/*
 * 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.MathException;
import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.special.Beta;
import org.apache.commons.math.util.MathUtils;

/**
 * The default implementation of {@link PascalDistribution}.
 * 
 * @version $Revision$ $Date$
 * @since 1.2
 */
public class PascalDistributionImpl extends AbstractIntegerDistribution implements PascalDistribution, Serializable {

	/** Serializable version identifier */
	private static final long serialVersionUID = 6751309484392813623L;

	/** The number of successes */
	private int numberOfSuccesses;

	/** The probability of success */
	private double probabilityOfSuccess;

	/**
	 * Create a binomial distribution with the given number of trials and
	 * probability of success.
	 * 
	 * @param r
	 *            the number of successes
	 * @param p
	 *            the probability of success
	 */
	public PascalDistributionImpl(int r, double p) {
		super();
		setNumberOfSuccesses(r);
		setProbabilityOfSuccess(p);
	}

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

	/**
	 * Access the probability of success for this distribution.
	 * 
	 * @return the probability of success
	 */
	public double getProbabilityOfSuccess() {
		return probabilityOfSuccess;
	}

	/**
	 * Change the number of successes for this distribution.
	 * 
	 * @param successes
	 *            the new number of successes
	 * @throws IllegalArgumentException
	 *             if <code>successes</code> is not positive.
	 */
	public void setNumberOfSuccesses(int successes) {
		if (successes < 0) {
			throw MathRuntimeException.createIllegalArgumentException("number of successes must be non-negative ({0})",
					successes);
		}
		numberOfSuccesses = successes;
	}

	/**
	 * Change the probability of success for this distribution.
	 * 
	 * @param p
	 *            the new probability of success
	 * @throws IllegalArgumentException
	 *             if <code>p</code> is not a valid probability.
	 */
	public void setProbabilityOfSuccess(double p) {
		if (p < 0.0 || p > 1.0) {
			throw MathRuntimeException.createIllegalArgumentException("{0} out of [{1}, {2}] range", p, 0.0, 1.0);
		}
		probabilityOfSuccess = p;
	}

	/**
	 * 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 -1;
	}

	/**
	 * 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) {
		// use MAX - 1 because MAX causes loop
		return Integer.MAX_VALUE - 1;
	}

	/**
	 * 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
	 * @throws MathException
	 *             if the cumulative probability can not be computed due to
	 *             convergence or other numerical errors
	 */
	@Override
	public double cumulativeProbability(int x) throws MathException {
		double ret;
		if (x < 0) {
			return numberOfSuccesses;
		} else {
			ret = Beta.regularizedBeta(getProbabilityOfSuccess(), getNumberOfSuccesses(), x + 1);
		}
		return ret;
	}

	/**
	 * 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;
		if (x < 0) {
			ret = 0.0;
		} else {
			ret = MathUtils.binomialCoefficientDouble(x + getNumberOfSuccesses() - 1, getNumberOfSuccesses() - 1)
					* Math.pow(getProbabilityOfSuccess(), getNumberOfSuccesses())
					* Math.pow(1.0 - getProbabilityOfSuccess(), x);
		}
		return ret;
	}

	/**
	 * For this distribution, X, this method returns the largest x, such that
	 * P(X &le; x) &le; <code>p</code>.
	 * <p>
	 * Returns <code>-1</code> for p=0 and <code>Integer.MAX_VALUE</code> for
	 * p=1.
	 * </p>
	 * 
	 * @param p
	 *            the desired probability
	 * @return the largest x such that P(X &le; x) <= p
	 * @throws MathException
	 *             if the inverse cumulative probability can not be computed due
	 *             to convergence or other numerical errors.
	 * @throws IllegalArgumentException
	 *             if p < 0 or p > 1
	 */
	@Override
	public int inverseCumulativeProbability(final double p) throws MathException {
		int ret;

		// handle extreme values explicitly
		if (p == 0) {
			ret = -1;
		} else if (p == 1) {
			ret = Integer.MAX_VALUE;
		} else {
			ret = super.inverseCumulativeProbability(p);
		}

		return ret;
	}
}
