/*
* 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.lazy;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Statistics;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.LearnerCapability;
import com.rapidminer.operator.learner.meta.Vote;
/**
* AttributeBasedVotingLearner is very lazy. Actually it does not learn at all but creates an
* {@link AttributeBasedVotingModel}. This model simply calculates the average of the
* attributes as prediction (for regression) or the mode of all attribute values
* (for classification). AttributeBasedVotingLearner is especially useful if it is used
* on an example set created by a meta learning scheme, e.g. by {@link Vote}.
*
* @author Ingo Mierswa
* @version $Id: AttributeBasedVotingLearner.java,v 1.5 2008/05/09 19:23:24 ingomierswa Exp $
*/
public class AttributeBasedVotingLearner extends AbstractLearner {
public AttributeBasedVotingLearner(OperatorDescription description) {
super(description);
}
public Model learn(ExampleSet exampleSet) {
exampleSet.recalculateAttributeStatistics(exampleSet.getAttributes().getLabel());
double majorityPrediction;
if (exampleSet.getAttributes().getLabel().isNominal()) {
majorityPrediction = exampleSet.getStatistics(exampleSet.getAttributes().getLabel(), Statistics.MODE);
} else {
majorityPrediction = exampleSet.getStatistics(exampleSet.getAttributes().getLabel(), Statistics.AVERAGE);
}
return new AttributeBasedVotingModel(exampleSet, majorityPrediction);
}
public boolean supportsCapability(LearnerCapability lc) {
if (lc == com.rapidminer.operator.learner.LearnerCapability.POLYNOMINAL_ATTRIBUTES)
return true;
if (lc == com.rapidminer.operator.learner.LearnerCapability.BINOMINAL_ATTRIBUTES)
return true;
if (lc == com.rapidminer.operator.learner.LearnerCapability.NUMERICAL_ATTRIBUTES)
return true;
if (lc == com.rapidminer.operator.learner.LearnerCapability.POLYNOMINAL_CLASS)
return true;
if (lc == com.rapidminer.operator.learner.LearnerCapability.BINOMINAL_CLASS)
return true;
if (lc == com.rapidminer.operator.learner.LearnerCapability.NUMERICAL_CLASS)
return true;
return false;
}
}