/*
 *  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.linear;

import java.util.Collections;
import java.util.List;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.UndefinedParameterError;

/**
 * This class implements the M5Prime feature selection method for Linear Regression.
 * 
 * @author Sebastian Land
 */
public class M5PLinearRegressionMethod implements LinearRegressionMethod {



    @Override
    public LinearRegressionResult applyMethod(LinearRegression regression, boolean useBias, double ridge, ExampleSet exampleSet, boolean[] isUsedAttribute, int numberOfExamples, int numberOfUsedAttributes, double[] means, double labelMean, double[] standardDeviations, double labelStandardDeviation, double[] coefficientsOnFullData, double errorOnFullData) throws UndefinedParameterError {
        LinearRegressionResult result = new LinearRegressionResult();
        result.isUsedAttribute = isUsedAttribute;
        result.coefficients = coefficientsOnFullData;
        result.error = errorOnFullData;

        // attribute removal as in M5 prime
        boolean improved = true;
        int currentNumberOfAttributes = numberOfUsedAttributes;
        double akaike = (numberOfExamples - numberOfUsedAttributes) + 2 * numberOfUsedAttributes;
        while (improved) {
            improved = false;
            currentNumberOfAttributes--;

            // find the attribute with the smallest standardized coefficient
            double minStadardizedCoefficient = 0;
            int attribute2Deselect = -1;
            int coefficientIndex = 0;
            for (int i = 0; i < isUsedAttribute.length; i++) {
                if (isUsedAttribute[i]) {
                    double standardizedCoefficient = Math.abs(coefficientsOnFullData[coefficientIndex] * standardDeviations[i] / labelStandardDeviation);
                    if ((coefficientIndex == 0) || (standardizedCoefficient < minStadardizedCoefficient)) {
                        minStadardizedCoefficient = standardizedCoefficient;
                        attribute2Deselect = i;
                    }
                    coefficientIndex++;
                }
            }

            // check if removing this attribute improves Akaike
            if (attribute2Deselect >= 0) {
                isUsedAttribute[attribute2Deselect] = false;
                double[] currentCoefficients = regression.performRegression(exampleSet, isUsedAttribute, means, labelMean, ridge);
                double currentError = regression.getSquaredError(exampleSet, isUsedAttribute, currentCoefficients, useBias);
                double currentAkaike = currentError / errorOnFullData * (numberOfExamples - numberOfUsedAttributes) + 2 * currentNumberOfAttributes;

                if (currentAkaike < akaike) {
                    improved = true;
                    akaike = currentAkaike;
                    result.coefficients = currentCoefficients;
                    result.error = currentError;
                } else {
                    isUsedAttribute[attribute2Deselect] = true;
                }
            }
        }
        return result;
    }


    @Override
    public List<ParameterType> getParameterTypes() {
        return Collections.emptyList();
    }
}