From 60b13abcffc67c7bb44a63afe3df7edfa72c3605 Mon Sep 17 00:00:00 2001
From: Vikas Shetty <vikass@sahajsoft.com>
Date: Sat, 19 Oct 2019 22:38:40 +0530
Subject: [PATCH] Adding callback parameters for Logistic Regression

---
 .../logistic_regression/logistic_regression.hpp | 15 +++++++++++----
 .../logistic_regression_impl.hpp                | 17 ++++++++++-------
 2 files changed, 21 insertions(+), 11 deletions(-)

diff --git a/src/mlpack/methods/logistic_regression/logistic_regression.hpp b/src/mlpack/methods/logistic_regression/logistic_regression.hpp
index cc23483808..b948752fe4 100644
--- a/src/mlpack/methods/logistic_regression/logistic_regression.hpp
+++ b/src/mlpack/methods/logistic_regression/logistic_regression.hpp
@@ -123,13 +123,16 @@ class LogisticRegression
    * parameters vector directly with Parameters() and modify it as desired.
    *
    * @tparam OptimizerType Type of optimizer to use to train the model.
+   * @tparam CallbackTypes Types of Callback Functions.
    * @param predictors Input training variables.
    * @param responses Outputs results from input training variables.
+   * @param callbacks Callback Functions.
    * @return The final objective of the trained model (NaN or Inf on error)
    */
-  template<typename OptimizerType = ens::L_BFGS>
+  template<typename OptimizerType = ens::L_BFGS, typename... CallbackTypes>
   double Train(const MatType& predictors,
-               const arma::Row<size_t>& responses);
+               const arma::Row<size_t>& responses,
+               CallbackTypes&&... callbacks);
 
   /**
    * Train the LogisticRegression model with the given instantiated optimizer.
@@ -143,15 +146,19 @@ class LogisticRegression
    * optimizer.Function().GetInitialPoint() to the current parameters vector,
    * accessible via Parameters().
    *
+   * @tparam OptimizerType Type of optimizer to use to train the model.
+   * @tparam CallbackTypes Types of Callback Functions.
    * @param predictors Input training variables.
    * @param responses Outputs results from input training variables.
    * @param optimizer Instantiated optimizer with instantiated error function.
+   * @param callbacks Callback Functions.
    * @return The final objective of the trained model (NaN or Inf on error)
    */
-  template<typename OptimizerType>
+  template<typename OptimizerType, typename... CallbackTypes>
   double Train(const MatType& predictors,
                const arma::Row<size_t>& responses,
-               OptimizerType& optimizer);
+               OptimizerType& optimizer,
+               CallbackTypes&&... callbacks);
 
   //! Return the parameters (the b vector).
   const arma::rowvec& Parameters() const { return parameters; }
diff --git a/src/mlpack/methods/logistic_regression/logistic_regression_impl.hpp b/src/mlpack/methods/logistic_regression/logistic_regression_impl.hpp
index 1973806fad..ef45d96300 100644
--- a/src/mlpack/methods/logistic_regression/logistic_regression_impl.hpp
+++ b/src/mlpack/methods/logistic_regression/logistic_regression_impl.hpp
@@ -67,20 +67,23 @@ LogisticRegression<MatType>::LogisticRegression(
 }
 
 template<typename MatType>
-template<typename OptimizerType>
-double LogisticRegression<MatType>::Train(const MatType& predictors,
-                                        const arma::Row<size_t>& responses)
+template<typename OptimizerType, typename... CallbackTypes>
+double LogisticRegression<MatType>::Train(
+        const MatType& predictors,
+        const arma::Row<size_t>& responses,
+        CallbackTypes&&... callbacks)
 {
   OptimizerType optimizer;
-  return Train(predictors, responses, optimizer);
+  return Train(predictors, responses, optimizer,  callbacks...);
 }
 
 template<typename MatType>
-template<typename OptimizerType>
+template<typename OptimizerType, typename... CallbackTypes>
 double LogisticRegression<MatType>::Train(
     const MatType& predictors,
     const arma::Row<size_t>& responses,
-    OptimizerType& optimizer)
+    OptimizerType& optimizer,
+    CallbackTypes&&... callbacks)
 {
   LogisticRegressionFunction<MatType> errorFunction(predictors,
                                                     responses,
@@ -88,7 +91,7 @@ double LogisticRegression<MatType>::Train(
   errorFunction.InitialPoint() = parameters;
 
   Timer::Start("logistic_regression_optimization");
-  const double out = optimizer.Optimize(errorFunction, parameters);
+  const double out = optimizer.Optimize(errorFunction, parameters, callbacks...);
   Timer::Stop("logistic_regression_optimization");
 
   Log::Info << "LogisticRegression::LogisticRegression(): final objective of "