/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 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.preprocessing.sampling;

import java.util.List;
import java.util.Random;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.MappedExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.MDInteger;
import com.rapidminer.operator.validation.IteratingPerformanceAverage;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.RandomGenerator;


/**
 * This operator constructs a bootstrapped sample from the given example set. That means
 * that a sampling with replacement will be performed. The usual sample size is the number 
 * of original examples. This operator also offers the possibility to create the inverse
 * example set, i.e. an example set containing all examples which are not part of the 
 * bootstrapped example set. This inverse example set might be used for a bootstrapped
 * validation (together with an {@link IteratingPerformanceAverage} operator.
 * 
 * @author Ingo Mierswa
 */
public abstract class AbstractBootstrapping extends AbstractSamplingOperator {


	/** The parameter name for "This ratio determines the size of the new example set." */
	public static final String PARAMETER_SAMPLE_RATIO = "sample_ratio";

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

	@Override
	protected MDInteger getSampledSize(ExampleSetMetaData emd) throws UndefinedParameterError {
		if (emd.getNumberOfExamples().isKnown())
			return new MDInteger((int) getParameterAsDouble(PARAMETER_SAMPLE_RATIO) * emd.getNumberOfExamples().getValue());
		return new MDInteger();
	}


	public abstract int[] createMapping(ExampleSet exampleSet, int size, Random random) throws OperatorException;

	@Override
	public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {         
		RandomGenerator random = RandomGenerator.getRandomGenerator(this);
		int[] mapping = createMapping(exampleSet, (int)Math.round(exampleSet.size() * getParameterAsDouble(PARAMETER_SAMPLE_RATIO)), random);
		MappedExampleSet bootstrappedExampleSet = new MappedExampleSet(exampleSet, mapping, true);
		return bootstrappedExampleSet;
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		ParameterType type = new ParameterTypeDouble(PARAMETER_SAMPLE_RATIO, "This ratio determines the size of the new example set.", 0.0d, Double.POSITIVE_INFINITY, 1.0d);
		type.setExpert(false);
		types.add(type);

		types.addAll(RandomGenerator.getRandomGeneratorParameters(this));

		return types;
	}
}