org.openimaj.math.statistics

Class MeanAndCovariance

java.lang.Object

org.openimaj.math.statistics.MeanAndCovariance

public class MeanAndCovariance
extends Object

Class to compute the mean and covariance of some given data.

Author:

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

Field Summary

Fields

Modifier and Type	Field and Description
Jama.Matrix	covar The covariance matrix (NxN)
Jama.Matrix	mean The mean vector (1xN)

Constructor Summary

Constructors

Constructor and Description
MeanAndCovariance(double[][] samples) Construct a new MeanAndCovariance containing the mean vector and covariance matrix of the given data (each row is a data point)
MeanAndCovariance(float[][] samples) Construct a new MeanAndCovariance containing the mean vector and covariance matrix of the given data (each row is a data point)
MeanAndCovariance(Jama.Matrix samples) Construct a new MeanAndCovariance containing the mean vector and covariance matrix of the given data (each row is a data point)

Method Summary

Modifier and Type	Method and Description
static Jama.Matrix	computeCovariance(double[][] samples) Get the covariance of the data
static Jama.Matrix	computeCovariance(float[][] samples) Get the covariance of the data
static Jama.Matrix	computeCovariance(Jama.Matrix samples) Get the covariance of the data
static Jama.Matrix	computeMean(double[][] samples) Get the mean of the data
static Jama.Matrix	computeMean(float[][] samples) Get the mean of the data
static Jama.Matrix	computeMean(Jama.Matrix samples) Get the mean of the data
Jama.Matrix	getCovariance() Get the covariance matrix
Jama.Matrix	getMean() Get the mean vector

Methods inherited from class java.lang.Object

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

Field Detail

mean

public final Jama.Matrix mean

The mean vector (1xN)

covar

public final Jama.Matrix covar

The covariance matrix (NxN)

Constructor Detail

MeanAndCovariance

public MeanAndCovariance(float[][] samples)

Construct a new MeanAndCovariance containing the mean vector and covariance matrix of the given data (each row is a data point)

Parameters:

samples - the data

MeanAndCovariance

public MeanAndCovariance(double[][] samples)

Construct a new MeanAndCovariance containing the mean vector and covariance matrix of the given data (each row is a data point)

Parameters:

samples - the data

MeanAndCovariance

public MeanAndCovariance(Jama.Matrix samples)

Construct a new MeanAndCovariance containing the mean vector and covariance matrix of the given data (each row is a data point)

Parameters:

samples - the data

Method Detail

getMean

public Jama.Matrix getMean()

Get the mean vector

Returns:

the mean vector

getCovariance

public Jama.Matrix getCovariance()

Get the covariance matrix

Returns:

the covariance matrix

computeMean

public static Jama.Matrix computeMean(float[][] samples)

Get the mean of the data

Parameters:

samples - the data

Returns:

the mean

computeCovariance

public static Jama.Matrix computeCovariance(float[][] samples)

Get the covariance of the data

Parameters:

samples - the data

Returns:

the covariance matrix

computeMean

public static Jama.Matrix computeMean(double[][] samples)

Get the mean of the data

Parameters:

samples - the data

Returns:

the mean

computeCovariance

public static Jama.Matrix computeCovariance(double[][] samples)

Get the covariance of the data

Parameters:

samples - the data

Returns:

the covariance matrix

computeMean

public static Jama.Matrix computeMean(Jama.Matrix samples)

Get the mean of the data

Parameters:

samples - the data

Returns:

the mean

computeCovariance

public static Jama.Matrix computeCovariance(Jama.Matrix samples)

Get the covariance of the data

Parameters:

samples - the data

Returns:

the covariance matrix