/**
* Copyright (C) 2001-2017 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 com.rapidminer.operator.OperatorDescription;
/**
* <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>
*
* @see RegularizedDiscriminantAnalysis
* @see LinearDiscriminantAnalysis
* @author Sebastian Land, Jan Czogalla
*/
public class QuadraticDiscriminantAnalysis extends RegularizedDiscriminantAnalysis {
/** The special alpha value for QDA */
static final double QDA_ALPHA = 0d;
public QuadraticDiscriminantAnalysis(OperatorDescription description) {
super(description);
}
@Override
protected boolean useAlphaParameter() {
return false;
}
@Override
protected double getAlpha() {
return QDA_ALPHA;
}
}