org.deeplearning4j.plot

Class BarnesHutTsne

java.lang.Object

org.deeplearning4j.plot.Tsne

org.deeplearning4j.plot.BarnesHutTsne

All Implemented Interfaces:

Serializable, Model

public class BarnesHutTsne
extends Tsne
implements Model

Barnes hut algorithm for TSNE, uses a dual tree approximation approach. Work based on: http://lvdmaaten.github.io/tsne/

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	BarnesHutTsne.Builder

Field Summary

Fields

Modifier and Type	Field and Description
static String	Y_GRAD

Fields inherited from class org.deeplearning4j.plot.Tsne

adaGrad, finalMomentum, gains, initialMomentum, iterationListener, learningRate, log, maxIter, minGain, momentum, normalize, perplexity, r, realMin, stopLyingIteration, switchMomentumIteration, tolerance, useAdaGrad, usePca, y, yIncs

Constructor Summary

Constructors

Constructor and Description
BarnesHutTsne(org.nd4j.linalg.api.ndarray.INDArray x, org.nd4j.linalg.api.ndarray.INDArray y, int numDimensions, double perplexity, double theta, int maxIter, int stopLyingIteration, int momentumSwitchIteration, double momentum, double finalMomentum, double learningRate)
BarnesHutTsne(org.nd4j.linalg.api.ndarray.INDArray x, org.nd4j.linalg.api.ndarray.INDArray y, int numDimensions, String simiarlityFunction, double theta, boolean invert, 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
void	accumulateScore(double accum) Sets a rolling tally for the score.
void	applyLearningRateScoreDecay() Update learningRate using for this model.
int	batchSize() The current inputs batch size
void	clear() Clear input
Pair<org.nd4j.linalg.api.ndarray.INDArray,Double>	computeGaussianKernel(org.nd4j.linalg.api.ndarray.INDArray distances, double beta, int k) Computes a gaussian kernel given a vector of squared distance distances
org.nd4j.linalg.api.ndarray.INDArray	computeGaussianPerplexity(org.nd4j.linalg.api.ndarray.INDArray d, double u) Convert data to probability co-occurrences (aka calculating the kernel)
void	computeGradientAndScore() Update the score
NeuralNetConfiguration	conf() The configuration for the neural network
void	fit() All models have a fit method
void	fit(org.nd4j.linalg.api.ndarray.INDArray data) Fit the model to the given data
void	fit(org.nd4j.linalg.api.ndarray.INDArray data, int nDims)
ConvexOptimizer	getOptimizer() Returns this models optimizer
org.nd4j.linalg.api.ndarray.INDArray	getParam(String param) Get the parameter
double	getPerplexity()
String	getSimiarlityFunction()
double	getTheta()
Gradient	gradient() Calculate a gradient
protected Pair<Double,org.nd4j.linalg.api.ndarray.INDArray>	gradient(org.nd4j.linalg.api.ndarray.INDArray p)
Pair<Gradient,Double>	gradientAndScore() Get the gradient and score
void	initParams() Initialize the parameters
org.nd4j.linalg.api.ndarray.INDArray	input() The input/feature matrix for the model
boolean	isInvert()
void	iterate(org.nd4j.linalg.api.ndarray.INDArray input) Run one iteration
int	numParams() the number of parameters for the model
int	numParams(boolean backwards) the number of parameters for the model
org.nd4j.linalg.api.ndarray.INDArray	params() Parameters of the model (if any)
Map<String,org.nd4j.linalg.api.ndarray.INDArray>	paramTable() The param table
void	plot(org.nd4j.linalg.api.ndarray.INDArray matrix, int nDims, List<String> labels, String path) Plot tsne
double	score() The score for the model
void	setConf(NeuralNetConfiguration conf) Setter for the configuration
void	setInvert(boolean invert)
void	setParam(String key, org.nd4j.linalg.api.ndarray.INDArray val) Set the parameter with a new ndarray
void	setParams(org.nd4j.linalg.api.ndarray.INDArray params) Set the parameters for this model.
void	setParamTable(Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable) Setter for the param table
void	setSimiarlityFunction(String simiarlityFunction)
void	step(org.nd4j.linalg.api.ndarray.INDArray p, int i) An individual iteration
org.nd4j.linalg.api.ndarray.INDArray	symmetrized(org.nd4j.linalg.api.ndarray.INDArray rowP, org.nd4j.linalg.api.ndarray.INDArray colP, org.nd4j.linalg.api.ndarray.INDArray valP) Symmetrize the value matrix
void	update(org.nd4j.linalg.api.ndarray.INDArray gradient, String paramType) Perform one update applying the gradient
void	validateInput() Validate the input

