/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/*
* MultiClassClassifier.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.meta;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.RandomizableSingleClassifierEnhancer;
import weka.classifiers.rules.ZeroR;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.MakeIndicator;
import weka.filters.unsupervised.instance.RemoveWithValues;
/**
<!-- globalinfo-start -->
* A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for increased accuracy.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -M <num>
* Sets the method to use. Valid values are 0 (1-against-all),
* 1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
* </pre>
*
* <pre> -R <num>
* Sets the multiplier when using random codes. (default 2.0)</pre>
*
* <pre> -P
* Use pairwise coupling (only has an effect for 1-against1)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of base classifier.
* (default: weka.classifiers.functions.Logistic)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.functions.Logistic:
* </pre>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -R <ridge>
* Set the ridge in the log-likelihood.</pre>
*
* <pre> -M <number>
* Set the maximum number of iterations (default -1, until convergence).</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Len Trigg (len@reeltwo.com)
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class MultiClassClassifier
extends RandomizableSingleClassifierEnhancer
implements OptionHandler {
/** for serialization */
static final long serialVersionUID = -3879602011542849141L;
/** The classifiers. */
protected Classifier [] m_Classifiers;
/** Use pairwise coupling with 1-vs-1 */
protected boolean m_pairwiseCoupling = false;
/** Needed for pairwise coupling */
protected double [] m_SumOfWeights;
/** The filters used to transform the class. */
protected Filter[] m_ClassFilters;
/** ZeroR classifier for when all base classifier return zero probability. */
private ZeroR m_ZeroR;
/** Internal copy of the class attribute for output purposes */
protected Attribute m_ClassAttribute;
/** A transformed dataset header used by the 1-against-1 method */
protected Instances m_TwoClassDataset;
/**
* The multiplier when generating random codes. Will generate
* numClasses * m_RandomWidthFactor codes
*/
private double m_RandomWidthFactor = 2.0;
/** The multiclass method to use */
protected int m_Method = METHOD_1_AGAINST_ALL;
/** 1-against-all */
public static final int METHOD_1_AGAINST_ALL = 0;
/** random correction code */
public static final int METHOD_ERROR_RANDOM = 1;
/** exhaustive correction code */
public static final int METHOD_ERROR_EXHAUSTIVE = 2;
/** 1-against-1 */
public static final int METHOD_1_AGAINST_1 = 3;
/** The error correction modes */
public static final Tag [] TAGS_METHOD = {
new Tag(METHOD_1_AGAINST_ALL, "1-against-all"),
new Tag(METHOD_ERROR_RANDOM, "Random correction code"),
new Tag(METHOD_ERROR_EXHAUSTIVE, "Exhaustive correction code"),
new Tag(METHOD_1_AGAINST_1, "1-against-1")
};
/**
* Constructor.
*/
public MultiClassClassifier() {
m_Classifier = new weka.classifiers.functions.Logistic();
}
/**
* String describing default classifier.
*
* @return the default classifier classname
*/
protected String defaultClassifierString() {
return "weka.classifiers.functions.Logistic";
}
/**
* Interface for the code constructors
*/
private abstract class Code
implements Serializable, RevisionHandler {
/** for serialization */
static final long serialVersionUID = 418095077487120846L;
/**
* Subclasses must allocate and fill these.
* First dimension is number of codes.
* Second dimension is number of classes.
*/
protected boolean [][]m_Codebits;
/**
* Returns the number of codes.
* @return the number of codes
*/
public int size() {
return m_Codebits.length;
}
/**
* Returns the indices of the values set to true for this code,
* using 1-based indexing (for input to Range).
*
* @param which the index
* @return the 1-based indices
*/
public String getIndices(int which) {
StringBuffer sb = new StringBuffer();
for (int i = 0; i < m_Codebits[which].length; i++) {
if (m_Codebits[which][i]) {
if (sb.length() != 0) {
sb.append(',');
}
sb.append(i + 1);
}
}
return sb.toString();
}
/**
* Returns a human-readable representation of the codes.
