/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* DistributionMetaClassifier.java
* Copyright (C) 2002 Richard Kirkby
*
*/
package weka.classifiers.meta;
import weka.classifiers.DistributionClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.*;
import java.util.Enumeration;
import java.util.Vector;
/**
* Class for wrapping a Classifier to make it return a distribution. Simply outputs
* a probabiltry of 1 for the predicted class and 0 for the others.
*
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class DistributionMetaClassifier extends DistributionClassifier
implements OptionHandler {
/** The classifier being wrapped */
private Classifier m_wrappedClassifier = new weka.classifiers.rules.ZeroR();
/**
* Default constructor.
*
*/
public DistributionMetaClassifier() {
}
/**
* Contructs a DistributionMetaClassifier wrapping a given Classifier.
*
* @param toWrap the classifier to wrap around
*/
public DistributionMetaClassifier(Classifier toWrap) {
setClassifier(toWrap);
}
/**
* Builds a classifier for a set of instances.
*
* @param instances the instances to train the classifier with
* @exception Exception if the classifier hasn't been set or something goes wrong
*/
public void buildClassifier(Instances data) throws Exception {
if (m_wrappedClassifier == null) {
throw new Exception("No classifier has been set");
}
m_wrappedClassifier.buildClassifier(data);
}
/**
* Returns the class probability distribution for an instance. Will simply have a
* probability of 1 for the predicted class and 0 for the others.
*
* @param instance the instance to be classified
* @return the probability distribution
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double predictedClass = m_wrappedClassifier.classifyInstance(instance);
double[] distribution = new double[instance.numClasses()];
if (!Instance.isMissingValue(predictedClass)) {
if (instance.classAttribute().type() == Attribute.NOMINAL) {
distribution[(int) predictedClass] = 1.0;
} else {
distribution[0] = predictedClass;
}
}
return distribution;
}
/**
* Returns a description of the classifier.
*
* @return a string containing a description of the classifier
*/
public String toString() {
return "DistributionMetaClassifier: " + m_wrappedClassifier.toString();
}
/**
* Sets the classifier to wrap.
*
* @param toWrap the classifier
*/
public void setClassifier(Classifier toWrap) {
m_wrappedClassifier = toWrap;
}
/**
* Gets the classifier being wrapped.
*
* @return the classifier
*/
public Classifier getClassifier() {
return m_wrappedClassifier;
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(1);
newVector.addElement(new Option(
"\tClassifier to wrap. (required)\n",
"W", 1,"-W <classifier name>"));
if ((m_wrappedClassifier != null) &&
(m_wrappedClassifier instanceof OptionHandler)) {
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to classifier "
+ m_wrappedClassifier.getClass().getName() + ":"));
Enumeration enum = ((OptionHandler)m_wrappedClassifier).listOptions();
while (enum.hasMoreElements()) {
newVector.addElement(enum.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -W classifier name <br>
* Classifier to wrap. (required) <p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String wString = Utils.getOption('W', options);
if (wString.length() != 0) {
setClassifier(Classifier.forName(wString,
Utils.partitionOptions(options)));
} else {
throw new Exception("A classifier must be specified with the -W option.");
}
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions() {
String [] classifierOptions = new String [0];
if ((m_wrappedClassifier != null) &&
(m_wrappedClassifier instanceof OptionHandler)) {
classifierOptions = ((OptionHandler)m_wrappedClassifier).getOptions();
}
String [] options = new String [classifierOptions.length + 3];
int current = 0;
if (getClassifier() != null) {
options[current++] = "-W";
options[current++] = getClassifier().getClass().getName();
}
options[current++] = "--";
System.arraycopy(classifierOptions, 0, options, current,
classifierOptions.length);
current += classifierOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
try {
System.out.println(Evaluation.evaluateModel(new DistributionMetaClassifier(),
argv));
} catch (Exception e) {
System.err.println(e.getMessage());
}
}
}