Methods inherited from class org.deeplearning4j.plot.Tsne

calculate, getIterationListener, getY, getYGradient, hBeta, plot, setIterationListener, setY

Methods inherited from class java.lang.Object

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

Field Detail

Y_GRAD

public static final String Y_GRAD

See Also:

Constant Field Values

Constructor Detail

BarnesHutTsne

public BarnesHutTsne(org.nd4j.linalg.api.ndarray.INDArray x,
                     org.nd4j.linalg.api.ndarray.INDArray y,
                     int numDimensions,
                     double perplexity,
                     double theta,
                     int maxIter,
                     int stopLyingIteration,
                     int momentumSwitchIteration,
                     double momentum,
                     double finalMomentum,
                     double learningRate)

BarnesHutTsne

public BarnesHutTsne(org.nd4j.linalg.api.ndarray.INDArray x,
                     org.nd4j.linalg.api.ndarray.INDArray y,
                     int numDimensions,
                     String simiarlityFunction,
                     double theta,
                     boolean invert,
                     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

getSimiarlityFunction

public String getSimiarlityFunction()

setSimiarlityFunction

public void setSimiarlityFunction(String simiarlityFunction)

isInvert

public boolean isInvert()

setInvert

public void setInvert(boolean invert)

getTheta

public double getTheta()

getPerplexity

public double getPerplexity()

computeGaussianPerplexity

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

Convert data to probability co-occurrences (aka calculating the kernel)

Overrides:

computeGaussianPerplexity in class Tsne

Parameters:

d - the data to convert

u - the perplexity of the model

Returns:

the probabilities of co-occurrence

input

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

Description copied from interface: Model

The input/feature matrix for the model

Specified by:

input in interface Model

Returns:

the input/feature matrix for the model

validateInput

public void validateInput()

Description copied from interface: Model

Validate the input

Specified by:

validateInput in interface Model

getOptimizer

public ConvexOptimizer getOptimizer()

Description copied from interface: Model

Returns this models optimizer

Specified by:

getOptimizer in interface Model

Returns:

this models optimizer

getParam

public org.nd4j.linalg.api.ndarray.INDArray getParam(String param)

Description copied from interface: Model

Get the parameter

Specified by:

getParam in interface Model

Parameters:

param - the key of the parameter

Returns:

the parameter vector/matrix with that particular key

initParams

public void initParams()

Description copied from interface: Model

Initialize the parameters

Specified by:

initParams in interface Model

paramTable

public Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable()

Description copied from interface: Model

The param table

Specified by:

paramTable in interface Model

Returns:

setParamTable

public void setParamTable(Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable)

Description copied from interface: Model

Setter for the param table

Specified by:

setParamTable in interface Model

setParam

public void setParam(String key,
                     org.nd4j.linalg.api.ndarray.INDArray val)

Description copied from interface: Model

Set the parameter with a new ndarray

Specified by:

setParam in interface Model

Parameters:

key - the key to se t

val - the new ndarray

clear

public void clear()

Description copied from interface: Model

Clear input

Specified by:

clear in interface Model

gradient

protected Pair<Double,org.nd4j.linalg.api.ndarray.INDArray> gradient(org.nd4j.linalg.api.ndarray.INDArray p)

