/*
 *  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 an internal forward selection for the linear regression.
 * It uses the Akaike Criterion that is maximized roundwise. Each round the attribute
 * minimizing the akaike criterion is deselected.
 * 
 * @author Sebastian Land
 */
public class GreedyLinearRegressionMethod 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 greedyResult = new LinearRegressionResult();
        greedyResult.isUsedAttribute = isUsedAttribute;
        greedyResult.coefficients =coefficientsOnFullData;
        greedyResult.error = errorOnFullData;

        boolean improved = true;
        double akaike = (numberOfExamples - numberOfUsedAttributes) + 2 * numberOfUsedAttributes;
        int currentNumberOfAttributes = numberOfUsedAttributes;

        // loop as long as improvements are found and deselect one attribute each time
        while (improved) {
            boolean[] currentlySelected = isUsedAttribute.clone();
            improved = false;
            currentNumberOfAttributes--;
            // for all remaining attributes: test if are best selection
            for (int i = 0; i < isUsedAttribute.length; i++) {
                if (currentlySelected[i]) {
                    // calculate the akaike value without this attribute
                    currentlySelected[i] = false;
                    double[] currentCoeffs = regression.performRegression(exampleSet, currentlySelected, means, labelMean, ridge);
                    double currentError = regression.getSquaredError(exampleSet, currentlySelected, currentCoeffs, useBias);
                    double currentAkaike = currentError / errorOnFullData * (numberOfExamples - numberOfUsedAttributes) + 2 * currentNumberOfAttributes;

                    // if the value is improved compared to the current best
                    if (currentAkaike < akaike) {
                        improved = true;
                        akaike = currentAkaike;
                        System.arraycopy(currentlySelected, 0, greedyResult.isUsedAttribute, 0, currentlySelected.length);
                        greedyResult.coefficients = currentCoeffs;
                        greedyResult.error = currentError;
                    }

                    // select it again for calculating other attributes
                    currentlySelected[i] = true;
                }
            }
        }
        return greedyResult;
    }


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