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A single jar with all the Cognitive Foundry components.
/*
* File: KernelPerceptron.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright July 18, 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.algorithm.perceptron.kernel;
import gov.sandia.cognition.algorithm.MeasurablePerformanceAlgorithm;
import gov.sandia.cognition.annotation.CodeReview;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner;
import gov.sandia.cognition.learning.algorithm.perceptron.Perceptron;
import gov.sandia.cognition.learning.function.kernel.Kernel;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.learning.function.categorization.DefaultKernelBinaryCategorizer;
import gov.sandia.cognition.util.DefaultNamedValue;
import gov.sandia.cognition.util.DefaultWeightedValue;
import gov.sandia.cognition.util.NamedValue;
import java.util.ArrayList;
import java.util.LinkedHashMap;
/**
* The KernelPerceptron class implements the kernel version of
* the Perceptron algorithm. That is, it replaces the inner-product used in the
* standard Perceptron algorithm with a kernel method. This allows the
* algorithm to be used with data and a kernel that would map it into a
* high-dimensional space but does not need to since the kernel can compute the
* inner-product in the high-dimensional space without actually creating the
* vectors for it.
*
* @param Input class of the {@code InputOutputPairs}
* @author Justin Basilico
* @since 2.0
* @see Perceptron
*/
@CodeReview(
reviewer="Kevin R. Dixon",
date="2008-07-23",
changesNeeded=false,
comments={
"Added PublicationReference to the original article.",
"Minor changes to javadoc.",
"Looks fine."
}
)
@PublicationReference(
author={
"Yoav Freund",
"Robert E. Schapire"
},
title="Large margin classification using the perceptron algorithm",
publication="Machine Learning",
type=PublicationType.Journal,
year=1999,
notes="Volume 37, Number 3",
pages={277,296},
url="http://www.cs.ucsd.edu/~yfreund/papers/LargeMarginsUsingPerceptron.pdf"
)
public class KernelPerceptron
extends AbstractAnytimeSupervisedBatchLearner>
implements MeasurablePerformanceAlgorithm
{
/** The default maximum number of iterations, {@value}. */
public static final int DEFAULT_MAX_ITERATIONS =
Perceptron.DEFAULT_MAX_ITERATIONS;
/** The default positive margin, {@value}. */
public static final double DEFAULT_MARGIN_POSITIVE =
Perceptron.DEFAULT_MARGIN_POSITIVE;
/** The default negative margin, {@value}. */
public static final double DEFAULT_MARGIN_NEGATIVE =
Perceptron.DEFAULT_MARGIN_NEGATIVE;
/** The kernel to use. */
private Kernel kernel;
/** The positive margin to enforce. */
private double marginPositive;
/** The negative margin to enforce. */
private double marginNegative;
/** The result categorizer. */
private DefaultKernelBinaryCategorizer result;
/** The number of errors on the most recent iteration. */
private int errorCount;
/** The mapping of weight objects to non-zero weighted examples
* (support vectors). */
private LinkedHashMap, DefaultWeightedValue> supportsMap;
/**
* Creates a new instance of KernelPerceptron.
*/
public KernelPerceptron()
{
this(null);
}
/**
* Creates a new KernelPerceptron with the given kernel.
*
* @param kernel The kernel to use.
*/
public KernelPerceptron(
final Kernel kernel)
{
this(kernel, DEFAULT_MAX_ITERATIONS);
}
/**
* Creates a new KernelPerceptron with the given kernel and maximum number
* of iterations.
*
* @param kernel The kernel to use.
* @param maxIterations The maximum number of iterations.
*/
public KernelPerceptron(
final Kernel kernel,
final int maxIterations)
{
this(kernel, maxIterations,
DEFAULT_MARGIN_POSITIVE, DEFAULT_MARGIN_NEGATIVE);
}
/**
* Creates a new KernelPerceptron with the given parameters.
*
* @param kernel The kernel to use.
* @param maxIterations The maximum number of iterations.
* @param marginPositive The positive margin to enforce.
* @param marginNegative The negative margin to enforce.
