001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 package org.apache.commons.math.distribution; 018 019 import java.io.Serializable; 020 021 import org.apache.commons.math.MathException; 022 import org.apache.commons.math.MathRuntimeException; 023 import org.apache.commons.math.special.Beta; 024 025 /** 026 * The default implementation of {@link BinomialDistribution}. 027 * 028 * @version $Revision: 920852 $ $Date: 2010-03-09 07:53:44 -0500 (Tue, 09 Mar 2010) $ 029 */ 030 public class BinomialDistributionImpl extends AbstractIntegerDistribution 031 implements BinomialDistribution, Serializable { 032 033 /** Serializable version identifier */ 034 private static final long serialVersionUID = 6751309484392813623L; 035 036 /** The number of trials. */ 037 private int numberOfTrials; 038 039 /** The probability of success. */ 040 private double probabilityOfSuccess; 041 042 /** 043 * Create a binomial distribution with the given number of trials and 044 * probability of success. 045 * 046 * @param trials the number of trials. 047 * @param p the probability of success. 048 */ 049 public BinomialDistributionImpl(int trials, double p) { 050 super(); 051 setNumberOfTrialsInternal(trials); 052 setProbabilityOfSuccessInternal(p); 053 } 054 055 /** 056 * Access the number of trials for this distribution. 057 * 058 * @return the number of trials. 059 */ 060 public int getNumberOfTrials() { 061 return numberOfTrials; 062 } 063 064 /** 065 * Access the probability of success for this distribution. 066 * 067 * @return the probability of success. 068 */ 069 public double getProbabilityOfSuccess() { 070 return probabilityOfSuccess; 071 } 072 073 /** 074 * Change the number of trials for this distribution. 075 * 076 * @param trials the new number of trials. 077 * @throws IllegalArgumentException if <code>trials</code> is not a valid 078 * number of trials. 079 * @deprecated as of 2.1 (class will become immutable in 3.0) 080 */ 081 @Deprecated 082 public void setNumberOfTrials(int trials) { 083 setNumberOfTrialsInternal(trials); 084 } 085 /** 086 * Change the number of trials for this distribution. 087 * 088 * @param trials the new number of trials. 089 * @throws IllegalArgumentException if <code>trials</code> is not a valid 090 * number of trials. 091 */ 092 private void setNumberOfTrialsInternal(int trials) { 093 if (trials < 0) { 094 throw MathRuntimeException.createIllegalArgumentException( 095 "number of trials must be non-negative ({0})", trials); 096 } 097 numberOfTrials = trials; 098 } 099 100 /** 101 * Change the probability of success for this distribution. 102 * 103 * @param p the new probability of success. 104 * @throws IllegalArgumentException if <code>p</code> is not a valid 105 * probability. 106 * @deprecated as of 2.1 (class will become immutable in 3.0) 107 */ 108 @Deprecated 109 public void setProbabilityOfSuccess(double p) { 110 setProbabilityOfSuccessInternal(p); 111 } 112 /** 113 * Change the probability of success for this distribution. 114 * 115 * @param p the new probability of success. 116 * @throws IllegalArgumentException if <code>p</code> is not a valid 117 * probability. 118 */ 119 private void setProbabilityOfSuccessInternal(double p) { 120 if (p < 0.0 || p > 1.0) { 121 throw MathRuntimeException.createIllegalArgumentException( 122 "{0} out of [{1}, {2}] range", p, 0.0, 1.0); 123 } 124 probabilityOfSuccess = p; 125 } 126 127 /** 128 * Access the domain value lower bound, based on <code>p</code>, used to 129 * bracket a PDF root. 130 * 131 * @param p the desired probability for the critical value 132 * @return domain value lower bound, i.e. P(X < <i>lower bound</i>) < 133 * <code>p</code> 134 */ 135 @Override 136 protected int getDomainLowerBound(double p) { 137 return -1; 138 } 139 140 /** 141 * Access the domain value upper bound, based on <code>p</code>, used to 142 * bracket a PDF root. 143 * 144 * @param p the desired probability for the critical value 145 * @return domain value upper bound, i.e. P(X < <i>upper bound</i>) > 146 * <code>p</code> 147 */ 148 @Override 149 protected int getDomainUpperBound(double p) { 150 return numberOfTrials; 151 } 152 153 /** 154 * For this distribution, X, this method returns P(X ≤ x). 155 * 156 * @param x the value at which the PDF is evaluated. 157 * @return PDF for this distribution. 158 * @throws MathException if the cumulative probability can not be computed 159 * due to convergence or other numerical errors. 160 */ 161 @Override 162 public double cumulativeProbability(int x) throws MathException { 163 double ret; 164 if (x < 0) { 165 ret = 0.0; 166 } else if (x >= numberOfTrials) { 167 ret = 1.0; 168 } else { 169 ret = 1.0 - Beta.regularizedBeta(getProbabilityOfSuccess(), 170 x + 1.0, numberOfTrials - x); 171 } 172 return ret; 173 } 174 175 /** 176 * For this distribution, X, this method returns P(X = x). 177 * 178 * @param x the value at which the PMF is evaluated. 179 * @return PMF for this distribution. 180 */ 181 public double probability(int x) { 182 double ret; 183 if (x < 0 || x > numberOfTrials) { 184 ret = 0.0; 185 } else { 186 ret = Math.exp(SaddlePointExpansion.logBinomialProbability(x, 187 numberOfTrials, probabilityOfSuccess, 188 1.0 - probabilityOfSuccess)); 189 } 190 return ret; 191 } 192 193 /** 194 * For this distribution, X, this method returns the largest x, such that 195 * P(X ≤ x) ≤ <code>p</code>. 196 * <p> 197 * Returns <code>-1</code> for p=0 and <code>Integer.MAX_VALUE</code> for 198 * p=1. 199 * </p> 200 * 201 * @param p the desired probability 202 * @return the largest x such that P(X ≤ x) <= p 203 * @throws MathException if the inverse cumulative probability can not be 204 * computed due to convergence or other numerical errors. 205 * @throws IllegalArgumentException if p < 0 or p > 1 206 */ 207 @Override 208 public int inverseCumulativeProbability(final double p) 209 throws MathException { 210 // handle extreme values explicitly 211 if (p == 0) { 212 return -1; 213 } 214 if (p == 1) { 215 return Integer.MAX_VALUE; 216 } 217 218 // use default bisection impl 219 return super.inverseCumulativeProbability(p); 220 } 221 }