Classes in this File | Line Coverage | Branch Coverage | Complexity | ||||||||
DistributionFactory |
|
| 1.1428571428571428;1.143 |
1 | /* |
|
2 | * Copyright 2003-2005 The Apache Software Foundation. |
|
3 | * |
|
4 | * Licensed under the Apache License, Version 2.0 (the "License"); |
|
5 | * you may not use this file except in compliance with the License. |
|
6 | * You may obtain a copy of the License at |
|
7 | * |
|
8 | * http://www.apache.org/licenses/LICENSE-2.0 |
|
9 | * |
|
10 | * Unless required by applicable law or agreed to in writing, software |
|
11 | * distributed under the License is distributed on an "AS IS" BASIS, |
|
12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
13 | * See the License for the specific language governing permissions and |
|
14 | * limitations under the License. |
|
15 | */ |
|
16 | ||
17 | package org.apache.commons.math.distribution; |
|
18 | ||
19 | import org.apache.commons.discovery.tools.DiscoverClass; |
|
20 | ||
21 | /** |
|
22 | * This factory provids the means to create common statistical distributions. |
|
23 | * The following distributions are supported: |
|
24 | * <ul> |
|
25 | * <li>Binomial</li> |
|
26 | * <li>Cauchy</li> |
|
27 | * <li>Chi-Squared</li> |
|
28 | * <li>Exponential</li> |
|
29 | * <li>F</li> |
|
30 | * <li>Gamma</li> |
|
31 | * <li>HyperGeometric</li> |
|
32 | * <li>Poisson</li> |
|
33 | * <li>Normal</li> |
|
34 | * <li>Student's t</li> |
|
35 | * <li>Weibull</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 $Revision$ $Date: 2005-06-26 15:20:57 -0700 (Sun, 26 Jun 2005) $ |
|
46 | */ |
|
47 | public abstract class DistributionFactory { |
|
48 | /** |
|
49 | * Default constructor. |
|
50 | */ |
|
51 | protected DistributionFactory() { |
|
52 | 464 | super(); |
53 | 464 | } |
54 | ||
55 | /** |
|
56 | * Create an instance of a <code>DistributionFactory</code> |
|
57 | * @return a new factory. |
|
58 | */ |
|
59 | public static DistributionFactory newInstance() { |
|
60 | 386 | DistributionFactory factory = null; |
61 | try { |
|
62 | 386 | DiscoverClass dc = new DiscoverClass(); |
63 | 386 | factory = (DistributionFactory) dc.newInstance( |
64 | 36 | DistributionFactory.class, |
65 | "org.apache.commons.math.distribution.DistributionFactoryImpl"); |
|
66 | 0 | } catch(Throwable t) { |
67 | 0 | return new DistributionFactoryImpl(); |
68 | 386 | } |
69 | 386 | return factory; |
70 | } |
|
71 | ||
72 | /** |
|
73 | * Create a binomial distribution with the given number of trials and |
|
74 | * probability of success. |
|
75 | * |
|
76 | * @param numberOfTrials the number of trials. |
|
77 | * @param probabilityOfSuccess the probability of success |
|
78 | * @return a new binomial distribution |
|
79 | */ |
|
80 | public abstract BinomialDistribution createBinomialDistribution( |
|
81 | int numberOfTrials, double probabilityOfSuccess); |
|
82 | ||
83 | /** |
|
84 | * Create a new cauchy distribution with the given median and scale. |
|
85 | * @param median the median of the distribution |
|
86 | * @param scale the scale |
|
87 | * @return a new cauchy distribution |
|
88 | * @since 1.1 |
|
89 | */ |
|
90 | public CauchyDistribution createCauchyDistribution( |
|
91 | double median, double scale) |
|
92 | { |
|
93 | 20 | return new CauchyDistributionImpl(median, scale); |
94 | } |
|
95 | ||
96 | /** |
|
97 | * Create a new chi-square distribution with the given degrees of freedom. |
|
98 | * |
|
99 | * @param degreesOfFreedom degrees of freedom |
|
100 | * @return a new chi-square distribution |
|
101 | */ |
|
102 | public abstract ChiSquaredDistribution createChiSquareDistribution( |