* @return a string representation of the codes
*/
public String toString() {
StringBuffer sb = new StringBuffer();
for(int i = 0; i < m_Codebits[0].length; i++) {
for (int j = 0; j < m_Codebits.length; j++) {
sb.append(m_Codebits[j][i] ? " 1" : " 0");
}
sb.append('\n');
}
return sb.toString();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}
/**
* Constructs a code with no error correction
*/
private class StandardCode
extends Code {
/** for serialization */
static final long serialVersionUID = 3707829689461467358L;
/**
* constructor
*
* @param numClasses the number of classes
*/
public StandardCode(int numClasses) {
m_Codebits = new boolean[numClasses][numClasses];
for (int i = 0; i < numClasses; i++) {
m_Codebits[i][i] = true;
}
//System.err.println("Code:\n" + this);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}
/**
* Constructs a random code assignment
*/
private class RandomCode
extends Code {
/** for serialization */
static final long serialVersionUID = 4413410540703926563L;
/** random number generator */
Random r = null;
/**
* constructor
*
* @param numClasses the number of classes
* @param numCodes the number of codes
* @param data the data to use
*/
public RandomCode(int numClasses, int numCodes, Instances data) {
r = data.getRandomNumberGenerator(m_Seed);
numCodes = Math.max(2, numCodes); // Need at least two classes
m_Codebits = new boolean[numCodes][numClasses];
int i = 0;
do {
randomize();
//System.err.println(this);
} while (!good() && (i++ < 100));
//System.err.println("Code:\n" + this);
}
private boolean good() {
boolean [] ninClass = new boolean[m_Codebits[0].length];
boolean [] ainClass = new boolean[m_Codebits[0].length];
for (int i = 0; i < ainClass.length; i++) {
ainClass[i] = true;
}
for (int i = 0; i < m_Codebits.length; i++) {
boolean ninCode = false;
boolean ainCode = true;
for (int j = 0; j < m_Codebits[i].length; j++) {
boolean current = m_Codebits[i][j];
ninCode = ninCode || current;
ainCode = ainCode && current;
ninClass[j] = ninClass[j] || current;
ainClass[j] = ainClass[j] && current;
}
if (!ninCode || ainCode) {
return false;
}
}
for (int j = 0; j < ninClass.length; j++) {
if (!ninClass[j] || ainClass[j]) {
return false;
}
}
return true;
}
/**
* randomizes
*/
private void randomize() {
for (int i = 0; i < m_Codebits.length; i++) {
for (int j = 0; j < m_Codebits[i].length; j++) {
double temp = r.nextDouble();
m_Codebits[i][j] = (temp < 0.5) ? false : true;
}
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}
/*
* TODO: Constructs codes as per:
* Bose, R.C., Ray Chaudhuri (1960), On a class of error-correcting
* binary group codes, Information and Control, 3, 68-79.
* Hocquenghem, A. (1959) Codes corecteurs d'erreurs, Chiffres, 2, 147-156.
*/
//private class BCHCode extends Code {...}
/** Constructs an exhaustive code assignment */
private class ExhaustiveCode
extends Code {
/** for serialization */
static final long serialVersionUID = 8090991039670804047L;
/**
* constructor
*
* @param numClasses the number of classes
*/
public ExhaustiveCode(int numClasses) {
int width = (int)Math.pow(2, numClasses - 1) - 1;
m_Codebits = new boolean[width][numClasses];
for (int j = 0; j < width; j++) {
m_Codebits[j][0] = true;
}
for (int i = 1; i < numClasses; i++) {
int skip = (int) Math.pow(2, numClasses - (i + 1));
for(int j = 0; j < width; j++) {
m_Codebits[j][i] = ((j / skip) % 2 != 0);
}
}
//System.err.println("Code:\n" + this);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// class
result.disableAllClasses();
result.disableAllClassDependencies();
result.enable(Capability.NOMINAL_CLASS);
return result;
}
/**
* Builds the classifiers.
*
* @param insts the training data.
* @throws Exception if a classifier can't be built
*/
public void buildClassifier(Instances insts) throws Exception {
Instances newInsts;
// can classifier handle the data?
