hex.singlenoderf

Class MSEStatistic

java.lang.Object

hex.singlenoderf.MSEStatistic

public class MSEStatistic
extends java.lang.Object

Computes the mse split statistics. For regression: Try to minimize the squared error at each split. Begin with the sum of the current target responses in the node, Yt. Iterate over the rows for the column and choose the split that minimizes the MSE. This will be the split s* that _maximizes_ the following expression: (Y_L^2 * weights_left) + (Y_R^2 * weights_right) Where Y_i = sum of the target responses going to node i (i is L or R), and weights_i = 1 / nobs_i (number of observations in the node).

Field Summary

Fields

Modifier and Type	Field and Description
protected int[][][]	_columnDists Column distributions: column x arity x classes Remembers the number of rows of the given column index, encodedValue, class.
protected float[][][]	_columnDistsRegression
protected int[]	_features
protected java.util.Random	_random

Constructor Summary

Constructors

Constructor and Description
MSEStatistic(Data data, int features, long seed, int exclusiveSplitLimit)

Method Summary

Methods

Modifier and Type	Method and Description
protected hex.singlenoderf.Statistic.Split	eqSplit(int colIndex, Data d, float[] dist, float distWeight, java.util.Random rand)
protected hex.singlenoderf.Statistic.Split	eqSplit(int colIndex, Data d, int[] dist, int distWeight, java.util.Random _)
protected hex.singlenoderf.Statistic.Split	ltSplit(int colIndex, Data d, float[] dist, float distWeight, java.util.Random rand)
protected hex.singlenoderf.Statistic.Split	ltSplit(int col, Data d, int[] dist, int distWeight, java.util.Random _) Returns the best split for a given column

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

_columnDists

protected final int[][][] _columnDists

Column distributions: column x arity x classes Remembers the number of rows of the given column index, encodedValue, class.

_features

protected final int[] _features

_random

protected java.util.Random _random

_columnDistsRegression

protected final float[][][] _columnDistsRegression

Constructor Detail

MSEStatistic

public MSEStatistic(Data data,
            int features,
            long seed,
            int exclusiveSplitLimit)

Method Detail

ltSplit

protected hex.singlenoderf.Statistic.Split ltSplit(int colIndex,
                                       Data d,
                                       float[] dist,
                                       float distWeight,
                                       java.util.Random rand)

eqSplit

protected hex.singlenoderf.Statistic.Split eqSplit(int colIndex,
                                       Data d,
                                       float[] dist,
                                       float distWeight,
                                       java.util.Random rand)

ltSplit

protected hex.singlenoderf.Statistic.Split ltSplit(int col,
                                       Data d,
                                       int[] dist,
                                       int distWeight,
                                       java.util.Random _)

Returns the best split for a given column

eqSplit

protected hex.singlenoderf.Statistic.Split eqSplit(int colIndex,
                                       Data d,
                                       int[] dist,
                                       int distWeight,
                                       java.util.Random _)