/**
 * 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.lazy;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Statistics;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.PredictionModel;
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
 *
 * @deprecated This learner is not used anymore.
 */
@Deprecated
public class AttributeBasedVotingLearner extends AbstractLearner {

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

	@Override
	public Class<? extends PredictionModel> getModelClass() {
		return AttributeBasedVotingModel.class;
	}

	@Override
	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);
	}

	@Override
	public boolean supportsCapability(OperatorCapability lc) {
		if (lc == com.rapidminer.operator.OperatorCapability.POLYNOMINAL_ATTRIBUTES) {
			return true;
		}
		if (lc == com.rapidminer.operator.OperatorCapability.BINOMINAL_ATTRIBUTES) {
			return true;
		}
		if (lc == com.rapidminer.operator.OperatorCapability.NUMERICAL_ATTRIBUTES) {
			return true;
		}

		if (lc == com.rapidminer.operator.OperatorCapability.POLYNOMINAL_LABEL) {
			return true;
		}
		if (lc == com.rapidminer.operator.OperatorCapability.BINOMINAL_LABEL) {
			return true;
		}
		if (lc == com.rapidminer.operator.OperatorCapability.NUMERICAL_LABEL) {
			return true;
		}

		return false;
	}
}