1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * http://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 18 package org.apache.commons.math.distribution; 19 20 /** 21 * This factory provids the means to create common statistical distributions. 22 * The following distributions are supported: 23 * <ul> 24 * <li>Binomial</li> 25 * <li>Cauchy</li> 26 * <li>Chi-Squared</li> 27 * <li>Exponential</li> 28 * <li>F</li> 29 * <li>Gamma</li> 30 * <li>HyperGeometric</li> 31 * <li>Poisson</li> 32 * <li>Normal</li> 33 * <li>Student's t</li> 34 * <li>Weibull</li> 35 * <li>Pascal</li> 36 * </ul> 37 * 38 * Common usage:<pre> 39 * DistributionFactory factory = DistributionFactory.newInstance(); 40 * 41 * // create a Chi-Square distribution with 5 degrees of freedom. 42 * ChiSquaredDistribution chi = factory.createChiSquareDistribution(5.0); 43 * </pre> 44 * 45 * @version $Revision: 545192 $ $Date: 2007-06-07 07:35:04 -0700 (Thu, 07 Jun 2007) $ 46 * @deprecated pluggability of distribution instances is now provided through 47 * constructors and setters. 48 */ 49 public abstract class DistributionFactory { 50 /** 51 * Default constructor. 52 */ 53 protected DistributionFactory() { 54 super(); 55 } 56 57 /** 58 * Create an instance of a <code>DistributionFactory</code> 59 * @return a new factory. 60 */ 61 public static DistributionFactory newInstance() { 62 return new DistributionFactoryImpl(); 63 } 64 65 /** 66 * Create a binomial distribution with the given number of trials and 67 * probability of success. 68 * 69 * @param numberOfTrials the number of trials. 70 * @param probabilityOfSuccess the probability of success 71 * @return a new binomial distribution 72 */ 73 public abstract BinomialDistribution createBinomialDistribution( 74 int numberOfTrials, double probabilityOfSuccess); 75 76 /** 77 * Create a Pascal distribution with the given number of successes and 78 * probability of success. 79 * 80 * @param numberOfSuccesses the number of successes. 81 * @param probabilityOfSuccess the probability of success 82 * @return a new Pascal distribution 83 * @since 1.2 84 */ 85 public PascalDistribution createPascalDistribution( 86 int numberOfSuccesses, double probabilityOfSuccess) { 87 return new PascalDistributionImpl(numberOfSuccesses, probabilityOfSuccess); 88 } 89 90 /** 91 * Create a new cauchy distribution with the given median and scale. 92 * @param median the median of the distribution 93 * @param scale the scale 94 * @return a new cauchy distribution 95 * @since 1.1 96 */ 97 public CauchyDistribution createCauchyDistribution( 98 double median, double scale) 99 { 100 return new CauchyDistributionImpl(median, scale); 101 } 102 103 /** 104 * Create a new chi-square distribution with the given degrees of freedom. 105 * 106 * @param degreesOfFreedom degrees of freedom 107 * @return a new chi-square distribution 108 */ 109 public abstract ChiSquaredDistribution createChiSquareDistribution( 110 double degreesOfFreedom); 111 112 /** 113 * Create a new exponential distribution with the given degrees of freedom. 114 * 115 * @param mean mean 116 * @return a new exponential distribution 117 */ 118 public abstract ExponentialDistribution createExponentialDistribution( 119 double mean); 120 121 /** 122 * Create a new F-distribution with the given degrees of freedom. 123 * 124 * @param numeratorDegreesOfFreedom numerator degrees of freedom 125 * @param denominatorDegreesOfFreedom denominator degrees of freedom 126 * @return a new F-distribution 127 */ 128 public abstract FDistribution createFDistribution( 129 double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom); 130 131 /** 132 * Create a new gamma distribution with the given shape and scale 133 * parameters. 134 * 135 * @param alpha the shape parameter 136 * @param beta the scale parameter 137 * 138 * @return a new gamma distribution 139 */ 140 public abstract GammaDistribution createGammaDistribution( 141 double alpha, double beta); 142 143 /** 144 * Create a new t distribution with the given degrees of freedom. 145 * 146 * @param degreesOfFreedom degrees of freedom 147 * @return a new t distribution 148 */ 149 public abstract TDistribution createTDistribution(double degreesOfFreedom); 150 151 /** 152 * Create a new hypergeometric distribution with the given the population 153 * size, the number of successes in the population, and the sample size. 154 * 155 * @param populationSize the population size 156 * @param numberOfSuccesses number of successes in the population 157 * @param sampleSize the sample size 158 * @return a new hypergeometric desitribution 159 */ 160 public abstract HypergeometricDistribution 161 createHypergeometricDistribution(int populationSize, 162 int numberOfSuccesses, int sampleSize); 163 164 /** 165 * Create a new normal distribution with the given mean and standard 166 * deviation. 167 * 168 * @param mean the mean of the distribution 169 * @param sd standard deviation 170 * @return a new normal distribution 171 */ 172 public abstract NormalDistribution 173 createNormalDistribution(double mean, double sd); 174 175 /** 176 * Create a new normal distribution with mean zero and standard 177 * deviation one. 178 * 179 * @return a new normal distribution. 180 */ 181 public abstract NormalDistribution createNormalDistribution(); 182 183 /** 184 * Create a new Poisson distribution with poisson parameter lambda. 185 * 186 * @param lambda poisson parameter 187 * @return a new poisson distribution. 188 */ 189 public abstract PoissonDistribution 190 createPoissonDistribution(double lambda); 191 192 /** 193 * Create a new Weibull distribution with the given shape and scale 194 * parameters. 195 * 196 * @param alpha the shape parameter. 197 * @param beta the scale parameter. 198 * @return a new Weibull distribution. 199 * @since 1.1 200 */ 201 public WeibullDistribution createWeibullDistribution( 202 double alpha, double beta) 203 { 204 return new WeibullDistributionImpl(alpha, beta); 205 } 206 }