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Algorithms and components for machine learning and statistics.
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/*
* File: BernoulliDistribution.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Feb 5, 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.collection.IntegerSpan;
import gov.sandia.cognition.math.ProbabilityUtil;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFactory;
import gov.sandia.cognition.statistics.AbstractClosedFormIntegerDistribution;
import gov.sandia.cognition.statistics.ClosedFormDiscreteUnivariateDistribution;
import gov.sandia.cognition.statistics.ClosedFormCumulativeDistributionFunction;
import gov.sandia.cognition.statistics.ProbabilityMassFunction;
import gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil;
import java.util.Collection;
import java.util.Random;
/**
* A Bernoulli distribution, which takes a value of "1" with probability "p"
* and value of "0" with probability "1-p". This is also known as flipping
* a weighted coin. A sum of Bernoulli random variables is a Binomial
* distribution.
* @author Kevin R. Dixon
* @since 3.0
*/
@PublicationReference(
author="Wikipedia",
title="Bernoulli distribution",
type=PublicationType.WebPage,
year=2009,
url="http://en.wikipedia.org/wiki/Bernoulli_distribution"
)
public class BernoulliDistribution
extends AbstractClosedFormIntegerDistribution
implements ClosedFormDiscreteUnivariateDistribution
{
/**
* Default Bernoulli parameter, {@value}
*/
public static final double DEFAULT_P = 0.5;
/**
* Bernoulli parameter, where the distribution takes value "1" with
* probability "p" and value "0" with probability 1-p.
*/
private double p;
/**
* Creates a new instance of BernoulliDistribution
*/
public BernoulliDistribution()
{
this( DEFAULT_P );
}
/**
* Creates a new instance of BernoulliDistribution
* @param p
* Bernoulli parameter, where the distribution takes value "1" with
* probability "p" and value "0" with probability 1-p.
*/
public BernoulliDistribution(
final double p )
{
this.setP( p );
}
/**
* Copy Constructor
* @param other BernoulliDistribution to copy
*/
public BernoulliDistribution(
final BernoulliDistribution other )
{
this( other.getP() );
}
@Override
public Double getMean()
{
return this.getMeanAsDouble();
}
@Override
public double getMeanAsDouble()
{
return this.getP();
}
@Override
public Integer getMinSupport()
{
return 0;
}
@Override
public Integer getMaxSupport()
{
return 1;
}
@Override
public void sampleInto(
final Random random,
final int sampleCount,
final Collection output)
{
for (int i = 0; i < sampleCount; i++)
{
output.add(Integer.valueOf(this.sampleAsInt(random)));
}
}
@Override
public int sampleAsInt(
final Random random)
{
if (this.sampleAsBoolean(random))
{
return 1;
}
else
{
return 0;
}
}
/**
* Samples from the Bernoulli distribution as a boolean.
*
* @param random
* The random number generator to use.
* @return
* A true or false value sampled from the distribution. The value is
* true with probability p.
*/
public boolean sampleAsBoolean(
final Random random)
{
return random.nextDouble() < this.p;
}
@Override
public Vector convertToVector()
{
return VectorFactory.getDefault().copyValues( this.getP() );
}
@Override
public void convertFromVector(
final Vector parameters )
{
if( parameters.getDimensionality() != 1 )
{
throw new IllegalArgumentException(
"parameter dimension must be 1" );
}
this.setP( parameters.getElement(0) );
}
@Override
public double getVariance()
{
return this.getP() * (1.0-this.getP());
}
@Override
public IntegerSpan getDomain()
{
return new IntegerSpan(0, 1);
}
@Override
public int getDomainSize()
{
return 2;
}
/**
* Getter for p
* @return
* Bernoulli parameter, where the distribution takes value "1" with
* probability "p" and value "0" with probability 1-p.
*/
public double getP()
{
return this.p;
}
/**
* Setter for p
* @param p
* Bernoulli parameter, where the distribution takes value "1" with
* probability "p" and value "0" with probability 1-p.
*/
public void setP(
final double p )
{
ProbabilityUtil.assertIsProbability(p);
this.p = p;
}
@Override
public BernoulliDistribution.CDF getCDF()
{
return new CDF( this );
}
@Override
public BernoulliDistribution.PMF getProbabilityFunction()
{
return new PMF( this );
}
@Override
public String toString()
{
return "p = " + this.getP();
}
/**
* PMF of the Bernoulli distribution.
*/
public static class PMF
extends BernoulliDistribution
implements ProbabilityMassFunction
{
/**
* Default constructor
*/
public PMF()
{
super();
}
/**
* Creates a new instance of PMF
* @param p
* Bernoulli parameter, where the distribution takes value "1" with
* probability "p" and value "0" with probability 1-p.
*/
public PMF(
final double p )
{
super( p );
}
/**
* Copy constructor
* @param other
* BernoulliDistribution to copy
*/
public PMF(
final BernoulliDistribution other )
{
super( other );
}
@Override
public double getEntropy()
{
return ProbabilityMassFunctionUtil.getEntropy( this );
}
@Override
public Double evaluate(
final Number input )
{
if( input.intValue() == 0 )
{
return 1.0-this.getP();
}
else if( input.intValue() == 1 )
{
return this.getP();
}
else
{
return 0.0;
}
}
@Override
public double logEvaluate(
final Number input)
{
return Math.log( this.evaluate(input) );
}
@Override
public BernoulliDistribution.PMF getProbabilityFunction()
{
return this;
}
}
/**
* CDF of a Bernoulli distribution.
*/
public static class CDF
extends BernoulliDistribution
implements ClosedFormCumulativeDistributionFunction
{
/**
* Default constructor
*/
public CDF()
{
super();
}
/**
* Creates a new instance of PMF
* @param p
* Bernoulli parameter, where the distribution takes value "1" with
* probability "p" and value "0" with probability 1-p.
*/
public CDF(
final double p )
{
super( p );
}
/**
* Copy constructor
* @param other
* BernoulliDistribution to copy
*/
public CDF(
final BernoulliDistribution other )
{
super( other );
}
@Override
public Double evaluate(
final Number input )
{
if( input.intValue() < 0 )
{
return 0.0;
}
else if( input.intValue() <= 0 )
{
return 1.0-this.getP();
}
else
{
return 1.0;
}
}
@Override
public BernoulliDistribution.CDF getCDF()
{
return this;
}
}
}