Facemorph.psm
Class DiagonalGaussian

java.lang.Object
  Facemorph.psm.DiagonalGaussian

public class DiagonalGaussian
extends java.lang.Object

DiagonalGaussian model

Constructor Summary

DiagonalGaussian(double[] mean, double[] variance)
DisagonalGaussian constructor

DiagonalGaussian(int dim)
DiagonalGaussian constructor

Method Summary

void	build(java.util.Vector samples) Build from a sample of double[] vectors
double	build(java.util.Vector samples, java.util.Vector<java.lang.Double> sampleWeights) Builds the mean and covariance matrix from the samples given
double	build(java.util.Vector samples, java.util.Vector sampleWeights, int index) Builds the mean and covariance matrix from the samples given
double[]	getMean() Get the mean vector
double[]	getRandomSample(java.util.Random rand) Gets a random vector sampled from this Gaussian distribution
double	probability(double[] sample) Calculates the probability of a sample vector
void	read(java.io.StreamTokenizer st) Read from a StreamTokenizer
void	write(java.io.PrintStream ps) Write to a PrintStream

Methods inherited from class java.lang.Object

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

Constructor Detail

DiagonalGaussian

public DiagonalGaussian(int dim)

DiagonalGaussian constructor

Parameters:

dim - the dimension of the space

DiagonalGaussian

public DiagonalGaussian(double[] mean,
                        double[] variance)

DisagonalGaussian constructor

Parameters:

mean - the mean position

variance - the variance along each axis

Method Detail

build

public void build(java.util.Vector samples)

Build from a sample of double[] vectors

Parameters:

samples - the samples to build from

build

public double build(java.util.Vector samples,
                    java.util.Vector sampleWeights,
                    int index)

Builds the mean and covariance matrix from the samples given

Parameters:

samples - the samples to build from

sampleWeights - the weights of the samples

index - the index of each element in sampleqweights to use for the weighting

Returns:

returns the sum of sample weights, or zero on an error

build

public double build(java.util.Vector samples,
                    java.util.Vector<java.lang.Double> sampleWeights)

Builds the mean and covariance matrix from the samples given

Parameters:

samples - the samples to build from

sampleWeights - the weights of each sample

Returns:

return the sample of samples weights, or 0 if something goes wrong

probability

public double probability(double[] sample)
                   throws BigMatException

Calculates the probability of a sample vector

Parameters:

sample - the sdample to evaluate

Returns:

returns the probability

Throws:

BigMatException

getRandomSample

public double[] getRandomSample(java.util.Random rand)

Gets a random vector sampled from this Gaussian distribution

Parameters:

rand - a Random number generator

Returns:

return a random sample

getMean

public double[] getMean()

Get the mean vector

Returns:

return teh mean vector

write

public void write(java.io.PrintStream ps)

Write to a PrintStream

Parameters:

ps - the PrintStream to write to

read

public void read(java.io.StreamTokenizer st)
          throws java.io.IOException

Read from a StreamTokenizer

Parameters:

st - the StreamTokenizer to read from

Throws:

java.io.IOException