/*
* 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.FastMath;
/**
* The default implementation of {@link BinomialDistribution}.
*
* @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
*/
public class BinomialDistributionImpl extends AbstractIntegerDistribution
implements BinomialDistribution, Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = 6751309484392813623L;
/** The number of trials. */
private int numberOfTrials;
/** The probability of success. */
private double probabilityOfSuccess;
/**
* Create a binomial distribution with the given number of trials and
* probability of success.
*
* @param trials the number of trials.
* @param p the probability of success.
*/
public BinomialDistributionImpl(int trials, double p) {
super();
setNumberOfTrialsInternal(trials);
setProbabilityOfSuccessInternal(p);
}
/**
* Access the number of trials for this distribution.
*
* @return the number of trials.
*/
public int getNumberOfTrials() {
return numberOfTrials;
}
/**
* Access the probability of success for this distribution.
*
* @return the probability of success.
*/
public double getProbabilityOfSuccess() {
return probabilityOfSuccess;
}
/**
* Change the number of trials for this distribution.
*
* @param trials the new number of trials.
* @throws IllegalArgumentException if <code>trials</code> is not a valid
* number of trials.
* @deprecated as of 2.1 (class will become immutable in 3.0)
*/
@Deprecated
public void setNumberOfTrials(int trials) {
setNumberOfTrialsInternal(trials);
}
/**
* Change the number of trials for this distribution.
*
* @param trials the new number of trials.
* @throws IllegalArgumentException if <code>trials</code> is not a valid
* number of trials.
*/
private void setNumberOfTrialsInternal(int trials) {
if (trials < 0) {
throw MathRuntimeException.createIllegalArgumentException(
LocalizedFormats.NEGATIVE_NUMBER_OF_TRIALS, trials);
}
numberOfTrials = trials;
}
/**
* 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) {
return numberOfTrials;
}
/**
* 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 if (x >= numberOfTrials) {
ret = 1.0;
} else {
ret = 1.0 - Beta.regularizedBeta(getProbabilityOfSuccess(),
x + 1.0, numberOfTrials - x);
}
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 || x > numberOfTrials) {
ret = 0.0;
} else {
ret = FastMath.exp(SaddlePointExpansion.logBinomialProbability(x,
numberOfTrials, probabilityOfSuccess,
1.0 - probabilityOfSuccess));
}
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 {
// handle extreme values explicitly
if (p == 0) {
return -1;
}
if (p == 1) {
return Integer.MAX_VALUE;
}
// use default bisection impl
return super.inverseCumulativeProbability(p);
}
/**
* Returns the lower bound of the support for the distribution.
*
* The lower bound of the support is always 0 no matter the number of trials
* and probability parameter.
*
* @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 the number of trials.
*
* @return upper bound of the support (equal to number of trials)
* @since 2.2
*/
public int getSupportUpperBound() {
return getNumberOfTrials();
}
/**
* Returns the mean.
*
* For <code>n</code> number of trials and
* probability parameter <code>p</code>, the mean is
* <code>n * p</code>
*
* @return the mean
* @since 2.2
*/
public double getNumericalMean() {
return (double)getNumberOfTrials() * getProbabilityOfSuccess();
}
/**
* Returns the variance.
*
* For <code>n</code> number of trials and
* probability parameter <code>p</code>, the variance is
* <code>n * p * (1 - p)</code>
*
* @return the variance
* @since 2.2
*/
public double getNumericalVariance() {
final double p = getProbabilityOfSuccess();
return (double)getNumberOfTrials() * p * (1 - p);
}
}