|
103 | double degreesOfFreedom); |
|
104 | ||
105 | /** |
|
106 | * Create a new exponential distribution with the given degrees of freedom. |
|
107 | * |
|
108 | * @param mean mean |
|
109 | * @return a new exponential distribution |
|
110 | */ |
|
111 | public abstract ExponentialDistribution createExponentialDistribution( |
|
112 | double mean); |
|
113 | ||
114 | /** |
|
115 | * Create a new F-distribution with the given degrees of freedom. |
|
116 | * |
|
117 | * @param numeratorDegreesOfFreedom numerator degrees of freedom |
|
118 | * @param denominatorDegreesOfFreedom denominator degrees of freedom |
|
119 | * @return a new F-distribution |
|
120 | */ |
|
121 | public abstract FDistribution createFDistribution( |
|
122 | double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom); |
|
123 | ||
124 | /** |
|
125 | * Create a new gamma distribution with the given shape and scale |
|
126 | * parameters. |
|
127 | * |
|
128 | * @param alpha the shape parameter |
|
129 | * @param beta the scale parameter |
|
130 | * |
|
131 | * @return a new gamma distribution |
|
132 | */ |
|
133 | public abstract GammaDistribution createGammaDistribution( |
|
134 | double alpha, double beta); |
|
135 | ||
136 | /** |
|
137 | * Create a new t distribution with the given degrees of freedom. |
|
138 | * |
|
139 | * @param degreesOfFreedom degrees of freedom |
|
140 | * @return a new t distribution |
|
141 | */ |
|
142 | public abstract TDistribution createTDistribution(double degreesOfFreedom); |
|
143 | ||
144 | /** |
|
145 | * Create a new hypergeometric distribution with the given the population |
|
146 | * size, the number of successes in the population, and the sample size. |
|
147 | * |
|
148 | * @param populationSize the population size |
|
149 | * @param numberOfSuccesses number of successes in the population |
|
150 | * @param sampleSize the sample size |
|
151 | * @return a new hypergeometric desitribution |
|
152 | */ |
|
153 | public abstract HypergeometricDistribution |
|
154 | createHypergeometricDistribution(int populationSize, |
|
155 | int numberOfSuccesses, int sampleSize); |
|
156 | ||
157 | /** |
|
158 | * Create a new normal distribution with the given mean and standard |
|
159 | * deviation. |
|
160 | * |
|
161 | * @param mean the mean of the distribution |
|
162 | * @param sd standard deviation |
|
163 | * @return a new normal distribution |
|
164 | */ |
|
165 | public abstract NormalDistribution |
|
166 | createNormalDistribution(double mean, double sd); |
|
167 | ||
168 | /** |
|
169 | * Create a new normal distribution with mean zero and standard |
|
170 | * deviation one. |
|
171 | * |
|
172 | * @return a new normal distribution. |
|
173 | */ |
|
174 | public abstract NormalDistribution createNormalDistribution(); |
|
175 | ||
176 | /** |
|
177 | * Create a new Poisson distribution with poisson parameter lambda. |
|
178 | * |
|
179 | * @param lambda poisson parameter |
|
180 | * @return a new poisson distribution. |
|
181 | */ |
|
182 | public abstract PoissonDistribution |
|
183 | createPoissonDistribution(double lambda); |
|
184 | ||
185 | /** |
|
186 | * Create a new Weibull distribution with the given shape and scale |
|
187 | * parameters. |
|
188 | * |
|
189 | * @param alpha the shape parameter. |
|
190 | * @param beta the scale parameter. |
|
191 | * @return a new Weibull distribution. |
|
192 | * @since 1.1 |
|
193 | */ |
|
194 | public WeibullDistribution createWeibullDistribution( |
|
195 | double alpha, double beta) |
|
196 | { |
|
197 | 26 | return new WeibullDistributionImpl(alpha, beta); |
198 | } |
|
199 | } |