*/
public KernelPerceptron(
final Kernel kernel,
final int maxIterations,
final double marginPositive,
final double marginNegative)
{
super(maxIterations);
this.setKernel(kernel);
this.setMarginPositive(marginPositive);
this.setMarginNegative(marginNegative);
this.setResult(null);
this.setErrorCount(0);
this.setSupportsMap(null);
}
@Override
protected boolean initializeAlgorithm()
{
if (this.getData() == null)
{
// Error: No data to learn on.
return false;
}
// Count the number of valid examples.
int validCount = 0;
for (InputOutputPair example : this.getData())
{
if (example != null)
{
validCount++;
}
}
if (validCount <= 0)
{
// Nothing to perform learning on.
return false;
}
// Set up the learning variables.
this.setErrorCount(validCount);
this.setSupportsMap(new LinkedHashMap, DefaultWeightedValue>());
this.setResult(new DefaultKernelBinaryCategorizer(
this.getKernel(), this.getSupportsMap().values(), 0.0));
return true;
}
@Override
protected boolean step()
{
// Reset the number of errors for the new iteration.
this.setErrorCount(0);
// Loop over all the training instances.
for (InputOutputPair example : this.getData())
{
if (example == null)
{
continue;
}
// Compute the predicted classification and get the actual
// classification.
final InputType input = example.getInput();
final boolean actual = example.getOutput();
final double prediction = this.result.evaluateAsDouble(input);
if ((actual && prediction <= +this.marginPositive) || (!actual && prediction >= -this.marginNegative))
{
// The classification was incorrect so we need to update
// the perceptron.
this.setErrorCount(this.getErrorCount() + 1);
// We are going to update the weight for this example and the
// global bias.
double weight = 0.0;
double bias = this.result.getBias();
// If the weight exists get it from the support for the
// example.
DefaultWeightedValue support =
this.supportsMap.get(example);
if (support != null)
{
weight = support.getWeight();
}
if (actual)
{
// Update for a positive example so add to the
// weights and the bias.
weight += 1.0;
bias += 1.0;
}
else
{
// Update for a negative example so subtract from
// the weights and the bias.
weight -= 1.0;
bias -= 1.0;
}
if (support == null)
{
// Add a support for this example.
support = new DefaultWeightedValue(input, weight);
this.supportsMap.put(example, support);
}
else if (weight == 0.0)
{
// This example is no longer a support.
this.supportsMap.remove(example);
}
else
{
// Update the weight for the support.
support.setWeight(weight);
}
// Update the bias.
this.result.setBias(bias);
}
// else - The classification was correct, no need to update.
}
// Keep going while the error count is positive.
return this.getErrorCount() > 0;
}
@Override
protected void cleanupAlgorithm()
{
if (this.getSupportsMap() != null)
{
// Make the result object have a more efficient backing collection
// at the end.
this.getResult().setExamples(
new ArrayList>(
this.getSupportsMap().values()));
this.setSupportsMap(null);
}
}
/**
* Gets the kernel to use.
*
* @return The kernel to use.
*/
public Kernel getKernel()
{
return this.kernel;
}
/**
* Sets the kernel to use.
*
* @param kernel The kernel to use.
*/
public void setKernel(
final Kernel kernel)
{
this.kernel = kernel;
}
/**
* Sets both the positive and negative margin to the same value.
*
* @param margin The new value for both the positive and negative margins.
*/
public void setMargin(
final double margin)
{
this.setMarginPositive(margin);
this.setMarginNegative(margin);
}
/**
* Gets the positive margin that is enforced.
*
* @return The positive margin that is enforced.
*/
public double getMarginPositive()
{
return this.marginPositive;
}
/**
* Sets the positive margin that is enforced.
*
* @param marginPositive The positive margin that is enforced.
*/
public void setMarginPositive(
final double marginPositive)
{
this.marginPositive = marginPositive;
}
/**
* Gets the negative margin that is enforced.
*
* @return The negative margin that is enforced.
*/
public double getMarginNegative()
{
return this.marginNegative;
}
/**
* Sets the negative margin that is enforced.
*
* @param marginNegative The negative margin that is enforced.
*/
public void setMarginNegative(
final double marginNegative)
{
this.marginNegative = marginNegative;
}
@Override
public DefaultKernelBinaryCategorizer getResult()
{
return this.result;
}
/**
* Sets the object currently being result.
*
* @param result The object currently being result.
*/
protected void setResult(
final DefaultKernelBinaryCategorizer result)
{
this.result = result;
}
/**
* Gets the error count of the most recent iteration.
*
* @return The current error count.
*/
public int getErrorCount()
{
return this.errorCount;
}
/**
* Sets the error count of the most recent iteration.
*
* @param errorCount The current error count.
*/
protected void setErrorCount(
final int errorCount)
{
this.errorCount = errorCount;
}
/**
* Gets the mapping of examples to weight objects (support vectors).
*
* @return The mapping of examples to weight objects.
*/
protected LinkedHashMap, DefaultWeightedValue> getSupportsMap()
{
return supportsMap;
}
/**
* Gets the mapping of examples to weight objects (support vectors).
*
* @param supportsMap The mapping of examples to weight objects.
*/
protected void setSupportsMap(
final LinkedHashMap, DefaultWeightedValue> supportsMap)
{
this.supportsMap = supportsMap;
}
@Override
public NamedValue getPerformance()
{
return new DefaultNamedValue("error count", this.getErrorCount());
}
}