org.deeplearning4j.plot

Class Tsne

java.lang.Object

org.deeplearning4j.plot.Tsne

All Implemented Interfaces:

Serializable

public class Tsne
extends Object
implements Serializable

Tsne calculation

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Tsne.Builder

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
org.nd4j.linalg.api.ndarray.INDArray	calculate(org.nd4j.linalg.api.ndarray.INDArray X, int nDims, double perplexity)
org.nd4j.linalg.api.ndarray.INDArray	d2p(org.nd4j.linalg.api.ndarray.INDArray d, double u) Convert data to probability co-occurrences
IterationListener	getIterationListener()
org.nd4j.linalg.api.ndarray.INDArray	getY()
Pair<org.nd4j.linalg.api.ndarray.INDArray,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 euclidean distances
void	plot(org.nd4j.linalg.api.ndarray.INDArray matrix, int nDims, List<String> labels) Plot tsne
void	setIterationListener(IterationListener iterationListener)
void	setY(org.nd4j.linalg.api.ndarray.INDArray y)
void	step(org.nd4j.linalg.api.ndarray.INDArray p, int i) An individual iteration

Methods inherited from class java.lang.Object

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

Constructor Detail

Tsne

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

Method Detail

hBeta

public Pair<org.nd4j.linalg.api.ndarray.INDArray,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 euclidean distances

Parameters:

d -

beta -

Returns:

d2p

public org.nd4j.linalg.api.ndarray.INDArray d2p(org.nd4j.linalg.api.ndarray.INDArray d,
                                                double u)

Convert data to probability co-occurrences

Parameters:

d - the data to convert

u - the perplexity of the model

Returns:

the probabilities of co-occurrence

calculate

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

Parameters:

X -

nDims -

perplexity -

step

public void step(org.nd4j.linalg.api.ndarray.INDArray p,
                 int i)

An individual iteration

Parameters:

p - the probabilities that certain points are near each other

i - the iteration (primarily for debugging purposes)

plot

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

Plot tsne

Parameters:

matrix - the matrix to plot

nDims - the number

labels -

Throws:

IOException

getY

public org.nd4j.linalg.api.ndarray.INDArray getY()

setY

public void setY(org.nd4j.linalg.api.ndarray.INDArray y)

getIterationListener

public IterationListener getIterationListener()

setIterationListener

public void setIterationListener(IterationListener iterationListener)