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/*
* File: AggressiveRelaxedOnlineMaximumMarginAlgorithm.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Foundry Learning Core
*
* Copyright January 27, 2011, 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.
* */
package gov.sandia.cognition.learning.algorithm.perceptron;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.learning.function.categorization.DefaultKernelBinaryCategorizer;
import gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer;
import gov.sandia.cognition.learning.function.kernel.KernelUtil;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFactory;
/**
* An implementation of the Aggressive Relaxed Online Maximum Margin Algorithm
* (AROMMA). It is an online learner for a linear binary categorizer that
* also has a kernel form.
*
* @author Justin Basilico
* @since 3.3.0
*/
@PublicationReference(
title="Ultraconservative online algorithms for multiclass problems",
author={"Koby Crammer", "Yoram Singer"},
year=2003,
type=PublicationType.Journal,
publication="The Journal of Machine Learning Research",
pages={951, 991},
url="http://portal.acm.org/citation.cfm?id=944936")
public class AggressiveRelaxedOnlineMaximumMarginAlgorithm
extends AbstractKernelizableBinaryCategorizerOnlineLearner
{
/**
* Creates a new {@code AggressiveRelaxedOnlineMaximumMarginAlgorithm}.
*/
public AggressiveRelaxedOnlineMaximumMarginAlgorithm()
{
this(VectorFactory.getDefault());
}
/**
* Creates a new {@code AggressiveRelaxedOnlineMaximumMarginAlgorithm} with
* the given vector factory.
*
* @param vectorFactory
* The vector factory to use.
*/
public AggressiveRelaxedOnlineMaximumMarginAlgorithm(
final VectorFactory vectorFactory)
{
super(vectorFactory);
}
@Override
public void update(
final LinearBinaryCategorizer target,
final Vector input,
final boolean label)
{
// Get the information about the example.
final double actual = label ? +1.0 : -1.0;
Vector weights = target.getWeights();
if (weights == null)
{
// This is the first example, so initialize the weight vector.
final double inputNorm = input.norm2Squared();
weights = this.getVectorFactory().copyVector(input);
weights.scaleEquals(actual / inputNorm);
target.setWeights(weights);
}
else
{
// Predict the output as a double (negative values are false, positive
// are true).
final double prediction = target.evaluateAsDouble(input);
final double error = actual * prediction;
final double inputNorm = input.norm2Squared();
final double weightsNorm = weights.norm2Squared();
if ((1.0 > error) && (error >= inputNorm * weightsNorm))
{
weights.zero();
if (inputNorm > 0.0)
{
weights.plusEquals(input);
weights.scaleEquals(actual / inputNorm);
}
}
else if (error < 1.0)
{
final double denominator = inputNorm * weightsNorm
- prediction * prediction;
// Compute the update value.
final double c = (inputNorm * weightsNorm - actual * prediction)
/ denominator;
final double d = (weightsNorm * (actual - prediction))
/ denominator;
weights.scaleEquals(c);
weights.plusEquals(input.scale(d));
}
// else - Passive when there is no loss.
}
}
@Override
public void update(
final DefaultKernelBinaryCategorizer target,
final InputType input,
final boolean label)
{
// Get the information about the example.
final double actual = label ? +1.0 : -1.0;
if (target.getExamples().isEmpty())
{
// Initialize the target on the first update.
final double inputNorm = target.getKernel().evaluate(input, input);
if (inputNorm > 0.0)
{
target.add(input, actual / inputNorm);
}
}
else
{
// Predict the output as a double (negative values are false, positive
// are true).
final double prediction = target.evaluateAsDouble(input);
final double error = actual * prediction;
final double inputNorm = target.getKernel().evaluate(input, input);
final double weightsNorm = KernelUtil.norm2Squared(target);
if ((1.0 > error) && (error >= inputNorm * weightsNorm))
{
target.getExamples().clear();
if (inputNorm > 0.0)
{
target.add(input, actual / inputNorm);
}
}
else if (error < 1.0)
{
final double denominator = inputNorm * weightsNorm
- prediction * prediction;
// Compute the update value.
final double c = (inputNorm * weightsNorm - actual * prediction)
/ denominator;
final double d = (weightsNorm * (actual - prediction))
/ denominator;
KernelUtil.scaleEquals(target, c);
target.add(input, d);
}
// else - Passive when there is no loss.
}
}
}