/*
* 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.exception.util.LocalizedFormats;
import org.apache.commons.math.special.Beta;
import org.apache.commons.math.util.MathUtils;
import org.apache.commons.math.util.FastMath;
/**
* The default implementation of {@link PascalDistribution}.
* @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
* @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 Pascal 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();
setNumberOfSuccessesInternal(r);
setProbabilityOfSuccessInternal(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.
* @deprecated as of 2.1 (class will become immutable in 3.0)
*/
@Deprecated
public void setNumberOfSuccesses(int successes) {
setNumberOfSuccessesInternal(successes);
}
/**
* Change the number of successes for this distribution.
* @param successes the new number of successes
* @throws IllegalArgumentException if <code>successes</code> is not
* positive.
*/
private void setNumberOfSuccessesInternal(int successes) {
if (successes < 0) {
throw MathRuntimeException.createIllegalArgumentException(
LocalizedFormats.NEGATIVE_NUMBER_OF_SUCCESSES,
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.
* @deprecated as of 2.1 (class will become immutable in 3.0)
*/
@Deprecated
public void setProbabilityOfSuccess(double p) {
setProbabilityOfSuccessInternal(p);
}
/**
* 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.
*/
private void setProbabilityOfSuccessInternal(double p) {
if (p < 0.0 || p > 1.0) {
throw MathRuntimeException.createIllegalArgumentException(
LocalizedFormats.OUT_OF_RANGE_SIMPLE, 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 < <i>lower bound</i>) <
* <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 < <i>upper bound</i>) >
* <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 ≤ 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) {
ret = 0.0;
} else {
ret = Beta.regularizedBeta(probabilityOfSuccess,
numberOfSuccesses, 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 +
numberOfSuccesses - 1, numberOfSuccesses - 1) *
FastMath.pow(probabilityOfSuccess, numberOfSuccesses) *
FastMath.pow(1.0 - probabilityOfSuccess, x);
}
return ret;
}
/**
* For this distribution, X, this method returns the largest x, such that
* P(X ≤ x) ≤ <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 ≤ 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;
}
/**
* Returns the lower bound of the support for the distribution.
*
* The lower bound of the support is always 0 no matter the parameters.
*
* @return lower bound of the support (always 0)
* @since 2.2
*/
public int getSupportLowerBound() {
return 0;
}
/**
* Returns the upper bound of the support for the distribution.
*
* The upper bound of the support is always positive infinity
* no matter the parameters. Positive infinity is represented
* by <code>Integer.MAX_VALUE</code> together with
* {@link #isSupportUpperBoundInclusive()} being <code>false</code>
*
* @return upper bound of the support (always <code>Integer.MAX_VALUE</code> for positive infinity)
* @since 2.2
*/
public int getSupportUpperBound() {
return Integer.MAX_VALUE;
}
/**
* Returns the mean.
*
* For number of successes <code>r</code> and
* probability of success <code>p</code>, the mean is
* <code>( r * p ) / ( 1 - p )</code>
*
* @return the mean
* @since 2.2
*/
public double getNumericalMean() {
final double p = getProbabilityOfSuccess();
final double r = getNumberOfSuccesses();
return ( r * p ) / ( 1 - p );
}
/**
* Returns the variance.
*
* For number of successes <code>r</code> and
* probability of success <code>p</code>, the mean is
* <code>( r * p ) / ( 1 - p )^2</code>
*
* @return the variance
* @since 2.2
*/
public double getNumericalVariance() {
final double p = getProbabilityOfSuccess();
final double r = getNumberOfSuccesses();
final double pInv = 1 - p;
return ( r * p ) / (pInv * pInv);
}
}