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benerator is a framework for creating realistic and valid high-volume test data, used for testing (unit/integration/load) and showcase setup. Metadata constraints are imported from systems and/or configuration files. Data can imported from and exported to files and systems, anonymized or generated from scratch. Domain packages provide reusable generators for creating domain-specific data as names and addresses internationalizable in language and region. It is strongly customizable with plugins and configuration options.

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/*
 * (c) Copyright 2010-2013 by Volker Bergmann. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, is permitted under the terms of the
 * GNU General Public License (GPL).
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * WITHOUT A WARRANTY OF ANY KIND. ALL EXPRESS OR IMPLIED CONDITIONS,
 * REPRESENTATIONS AND WARRANTIES, INCLUDING ANY IMPLIED WARRANTY OF
 * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR NON-INFRINGEMENT, ARE
 * HEREBY EXCLUDED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 * POSSIBILITY OF SUCH DAMAGE.
 */

package org.databene.benerator.distribution;

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

import org.databene.benerator.Generator;
import org.databene.benerator.NonNullGenerator;
import org.databene.benerator.primitive.number.AbstractNonNullNumberGenerator;
import org.databene.benerator.sample.ConstantGenerator;
import org.databene.benerator.sample.SampleGenerator;
import org.databene.benerator.util.GeneratorUtil;
import org.databene.commons.Converter;
import org.databene.commons.converter.ConverterManager;

/**
 * {@link Distribution} implementation which uses the inverse of a probability function integral 
 * for efficiently generating numbers with a given probability distribution. 
 * See Random 
 * Number Generation from Non-uniform Distributions.


 * Created: 12.03.2010 13:31:16
 * @since 0.6.0
 * @author Volker Bergmann
 */
public abstract class CumulativeDistributionFunction implements Distribution {
	
	public abstract double cumulativeProbability(double value);
	
	public abstract double inverse(double probability);

	@Override
	public  Generator applyTo(Generator source, boolean unique) {
		if (unique)
			throw new IllegalArgumentException(this + " cannot generate unique values");
	    List allProducts = GeneratorUtil.allProducts(source);
	    if (allProducts.size() == 1)
	    	return new ConstantGenerator(allProducts.get(0));
	    return new SampleGenerator(source.getGeneratedType(), this, unique, allProducts);
    }

	@Override
	public  NonNullGenerator createNumberGenerator(
			Class numberType, T min, T max, T granularity, boolean unique) {
		if (unique)
			throw new IllegalArgumentException(this + " cannot generate unique values");
	    return new IPINumberGenerator(this, numberType, min, max, granularity);
    }
	
	@Override
	public String toString() {
	    return getClass().getSimpleName();
	}
	
    /**
     * Generates numbers according to an {@link CumulativeDistributionFunction}.


     * Created: 12.03.2010 14:37:33
     * @since 0.6.0
     * @author Volker Bergmann
     */
    public static class IPINumberGenerator extends AbstractNonNullNumberGenerator {
    	
    	private CumulativeDistributionFunction fcn;
    	private Random random = new Random();
		private Converter converter;
		private double minProb;
		private double probScale;
		private double minD;
		private double maxD;
		private double granularityD;
    	
		public IPINumberGenerator(CumulativeDistributionFunction fcn, Class targetType, E min, E max, E granularity) {
			super(targetType, min, max, granularity);
			this.fcn = fcn;
			this.minD = (min != null ? min.doubleValue() : (max != null ? maxD - 9 : 0));
			this.maxD = (max != null ? max.doubleValue() : (min != null ? minD + 9 : 0));
			this.granularityD = granularity.doubleValue();
			this.minProb = fcn.cumulativeProbability(minD);
			this.probScale = fcn.cumulativeProbability(maxD + granularityD) - this.minProb;
			this.converter = ConverterManager.getInstance().createConverter(Double.class, targetType);
        }

		@Override
		public E generate() {
			double tmp;
			double prob = minProb + random.nextDouble() * probScale;
			tmp = fcn.inverse(prob);
			tmp = Math.floor((tmp - minD) / granularityD) * granularityD + minD;
			return converter.convert(tmp);
        }

    }

}