/*
* RapidMiner
*
* Copyright (C) 2001-2008 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 com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.LearnerCapability;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
/**
* Naive Bayes learner.
*
* @author Tobias Malbrecht
* @version $Id: NaiveBayes.java,v 1.15 2008/06/03 14:17:47 tobiasmalbrecht Exp $
*/
public class NaiveBayes extends AbstractLearner {
public static final String PARAMETER_LAPLACE_CORRECTION = "laplace_correction";
public NaiveBayes(OperatorDescription description) {
super(description);
}
public Model learn(ExampleSet exampleSet) throws OperatorException {
boolean laplaceCorrectionEnabled = getParameterAsBoolean(PARAMETER_LAPLACE_CORRECTION);
return new DistributionModel(exampleSet, laplaceCorrectionEnabled);
}
public boolean supportsCapability(LearnerCapability lc) {
if (lc == LearnerCapability.POLYNOMINAL_ATTRIBUTES)
return true;
if (lc == LearnerCapability.BINOMINAL_ATTRIBUTES)
return true;
if (lc == LearnerCapability.NUMERICAL_ATTRIBUTES)
return true;
if (lc == LearnerCapability.POLYNOMINAL_CLASS)
return true;
if (lc == LearnerCapability.BINOMINAL_CLASS)
return true;
if (lc == LearnerCapability.WEIGHTED_EXAMPLES)
return true;
return false;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeBoolean(PARAMETER_LAPLACE_CORRECTION, "Use Laplace correction to prevent high influence of zero probabilities.", true);
type.setExpert(true);
types.add(type);
return types;
}
}