Fixed a bug in constructor

core/src/main/java/hivemall/smile/classification/DecisionTree.java

@@ -118,7 +118,7 @@
  * Some techniques such as bagging, boosting, and random forest use more than one decision tree for
  * their analysis.
  */
-public class DecisionTree implements Classifier<double[]> {
+public final class DecisionTree implements Classifier<double[]> {
     /**
      * The attributes of independent variable.
      */
@@ -193,7 +193,7 @@ public static enum SplitRule {
     /**
      * Classification tree node.
      */
-    public static class Node implements Externalizable {
+    public static final class Node implements Externalizable {
 
         /**
          * Predicted class label for this node.
@@ -412,7 +412,7 @@ private static void indent(final StringBuilder builder, final int depth) {
     /**
      * Classification tree node for training purpose.
      */
-    class TrainNode implements Comparable<TrainNode> {
+    private final class TrainNode implements Comparable<TrainNode> {
         /**
          * The associated regression tree node.
          */
@@ -464,19 +464,7 @@ public boolean findBestSplit() {
 
             // Sample count in each class.
             final int[] count = new int[_k];
-            int label = -1;
-            boolean pure = true;
-            for (int i = 0; i < numSamples; i++) {
-                int index = bags[i];
-                int y_i = y[index];
-                count[y_i]++;
-
-                if (label == -1) {
-                    label = y_i;
-                } else if (y_i != label) {
-                    pure = false;
-                }
-            }
+            final boolean pure = sampleCount(count);
 
             // Since all instances have same label, stop splitting.
             if (pure) {
@@ -513,6 +501,23 @@ public boolean findBestSplit() {
             return (node.splitFeature != -1);
         }
 
+        private boolean sampleCount(@Nonnull final int[] count) {
+            int label = -1;
+            boolean pure = true;
+            for (int i = 0; i < bags.length; i++) {
+                int index = bags[i];
+                int y_i = y[index];
+                count[y_i]++;
+
+                if (label == -1) {
+                    label = y_i;
+                } else if (y_i != label) {
+                    pure = false;
+                }
+            }
+            return pure;
+        }
+
         /**
          * Finds the best split cutoff for attribute j at the current node.
          * 
@@ -814,8 +819,8 @@ public DecisionTree(@Nullable Attribute[] attributes, @Nonnull double[][] x, @No
         this._minSplit = minSplits;
         this._minLeafSize = minLeafSize;
         this._rule = rule;
-        this._order = (order == null) ? SmileExtUtils.sort(attributes, x) : order;
-        this._importance = new double[attributes.length];
+        this._order = (order == null) ? SmileExtUtils.sort(_attributes, x) : order;
+        this._importance = new double[_attributes.length];
         this._rnd = (rand == null) ? new smile.math.Random() : rand;
 
         final int n = y.length;

core/src/main/java/hivemall/smile/regression/RegressionTree.java

@@ -106,7 +106,7 @@
  * @see GradientTreeBoost
  * @see RandomForest
  */
-public class RegressionTree implements Regression<double[]> {
+public final class RegressionTree implements Regression<double[]> {
     /**
      * The attributes of independent variable.
      */
@@ -167,7 +167,7 @@ public interface NodeOutput {
     /**
      * Regression tree node.
      */
-    public static class Node implements Externalizable {
+    public static final class Node implements Externalizable {
 
         /**
          * Predicted real value for this node.
@@ -396,7 +396,7 @@ private static void indent(final StringBuilder builder, final int depth) {
     /**
      * Regression tree node for training purpose.
      */
-    class TrainNode implements Comparable<TrainNode> {
+    private final class TrainNode implements Comparable<TrainNode> {
 
         /**
          * The associated regression tree node.
@@ -480,14 +480,12 @@ public boolean findBestSplit() {
                 variables[i] = i;
             }
 
-            // Loop through features and compute the reduction of squared error,
-            // which is trueCount * trueMean^2 + falseCount * falseMean^2 - count * parentMean^2                    
             if (_numVars < p) {
-                // Training of Random Forest will get into this race condition.
-                // smile.math.Math uses a static object of random number generator.
                 SmileExtUtils.shuffle(variables, _rnd);
             }
 
+            // Loop through features and compute the reduction of squared error,
+            // which is trueCount * trueMean^2 + falseCount * falseMean^2 - count * parentMean^2      
             final int[] samples = _hasNumericType ? SmileExtUtils.bagsToSamples(bags, x.length)
                     : null;
             for (int j = 0; j < _numVars; j++) {
@@ -749,7 +747,7 @@ public RegressionTree(@Nullable Attribute[] attributes, @Nonnull double[][] x,
         checkArgument(x, y, numVars, maxDepth, maxLeafs, minSplits, minLeafSize);
 
         this._attributes = SmileExtUtils.attributeTypes(attributes, x);
-        if (attributes.length != x[0].length) {
+        if (_attributes.length != x[0].length) {
             throw new IllegalArgumentException("-attrs option is invliad: "
                     + Arrays.toString(attributes));
         }
@@ -759,8 +757,8 @@ public RegressionTree(@Nullable Attribute[] attributes, @Nonnull double[][] x,
         this._maxDepth = maxDepth;
         this._minSplit = minSplits;
         this._minLeafSize = minLeafSize;
-        this._order = (order == null) ? SmileExtUtils.sort(attributes, x) : order;
-        this._importance = new double[attributes.length];
+        this._order = (order == null) ? SmileExtUtils.sort(_attributes, x) : order;
+        this._importance = new double[_attributes.length];
         this._rnd = (rand == null) ? new smile.math.Random() : rand;
         this._nodeOutput = output;