Reverted splitInfo to arma::vec for old decision tree for numric splits

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);
 
   /**

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;
     }

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

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);
 

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;

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])