org.deeplearning4j.plot

Class Tsne

java.lang.Object

org.deeplearning4j.plot.Tsne

public class Tsne
extends java.lang.Object

dl4j port of original t-sne algorithm described/implemented by van der Maaten and Hinton

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Tsne.Builder

Field Summary

Fields

Modifier and Type	Field and Description
protected org.nd4j.linalg.learning.AdaGrad	adaGrad
protected double	finalMomentum
protected double	initialMomentum
protected double	learningRate
protected static org.slf4j.Logger	logger
protected int	maxIter
protected double	minGain
protected double	momentum
protected boolean	normalize
protected double	perplexity
protected double	realMin
protected int	stopLyingIteration
protected int	switchMomentumIteration
protected double	tolerance
protected boolean	useAdaGrad
protected boolean	usePca
protected org.nd4j.linalg.api.ndarray.INDArray	Y

Constructor Summary

Constructors

Constructor and Description
Tsne(int maxIter, double realMin, double initialMomentum, double finalMomentum, double minGain, double momentum, int switchMomentumIteration, boolean normalize, boolean usePca, int stopLyingIteration, double tolerance, double learningRate, boolean useAdaGrad, double perplexity)

Method Summary

Modifier and Type	Method and Description
org.nd4j.linalg.api.ndarray.INDArray	calculate(org.nd4j.linalg.api.ndarray.INDArray X, int targetDimensions, double perplexity)
org.nd4j.linalg.api.ndarray.INDArray	diag(org.nd4j.linalg.api.ndarray.INDArray ds)
Pair<java.lang.Double,org.nd4j.linalg.api.ndarray.INDArray>	hBeta(org.nd4j.linalg.api.ndarray.INDArray d, double beta) Computes a gaussian kernel given a vector of squared distance distances
protected void	init()
void	plot(org.nd4j.linalg.api.ndarray.INDArray matrix, int nDims, java.util.List<java.lang.String> labels, java.lang.String path)

Methods inherited from class java.lang.Object

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

Field Detail

maxIter

protected int maxIter

realMin

protected double realMin

initialMomentum

protected double initialMomentum

finalMomentum

protected double finalMomentum

minGain

protected double minGain

momentum

protected double momentum

switchMomentumIteration

protected int switchMomentumIteration

normalize

protected boolean normalize

usePca

protected boolean usePca

stopLyingIteration

protected int stopLyingIteration

tolerance

protected double tolerance

learningRate

protected double learningRate

adaGrad

protected org.nd4j.linalg.learning.AdaGrad adaGrad

useAdaGrad

protected boolean useAdaGrad

perplexity

protected double perplexity

Y

protected org.nd4j.linalg.api.ndarray.INDArray Y

logger

protected static final org.slf4j.Logger logger

Constructor Detail

Tsne

public Tsne(int maxIter,
            double realMin,
            double initialMomentum,
            double finalMomentum,
            double minGain,
            double momentum,
            int switchMomentumIteration,
            boolean normalize,
            boolean usePca,
            int stopLyingIteration,
            double tolerance,
            double learningRate,
            boolean useAdaGrad,
            double perplexity)

Method Detail

init

protected void init()

calculate

public org.nd4j.linalg.api.ndarray.INDArray calculate(org.nd4j.linalg.api.ndarray.INDArray X,
                                                      int targetDimensions,
                                                      double perplexity)

diag

public org.nd4j.linalg.api.ndarray.INDArray diag(org.nd4j.linalg.api.ndarray.INDArray ds)

plot

public void plot(org.nd4j.linalg.api.ndarray.INDArray matrix,
                 int nDims,
                 java.util.List<java.lang.String> labels,
                 java.lang.String path)
          throws java.io.IOException

Throws:

java.io.IOException

hBeta

public Pair<java.lang.Double,org.nd4j.linalg.api.ndarray.INDArray> hBeta(org.nd4j.linalg.api.ndarray.INDArray d,
                                                                         double beta)

Computes a gaussian kernel given a vector of squared distance distances

Parameters:

d - the data

beta -

Returns: