org.openimaj.math.statistics.distribution

Class DiagonalMultivariateGaussian

java.lang.Object

org.openimaj.math.statistics.distribution.AbstractMultivariateGaussian

org.openimaj.math.statistics.distribution.DiagonalMultivariateGaussian

All Implemented Interfaces:

MultivariateDistribution, MultivariateGaussian

public class DiagonalMultivariateGaussian
extends AbstractMultivariateGaussian

Implementation of a MultivariateGaussian with a diagonal covariance matrix.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Field Summary

Fields

Modifier and Type	Field and Description
double[]	variance The diagonal of the covariance matrix

Fields inherited from class org.openimaj.math.statistics.distribution.AbstractMultivariateGaussian

mean

Constructor Summary

Constructors

Constructor and Description
DiagonalMultivariateGaussian(int ndims) Construct the Gaussian with the zero mean and unit variance
DiagonalMultivariateGaussian(Jama.Matrix mean, double[] variance) Construct the Gaussian with the provided center and covariance

Method Summary

Modifier and Type	Method and Description
double	estimateLogProbability(double[] sample) Get the log probability for a given point in space relative to the PDF represented by this distribution.
double[]	estimateLogProbability(double[][] samples) Get the log probability for a given point in space relative to the PDF represented by this distribution.
double	estimateProbability(double[] sample) Get the probability for a given point in space relative to the PDF represented by this distribution.
Jama.Matrix	getCovariance() Get the covariance
double	getCovariance(int row, int col) Get a covariance value from the covariance matrix.
double[][]	sample(int nsamples, Random rng) Sample the distribution.

Methods inherited from class org.openimaj.math.statistics.distribution.AbstractMultivariateGaussian

getMean, numDims, sample, toString

Methods inherited from class java.lang.Object

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

Field Detail

variance

public double[] variance

The diagonal of the covariance matrix

Constructor Detail

DiagonalMultivariateGaussian

public DiagonalMultivariateGaussian(Jama.Matrix mean,
                                    double[] variance)

Construct the Gaussian with the provided center and covariance

Parameters:

mean - centre of the Gaussian

variance - variance of the Gaussian

DiagonalMultivariateGaussian

public DiagonalMultivariateGaussian(int ndims)

Construct the Gaussian with the zero mean and unit variance

Parameters:

ndims - number of dimensions

Method Detail

getCovariance

public Jama.Matrix getCovariance()

Description copied from interface: MultivariateGaussian

Get the covariance

Returns:

the covariance

getCovariance

public double getCovariance(int row,
                            int col)

Description copied from interface: MultivariateGaussian

Get a covariance value from the covariance matrix.

This method is provided for efficiency as not all implementations will store the full matrix, and it would be wasteful to create it each time a value is needed.

Parameters:

row - the row of the matrix value to get

col - the column of the matrix value to get

Returns:

the covariance at the given row and column

estimateProbability

public double estimateProbability(double[] sample)

Description copied from interface: MultivariateDistribution

Get the probability for a given point in space relative to the PDF represented by this distribution.

Specified by:

estimateProbability in interface MultivariateDistribution

Overrides:

estimateProbability in class AbstractMultivariateGaussian

Parameters:

sample - the point

Returns:

the probability

estimateLogProbability

public double estimateLogProbability(double[] sample)

Description copied from interface: MultivariateDistribution

Get the log probability for a given point in space relative to the PDF represented by this distribution.

Specified by:

estimateLogProbability in interface MultivariateDistribution

Overrides:

estimateLogProbability in class AbstractMultivariateGaussian

Parameters:

sample - the point

Returns:

the log-probability

estimateLogProbability

public double[] estimateLogProbability(double[][] samples)

Description copied from interface: MultivariateDistribution

Get the log probability for a given point in space relative to the PDF represented by this distribution.

Specified by:

estimateLogProbability in interface MultivariateDistribution

Overrides:

estimateLogProbability in class AbstractMultivariateGaussian

Parameters:

samples - the samples

Returns:

the log-probability

sample

public double[][] sample(int nsamples,
                         Random rng)

Description copied from interface: MultivariateDistribution

Sample the distribution.

Specified by:

sample in interface MultivariateDistribution

Overrides:

sample in class AbstractMultivariateGaussian

Parameters:

nsamples - the number of samples to draw

rng - the random number generator

Returns:

a list of sample vectors from this distribution