getCapabilities().testWithFail(insts);
// zero training instances - could be incremental
boolean zeroTrainingInstances = insts.numInstances() == 0;
// remove instances with missing class
insts = new Instances(insts);
insts.deleteWithMissingClass();
if (m_Classifier == null) {
throw new Exception("No base classifier has been set!");
}
m_ZeroR = new ZeroR();
m_ZeroR.buildClassifier(insts);
m_TwoClassDataset = null;
int numClassifiers = insts.numClasses();
if (numClassifiers <= 2) {
m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, 1);
m_Classifiers[0].buildClassifier(insts);
m_ClassFilters = null;
} else if (m_Method == METHOD_1_AGAINST_1) {
// generate fastvector of pairs
FastVector pairs = new FastVector();
for (int i=0; i<insts.numClasses(); i++) {
for (int j=0; j<insts.numClasses(); j++) {
if (j<=i) continue;
int[] pair = new int[2];
pair[0] = i; pair[1] = j;
pairs.addElement(pair);
}
}
numClassifiers = pairs.size();
m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, numClassifiers);
m_ClassFilters = new Filter[numClassifiers];
m_SumOfWeights = new double[numClassifiers];
// generate the classifiers
for (int i=0; i<numClassifiers; i++) {
RemoveWithValues classFilter = new RemoveWithValues();
classFilter.setAttributeIndex("" + (insts.classIndex() + 1));
classFilter.setModifyHeader(true);
classFilter.setInvertSelection(true);
classFilter.setNominalIndicesArr((int[])pairs.elementAt(i));
Instances tempInstances = new Instances(insts, 0);
tempInstances.setClassIndex(-1);
classFilter.setInputFormat(tempInstances);
newInsts = Filter.useFilter(insts, classFilter);
if (newInsts.numInstances() > 0 || zeroTrainingInstances) {
newInsts.setClassIndex(insts.classIndex());
m_Classifiers[i].buildClassifier(newInsts);
m_ClassFilters[i] = classFilter;
m_SumOfWeights[i] = newInsts.sumOfWeights();
} else {
m_Classifiers[i] = null;
m_ClassFilters[i] = null;
}
}
// construct a two-class header version of the dataset
m_TwoClassDataset = new Instances(insts, 0);
int classIndex = m_TwoClassDataset.classIndex();
m_TwoClassDataset.setClassIndex(-1);
m_TwoClassDataset.deleteAttributeAt(classIndex);
FastVector classLabels = new FastVector();
classLabels.addElement("class0");
classLabels.addElement("class1");
m_TwoClassDataset.insertAttributeAt(new Attribute("class", classLabels),
classIndex);
m_TwoClassDataset.setClassIndex(classIndex);
} else { // use error correcting code style methods
Code code = null;
switch (m_Method) {
case METHOD_ERROR_EXHAUSTIVE:
code = new ExhaustiveCode(numClassifiers);
break;
case METHOD_ERROR_RANDOM:
code = new RandomCode(numClassifiers,
(int)(numClassifiers * m_RandomWidthFactor),
insts);
break;
case METHOD_1_AGAINST_ALL:
code = new StandardCode(numClassifiers);
break;
default:
throw new Exception("Unrecognized correction code type");
}
numClassifiers = code.size();
m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, numClassifiers);
m_ClassFilters = new MakeIndicator[numClassifiers];
for (int i = 0; i < m_Classifiers.length; i++) {
m_ClassFilters[i] = new MakeIndicator();
MakeIndicator classFilter = (MakeIndicator) m_ClassFilters[i];
classFilter.setAttributeIndex("" + (insts.classIndex() + 1));
classFilter.setValueIndices(code.getIndices(i));
classFilter.setNumeric(false);
classFilter.setInputFormat(insts);
newInsts = Filter.useFilter(insts, m_ClassFilters[i]);
m_Classifiers[i].buildClassifier(newInsts);
}
}
m_ClassAttribute = insts.classAttribute();
}
/**
* Returns the individual predictions of the base classifiers
* for an instance. Used by StackedMultiClassClassifier.
* Returns the probability for the second "class" predicted
* by each base classifier.
*
* @param inst the instance to get the prediction for
* @return the individual predictions
* @throws Exception if the predictions can't be computed successfully
*/
public double[] individualPredictions(Instance inst) throws Exception {
double[] result = null;
if (m_Classifiers.length == 1) {
result = new double[1];
result[0] = m_Classifiers[0].distributionForInstance(inst)[1];
} else {
result = new double[m_ClassFilters.length];
for(int i = 0; i < m_ClassFilters.length; i++) {
if (m_Classifiers[i] != null) {
if (m_Method == METHOD_1_AGAINST_1) {
Instance tempInst = (Instance)inst.copy();
tempInst.setDataset(m_TwoClassDataset);
result[i] = m_Classifiers[i].distributionForInstance(tempInst)[1];
} else {
m_ClassFilters[i].input(inst);
m_ClassFilters[i].batchFinished();
result[i] = m_Classifiers[i].
distributionForInstance(m_ClassFilters[i].output())[1];
}
}
}
}
return result;
}
/**
* Returns the distribution for an instance.
