/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.functions.kernel;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.Kernel;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.svm.SVMInterface;
import com.rapidminer.operator.learner.functions.kernel.logistic.KLR;
/**
* The model for the MyKLR learner by Stefan Rueping.
*
* @author Ingo Mierswa
* @version $Id: MyKLRModel.java,v 1.9 2008/05/09 19:23:01 ingomierswa Exp $
*/
public class MyKLRModel extends AbstractMySVMModel {
private static final long serialVersionUID = 8033254475867697195L;
public MyKLRModel(ExampleSet exampleSet, com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples model, Kernel kernel, int kernelType) {
super(exampleSet, model, kernel, kernelType);
}
public String getModelInfo() {
return
"KLR Model (" + getNumberOfSupportVectors() + " support vectors)";
}
public SVMInterface createSVM() {
return new KLR();
}
public void setPrediction(Example example, double _prediction) {
double prediction = _prediction - 0.5d;
Attribute predLabel = example.getAttributes().getPredictedLabel();
int index = prediction > 0.0 ? predLabel.getMapping().getPositiveIndex() : predLabel.getMapping().getNegativeIndex();
example.setValue(predLabel, index);
example.setConfidence(predLabel.getMapping().getPositiveString(), 1.0d / (1.0d + java.lang.Math.exp(-prediction)));
example.setConfidence(predLabel.getMapping().getNegativeString(), 1.0d / (1.0d + java.lang.Math.exp(prediction)));
}
}