org.apache.commons.math.distribution.BinomialDistributionImpl Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of commons-math Show documentation
Show all versions of commons-math Show documentation
The Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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 BinomialDistribution}.
*
* @version $Revision: 772119 $ $Date: 2009-05-06 05:43:28 -0400 (Wed, 06 May 2009) $
*/
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();
setNumberOfTrials(trials);
setProbabilityOfSuccess(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 trials is not a valid
* number of trials.
*/
public void setNumberOfTrials(int trials) {
if (trials < 0) {
throw MathRuntimeException.createIllegalArgumentException(
"number of trials must be non-negative ({0})", trials);
}
numberOfTrials = trials;
}
/**
* Change the probability of success for this distribution.
* @param p the new probability of success.
* @throws IllegalArgumentException if p 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 p, used to
* bracket a PDF root.
*
* @param p the desired probability for the critical value
* @return domain value lower bound, i.e.
* P(X < lower bound) < p
*/
@Override
protected int getDomainLowerBound(double p) {
return -1;
}
/**
* Access the domain value upper bound, based on p, used to
* bracket a PDF root.
*
* @param p the desired probability for the critical value
* @return domain value upper bound, i.e.
* P(X < upper bound) > p
*/
@Override
protected int getDomainUpperBound(double p) {
return getNumberOfTrials();
}
/**
* 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 >= getNumberOfTrials()) {
ret = 1.0;
} else {
ret =
1.0 - Beta.regularizedBeta(
getProbabilityOfSuccess(),
x + 1.0,
getNumberOfTrials() - 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 > getNumberOfTrials()) {
ret = 0.0;
} else {
ret = MathUtils.binomialCoefficientDouble(
getNumberOfTrials(), x) *
Math.pow(getProbabilityOfSuccess(), x) *
Math.pow(1.0 - getProbabilityOfSuccess(),
getNumberOfTrials() - x);
}
return ret;
}
/**
* For this distribution, X, this method returns the largest x, such
* that P(X ≤ x) ≤ p.
*
* Returns -1 for p=0 and Integer.MAX_VALUE for
* p=1.
*
* @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);
}
}
© 2015 - 2024 Weber Informatics LLC | Privacy Policy