/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math.distribution;

import java.io.Serializable;

import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.util.MathUtils;

/**
 * The default implementation of {@link HypergeometricDistribution}.
 *
 * @version $Revision$ $Date$
 */
public class HypergeometricDistributionImpl extends AbstractIntegerDistribution
    implements HypergeometricDistribution, Serializable 
{

    /** Serializable version identifier */
    private static final long serialVersionUID = -436928820673516179L;

    /** The number of successes in the population. */
    private int numberOfSuccesses;
    
    /** The population size. */
    private int populationSize;
    
    /** The sample size. */
    private int sampleSize;
    
    /**
     * Construct a new hypergeometric distribution with the given the population
     * size, the number of successes in the population, and the sample size.
     * @param populationSize the population size.
     * @param numberOfSuccesses number of successes in the population.
     * @param sampleSize the sample size.
     */
    public HypergeometricDistributionImpl(int populationSize,
        int numberOfSuccesses, int sampleSize) {
        super();
        if (numberOfSuccesses > populationSize) {
            throw MathRuntimeException.createIllegalArgumentException(
                "number of successes ({0}) must be less than or equal to population size ({1})",
                numberOfSuccesses, populationSize);
        }
        if (sampleSize > populationSize) {
            throw MathRuntimeException.createIllegalArgumentException(
                  "sample size ({0}) must be less than or equal to population size ({1})",
                  sampleSize, populationSize);
        }
        setPopulationSize(populationSize);
        setSampleSize(sampleSize);
        setNumberOfSuccesses(numberOfSuccesses);
    }

    /**
     * For this distribution, X, this method returns P(X &le; x).
     * @param x the value at which the PDF is evaluated.
     * @return PDF for this distribution. 
     */
    @Override
    public double cumulativeProbability(int x) {
        double ret;
        
        int n = getPopulationSize();
        int m = getNumberOfSuccesses();
        int k = getSampleSize();

        int[] domain = getDomain(n, m, k);
        if (x < domain[0]) {
            ret = 0.0;
        } else if(x >= domain[1]) {
            ret = 1.0;
        } else {
            ret = innerCumulativeProbability(domain[0], x, 1, n, m, k);
        }
        
        return ret;
    }

    /**
     * Return the domain for the given hypergeometric distribution parameters.
     * @param n the population size.
     * @param m number of successes in the population.
     * @param k the sample size.
     * @return a two element array containing the lower and upper bounds of the
     *         hypergeometric distribution.  
     */
    private int[] getDomain(int n, int m, int k){
        return new int[]{
            getLowerDomain(n, m, k),
            getUpperDomain(m, k)
        };
    }
    
    /**
     * Access the domain value lower bound, based on <code>p</code>, used to
     * bracket a PDF root.
     * 
     * @param p the desired probability for the critical value
     * @return domain value lower bound, i.e.
     *         P(X &lt; <i>lower bound</i>) &lt; <code>p</code> 
     */
    @Override
    protected int getDomainLowerBound(double p) {
        return getLowerDomain(getPopulationSize(), getNumberOfSuccesses(),
            getSampleSize());
    }
    
    /**
     * Access the domain value upper bound, based on <code>p</code>, used to
     * bracket a PDF root.
     * 
     * @param p the desired probability for the critical value
     * @return domain value upper bound, i.e.
     *         P(X &lt; <i>upper bound</i>) &gt; <code>p</code> 
     */
    @Override
    protected int getDomainUpperBound(double p) {
        return getUpperDomain(getSampleSize(), getNumberOfSuccesses());
    }

    /**
     * Return the lowest domain value for the given hypergeometric distribution
     * parameters.
     * @param n the population size.
     * @param m number of successes in the population.
     * @param k the sample size.
     * @return the lowest domain value of the hypergeometric distribution.  
     */
    private int getLowerDomain(int n, int m, int k) {
        return Math.max(0, m - (n - k));
    }

    /**
     * Access the number of successes.
     * @return the number of successes.
     */
    public int getNumberOfSuccesses() {
        return numberOfSuccesses;
    }

    /**
     * Access the population size.
     * @return the population size.
     */
    public int getPopulationSize() {
        return populationSize;
    }

