learnerfeaturegeneration.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.features;

import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.operator.OperatorChain;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.IOObject;
import edu.udo.cs.yale.operator.IODescription;
import edu.udo.cs.yale.operator.IOContainer;
import edu.udo.cs.yale.operator.IllegalInputException;
import edu.udo.cs.yale.operator.learner.Model;
import edu.udo.cs.yale.example.ExampleSet;

import java.util.List;

/** This operator uses its inner learners for model creation. A new attribute for each model is created.
 *  This operator replaces the ModelContainerLearner of Yale 2.2.
 *
 *  @version $Id: LearnerFeatureGeneration.java,v 2.3 2004/09/09 12:00:52 ingomierswa Exp $
 */
public class LearnerFeatureGeneration extends OperatorChain {

    private static final Class[]  INPUT_CLASSES  = { ExampleSet.class };    
    private static final Class[]  OUTPUT_CLASSES = { ExampleSet.class };


    public IOObject[] apply() throws OperatorException {
	ExampleSet exampleSet = (ExampleSet)getInput(ExampleSet.class);


	Model[] models = new Model[getNumberOfOperators()];

	for (int i = 0; i < models.length; i++) {
	    IOContainer learnResult = getOperator(i).apply(getInput().append(new IOObject[] { exampleSet }));
	    models[i] = (Model)learnResult.getInput(Model.class);
	}

	applyModels(exampleSet, models, getParameterAsBoolean("keep_all"));

	return new IOObject[] { exampleSet };
    }


    private void applyModels(ExampleSet exampleSet, Model[] models, boolean keepAll) throws OperatorException {
	ExampleSet[] eSet   = new ExampleSet[models.length];
	
	for (int i = 0; i < models.length; i++) {
	    Model model = models[i];
	    eSet[i] = (ExampleSet)exampleSet.clone();
	    model.createPredictedLabel(eSet[i], model.getName()+"_"+i);
	    model.apply(eSet[i]);
	}

	// remove old attributes ?
	if (!keepAll) {
	    exampleSet.removeAllAttributes();
	}

	// add the predictions as normal attribute to the original example set
	for (int i = 0; i < models.length; i++)
	    exampleSet.addAttribute(eSet[i].getPredictedLabel());

	exampleSet.recalculateAllAttributeStatistics();
    }


    // ================================================================================

    public Class[] getInputClasses() { return INPUT_CLASSES; }

    public Class[] getOutputClasses() { return OUTPUT_CLASSES; }

    /** Ok if the only inner operator returns a model. */
    public Class[] checkIO(Class[] input) throws IllegalInputException {
	for (int i = 0; i < getNumberOfOperators(); i++) {
	    Class[] innerResult = getOperator(i).checkIO(input);
	    if (!IODescription.containsClass(Model.class, innerResult))
		throw new IllegalInputException(this, getOperator(0), Model.class);
	}
	return OUTPUT_CLASSES;
    }

    /** Returns the maximum number of innner operators. */
    public int getMinNumberOfInnerOperators() { return 1; }
    /** Returns the minimum number of innner operators. */
    public int getMaxNumberOfInnerOperators() { return Integer.MAX_VALUE; }

    public int getNumberOfSteps() {
	return getNumberOfChildrensSteps();
    }


    public List getParameterTypes() {
	List types = super.getParameterTypes();
	ParameterType type = new ParameterTypeBoolean("keep_all", "Indicates if the original attributes should be deleted during applying the model.", false);
	type.setExpert(false);
	types.add(type);
	return types;
    }
}