/*
 *  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.tree;

import java.util.LinkedList;
import java.util.List;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;

/**
 * This operator learns decision trees without pruning using nominal
 * attributes only.
 * Decision trees are powerful classification methods which often can also
 * easily be understood. This decision tree learner works similar to Quinlan's
 * ID3. 
 * 
 * @author Ingo Mierswa
 */
public class ID3Learner extends AbstractTreeLearner {

	public ID3Learner(OperatorDescription description) {
		super(description);
	}

	@Override
	public Pruner getPruner() throws OperatorException {
		return null;
	}

	@Override
	public List<Terminator> getTerminationCriteria(ExampleSet exampleSet) {
		List<Terminator> result = new LinkedList<Terminator>();
		result.add(new SingleLabelTermination());
		result.add(new NoAttributeLeftTermination());
		result.add(new EmptyTermination());
		result.add(new MaxDepthTermination(exampleSet.size()));
		return result;
	}

	public boolean supportsCapability(OperatorCapability capability) {
		switch (capability) {
		case BINOMINAL_ATTRIBUTES:
		case POLYNOMINAL_ATTRIBUTES:
		case POLYNOMINAL_LABEL:
		case BINOMINAL_LABEL:
		case WEIGHTED_EXAMPLES:
			return true;
		case MISSING_VALUES:
		default:
			return false;
		}
	}

	@Override
	protected TreeBuilder getTreeBuilder(ExampleSet exampleSet) throws OperatorException {
		return new TreeBuilder(createCriterion(getParameterAsDouble(PARAMETER_MINIMAL_GAIN)),
				getTerminationCriteria(exampleSet),
				getPruner(),
				getSplitPreprocessing(),
				new DecisionTreeLeafCreator(),
				true,
				0,
				getParameterAsInt(PARAMETER_MINIMAL_SIZE_FOR_SPLIT),
				getParameterAsInt(PARAMETER_MINIMAL_LEAF_SIZE));
	}
}