/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 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.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.annotation.ResourceConsumptionEstimator;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.tools.OperatorResourceConsumptionHandler;


/**
 * Naive Bayes learner.
 * 
 * @author Tobias Malbrecht
 */
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 {
		return new SimpleDistributionModel(exampleSet, getParameterAsBoolean(PARAMETER_LAPLACE_CORRECTION));
	}

	@Override
	public Class<? extends PredictionModel> getModelClass() {
		return DistributionModel.class;
	}
	
	public boolean supportsCapability(OperatorCapability lc) {
		switch (lc) {
		case POLYNOMINAL_ATTRIBUTES:
		case BINOMINAL_ATTRIBUTES:
		case NUMERICAL_ATTRIBUTES:
		case POLYNOMINAL_LABEL:
		case BINOMINAL_LABEL:
		case WEIGHTED_EXAMPLES:
		case UPDATABLE:
		case MISSING_VALUES:
			return true;
		default: 
			return false;
		}
	}

	@Override
	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, false);
		type.setExpert(true);
		types.add(type);
		return types;
	}
	
	@Override
	public ResourceConsumptionEstimator getResourceConsumptionEstimator() {
		return OperatorResourceConsumptionHandler.getResourceConsumptionEstimator(getExampleSetInputPort(), NaiveBayes.class, null);
	}
}