/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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.jmysvm.svm.SVMpattern;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.svm.SVMregression;
/**
* The implementation for the mySVM model (Java version) by Stefan Rueping.
*
* @author Ingo Mierswa
*/
public class JMySVMModel extends AbstractMySVMModel {
private static final long serialVersionUID = 7748169156351553025L;
public JMySVMModel(ExampleSet exampleSet,
com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples model, Kernel kernel, int kernelType) {
super(exampleSet, model, kernel, kernelType);
}
@Override
public SVMInterface createSVM() {
if (getLabel().isNominal()) {
return new SVMpattern();
} else {
return new SVMregression();
}
}
@Override
public void setPrediction(Example example, double prediction) {
Attribute predLabel = example.getAttributes().getPredictedLabel();
if (predLabel.isNominal()) {
int index = prediction > 0 ? predLabel.getMapping().getPositiveIndex() : predLabel.getMapping()
.getNegativeIndex();
example.setValue(predLabel, index);
// set confidence to numerical prediction, such that can be scaled later
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)));
} else {
example.setValue(predLabel, prediction);
}
}
}