/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 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 java.util.Map;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples.MeanVariance;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.Kernel;


/**
 * An optimized implementation for Linear MySVM Models that only store the coefficients
 * to save memory and apply these weights directly without kernel transformations.
 * 
 * @author Tobias Malbrecht
 */
public class LinearMySVMModel extends PredictionModel {

    private static final long serialVersionUID = 2812901947459843681L;

    private Map<Integer, MeanVariance> meanVariances;

    private double bias;

    private double[] weights = null;

    public LinearMySVMModel(ExampleSet exampleSet, com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples model, Kernel kernel, int kernelType) {
        super(exampleSet);
        this.meanVariances = model.getMeanVariances();
        this.bias = model.get_b();
        this.weights = new double[model.get_dim()];
        for (int i = 0; i < model.count_examples(); i++) {
            double[] x = model.get_example(i).toDense(model.get_dim());
            double alpha = model.get_alpha(i);
            double y = model.get_y(i);
            if (y != 0.0d) {
                alpha /= y;
            }
            for (int j = 0; j < weights.length; j++) {
                weights[j] += y * alpha * x[j];
            }
        }
    }

    @Override
    public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) throws OperatorException {
        for (Example example : exampleSet) {
            double prediction = bias;
            int a = 0;
            for (Attribute attribute : exampleSet.getAttributes()) {
                double value = example.getValue(attribute);
                MeanVariance meanVariance = meanVariances.get(a);
                if (meanVariance != null) {
                    if (meanVariance.getVariance() == 0.0d)
                        value = 0.0d;
                    else
                        value = (value - meanVariance.getMean()) / Math.sqrt(meanVariance.getVariance());
                }
                prediction += weights[a] * value;
                a++;
            }
            if (predictedLabelAttribute.isNominal()) {
                int index = prediction > 0 ? predictedLabelAttribute.getMapping().getPositiveIndex() : predictedLabelAttribute.getMapping().getNegativeIndex();
                example.setValue(predictedLabelAttribute, index);
                // set confidence to numerical prediction, such that can be scaled later
                example.setConfidence(predictedLabelAttribute.getMapping().getPositiveString(), 1.0d / (1.0d + java.lang.Math.exp(-prediction)));
                example.setConfidence(predictedLabelAttribute.getMapping().getNegativeString(), 1.0d / (1.0d + java.lang.Math.exp(prediction)));
            } else {
                example.setValue(predictedLabelAttribute, prediction);
            }
        }
        return exampleSet;
    }
}