org.nd4j.linalg.util

Class FeatureUtil

java.lang.Object

org.nd4j.linalg.util.FeatureUtil

public class FeatureUtil
extends Object

Feature matrix related jcuda.utils

Constructor Summary

Constructors

Constructor and Description
FeatureUtil()

Method Summary

Modifier and Type	Method and Description
static void	normalizeMatrix(INDArray toNormalize)
static void	scaleByMax(INDArray toScale) Divides each row by its max
static void	scaleMinMax(double min, double max, INDArray toScale) Scales the ndarray columns to the given min/max values
static INDArray	toOutcomeMatrix(int[] index, int numOutcomes) Creates an out come vector from the specified inputs
static INDArray	toOutcomeVector(int index, int numOutcomes) Creates an out come vector from the specified inputs

Methods inherited from class java.lang.Object

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

Constructor Detail

FeatureUtil

public FeatureUtil()

Method Detail

toOutcomeVector

public static INDArray toOutcomeVector(int index,
                                       int numOutcomes)

Creates an out come vector from the specified inputs

Parameters:

index - the index of the label

numOutcomes - the number of possible outcomes

Returns:

a binary label matrix used for supervised learning

toOutcomeMatrix

public static INDArray toOutcomeMatrix(int[] index,
                                       int numOutcomes)

Creates an out come vector from the specified inputs

Parameters:

index - the index of the label

numOutcomes - the number of possible outcomes

Returns:

a binary label matrix used for supervised learning

normalizeMatrix

public static void normalizeMatrix(INDArray toNormalize)

scaleByMax

public static void scaleByMax(INDArray toScale)

Divides each row by its max

Parameters:

toScale - the matrix to divide by its row maxes

scaleMinMax

public static void scaleMinMax(double min,
                               double max,
                               INDArray toScale)

Scales the ndarray columns to the given min/max values

Parameters:

min - the minimum number

max - the max number