be.ac.ulg.montefiore.run.jahmm
Class OpdfGaussian

java.lang.Object
  be.ac.ulg.montefiore.run.jahmm.OpdfGaussian

All Implemented Interfaces:

Opdf<ObservationReal>, java.io.Serializable, java.lang.Cloneable

public class OpdfGaussian
extends java.lang.Object
implements Opdf<ObservationReal>

This class represents a (monovariate) gaussian distribution function.

See Also:

Serialized Form

Constructor Summary

OpdfGaussian()
Builds a new gaussian probability distribution with zero mean and unit variance.

OpdfGaussian(double mean, double variance)
Builds a new gaussian probability distribution with a given mean and covariance matrix.

Method Summary

OpdfGaussian	clone()
void	fit(java.util.Collection<? extends ObservationReal> co) Fits this observation probability (distribution) function to a (non empty) set of observations.
void	fit(java.util.Collection<? extends ObservationReal> co, double[] weights) Fits this observation probability (distribution) function to a weighted (non empty) set of observations.
void	fit(ObservationReal... oa) Fits this observation probability (distribution) function to a (non empty) set of observations.
void	fit(ObservationReal[] o, double[] weights) Fits this observation probability (distribution) function to a weighted (non empty) set of observations.
ObservationReal	generate() Generates a (pseudo) random observation according to this distribution.
double	mean() Returns this distribution's mean value.
double	probability(ObservationReal o) Returns the probability (density) of an observation given a distribution.
java.lang.String	toString()
java.lang.String	toString(java.text.NumberFormat numberFormat) Returns a String describing this distribution.
double	variance() Returns this distribution's variance.

Methods inherited from class java.lang.Object

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

Constructor Detail

OpdfGaussian

public OpdfGaussian()

Builds a new gaussian probability distribution with zero mean and unit variance.

OpdfGaussian

public OpdfGaussian(double mean,
                    double variance)

Builds a new gaussian probability distribution with a given mean and covariance matrix.

Parameters:

mean - The distribution's mean.

variance - The distribution's variance.

Method Detail

mean

public double mean()

Returns this distribution's mean value.

Returns:

This distribution's mean value.

variance

public double variance()

Returns this distribution's variance.

Returns:

This distribution's variance.

probability

public double probability(ObservationReal o)

Description copied from interface: Opdf

Returns the probability (density) of an observation given a distribution.

Specified by:

probability in interface Opdf<ObservationReal>

Parameters:

o - An observation.

Returns:

The probability (density, if o takes continuous values) of o for this function.

generate

public ObservationReal generate()

Description copied from interface: Opdf

Generates a (pseudo) random observation according to this distribution.

Specified by:

generate in interface Opdf<ObservationReal>

Returns:

An observation.

fit

public void fit(ObservationReal... oa)

Description copied from interface: Opdf

Fits this observation probability (distribution) function to a (non empty) set of observations. The meaning to give to fits should be has the maximum likelihood if possible.

Specified by:

fit in interface Opdf<ObservationReal>

Parameters:

oa - An array of observations compatible with this function.

fit

public void fit(java.util.Collection<? extends ObservationReal> co)

Description copied from interface: Opdf

Fits this observation probability (distribution) function to a (non empty) set of observations. The meaning to give to fits should be has the maximum likelihood if possible.

Specified by:

fit in interface Opdf<ObservationReal>

Parameters:

co - A set of observations compatible with this function.

fit

public void fit(ObservationReal[] o,
                double[] weights)

Description copied from interface: Opdf

Fits this observation probability (distribution) function to a weighted (non empty) set of observations. Equations (53) and (54) of Rabiner's A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition explain how the weights can be used.

Specified by:

fit in interface Opdf<ObservationReal>

Parameters:

o - An array of observations compatible with this factory.

weights - The weight associated to each observation (such that weight.length == o.length and the sum of all the elements equals 1).

fit

public void fit(java.util.Collection<? extends ObservationReal> co,
                double[] weights)

Description copied from interface: Opdf

Fits this observation probability (distribution) function to a weighted (non empty) set of observations. Equations (53) and (54) of Rabiner's A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition explain how the weights can be used.

Specified by:

fit in interface Opdf<ObservationReal>

Parameters:

co - A set of observations compatible with this factory.

weights - The weight associated to each observation (such that weight.length == o.length and the sum of all the elements equals 1).

clone

public OpdfGaussian clone()

Specified by:

clone in interface Opdf<ObservationReal>

Overrides:

clone in class java.lang.Object

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

toString

public java.lang.String toString(java.text.NumberFormat numberFormat)

Description copied from interface: Opdf

Returns a String describing this distribution.

Specified by:

toString in interface Opdf<ObservationReal>

Parameters:

numberFormat - A formatter used to convert the numbers (e.g. probabilities) to strings.

Returns:

A String describing this distribution.