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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 -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)
*
*
* -R <num>
* Sets the multiplier when using random codes. (default 2.0)
*
* -P
* Use pairwise coupling (only has an effect for 1-against1)
*
* -S <num>
* Random number seed.
* (default 1)
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
* -W
* Full name of base classifier.
* (default: weka.classifiers.functions.SGD)
*
*
* @author Eibe Frank ([email protected])
* @author Len Trigg ([email protected])
* @author Richard Kirkby ([email protected])
* @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
*
* @version $Revision: 9248 $
*/
public class MultiClassClassifierUpdateable extends MultiClassClassifier
implements OptionHandler, UpdateableClassifier {
/** For serialization */
private static final long serialVersionUID = -1619685269774366430L;
/**
* Constructor
*/
public MultiClassClassifierUpdateable() {
m_Classifier = new weka.classifiers.functions.SGD();
}
/**
* @return a description of the classifier suitable for displaying in the
* explorer/experimenter gui
*/
@Override
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. "
+ "The base classifier must be an updateable classifier";
}
@Override
public void buildClassifier(Instances insts) throws Exception {
if (m_Classifier == null) {
throw new Exception("No base classifier has been set!");
}
if (!(m_Classifier instanceof UpdateableClassifier)) {
throw new Exception("Base classifier must be updateable!");
}
super.buildClassifier(insts);
}
/**
* Updates the classifier with the given instance.
*
* @param instance the new training instance to include in the model
* @exception Exception if the instance could not be incorporated in the
* model.
*/
@Override
public void updateClassifier(Instance instance) throws Exception {
if (!instance.classIsMissing()) {
if (m_Classifiers.length == 1) {
((UpdateableClassifier) m_Classifiers[0]).updateClassifier(instance);
return;
}
for (int i = 0; i < m_Classifiers.length; i++) {
if (m_Classifiers[i] != null) {
m_ClassFilters[i].input(instance);
Instance converted = m_ClassFilters[i].output();
if (converted != null) {
converted.dataset().setClassIndex(m_ClassAttribute.index());
((UpdateableClassifier) m_Classifiers[i])
.updateClassifier(converted);
if (m_Method == METHOD_1_AGAINST_1) {
m_SumOfWeights[i] += converted.weight();
}
}
}
}
}
}
/**
* 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
*/
@Override
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 && m_SumOfWeights[i] > 0) {
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) {
try {
return pairwiseCoupling(n, r);
} catch (IllegalArgumentException ex) {
}
}
if (Utils.gr(Utils.sum(probs), 0)) {
Utils.normalize(probs);
}
return probs;
} else {
probs = super.distributionForInstance(inst);
}
/*
* if (probs.length == 1) { // ZeroR made the prediction return new
* double[m_ClassAttribute.numValues()]; }
*/
return probs;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 9248 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String[] argv) {
runClassifier(new MultiClassClassifierUpdateable(), argv);
}
}