Overrides:

gradient in class Tsne

symmetrized

public org.nd4j.linalg.api.ndarray.INDArray symmetrized(org.nd4j.linalg.api.ndarray.INDArray rowP,
                                                        org.nd4j.linalg.api.ndarray.INDArray colP,
                                                        org.nd4j.linalg.api.ndarray.INDArray valP)

Symmetrize the value matrix

Parameters:

rowP -

colP -

valP -

Returns:

computeGaussianKernel

public Pair<org.nd4j.linalg.api.ndarray.INDArray,Double> computeGaussianKernel(org.nd4j.linalg.api.ndarray.INDArray distances,
                                                                               double beta,
                                                                               int k)

Computes a gaussian kernel given a vector of squared distance distances

Parameters:

distances -

beta -

Returns:

fit

public void fit()

Description copied from interface: Model

All models have a fit method

Specified by:

fit in interface Model

step

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

An individual iteration

Overrides:

step in class Tsne

Parameters:

p - the probabilities that certain points are near each other

i - the iteration (primarily for debugging purposes)

update

public void update(org.nd4j.linalg.api.ndarray.INDArray gradient,
                   String paramType)

Description copied from interface: Model

Perform one update applying the gradient

Specified by:

update in interface Model

Parameters:

gradient - the gradient to apply

plot

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

Plot tsne

Overrides:

plot in class Tsne

Parameters:

matrix - the matrix to plot

nDims - the number

labels -

path - the path to write

Throws:

IOException

score

public double score()

Description copied from interface: Model

The score for the model

Specified by:

score in interface Model

Returns:

the score for the model

computeGradientAndScore

public void computeGradientAndScore()

Description copied from interface: Model

Update the score

Specified by:

computeGradientAndScore in interface Model

accumulateScore

public void accumulateScore(double accum)

Description copied from interface: Model

Sets a rolling tally for the score. This is useful for mini batch learning when you are accumulating error across a dataset.

Specified by:

accumulateScore in interface Model

Parameters:

accum - the amount to accum

params

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

Description copied from interface: Model

Parameters of the model (if any)

Specified by:

params in interface Model

Returns:

the parameters of the model

numParams

public int numParams()

Description copied from interface: Model

the number of parameters for the model

Specified by:

numParams in interface Model

Returns:

the number of parameters for the model

numParams

public int numParams(boolean backwards)

Description copied from interface: Model

the number of parameters for the model

Specified by:

numParams in interface Model

Returns:

the number of parameters for the model

setParams

public void setParams(org.nd4j.linalg.api.ndarray.INDArray params)

Description copied from interface: Model

Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model

Specified by:

setParams in interface Model

Parameters:

params - the parameters for the model

applyLearningRateScoreDecay

public void applyLearningRateScoreDecay()

Description copied from interface: Model

Update learningRate using for this model. Use the learningRateScoreBasedDecay to adapt the score if the Eps termination condition is met

Specified by:

applyLearningRateScoreDecay in interface Model

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray data)

Description copied from interface: Model

Fit the model to the given data

Specified by:

fit in interface Model

Parameters:

data - the data to fit the model to

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray data,
                int nDims)

iterate

public void iterate(org.nd4j.linalg.api.ndarray.INDArray input)

Description copied from interface: Model

Run one iteration

Specified by:

iterate in interface Model

Parameters:

input - the input to iterate on

gradient

public Gradient gradient()

Description copied from interface: Model

Calculate a gradient

Specified by:

gradient in interface Model

Returns:

the gradient for this model

gradientAndScore

public Pair<Gradient,Double> gradientAndScore()

Description copied from interface: Model

Get the gradient and score

Specified by:

gradientAndScore in interface Model

Returns:

the gradient and score

batchSize

public int batchSize()

Description copied from interface: Model

The current inputs batch size

Specified by:

batchSize in interface Model

Returns:

the current inputs batch size

conf

public NeuralNetConfiguration conf()

Description copied from interface: Model

The configuration for the neural network

Specified by:

conf in interface Model

Returns:

the configuration for the neural network

setConf

public void setConf(NeuralNetConfiguration conf)

Description copied from interface: Model

Setter for the configuration

Specified by:

setConf in interface Model