/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.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 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.UndefinedParameterError;
import com.rapidminer.tools.math.matrix.CovarianceMatrix;

/**
 * <p>This operator performs a quadratic discriminant analysis (QDA). QDA is closely related to 
 * linear discriminant analysis (LDA), where it is assumed that the measurements are normally distributed. 
 * Unlike LDA however, in QDA there is no assumption that the covariance of each of the classes is identical.</p>
 * 
 * @author Sebastian Land
 */
public class QuadraticDiscriminantAnalysis extends LinearDiscriminantAnalysis {

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

	@Override
	protected DiscriminantModel getModel(ExampleSet exampleSet, String[] labels, Matrix[] meanVectors, Matrix[] inverseCovariances, double[] aprioriProbabilities) {
		return new DiscriminantModel(exampleSet, labels, meanVectors, inverseCovariances, aprioriProbabilities, 1d);		
	}

	@Override
	protected Matrix[] getInverseCovarianceMatrices(ExampleSet exampleSet, String[] labels) throws UndefinedParameterError {
		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 apropriate 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 = CovarianceMatrix.getCovarianceMatrix(labelSet).inverse();
			classInverseCovariances[labelIndex] = inverse;
			labelIndex++;
		}
		return classInverseCovariances;
	}
}