/*
 *  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.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
 * @version $Id: JMySVMModel.java,v 1.8 2008/05/09 19:23:01 ingomierswa Exp $
 */
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);
	}

	public SVMInterface createSVM() {
		if (getLabel().isNominal())
			return new SVMpattern();
		else
			return new SVMregression();
	}

	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);
		}
	}
}