org.openimaj.math.statistics.distribution

Class AbstractMultivariateGaussian

java.lang.Object

org.openimaj.math.statistics.distribution.AbstractMultivariateGaussian

All Implemented Interfaces:

MultivariateDistribution, MultivariateGaussian

Direct Known Subclasses:

CachingMultivariateGaussian, DiagonalMultivariateGaussian, FullMultivariateGaussian, SphericalMultivariateGaussian

public abstract class AbstractMultivariateGaussian
extends Object
implements MultivariateGaussian

Abstract base class for MultivariateGaussian implementations

Author:

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

Field Summary

Fields

Modifier and Type	Field and Description
Jama.Matrix	mean The mean vector

Constructor Summary

Constructors

Constructor and Description
AbstractMultivariateGaussian()

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[][] x) 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	getMean() Get the mean
int	numDims() Get the dimensionality
double[][]	sample(int nsamples, Random rng) Sample the distribution.
double[]	sample(Random rng) Sample the distribution.
String	toString()

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.openimaj.math.statistics.distribution.MultivariateGaussian

getCovariance, getCovariance

Field Detail

mean

public Jama.Matrix mean

The mean vector

Constructor Detail

AbstractMultivariateGaussian

public AbstractMultivariateGaussian()

Method Detail

getMean

public Jama.Matrix getMean()

Description copied from interface: MultivariateGaussian

Get the mean

Specified by:

getMean in interface MultivariateGaussian

Returns:

the mean

sample

public double[] sample(Random rng)

Description copied from interface: MultivariateDistribution

Sample the distribution. the number of samples to draw

Specified by:

sample in interface MultivariateDistribution

Parameters:

rng - the random number generator

Returns:

a list of sample vectors from this distribution

sample

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

Description copied from interface: MultivariateDistribution

Sample the distribution.

Specified by:

sample in interface MultivariateDistribution

Parameters:

nsamples - the number of samples to draw

rng - the random number generator

Returns:

a list of sample vectors from this distribution

numDims

public int numDims()

Description copied from interface: MultivariateGaussian

Get the dimensionality

Specified by:

numDims in interface MultivariateGaussian

Returns:

number of dimensions

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

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

Parameters:

sample - the point

Returns:

the log-probability

toString

public String toString()

Overrides:

toString in class Object

estimateLogProbability

public double[] estimateLogProbability(double[][] x)

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

Parameters:

x - the samples

Returns:

the log-probability