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/interfaces inherited from class org.deeplearning4j.plot.Tsne

Tsne.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, r, r2, realMin, stopLyingIteration, switchMomentumIteration, tolerance, useAdaGrad, usePca, y, yIncs

Constructor Summary

Constructors

Constructor and Description
BarnesHutTsne(org.nd4j.linalg.api.ndarray.INDArray x, int n, int d, 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)

Method Summary

Modifier and Type	Method and Description
int	batchSize() The current inputs batch size
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)
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
Gradient	getGradient() Calculate a gradient
org.nd4j.linalg.api.ndarray.INDArray	getYGradient(int n, org.nd4j.linalg.api.ndarray.INDArray PQ, org.nd4j.linalg.api.ndarray.INDArray qu)
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
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	hBeta(org.nd4j.linalg.api.ndarray.INDArray d, org.nd4j.linalg.api.ndarray.INDArray distances, double beta) Computes a gaussian kernel given a vector of squared euclidean distances
org.nd4j.linalg.api.ndarray.INDArray	input() The input/feature matrix for the model
void	iterate(org.nd4j.linalg.api.ndarray.INDArray input) Run one iteration
int	numParams() the number of parameters for the model
org.nd4j.linalg.api.ndarray.INDArray	params() Parameters of the model (if any)
double	score() The score for the model
void	setConf(NeuralNetConfiguration conf) Setter for the configuration
void	setParams(org.nd4j.linalg.api.ndarray.INDArray params) Set the parameters for this model.
org.nd4j.linalg.api.ndarray.INDArray	transform(org.nd4j.linalg.api.ndarray.INDArray data) Transform the data based on the model's output.
void	update(Gradient gradient) Perform one update applying the gradient
void	validateInput() Validate the input

Methods inherited from class org.deeplearning4j.plot.Tsne

calculate, getIterationListener, getY, hBeta, loadIntoTmp, plot, plot, setIterationListener, setY, step

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,
                     int n,
                     int d,
                     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)

Method Detail

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

gradient

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

Overrides:

gradient in class Tsne

getYGradient

public org.nd4j.linalg.api.ndarray.INDArray getYGradient(int n,
                                                         org.nd4j.linalg.api.ndarray.INDArray PQ,
                                                         org.nd4j.linalg.api.ndarray.INDArray qu)

Overrides:

getYGradient in class Tsne

hBeta

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

Computes a gaussian kernel given a vector of squared euclidean distances

Parameters:

d - the data

beta -

Returns:

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

Perform one update applying the gradient

Specified by:

update in interface Model

Parameters:

gradient - the gradient to apply

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

transform

public org.nd4j.linalg.api.ndarray.INDArray transform(org.nd4j.linalg.api.ndarray.INDArray data)

Description copied from interface: Model

Transform the data based on the model's output. This can be anything from a number to reconstructions.

Specified by:

transform in interface Model

Parameters:

data - the data to transform

Returns:

the transformed data

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

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

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

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

getGradient

public Gradient getGradient()

Description copied from interface: Model

Calculate a gradient

Specified by:

getGradient 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