    /**
     * Access the sample size.
     * @return the sample size.
     */
    public int getSampleSize() {
        return sampleSize;
    }

    /**
     * Return the highest domain value for the given hypergeometric distribution
     * parameters.
     * @param m number of successes in the population.
     * @param k the sample size.
     * @return the highest domain value of the hypergeometric distribution.  
     */
    private int getUpperDomain(int m, int k){
        return Math.min(k, m);
    }

    /**
     * For this distribution, X, this method returns P(X = x).
     * 
     * @param x the value at which the PMF is evaluated.
     * @return PMF for this distribution. 
     */
    public double probability(int x) {
        double ret;
        
        int n = getPopulationSize();
        int m = getNumberOfSuccesses();
        int k = getSampleSize();

        int[] domain = getDomain(n, m, k);
        if(x < domain[0] || x > domain[1]){
            ret = 0.0;
        } else {
            ret = probability(n, m, k, x);
        }
        
        return ret;
    }
    
    /**
     * For the distribution, X, defined by the given hypergeometric distribution
     * parameters, this method returns P(X = x).
     * 
     * @param n the population size.
     * @param m number of successes in the population.
     * @param k the sample size.
     * @param x the value at which the PMF is evaluated.
     * @return PMF for the distribution. 
     */
    private double probability(int n, int m, int k, int x) {
        return Math.exp(MathUtils.binomialCoefficientLog(m, x) +
            MathUtils.binomialCoefficientLog(n - m, k - x) -
            MathUtils.binomialCoefficientLog(n, k));
    }

    /**
     * Modify the number of successes.
     * @param num the new number of successes.
     * @throws IllegalArgumentException if <code>num</code> is negative.
     */
    public void setNumberOfSuccesses(int num) {
        if(num < 0){
            throw MathRuntimeException.createIllegalArgumentException(
                  "number of successes must be non-negative ({0})",
                  num);
        }
        numberOfSuccesses = num;
    }

    /**
     * Modify the population size.
     * @param size the new population size.
     * @throws IllegalArgumentException if <code>size</code> is not positive.
     */
    public void setPopulationSize(int size) {
        if(size <= 0){
            throw MathRuntimeException.createIllegalArgumentException(
                  "population size must be positive ({0})",
                  size);
        }
        populationSize = size;
    }
    
    /**
     * Modify the sample size.
     * @param size the new sample size.
     * @throws IllegalArgumentException if <code>size</code> is negative.
     */
    public void setSampleSize(int size) {
        if (size < 0) {
            throw MathRuntimeException.createIllegalArgumentException(
                  "sample size must be positive ({0})",
                  size);
        }    
        sampleSize = size;
    }

    /**
     * For this distribution, X, this method returns P(X &ge; x).
     * @param x the value at which the CDF is evaluated.
     * @return upper tail CDF for this distribution.
     * @since 1.1
     */
    public double upperCumulativeProbability(int x) {
        double ret;
        
        int n = getPopulationSize();
        int m = getNumberOfSuccesses();
        int k = getSampleSize();

        int[] domain = getDomain(n, m, k);
        if (x < domain[0]) {
            ret = 1.0;
        } else if(x > domain[1]) {
            ret = 0.0;
        } else {
            ret = innerCumulativeProbability(domain[1], x, -1, n, m, k);
        }
        
        return ret;
    }
    
    /**
     * For this distribution, X, this method returns P(x0 &le; X &le; x1).  This
     * probability is computed by summing the point probabilities for the values
     * x0, x0 + 1, x0 + 2, ..., x1, in the order directed by dx. 
     * @param x0 the inclusive, lower bound
     * @param x1 the inclusive, upper bound
     * @param dx the direction of summation. 1 indicates summing from x0 to x1.
     *           0 indicates summing from x1 to x0.
     * @param n the population size.
     * @param m number of successes in the population.
     * @param k the sample size.
     * @return P(x0 &le; X &le; x1). 
     */
    private double innerCumulativeProbability(
        int x0, int x1, int dx, int n, int m, int k)
    {
        double ret = probability(n, m, k, x0);
        while (x0 != x1) {
            x0 += dx;
            ret += probability(n, m, k, x0);
        }
        return ret;
    }
}
