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Algorithms and components for machine learning and statistics.
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/*
* File: DirichletDistribution.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Dec 14, 2009, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.statistics.distribution;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.math.MathUtil;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFactory;
import gov.sandia.cognition.math.matrix.VectorInputEvaluator;
import gov.sandia.cognition.statistics.AbstractDistribution;
import gov.sandia.cognition.statistics.ClosedFormComputableDistribution;
import gov.sandia.cognition.statistics.ProbabilityDensityFunction;
import gov.sandia.cognition.util.ObjectUtil;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Random;
/**
* The Dirichlet distribution is the multivariate generalization of the beta
* distribution. It describes the belief that the probabilities of K
* mutually exclusive events "x_i" have been observed "a_i -1" times. The
* Dirichlet distribution is the conjugate prior of the multinomial
* distribution.
*
* @author Kevin R. Dixon
* @since 3.0
*/
@PublicationReference(
author="Wikipedia",
title="Dirichlet distribution",
type=PublicationType.WebPage,
year=2009,
url="http://en.wikipedia.org/wiki/Dirichlet_distribution"
)
public class DirichletDistribution
extends AbstractDistribution
implements ClosedFormComputableDistribution
{
/**
* Parameters of the Dirichlet distribution, must be at least 2-dimensional
* and each element must be positive.
*/
protected Vector parameters;
/**
* Creates a new instance of DirichletDistribution
*/
public DirichletDistribution()
{
this( 2 );
}
/**
* Creates a new instance of DirichletDistribution
* @param dimensionality
* Dimensionality of the distribution
*/
public DirichletDistribution(
final int dimensionality )
{
this( VectorFactory.getDefault().createVector(dimensionality,1.0) );
}
/**
* Creates a new instance of DirichletDistribution
* @param parameters
* Parameters of the Dirichlet distribution, must be at least 2-dimensional
* and each element must be positive.
*
*/
public DirichletDistribution(
final Vector parameters )
{
this.setParameters(parameters);
}
/**
* Copy Constructor.
* @param other
* DirichletDistribution to copy.
*/
public DirichletDistribution(
final DirichletDistribution other )
{
this( ObjectUtil.cloneSafe( other.getParameters() ) );
}
@Override
public DirichletDistribution clone()
{
DirichletDistribution clone = (DirichletDistribution) super.clone();
clone.setParameters( ObjectUtil.cloneSafe(this.getParameters()) );
return clone;
}
@Override
public Vector getMean()
{
return this.parameters.scale(1.0 / this.parameters.norm1());
}
@Override
public Vector sample(
final Random random)
{
// Create the result vector.
final int K = this.getParameters().getDimensionality();
final Vector y = VectorFactory.getDenseDefault().createVector(K);
double sum = 0.0;
for (int i = 0; i < K; i++)
{
final double yi = GammaDistribution.sampleStandard(
this.parameters.get(i), random);
y.set(i, yi);
sum += yi;
}
if (sum != 0.0)
{
y.scaleEquals(1.0 / sum);
}
return y;
}
@Override
public void sampleInto(
final Random random,
final int numSamples,
final Collection output)
{
GammaDistribution.CDF gammaRV = new GammaDistribution.CDF(1.0, 1.0);
int K = this.getParameters().getDimensionality();
double[][] gammaData = new double[K][];
for (int i = 0; i < K; i++)
{
double ai = this.parameters.get(i);
gammaRV.setShape(ai);
gammaData[i] = gammaRV.sampleAsDoubles(random, numSamples);
}
for (int n = 0; n < numSamples; n++)
{
Vector y = VectorFactory.getDenseDefault().createVector(K);
double sum = 0.0;
for (int i = 0; i < K; i++)
{
double yin = gammaData[i][n];
y.set(i, yin);
sum += yin;
}
if (sum != 0.0)
{
y.scaleEquals(1.0 / sum);
}
output.add(y);
}
}
@Override
public Vector convertToVector()
{
return ObjectUtil.cloneSafe(this.getParameters());
}
@Override
public void convertFromVector(
final Vector parameters)
{
parameters.assertSameDimensionality( this.getParameters() );
this.setParameters( ObjectUtil.cloneSafe(parameters) );
}
/**
* Getter for parameters
* @return
* Parameters of the Dirichlet distribution, must be at least 2-dimensional
* and each element must be positive.
*/
public Vector getParameters()
{
return this.parameters;
}
/**
* Setter for parameters
* @param parameters
* Parameters of the Dirichlet distribution, must be at least 2-dimensional
* and each element must be positive.
*/
public void setParameters(
final Vector parameters)
{
final int N = parameters.getDimensionality();
if( N < 2 )
{
throw new IllegalArgumentException( "Dimensionality must be >= 2" );
}
for( int i = 0; i < N; i++ )
{
if( parameters.getElement(i) <= 0.0 )
{
throw new IllegalArgumentException(
"All parameter elements must be > 0.0" );
}
}
this.parameters = parameters;
}
@Override
public DirichletDistribution.PDF getProbabilityFunction()
{
return new DirichletDistribution.PDF( this );
}
/**
* PDF of the Dirichlet distribution.
*/
public static class PDF
extends DirichletDistribution
implements ProbabilityDensityFunction,
VectorInputEvaluator
{
/**
* Default constructor.
*/
public PDF()
{
super();
}
/**
* Creates a new instance of PDF
* @param parameters
* Parameters of the Dirichlet distribution, must be at least 2-dimensional
* and each element must be positive.
*/
public PDF(
final Vector parameters )
{
super( parameters );
}
/**
* Copy Constructor.
* @param other
* DirichletDistribution to copy.
*/
public PDF(
final DirichletDistribution other )
{
super( other );
}
/**
* Evaluates the Dirichlet PDF about the given input. Note that we
* normalize the given input by its L1 norm to ensure that its entries
* sum to 1.
* @param input
* Input to consider, automatically normalized by its L1 norm without
* side-effect.
* @return
* Dirichlet PDF evaluated about the given (unnormalized) input.
*/
@Override
public Double evaluate(
final Vector input)
{
Vector xn = input.scale( 1.0 / input.norm1() );
Vector a = this.getParameters();
input.assertSameDimensionality( a );
double logsum = 0.0;
final int K = a.getDimensionality();
for( int i = 0; i < K; i++ )
{
double xi = xn.getElement(i);
if( (xi <= 0.0) || (1.0 <= xi) )
{
throw new IllegalArgumentException(
"Expected all inputs to be (0.0,infinity): " + input );
}
double ai = a.getElement(i);
logsum += (ai-1.0) * Math.log( xi );
}
logsum -= MathUtil.logMultinomialBetaFunction( a );
return Math.exp(logsum);
}
@Override
public double logEvaluate(
final Vector input)
{
Vector xn = input.scale( 1.0 / input.norm1() );
Vector a = this.getParameters();
input.assertSameDimensionality( a );
double logsum = 0.0;
final int K = a.getDimensionality();
for( int i = 0; i < K; i++ )
{
double xi = xn.getElement(i);
if( (xi <= 0.0) || (1.0 <= xi) )
{
throw new IllegalArgumentException(
"Expected all inputs to be (0.0,infinity): " + input );
}
double ai = a.getElement(i);
logsum += (ai-1.0) * Math.log( xi );
}
logsum -= MathUtil.logMultinomialBetaFunction( a );
return logsum;
}
@Override
public int getInputDimensionality()
{
return (this.parameters != null) ? this.parameters.getDimensionality() : 0;
}
@Override
public DirichletDistribution.PDF getProbabilityFunction()
{
return this;
}
}
}