defaultlearner.java

著名的开源仿真软件yale

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2002, 2003
 *      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;

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.BatchedExampleSet;
import edu.udo.cs.yale.example.ExampleReader;
import edu.udo.cs.yale.example.SkipNANExampleReader;
import edu.udo.cs.yale.operator.Operator;
import edu.udo.cs.yale.operator.UserError;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.FatalException;
import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.operator.learner.Learner;
import edu.udo.cs.yale.operator.learner.Model;
import edu.udo.cs.yale.operator.performance.PerformanceVector;
import edu.udo.cs.yale.operator.performance.EstimatedPerformance;
import edu.udo.cs.yale.tools.ParameterService;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.tools.TempFileService;
import edu.udo.cs.yale.tools.Tools;
import edu.udo.cs.yale.tools.OutputStreamMultiplier;
import edu.udo.cs.yale.tools.Ontology;

import java.util.List;
import java.util.LinkedList;
import java.util.StringTokenizer;
import java.util.ArrayList;
import java.util.Arrays;
import java.io.*;


/** This learner creates a model, that will simply predict a default value for all examples,
 *  i.e. the average or median of the true labels or a fixed specified value.
 *  This learner can be used to compare the results of "real" learning schemes with
 *  guessing.
 *
 *  @yale.xmlclass DefaultLearner
 *  @yale.index DefaultLearner
 *  @author  Stefan R黳ing
 *  @version $Id: DefaultLearner.java,v 2.4 2003/08/14 10:24:57 fischer Exp $
 *  @see     edu.udo.cs.yale.operator.learner.DefaultModel
 *  @see     edu.udo.cs.yale.example.ExampleSet
 *  @yale.todo H鋟figste Klasse f黵 Klassifikation zuf黦en, in Datei speichern, imports aufr鋟men (kopiert von MySVMLearner
 */
public class  DefaultLearner  extends Learner {


    /** Kernel parameters of the mySVM. */
    private static final String[]  METHODS = { "median","average","constant" };


    public Model  learn(ExampleSet exampleSet)  throws OperatorException {
	double value = 0.0;
	String param = getParameterAsString("method");
	int labelIndex = exampleSet.getLabel().getIndex();
	if(param.equals("median")){
	    double[] labels = new double[exampleSet.getSize()];
	    ExampleReader  r = new SkipNANExampleReader(exampleSet.getExampleReader());
	    int  numberOfExamples = 0;
	    while (r.hasNext()) {
		// ---- read next example ----
		Example  example = (Example)r.next();
		labels[numberOfExamples] = example.getLabel();
		numberOfExamples++;
	    };
	    java.util.Arrays.sort(labels,0,numberOfExamples-1);
	    value = labels[numberOfExamples/2];
	}
	else if(param.equals("average")){
	    ExampleReader  r = new SkipNANExampleReader(exampleSet.getExampleReader());
	    int  numberOfExamples = 0;
	    while (r.hasNext()) {
		// ---- read next example ----
		Example  example = (Example)r.next();
		numberOfExamples++;
		value += example.getLabel();
	    };
	    value /= (double)numberOfExamples;
	}
	else if(param.equals("constant")){
	    try{
		value = getParameterAsDouble("value");
	    }
	    catch(Exception e){
		LogService.logMessage("No constant set.",LogService.ERROR);
	    };
	}
	else{
	    LogService.logMessage("No method set.",LogService.ERROR);
	};
	LogService.logMessage("Default value is "+value,LogService.TASK);
	return new DefaultModel(value);
    }

    /** sepcifies the parameters of the <tt>MySVMLearner</tt>, their types, 
     *  their default values, and descriptions of them.
     */
    public List getParameterTypes() {
	List types = super.getParameterTypes();

	types.add(new ParameterTypeStringCategory("method", "The method to compute the default.",
						  METHODS));
	types.add(new ParameterTypeDouble("constant", "Value returned when method = \"constant\".", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY,0.0));

	return types;
    }


}