*
* @param inst the instance to get the distribution for
* @return the distribution
* @throws Exception if the distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance inst) throws Exception {
if (m_Classifiers.length == 1) {
return m_Classifiers[0].distributionForInstance(inst);
}
double[] probs = new double[inst.numClasses()];
if (m_Method == METHOD_1_AGAINST_1) {
double[][] r = new double[inst.numClasses()][inst.numClasses()];
double[][] n = new double[inst.numClasses()][inst.numClasses()];
for(int i = 0; i < m_ClassFilters.length; i++) {
if (m_Classifiers[i] != null) {
Instance tempInst = (Instance)inst.copy();
tempInst.setDataset(m_TwoClassDataset);
double [] current = m_Classifiers[i].distributionForInstance(tempInst);
Range range = new Range(((RemoveWithValues)m_ClassFilters[i])
.getNominalIndices());
range.setUpper(m_ClassAttribute.numValues());
int[] pair = range.getSelection();
if (m_pairwiseCoupling && inst.numClasses() > 2) {
r[pair[0]][pair[1]] = current[0];
n[pair[0]][pair[1]] = m_SumOfWeights[i];
} else {
if (current[0] > current[1]) {
probs[pair[0]] += 1.0;
} else {
probs[pair[1]] += 1.0;
}
}
}
}
if (m_pairwiseCoupling && inst.numClasses() > 2) {
return pairwiseCoupling(n, r);
}
} else {
// error correcting style methods
for(int i = 0; i < m_ClassFilters.length; i++) {
m_ClassFilters[i].input(inst);
m_ClassFilters[i].batchFinished();
double [] current = m_Classifiers[i].
distributionForInstance(m_ClassFilters[i].output());
for (int j = 0; j < m_ClassAttribute.numValues(); j++) {
if (((MakeIndicator)m_ClassFilters[i]).getValueRange().isInRange(j)) {
probs[j] += current[1];
} else {
probs[j] += current[0];
}
}
}
}
if (Utils.gr(Utils.sum(probs), 0)) {
Utils.normalize(probs);
return probs;
} else {
return m_ZeroR.distributionForInstance(inst);
}
}
/**
* Prints the classifiers.
*
* @return a string representation of the classifier
*/
public String toString() {
if (m_Classifiers == null) {
return "MultiClassClassifier: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("MultiClassClassifier\n\n");
for (int i = 0; i < m_Classifiers.length; i++) {
text.append("Classifier ").append(i + 1);
if (m_Classifiers[i] != null) {
if ((m_ClassFilters != null) && (m_ClassFilters[i] != null)) {
if (m_ClassFilters[i] instanceof RemoveWithValues) {
Range range = new Range(((RemoveWithValues)m_ClassFilters[i])
.getNominalIndices());
range.setUpper(m_ClassAttribute.numValues());
int[] pair = range.getSelection();
text.append(", " + (pair[0]+1) + " vs " + (pair[1]+1));
} else if (m_ClassFilters[i] instanceof MakeIndicator) {
text.append(", using indicator values: ");
text.append(((MakeIndicator)m_ClassFilters[i]).getValueRange());
}
}
text.append('\n');
text.append(m_Classifiers[i].toString() + "\n\n");
} else {
text.append(" Skipped (no training examples)\n");
}
}
return text.toString();
}
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Vector vec = new Vector(4);
vec.addElement(new Option(
"\tSets the method to use. Valid values are 0 (1-against-all),\n"
+"\t1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)\n",
"M", 1, "-M <num>"));
vec.addElement(new Option(
"\tSets the multiplier when using random codes. (default 2.0)",
"R", 1, "-R <num>"));
vec.addElement(new Option(
"\tUse pairwise coupling (only has an effect for 1-against1)",
"P", 0, "-P"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
vec.addElement(enu.nextElement());
}
return vec.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -M <num>
* Sets the method to use. Valid values are 0 (1-against-all),
* 1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)
* </pre>
*
* <pre> -R <num>
* Sets the multiplier when using random codes. (default 2.0)</pre>
*
* <pre> -P
* Use pairwise coupling (only has an effect for 1-against1)</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of base classifier.
* (default: weka.classifiers.functions.Logistic)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.functions.Logistic:
* </pre>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -R <ridge>
* Set the ridge in the log-likelihood.</pre>
*
* <pre> -M <number>
* Set the maximum number of iterations (default -1, until convergence).</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String errorString = Utils.getOption('M', options);
if (errorString.length() != 0) {
setMethod(new SelectedTag(Integer.parseInt(errorString),
TAGS_METHOD));
} else {
setMethod(new SelectedTag(METHOD_1_AGAINST_ALL, TAGS_METHOD));
}
String rfactorString = Utils.getOption('R', options);
if (rfactorString.length() != 0) {
setRandomWidthFactor((new Double(rfactorString)).doubleValue());
} else {
setRandomWidthFactor(2.0);
}
setUsePairwiseCoupling(Utils.getFlag('P', options));
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] superOptions = super.getOptions();
String [] options = new String [superOptions.length + 5];
int current = 0;
options[current++] = "-M";
options[current++] = "" + m_Method;
if (getUsePairwiseCoupling()) {
options[current++] = "-P";
}
options[current++] = "-R";
options[current++] = "" + m_RandomWidthFactor;
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
current += superOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "A metaclassifier for handling multi-class datasets with 2-class "
+ "classifiers. This classifier is also capable of "
+ "applying error correcting output codes for increased accuracy.";
}
/**
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String randomWidthFactorTipText() {
return "Sets the width multiplier when using random codes. The number "
+ "of codes generated will be thus number multiplied by the number of "
+ "classes.";
}
/**
* Gets the multiplier when generating random codes. Will generate
* numClasses * m_RandomWidthFactor codes.
