_CASE("RawCountSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingT
  * These parameters are the default ones. The output type is
  * vector<vector<double>>.
  */
-TEST_CASE("VectorRawCountSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("VectorRawCountSmoothIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   std::vector<string> input = {
     "GACCA",
@@ -1067,7 +1071,8 @@ TEST_CASE("VectorTfIdfRawCountEncodingTest", "[StringEncodingTest]")
  * raw count term frequency type and the non-smooth inverse document frequency
  * type.
  */
-TEST_CASE("RawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("RawCountTfIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   vector<string> input = {
     "GACCA",
@@ -1098,7 +1103,8 @@ TEST_CASE("RawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]
  * raw count term frequency type and the non-smooth inverse document frequency
  * type. The output type is vector<vector<double>>.
  */
-TEST_CASE("VectorRawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("VectorRawCountTfIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   std::vector<string> input = {
     "GACCA",
@@ -1128,7 +1134,8 @@ TEST_CASE("VectorRawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodin
  * Test the Tf-Idf encoding algorithm for individual characters with the
  * binary term frequency type and the smooth inverse document frequency type.
  */
-TEST_CASE("BinarySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("BinarySmoothIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   vector<string> input = {
     "GACCA",
@@ -1159,7 +1166,8 @@ TEST_CASE("BinarySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTes
  * binary term frequency type and the smooth inverse document frequency type.
  * The output type is vector<vector<double>>.
  */
-TEST_CASE("VectorBinarySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("VectorBinarySmoothIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   std::vector<string> input = {
     "GACCA",
@@ -1221,7 +1229,8 @@ TEST_CASE("BinaryTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
  * sublinear term frequency type and the smooth inverse document frequency
  * type.
  */
-TEST_CASE("SublinearSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("SublinearSmoothIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   vector<string> input = {
     "GACCA",
@@ -1253,7 +1262,8 @@ TEST_CASE("SublinearSmoothIdfEncodingIndividualCharactersTest", "[StringEncoding
  * sublinear term frequency type and the non-smooth inverse document frequency
  * type.
  */
-TEST_CASE("SublinearTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("SublinearTfIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   vector<string> input = {
     "GACCA",
@@ -1285,7 +1295,8 @@ TEST_CASE("SublinearTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest
  * standard term frequency type and the smooth inverse document frequency
  * type.
  */
-TEST_CASE("TermFrequencySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("TermFrequencySmoothIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   vector<string> input = {
     "GACCA",
@@ -1366,7 +1377,8 @@ TEST_CASE("TermFrequencySmoothIdfEncodingIndividualCharactersTest", "[StringEnco
  * standard term frequency type and the non-smooth inverse document frequency
  * type.
  */
-TEST_CASE("TermFrequencyTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
+TEST_CASE("TermFrequencyTfIdfEncodingIndividualCharactersTest",
+          "[StringEncodingTest]")
 {
   vector<string> input = {
     "GACCA",