/*
 *  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;

import java.util.Iterator;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;


/**
 * <p>A model that can be applied to an example set by applying it to each example
 * separately. In contrast to the usual {@link com.rapidminer.operator.learner.SimplePredictionModel},
 * this model should only be used for binary classification problems. The method {@link #predict(Example)}
 * must deliver a function value. A value greater than 0 will be mapped to the positive class, a value
 * smaller than 0 to the negative class index. The confidence values will be calculated by the fast 
 * and simple scaling suggested by Rueping.
 * 
 * @author Ingo Mierswa
 * @version $Id: SimpleBinaryPredictionModel.java,v 1.6 2008/07/31 17:43:40 ingomierswa Exp $
 */
public abstract class SimpleBinaryPredictionModel extends PredictionModel {

    /**
	 * 
	 */
	private static final long serialVersionUID = 1540861516979781090L;
	private double threshold = 0.0d;
	
	protected SimpleBinaryPredictionModel(ExampleSet exampleSet, double threshold) {
		super(exampleSet);
        this.threshold = threshold;
	}
	
	/**
	 * Applies the model to a single example and returns the predicted class
	 * value.
	 */
	public abstract double predict(Example example) throws OperatorException;

	/** Iterates over all examples and applies the model to them. */
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
		// checks
		if (!predictedLabel.isNominal())
			throw new UserError(null, 101, getName(), predictedLabel.getName());
		if (predictedLabel.getMapping().getValues().size() != 2)
			throw new UserError(null, 114, getName(), predictedLabel.getName());
		
		Iterator<Example> r = exampleSet.iterator();
		while (r.hasNext()) {
			Example example = r.next();
			double functionValue = predict(example) - threshold;
			
			// map prediction
			if (functionValue > 0.0d) {	
				example.setValue(predictedLabel, getLabel().getMapping().getPositiveIndex());
			} else {
				example.setValue(predictedLabel, getLabel().getMapping().getNegativeIndex());
			}
			
			// set confidence values
			example.setConfidence(getLabel().getMapping().mapIndex(predictedLabel.getMapping().getPositiveIndex()), 1.0d / (1.0d + java.lang.Math.exp(-functionValue)));
			example.setConfidence(getLabel().getMapping().mapIndex(predictedLabel.getMapping().getNegativeIndex()), 1.0d / (1.0d + java.lang.Math.exp(functionValue)));
		}
		
		return exampleSet;
	}
}