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Diffstat (limited to 'src/main/java/org/apache/commons/math/distribution/BinomialDistributionImpl.java')
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diff --git a/src/main/java/org/apache/commons/math/distribution/BinomialDistributionImpl.java b/src/main/java/org/apache/commons/math/distribution/BinomialDistributionImpl.java new file mode 100644 index 0000000..9ebb629 --- /dev/null +++ b/src/main/java/org/apache/commons/math/distribution/BinomialDistributionImpl.java @@ -0,0 +1,279 @@ +/* + * 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); + } +} |