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] 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;