/*
 *  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;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.learner.SimpleBinaryPredictionModel;
import com.rapidminer.tools.Tools;


/**
 * The model determined by the {@link LogisticRegression} operator.
 * 
 * @author Ingo Mierswa, Tobias Malbrecht
 * @version $Id: LogisticRegressionModel.java,v 1.8 2008/05/09 19:22:56 ingomierswa Exp $
 */
public class LogisticRegressionModel extends SimpleBinaryPredictionModel {
    
    private static final long serialVersionUID = -966943348790852574L;
    
    private double[] beta = null;
    
    private double[] standardError = null;
    
    private double[] waldStatistic = null;

    private String[] attributeNames;
    
    private boolean interceptAdded;
    
    public LogisticRegressionModel(ExampleSet exampleSet, double[] beta, double[] variance, boolean interceptAdded) {
        super(exampleSet, 0.5d);
        this.attributeNames = com.rapidminer.example.Tools.getRegularAttributeNames(exampleSet);
        this.beta = beta;
        this.interceptAdded = interceptAdded;

        standardError = new double[variance.length];
        waldStatistic = new double[variance.length];
        for (int j = 0; j < beta.length; j++) {
        	standardError[j] = Math.sqrt(variance[j]);
        	waldStatistic[j] = beta[j] * beta[j] / variance[j];
        }
    }
    
    public double predict(Example example) {
        double eta = 0.0d;
        int i = 0;
        for (Attribute attribute : example.getAttributes()) {
            double value = example.getValue(attribute);
            eta += beta[i] * value;
            i++;
        }
        if (interceptAdded) {
        	eta += beta[beta.length - 1];
        }
        return Math.exp(eta) / (1 + Math.exp(eta));
    }
    
    public String toString() {
    	StringBuffer result = new StringBuffer();
    	if (interceptAdded) {
    		result.append("Bias (offset): " + Tools.formatNumber(beta[beta.length - 1]));
    		result.append("  \t(SE: " + Tools.formatNumber(standardError[standardError.length - 1]));
    		result.append(", Wald: " + Tools.formatNumber(waldStatistic[waldStatistic.length - 1])+ ")" + Tools.getLineSeparators(2));
    	}
    	result.append("Coefficients:" + Tools.getLineSeparator());
		for (int j = 0; j < beta.length - 1; j++) {
			result.append("beta(" + attributeNames[j] + ") = " + Tools.formatNumber(beta[j]));
			result.append(" \t\t(SE: " + Tools.formatNumber(standardError[j]));
			result.append(", Wald: " + Tools.formatNumber(waldStatistic[j]) + ")" + Tools.getLineSeparator());
		}
    	result.append(Tools.getLineSeparator() + "Odds Ratios:" + Tools.getLineSeparator());
		for (int j = 0; j < beta.length - 1; j++) {
			result.append("odds_ratio(" + attributeNames[j] + ") = " + Tools.formatNumber(Math.exp(beta[j])) + Tools.getLineSeparator());
		}
    	return result.toString();
    }
}