multimodelbyregression.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.learner.meta;

import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.learner.IOModel;
import edu.udo.cs.yale.operator.learner.Model;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.ExampleReader;
import edu.udo.cs.yale.tools.LogService;

import java.io.*;

/** MultiModels are used for multi class learning tasks. A MultiModel contains a set of Models that can 
 *  handle only two-class decisions. */
public class MultiModelByRegression extends IOModel {

    public static final String ID = "YALE MultiModel";

    private static final int FILE_MODEL = 1;
    private static final int IO_MODEL = 2;

    private Model[] models;

    public MultiModelByRegression(Attribute label, Model[] models) {
	super(label);
	this.models = models;
    }

    public int getNumberOfModels() {
	return models.length;
    }

    /** Returns a binary decision model for the given classification index. */
    public Model getModel(int index) {
	return models[index];
    }

    /** Iterates over all classes of the label and applies one model for each class. For each example
     *  the predicted label is determined by choosing the model with the highest confidence. */
    public void apply(ExampleSet exampleSet) throws OperatorException {

	ExampleSet[] eSet   = new ExampleSet[getNumberOfModels()];
	
	for (int i = 0; i < getNumberOfModels(); i++) {
	    Model model = getModel(i);
	    eSet[i] = (ExampleSet)exampleSet.clone();
	    Attribute predLabel = model.createPredictedLabel(eSet[i]);
	    model.apply(eSet[i]);
	}

	ExampleReader[] reader = new ExampleReader[eSet.length];
	for (int r = 0; r < reader.length; r++) 
	    reader[r] = eSet[r].getExampleReader();


	ExampleReader originalReader = exampleSet.getExampleReader();
	while (originalReader.hasNext()) {
	    double bestLabel = Double.NaN;
	    double highestFunctionValue = Double.NEGATIVE_INFINITY;
	    for (int k = 0; k < reader.length; k++) { 
		double functionValue = reader[k].next().getPredictedLabel();
		if (functionValue > highestFunctionValue) {
		    highestFunctionValue = functionValue;
		    bestLabel = k + Attribute.FIRST_CLASS_INDEX;
		}
	    }
	    originalReader.next().setPredictedLabel(bestLabel);
	}
    }

    /** Writes the models subsequently to the output stream. */
    public void writeData(ObjectOutputStream out) throws IOException {
	out.writeInt(models.length);
	for (int i = 0; i < models.length; i++) {
	    models[i].writeModel(out);
	}
    }

    /** Reads all models from the file. */
    public void readData(ObjectInputStream in) throws IOException {

	this.models = new Model[in.readInt()];
	for (int i = 0; i < models.length; i++) {
	    models[i] = Model.readModel(in);
	}
    }

    public String getIdentifier() { return ID; }

    public String toString() {
	String result = super.toString() + "\n";
	for (int i = 0; i < models.length; i++) 
	    result += (i>0?"\n":"") + models[i].toString();
	return result;
    }
}