org.encog.ml.hmm.distributions

Class ContinousDistribution

java.lang.Object

org.encog.ml.hmm.distributions.ContinousDistribution

All Implemented Interfaces:

Serializable, Cloneable, StateDistribution

public class ContinousDistribution
extends Object
implements StateDistribution

A continuous distribution represents an infinite range of choices between two real numbers. A gaussian distribution is used to distribute the probability.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ContinousDistribution(double[] mean, double[][] covariance) Construct a continuous distribution.
ContinousDistribution(int dimension) Construct a continuous distribution with the specified number of dimensions.

Method Summary

Methods

Modifier and Type	Method and Description
ContinousDistribution	clone()
void	fit(MLDataSet co) Fit this distribution to the specified data set.
void	fit(MLDataSet co, double[] weights) Fit this distribution to the specified data set, given the specified weights, per element.
MLDataPair	generate() Generate a random data pair, based on the probabilities.
Matrix	getCovariance()
double[]	getMean()
double	probability(MLDataPair o) Determine the probability of the specified data pair.
void	update(double[][] covariance) Update the covariance.

Methods inherited from class java.lang.Object

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

Constructor Detail

ContinousDistribution

public ContinousDistribution(double[] mean,
                     double[][] covariance)

Construct a continuous distribution.

Parameters:

mean - The mean.

covariance - The covariance.

ContinousDistribution

public ContinousDistribution(int dimension)

Construct a continuous distribution with the specified number of dimensions.

Parameters:

dimension - The dimensions.

Method Detail

clone

public ContinousDistribution clone()

Specified by:

clone in interface StateDistribution

Overrides:

clone in class Object

Returns:

A clone of this distribution.

fit

public void fit(MLDataSet co)

Fit this distribution to the specified data set.

Specified by:

fit in interface StateDistribution

Parameters:

co - The data set to fit to.

fit

public void fit(MLDataSet co,
       double[] weights)

Fit this distribution to the specified data set, given the specified weights, per element.

Specified by:

fit in interface StateDistribution

Parameters:

co - The data set to fit to.

weights - The weights.

generate

public MLDataPair generate()

Generate a random data pair, based on the probabilities.

Specified by:

generate in interface StateDistribution

Returns:

A random data pair.

probability

public double probability(MLDataPair o)

Determine the probability of the specified data pair.

Specified by:

probability in interface StateDistribution

Parameters:

o - The pair to consider.

Returns:

The probability.

update

public void update(double[][] covariance)

Update the covariance.

Parameters:

covariance - The new covariance.

getMean

public double[] getMean()

Returns:

The mean for the dimensions of the gaussian curve.

getCovariance

public Matrix getCovariance()

Returns:

The covariance matrix.