adding more docs for nested methods

Author: manishamde

mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala

@@ -306,6 +306,20 @@ object DecisionTree extends Serializable with Logging {
       arr
     }
 
+    /**
+    Performs a sequential aggregation over a partition for classification.
+
+    for p bins, k features, l nodes (level = log2(l)) storage is of the form:
+    b111_left_count,b111_right_count, .... , ..
+    .. bpk1_left_count, bpk1_right_count, .... , ..
+    .. bpkl_left_count, bpkl_right_count
+
+    @param agg Array[Double] storing aggregate calculation of size
+    2*numSplits*numFeatures*numNodes for classification
+    @param arr Array[Double] of size 1+(numFeatures*numNodes)
+    @return Array[Double] storing aggregate calculation of size 2*numSplits*numFeatures*numNodes
+    for classification
+    */
     def classificationBinSeqOp(arr: Array[Double], agg: Array[Double]) {
       for (node <- 0 until numNodes) {
         val validSignalIndex = 1 + numFeatures * node
@@ -326,6 +340,20 @@ object DecisionTree extends Serializable with Logging {
       }
     }
 
+    /**
+    Performs a sequential aggregation over a partition for regression.
+
+    for p bins, k features, l nodes (level = log2(l)) storage is of the form:
+    b111_count,b111_sum, b111_sum_squares .... , ..
+    .. bpk1_count, bpk1_sum, bpk1_sum_squares, .... , ..
+    .. bpkl_count, bpkl_sum, bpkl_sum_squares
+
+    @param agg Array[Double] storing aggregate calculation of size
+    3*numSplits*numFeatures*numNodes for classification
+    @param arr Array[Double] of size 1+(numFeatures*numNodes)
+    @return Array[Double] storing aggregate calculation of size 3*numSplits*numFeatures*numNodes
+    for regression
+    */
     def regressionBinSeqOp(arr: Array[Double], agg: Array[Double]) {
       for (node <- 0 until numNodes) {
         val validSignalIndex = 1 + numFeatures * node
@@ -354,11 +382,11 @@ object DecisionTree extends Serializable with Logging {
     .. bpk1_left_count, bpk1_right_count, .... , ..
     .. bpkl_left_count, bpkl_right_count
 
-    @param agg Array[Double] storing aggregate calculation of size
-    2*numSplits*numFeatures*numNodes for classification
+    @param agg Array[Double] storing aggregate calculation of size 2*numSplits*numFeatures*numNodes for classification
+                          and 3*numSplits*numFeatures*numNodes for regression
     @param arr Array[Double] of size 1+(numFeatures*numNodes)
-    @return Array[Double] storing aggregate calculation of size 2*numSplits*numFeatures*numNodes
-    for classification
+    @return Array[Double] storing aggregate calculation of size 2*numSplits*numFeatures*numNodes for classification
+                    and 3*numSplits*numFeatures*numNodes for regression
     */
     def binSeqOp(agg : Array[Double], arr: Array[Double]) : Array[Double] = {
       strategy.algo match {
@@ -411,7 +439,15 @@ object DecisionTree extends Serializable with Logging {
     logDebug("binAggregates.length = " + binAggregates.length)
     //binAggregates.foreach(x => logDebug(x))
 
-
+    /**
+     * Calculates the information gain for all splits
+     * @param leftNodeAgg left node aggregates
+     * @param featureIndex feature index
+     * @param splitIndex split index
+     * @param rightNodeAgg right node aggregate
+     * @param topImpurity impurity of the parent node
+     * @return information gain and statistics for all splits
+     */
     def calculateGainForSplit(leftNodeAgg: Array[Array[Double]],
                               featureIndex: Int,
                               splitIndex: Int,