weka.classifiers.IteratedSingleClassifierEnhancer Maven / Gradle / Ivy
/*
* 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.
*/
/*
* IteratedSingleClassifierEnhancer.java
* Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers;
import weka.core.Instances;
import weka.core.Option;
import weka.core.Utils;
import java.util.Enumeration;
import java.util.Vector;
/**
* Abstract utility class for handling settings common to
* meta classifiers that build an ensemble from a single base learner.
*
* @author Eibe Frank ([email protected])
* @version $Revision: 1.4 $
*/
public abstract class IteratedSingleClassifierEnhancer
extends SingleClassifierEnhancer {
/** for serialization */
private static final long serialVersionUID = -6217979135443319724L;
/** Array for storing the generated base classifiers. */
protected Classifier[] m_Classifiers;
/** The number of iterations. */
protected int m_NumIterations = 10;
/**
* Stump method for building the classifiers.
*
* @param data the training data to be used for generating the
* bagged classifier.
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifier == null) {
throw new Exception("A base classifier has not been specified!");
}
m_Classifiers = Classifier.makeCopies(m_Classifier, m_NumIterations);
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(2);
newVector.addElement(new Option(
"\tNumber of iterations.\n"
+ "\t(default 10)",
"I", 1, "-I "));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:
*
* -W classname
* Specify the full class name of the base learner.
*
* -I num
* Set the number of iterations (default 10).
*
* Options after -- are passed to the designated classifier.
*
* @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 iterations = Utils.getOption('I', options);
if (iterations.length() != 0) {
setNumIterations(Integer.parseInt(iterations));
} else {
setNumIterations(10);
}
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 + 2];
int current = 0;
options[current++] = "-I";
options[current++] = "" + getNumIterations();
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
return options;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numIterationsTipText() {
return "The number of iterations to be performed.";
}
/**
* Sets the number of bagging iterations
*/
public void setNumIterations(int numIterations) {
m_NumIterations = numIterations;
}
/**
* Gets the number of bagging iterations
*
* @return the maximum number of bagging iterations
*/
public int getNumIterations() {
return m_NumIterations;
}
}