thresholdapplier.java

一个很好的LIBSVM的JAVA源码。对于要研究和改进SVM算法的学者。可以参考。来自数据挖掘工具YALE工具包。

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2001-2004
 *      Simon Fischer, Ralf Klinkenberg, Ingo Mierswa, 
 *          Katharina Morik, Oliver Ritthoff
 *      Artificial Intelligence Unit
 *      Computer Science Department
 *      University of Dortmund
 *      44221 Dortmund,  Germany
 *  email: [e-mail]
 *  web:   http://yale.cs.uni-dortmund.de/
 *
 *  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., 59 Temple Place, Suite 330, Boston, MA 02111-1307
 *  USA.
 */
package edu.udo.cs.yale.operator;

import java.util.Arrays;
import java.util.List;

import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleReader;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.tools.Ontology;

/** This operator applies the given threshold to an example set and maps a soft prediction to crisp values.
 *
 *  @version $Id: ThresholdApplier.java,v 2.5 2004/09/09 12:00:52 ingomierswa Exp $ 
 */
public class ThresholdApplier extends Operator {

    public Class[] getInputClasses() {	return new Class[] { ExampleSet.class, Threshold.class }; }

    public Class[] getOutputClasses() {	return new Class[] { ExampleSet.class }; }

    public IOObject[] apply() throws OperatorException {
	ExampleSet exampleSet = (ExampleSet)getInput(ExampleSet.class);
	Threshold threshold   = (Threshold)getInput(Threshold.class);
	
	
	Attribute predictedLabel = exampleSet.getPredictedLabel();
	if (predictedLabel == null)
	    throw new UserError(this, 107);

	predictedLabel.setValueType(Ontology.NOMINAL);
	int zeroIndex = predictedLabel.mapString(threshold.getZeroClass());
	int oneIndex  = predictedLabel.mapString(threshold.getOneClass());

	ExampleReader reader = exampleSet.getExampleReader();
	while (reader.hasNext()) {
	    Example example = reader.next();
	    double softPrediction = example.getValue(predictedLabel);
	    double crispPrediction = softPrediction <= threshold.getThreshold() ? zeroIndex : oneIndex;  // IM: kleiner oder kleiner gleich?
	    example.setValue(predictedLabel, crispPrediction);
	}

	exampleSet.recalculateAttributeStatistics(predictedLabel);

	return new IOObject[] { exampleSet };
    }	
}