/*
 *  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.features.weighting;

import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Statistics;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;


/**
 * <p>
 * Creates weights from the standard deviations of all attributes. The values can
 * be normalized by the average, the minimum, or the maximum of the attribute.
 * </p>
 * 
 * @author Ingo Mierswa
 * @version $Id: StandardDeviationWeighting.java,v 1.8 2006/03/27 13:22:00
 *          ingomierswa Exp $
 */
public class StandardDeviationWeighting extends AbstractWeighting {

	/** The parameter name for "Indicates if the standard deviation should be divided by the minimum, maximum, or average of the attribute." */
	public static final String PARAMETER_NORMALIZE = "normalize";
	
	private static final String[] NORMALIZATIONS = { "none", "average", "minimum", "maximum" };

	private static final int NONE = 0;

	private static final int AVERAGE = 1;

	private static final int MINIMUM = 2;

	private static final int MAXIMUM = 3;

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

	public AttributeWeights calculateWeights(ExampleSet exampleSet) throws OperatorException {		
		exampleSet.recalculateAllAttributeStatistics();

		int normalization = getParameterAsInt(PARAMETER_NORMALIZE);
		AttributeWeights weights = new AttributeWeights();

		for (Attribute attribute : exampleSet.getAttributes()) {
			double data = Math.sqrt(exampleSet.getStatistics(attribute, Statistics.VARIANCE));
			switch (normalization) {
				case NONE:
					break;
				case AVERAGE:
					data /= exampleSet.getStatistics(attribute, Statistics.AVERAGE);
					break;
				case MINIMUM:
					data /= exampleSet.getStatistics(attribute, Statistics.MINIMUM);
					break;
				case MAXIMUM:
					data /= exampleSet.getStatistics(attribute, Statistics.MAXIMUM);
					break;
				default:
					break;
			}
			data = Math.abs(data);
			weights.setWeight(attribute.getName(), data);
		}

		return weights;
	}
	
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		ParameterType type = new ParameterTypeCategory(PARAMETER_NORMALIZE, "Indicates if the standard deviation should be divided by the minimum, maximum, or average of the attribute.", NORMALIZATIONS, 0);
		type.setExpert(false);
		types.add(type);
		return types;
	}
}