org.deeplearning4j.plot

Class BarnesHutTsne

java.lang.Object

org.deeplearning4j.plot.BarnesHutTsne

All Implemented Interfaces:

Model

public class BarnesHutTsne
extends java.lang.Object
implements Model

Barnes hut algorithm for TSNE, uses a dual tree approximation approach. Work based on: http://lvdmaaten.github.io/tsne/ For hight dimensions, it's recommended to reduce the dimension up to 50 using another method (PCA or other)

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	BarnesHutTsne.Builder

Field Summary

Fields

Modifier and Type	Field and Description
protected org.nd4j.linalg.learning.AdaGrad	adaGrad
protected double	finalMomentum
protected double	initialMomentum
protected IterationListener	iterationListener
protected double	learningRate
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
static java.lang.String	Y_GRAD

Constructor Summary

Constructors

Constructor and Description
BarnesHutTsne(int numDimensions, java.lang.String simiarlityFunction, double theta, boolean invert, int maxIter, double realMin, double initialMomentum, double finalMomentum, double momentum, int switchMomentumIteration, boolean normalize, int stopLyingIteration, double tolerance, double learningRate, boolean useAdaGrad, double perplexity, IterationListener iterationListener, 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,java.lang.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) Deprecated. Use fit(INDArray)
org.nd4j.linalg.api.ndarray.INDArray	getData() Return the matrix reduce to the NDim.
int	getNumDimensions()
ConvexOptimizer	getOptimizer() Returns this models optimizer
org.nd4j.linalg.api.ndarray.INDArray	getParam(java.lang.String param) Get the parameter
double	getPerplexity()
java.lang.String	getSimiarlityFunction()
double	getTheta()
Gradient	gradient() Calculate a gradient
protected Pair<java.lang.Double,org.nd4j.linalg.api.ndarray.INDArray>	gradient(org.nd4j.linalg.api.ndarray.INDArray p)
Pair<Gradient,java.lang.Double>	gradientAndScore() Get the gradient and score
void	init() Init the model
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)
java.util.Map<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray>	paramTable() The param table
java.util.Map<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray>	paramTable(boolean backprapParamsOnly) Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
void	plot(org.nd4j.linalg.api.ndarray.INDArray matrix, int nDims, java.util.List<java.lang.String> labels, java.lang.String path) Deprecated. use fit(INDArray) and saveAsFile(List, String) instead.
void	saveAsFile(java.util.List<java.lang.String> labels, java.lang.String path) Save the model as a file with a csv format, adding the label as the last column.
double	score() The score for the model
void	setBackpropGradientsViewArray(org.nd4j.linalg.api.ndarray.INDArray gradients) Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
void	setConf(NeuralNetConfiguration conf) Setter for the configuration
void	setData(org.nd4j.linalg.api.ndarray.INDArray data)
void	setInvert(boolean invert)
void	setListeners(java.util.Collection<IterationListener> listeners) Set the IterationListeners for the ComputationGraph (and all layers in the network)
void	setListeners(IterationListener... listeners) Set the IterationListeners for the ComputationGraph (and all layers in the network)
void	setNumDimensions(int numDimensions)
void	setParam(java.lang.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	setParamsViewArray(org.nd4j.linalg.api.ndarray.INDArray params) Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
void	setParamTable(java.util.Map<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray> paramTable) Setter for the param table
void	setSimiarlityFunction(java.lang.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(Gradient gradient) Update layer weights and biases with gradient change
void	update(org.nd4j.linalg.api.ndarray.INDArray gradient, java.lang.String paramType) Perform one update applying the gradient
void	validateInput() Validate the input

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

Y_GRAD

public static final java.lang.String Y_GRAD

See Also:

Constant Field Values

iterationListener

protected transient IterationListener iterationListener

Constructor Detail

BarnesHutTsne

public BarnesHutTsne(int numDimensions,
                     java.lang.String simiarlityFunction,
                     double theta,
                     boolean invert,
                     int maxIter,
                     double realMin,
                     double initialMomentum,
                     double finalMomentum,
                     double momentum,
                     int switchMomentumIteration,
                     boolean normalize,
                     int stopLyingIteration,
                     double tolerance,
                     double learningRate,
                     boolean useAdaGrad,
                     double perplexity,
                     IterationListener iterationListener,
                     double minGain)

Method Detail

getSimiarlityFunction

public java.lang.String getSimiarlityFunction()

setSimiarlityFunction

public void setSimiarlityFunction(java.lang.String simiarlityFunction)

isInvert

public boolean isInvert()

setInvert

public void setInvert(boolean invert)

getTheta

public double getTheta()

getPerplexity

public double getPerplexity()

getNumDimensions

public int getNumDimensions()

setNumDimensions

public void setNumDimensions(int numDimensions)

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)

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(java.lang.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 java.util.Map<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray> paramTable()

Description copied from interface: Model

The param table

Specified by:

paramTable in interface Model

Returns:

paramTable

public java.util.Map<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray> paramTable(boolean backprapParamsOnly)

Description copied from interface: Model

Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters

Specified by:

paramTable in interface Model

Parameters:

backprapParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())

setParamTable

public void setParamTable(java.util.Map<java.lang.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(java.lang.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<java.lang.Double,org.nd4j.linalg.api.ndarray.INDArray> gradient(org.nd4j.linalg.api.ndarray.INDArray p)

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,java.lang.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:

init

public void init()

Init the model

Specified by:

init in interface Model

setListeners

public void setListeners(java.util.Collection<IterationListener> listeners)

Set the IterationListeners for the ComputationGraph (and all layers in the network)

Specified by:

setListeners in interface Model

Parameters:

listeners -

setListeners

public void setListeners(IterationListener... listeners)

Set the IterationListeners for the ComputationGraph (and all layers in the network)

Specified by:

setListeners in interface Model

Parameters:

listeners -

fit

public void fit()

Description copied from interface: Model

All models have a fit method

Specified by:

fit in interface Model

update

public void update(Gradient gradient)

Description copied from interface: Model

Update layer weights and biases with gradient change

Specified by:

update in interface Model

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)

update

public void update(org.nd4j.linalg.api.ndarray.INDArray gradient,
                   java.lang.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

saveAsFile

public void saveAsFile(java.util.List<java.lang.String> labels,
                       java.lang.String path)
                throws java.io.IOException

Save the model as a file with a csv format, adding the label as the last column.

Parameters:

labels -

path - the path to write

Throws:

java.io.IOException

plot

@Deprecated
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

Deprecated. use fit(INDArray) and saveAsFile(List, String) instead.

Plot tsne

Parameters:

matrix - the matrix to plot

nDims - the number

labels -

path - the path to write

Throws:

java.io.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

setParamsViewArray

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

Description copied from interface: Model

Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

Specified by:

setParamsViewArray in interface Model

Parameters:

params - a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array

setBackpropGradientsViewArray

public void setBackpropGradientsViewArray(org.nd4j.linalg.api.ndarray.INDArray gradients)

Description copied from interface: Model

Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

Specified by:

setBackpropGradientsViewArray in interface Model

Parameters:

gradients - a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array

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

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

Deprecated. Use fit(INDArray)

Change the dimensions with

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,java.lang.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

getData

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

Return the matrix reduce to the NDim.

setData

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