/*
 *  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.bayes;

import java.util.List;

import Jama.Matrix;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.math.MathFunctions;
import com.rapidminer.tools.math.matrix.CovarianceMatrix;
/**
 * <p>This is a regularized discriminant analysis (RDA) which is a generalization of the LDA or QDA.
 * Both algorithms are special cases of this algorithm, using parameter alpha = 1 respectively alpha = 0.</p>
 * 
 * @author Sebastian Land
 */
public class RegularizedDiscriminantAnalysis extends LinearDiscriminantAnalysis {

	public static final String PARAMETER_ALPHA = "alpha";

	public RegularizedDiscriminantAnalysis(OperatorDescription description) {
		super(description);
	}

	@Override
	protected Matrix[] getInverseCovarianceMatrices(ExampleSet exampleSet, String[] labels) throws UndefinedParameterError {
		double alpha = getParameterAsDouble(PARAMETER_ALPHA);
		Matrix[] globalInverseCovariances = super.getInverseCovarianceMatrices(exampleSet, labels); 

		Matrix[] classInverseCovariances = new Matrix[labels.length];
		Attribute labelAttribute = exampleSet.getAttributes().getLabel();
		SplittedExampleSet labelSet = SplittedExampleSet.splitByAttribute(exampleSet, exampleSet.getAttributes().getLabel());
		int labelIndex = 0;
		for (String label: labels) {
			// select appropriate subset
			for (int i = 0; i < labels.length; i++) {
				labelSet.selectSingleSubset(i);
				if (labelSet.getExample(0).getNominalValue(labelAttribute).equals(label))
					break;
			}
			// calculate inverse matrix
			Matrix inverse = MathFunctions.invertMatrix(CovarianceMatrix.getCovarianceMatrix(labelSet));
			classInverseCovariances[labelIndex] = inverse;
			labelIndex++;
		}

		// weighting of the matrices
		Matrix[] regularizedMatrices = new Matrix[classInverseCovariances.length];
		for (int i = 0; i < labels.length; i++) {
			regularizedMatrices[i] = globalInverseCovariances[i].times(alpha).plus(classInverseCovariances[i].times(1d - alpha));
		}
		return classInverseCovariances;
	}

	@Override
	protected DiscriminantModel getModel(ExampleSet exampleSet, String[] labels, Matrix[] meanVectors, Matrix[] inverseCovariances, double[] aprioriProbabilities) throws UndefinedParameterError {
		return new DiscriminantModel(exampleSet, labels, meanVectors, inverseCovariances, aprioriProbabilities, getParameterAsDouble(PARAMETER_ALPHA));		
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> list = super.getParameterTypes();

		list.add(new ParameterTypeDouble(PARAMETER_ALPHA, "This is the strength of regularization. 1: Only global covariance is used, 0: Only per class covariance is used.", 0d, 1d, 0.5d, false));

		return list;
	}
}