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/*
* File: FisherLinearDiscriminantBinaryCategorizer.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright October 9, 2007, 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.learning.function.categorization;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.learning.algorithm.SupervisedBatchLearner;
import gov.sandia.cognition.learning.data.DatasetUtil;
import gov.sandia.cognition.learning.data.DefaultInputOutputPair;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.learning.function.scalar.LinearDiscriminant;
import gov.sandia.cognition.math.MultivariateStatisticsUtil;
import gov.sandia.cognition.math.matrix.Matrix;
import gov.sandia.cognition.math.matrix.MatrixFactory;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic;
import gov.sandia.cognition.statistics.distribution.MultivariateGaussian;
import gov.sandia.cognition.util.AbstractCloneableSerializable;
import gov.sandia.cognition.util.DefaultPair;
import gov.sandia.cognition.util.Pair;
import java.util.ArrayList;
import java.util.Collection;
import java.util.LinkedList;
/**
* A Fisher Linear Discriminant classifier, which creates an optimal linear
* separating plane between two Gaussian classes of different covariances.
*
* @author Kevin R. Dixon
* @since 2.0
*/
@PublicationReference(
author="Wikipedia",
title="Linear discriminant analysis",
type=PublicationType.WebPage,
year=2009,
url="http://en.wikipedia.org/wiki/Linear_discriminant_analysis#Fisher.27s_linear_discriminant"
)
public class FisherLinearDiscriminantBinaryCategorizer
extends ScalarFunctionToBinaryCategorizerAdapter
{
/**
* Default constructor
*/
public FisherLinearDiscriminantBinaryCategorizer()
{
this( (Vector) null, DEFAULT_THRESHOLD );
}
/**
* Creates a new of {@code FisherLinearDiscriminantBinaryCategorizer}.
*
* @param weightVector The weight vector.
* @param threshold The threshold.
*/
public FisherLinearDiscriminantBinaryCategorizer(
final Vector weightVector,
final double threshold)
{
this(new LinearDiscriminant(weightVector),threshold);
}
/**
* Creates a new of {@code FisherLinearDiscriminantBinaryCategorizer}.
*
* @param discriminant The linear discriminant to use.
* @param threshold The threshold.
*/
public FisherLinearDiscriminantBinaryCategorizer(
final LinearDiscriminant discriminant,
final double threshold )
{
super(discriminant, threshold);
}
@Override
public FisherLinearDiscriminantBinaryCategorizer clone()
{
return (FisherLinearDiscriminantBinaryCategorizer) super.clone();
}
/**
* This class implements a closed form solver for the Fisher linear
* discriminant binary categorizer.
*/
public static class ClosedFormSolver
extends AbstractCloneableSerializable
implements SupervisedBatchLearner
{
/** The default covariance. */
private double defaultCovariance;
/**
* Default constructor.
*/
public ClosedFormSolver()
{
this( MultivariateGaussian.MaximumLikelihoodEstimator.DEFAULT_COVARIANCE );
}
/**
* Creates a new {@code ClosedFormSolver}.
*
* @param defaultCovariance The default covariance.
*/
public ClosedFormSolver(
double defaultCovariance)
{
this.defaultCovariance = defaultCovariance;
}
public FisherLinearDiscriminantBinaryCategorizer learn(
Collection> data)
{
return ClosedFormSolver.learn(data, this.defaultCovariance);
}
/**
* Closed-form learning algorithm for a Fisher Linear Discriminant.
*
* @param data The data to learn the discriminant categorizer from.
* @param defaultCovariance The default covariance.
* @return A discriminant categorizer learned from the data.
*/
public static FisherLinearDiscriminantBinaryCategorizer learn(
Collection> data,
final double defaultCovariance)
{
// Split the data into two classes based on their
DefaultPair, LinkedList> pair =
DatasetUtil.splitDatasets(data);
LinkedList d1 = pair.getFirst();
LinkedList d0 = pair.getSecond();
// This is faster than estimating a MultivariateGaussian as
// the Gaussian will automatically invert the covariance matrix
// and cache that
Pair r1 =
MultivariateStatisticsUtil.computeMeanAndCovariance(d1);
Vector m1 = r1.getFirst();
Matrix c1 = r1.getSecond();
Pair r0 =
MultivariateStatisticsUtil.computeMeanAndCovariance(d0);
Vector m0 = r0.getFirst();
Matrix c0 = r0.getSecond();
Matrix cinverse;
if (defaultCovariance != 0.0)
{
int M = m0.getDimensionality();
Matrix ci = MatrixFactory.getDefault().createIdentity(M, M).scale(defaultCovariance);
cinverse = c0.plus(c1.plus(ci)).inverse();
}
else
{
cinverse = c0.plus(c1).inverse();
}
Vector weightVector = cinverse.times(m1.minus(m0));
// Technically, the threshold is supposed to be zero, but we might
// try to do better
LinearDiscriminant discriminant =
new LinearDiscriminant(weightVector);
ArrayList> doubleData =
new ArrayList>(data.size());
for (InputOutputPair sample : data)
{
Double value = discriminant.evaluate(sample.getInput());
doubleData.add(new DefaultInputOutputPair(
value, sample.getOutput()));
}
ReceiverOperatingCharacteristic roc =
ReceiverOperatingCharacteristic.create(doubleData);
ReceiverOperatingCharacteristic.Statistic stats =
roc.computeStatistics();
return new FisherLinearDiscriminantBinaryCategorizer(
discriminant, stats.getOptimalThreshold().getClassifier().getThreshold() );
}
}
}