/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 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.logistic.KLR;


/**
 * The model for the MyKLR learner by Stefan Rueping.
 * 
 * @author Ingo Mierswa
 */
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)";
    }

	@Override
	public SVMInterface createSVM() {
		return new KLR();
	}

	@Override
	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)));
	}
}