*
* @return the width multiplier
*/
public double getRandomWidthFactor() {
return m_RandomWidthFactor;
}
/**
* Sets the multiplier when generating random codes. Will generate
* numClasses * m_RandomWidthFactor codes.
*
* @param newRandomWidthFactor the new width multiplier
*/
public void setRandomWidthFactor(double newRandomWidthFactor) {
m_RandomWidthFactor = newRandomWidthFactor;
}
/**
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String methodTipText() {
return "Sets the method to use for transforming the multi-class problem into "
+ "several 2-class ones.";
}
/**
* Gets the method used. Will be one of METHOD_1_AGAINST_ALL,
* METHOD_ERROR_RANDOM, METHOD_ERROR_EXHAUSTIVE, or METHOD_1_AGAINST_1.
*
* @return the current method.
*/
public SelectedTag getMethod() {
return new SelectedTag(m_Method, TAGS_METHOD);
}
/**
* Sets the method used. Will be one of METHOD_1_AGAINST_ALL,
* METHOD_ERROR_RANDOM, METHOD_ERROR_EXHAUSTIVE, or METHOD_1_AGAINST_1.
*
* @param newMethod the new method.
*/
public void setMethod(SelectedTag newMethod) {
if (newMethod.getTags() == TAGS_METHOD) {
m_Method = newMethod.getSelectedTag().getID();
}
}
/**
* Set whether to use pairwise coupling with 1-vs-1
* classification to improve probability estimates.
*
* @param p true if pairwise coupling is to be used
*/
public void setUsePairwiseCoupling(boolean p) {
m_pairwiseCoupling = p;
}
/**
* Gets whether to use pairwise coupling with 1-vs-1
* classification to improve probability estimates.
*
* @return true if pairwise coupling is to be used
*/
public boolean getUsePairwiseCoupling() {
return m_pairwiseCoupling;
}
/**
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String usePairwiseCouplingTipText() {
return "Use pairwise coupling (only has an effect for 1-against-1).";
}
/**
* Implements pairwise coupling.
*
* @param n the sum of weights used to train each model
* @param r the probability estimate from each model
* @return the coupled estimates
*/
public static double[] pairwiseCoupling(double[][] n, double[][] r) {
// Initialize p and u array
double[] p = new double[r.length];
for (int i =0; i < p.length; i++) {
p[i] = 1.0 / (double)p.length;
}
double[][] u = new double[r.length][r.length];
for (int i = 0; i < r.length; i++) {
for (int j = i + 1; j < r.length; j++) {
u[i][j] = 0.5;
}
}
// firstSum doesn't change
double[] firstSum = new double[p.length];
for (int i = 0; i < p.length; i++) {
for (int j = i + 1; j < p.length; j++) {
firstSum[i] += n[i][j] * r[i][j];
firstSum[j] += n[i][j] * (1 - r[i][j]);
}
}
// Iterate until convergence
boolean changed;
do {
changed = false;
double[] secondSum = new double[p.length];
for (int i = 0; i < p.length; i++) {
for (int j = i + 1; j < p.length; j++) {
secondSum[i] += n[i][j] * u[i][j];
secondSum[j] += n[i][j] * (1 - u[i][j]);
}
}
for (int i = 0; i < p.length; i++) {
if ((firstSum[i] == 0) || (secondSum[i] == 0)) {
if (p[i] > 0) {
changed = true;
}
p[i] = 0;
} else {
double factor = firstSum[i] / secondSum[i];
double pOld = p[i];
p[i] *= factor;
if (Math.abs(pOld - p[i]) > 1.0e-3) {
changed = true;
}
}
}
Utils.normalize(p);
for (int i = 0; i < r.length; i++) {
for (int j = i + 1; j < r.length; j++) {
u[i][j] = p[i] / (p[i] + p[j]);
}
}
} while (changed);
return p;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new MultiClassClassifier(), argv);
}
}