From e5935d4eb0d07ba9fd2e62c102e12c2fcafc28ff Mon Sep 17 00:00:00 2001
From: Rishabh Garg <56191449+RishabhGarg108@users.noreply.github.com>
Date: Wed, 17 Mar 2021 18:51:00 +0530
Subject: [PATCH] Added warmStart to Train function definition

---
 src/mlpack/methods/random_forest/random_forest.hpp   | 12 ++++++++----
 .../methods/random_forest/random_forest_impl.hpp     | 12 ++++++++----
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/src/mlpack/methods/random_forest/random_forest.hpp b/src/mlpack/methods/random_forest/random_forest.hpp
index 23673a9592..f3aea15896 100644
--- a/src/mlpack/methods/random_forest/random_forest.hpp
+++ b/src/mlpack/methods/random_forest/random_forest.hpp
@@ -179,7 +179,8 @@ class RandomForest
                const double minimumGainSplit = 1e-7,
                const size_t maximumDepth = 0,
                DimensionSelectionType dimensionSelector =
-                   DimensionSelectionType());
+                   DimensionSelectionType(),
+               bool warmStart = false);
 
   /**
    * Train the random forest on the given labeled training data with the given
@@ -211,7 +212,8 @@ class RandomForest
                const double minimumGainSplit = 1e-7,
                const size_t maximumDepth = 0,
                DimensionSelectionType dimensionSelector =
-                   DimensionSelectionType());
+                   DimensionSelectionType(),
+               bool warmStart = false);
 
   /**
    * Train the random forest on the given weighted labeled training data with
@@ -241,7 +243,8 @@ class RandomForest
                const double minimumGainSplit = 1e-7,
                const size_t maximumDepth = 0,
                DimensionSelectionType dimensionSelector =
-                   DimensionSelectionType());
+                   DimensionSelectionType(),
+               bool warmStart = false);
 
   /**
    * Train the random forest on the given weighted labeled training data with
@@ -274,7 +277,8 @@ class RandomForest
                const double minimumGainSplit = 1e-7,
                const size_t maximumDepth = 0,
                DimensionSelectionType dimensionSelector =
-                   DimensionSelectionType());
+                   DimensionSelectionType(),
+               bool warmStart = false);
 
   /**
    * Predict the class of the given point.  If the random forest has not been
diff --git a/src/mlpack/methods/random_forest/random_forest_impl.hpp b/src/mlpack/methods/random_forest/random_forest_impl.hpp
index d79b252791..da2c6ef470 100644
--- a/src/mlpack/methods/random_forest/random_forest_impl.hpp
+++ b/src/mlpack/methods/random_forest/random_forest_impl.hpp
@@ -160,7 +160,8 @@ double RandomForest<
          const size_t minimumLeafSize,
          const double minimumGainSplit,
          const size_t maximumDepth,
-         DimensionSelectionType dimensionSelector)
+         DimensionSelectionType dimensionSelector,
+         bool warmStart)
 {
   // Pass off to Train().
   data::DatasetInfo info; // Ignored by Train().
@@ -192,7 +193,8 @@ double RandomForest<
          const size_t minimumLeafSize,
          const double minimumGainSplit,
          const size_t maximumDepth,
-         DimensionSelectionType dimensionSelector)
+         DimensionSelectionType dimensionSelector,
+         bool warmStart)
 {
   // Pass off to Train().
   arma::rowvec weights; // Ignored by Train().
@@ -223,7 +225,8 @@ double RandomForest<
          const size_t minimumLeafSize,
          const double minimumGainSplit,
          const size_t maximumDepth,
-         DimensionSelectionType dimensionSelector)
+         DimensionSelectionType dimensionSelector,
+         bool warmStart)
 {
   // Pass off to Train().
   data::DatasetInfo info; // Ignored by Train().
@@ -255,7 +258,8 @@ double RandomForest<
          const size_t minimumLeafSize,
          const double minimumGainSplit,
          const size_t maximumDepth,
-         DimensionSelectionType dimensionSelector)
+         DimensionSelectionType dimensionSelector,
+         bool warmStart)
 {
   // Pass off to Train().
   return Train<true, true, false>(dataset, datasetInfo, labels, numClasses, weights,