Coverage Report - org.apache.commons.math.distribution.HypergeometricDistributionImpl

Classes in this File	Line Coverage	Branch Coverage	Complexity
HypergeometricDistributionImpl	97%	100%	1.9411764705882353;1.941

1		/*
2		* Copyright 2003-2004 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 java.io.Serializable;
20
21		import org.apache.commons.math.util.MathUtils;
22
23		/**
24		* The default implementation of {@link HypergeometricDistribution}.
25		*
26		* @version $Revision$ $Date: 2005-08-26 07:05:45 -0700 (Fri, 26 Aug 2005) $
27		*/
28		public class HypergeometricDistributionImpl extends AbstractIntegerDistribution
29		implements HypergeometricDistribution, Serializable
30		{
31
32		/** Serializable version identifier */
33		static final long serialVersionUID = -436928820673516179L;
34
35		/** The number of successes in the population. */
36		private int numberOfSuccesses;
37
38		/** The population size. */
39		private int populationSize;
40
41		/** The sample size. */
42		private int sampleSize;
43
44		/**
45		* Construct a new hypergeometric distribution with the given the population
46		* size, the number of successes in the population, and the sample size.
47		* @param populationSize the population size.
48		* @param numberOfSuccesses number of successes in the population.
49		* @param sampleSize the sample size.
50		*/
51		public HypergeometricDistributionImpl(int populationSize,
52		int numberOfSuccesses, int sampleSize) {
53	46	super();
54	46	if (numberOfSuccesses > populationSize) {
55	4	throw new IllegalArgumentException(
56		"number of successes must be less than or equal to " +
57		"population size");
58		}
59	42	if (sampleSize > populationSize) {
60	2	throw new IllegalArgumentException(
61		"sample size must be less than or equal to population size");
62		}
63	40	setPopulationSize(populationSize);
64	40	setSampleSize(sampleSize);
65	38	setNumberOfSuccesses(numberOfSuccesses);
66	36	}
67
68		/**
69		* For this disbution, X, this method returns P(X ≤ x).
70		* @param x the value at which the PDF is evaluated.
71		* @return PDF for this distribution.
72		*/
73		public double cumulativeProbability(int x) {
74		double ret;
75
76	272	int n = getPopulationSize();
77	272	int m = getNumberOfSuccesses();
78	272	int k = getSampleSize();
79
80	272	int[] domain = getDomain(n, m, k);
81	272	if (x < domain[0]) {
82	32	ret = 0.0;
83	240	} else if(x >= domain[1]) {
84	40	ret = 1.0;
85		} else {
86	200	ret = innerCumulativeProbability(domain[0], x, 1, n, m, k);
87		}
88
89	272	return ret;
90		}
91
92		/**
93		* Return the domain for the given hypergeometric distribution parameters.
94		* @param n the population size.
95		* @param m number of successes in the population.
96		* @param k the sample size.
97		* @return a two element array containing the lower and upper bounds of the
98		* hypergeometric distribution.
99		*/
100		private int[] getDomain(int n, int m, int k){
101	462	return new int[]{
102		getLowerDomain(n, m, k),
103		getUpperDomain(m, k)
104		};
105		}
106
107		/**
108		* Access the domain value lower bound, based on <code>p</code>, used to
109		* bracket a PDF root.
110		*
111		* @param p the desired probability for the critical value
112		* @return domain value lower bound, i.e.
113		* P(X &lt; <i>lower bound</i>) &lt; <code>p</code>
114		*/
115		protected int getDomainLowerBound(double p) {
116	36	return getLowerDomain(getPopulationSize(), getNumberOfSuccesses(),
117		getSampleSize());
118		}
119
120		/**
121		* Access the domain value upper bound, based on <code>p</code>, used to
122		* bracket a PDF root.
123		*
124		* @param p the desired probability for the critical value
125		* @return domain value upper bound, i.e.
126		* P(X &lt; <i>upper bound</i>) &gt; <code>p</code>
127		*/
128		protected int getDomainUpperBound(double p) {
129	36	return getUpperDomain(getSampleSize(), getNumberOfSuccesses());
130		}
131
132		/**
133		* Return the lowest domain value for the given hypergeometric distribution
134		* parameters.
135		* @param n the population size.
136		* @param m number of successes in the population.
137		* @param k the sample size.
138		* @return the lowest domain value of the hypergeometric distribution.
139		*/
140		private int getLowerDomain(int n, int m, int k) {
141	498	return Math.max(0, m - (n - k));
142		}
143
144		/**
145		* Access the number of successes.
146		* @return the number of successes.
147		*/
148		public int getNumberOfSuccesses() {
149	534	return numberOfSuccesses;
150		}
151
152		/**
153		* Access the population size.
154		* @return the population size.
155		*/
156		public int getPopulationSize() {
157	500	return populationSize;
158		}
159
160		/**
161		* Access the sample size.
162		* @return the sample size.
163		*/
164		public int getSampleSize() {
165	534	return sampleSize;
166		}
167
168		/**
169		* Return the highest domain value for the given hypergeometric distribution
170		* parameters.
171		* @param m number of successes in the population.
172		* @param k the sample size.
173		* @return the highest domain value of the hypergeometric distribution.
174		*/
175		private int getUpperDomain(int m, int k){
176	498	return Math.min(k, m);
177		}
178
179		/**
180		* For this disbution, X, this method returns P(X = x).
181		*
182		* @param x the value at which the PMF is evaluated.
183		* @return PMF for this distribution.
184		*/
185		public double probability(int x) {
186		double ret;
187
188	118	int n = getPopulationSize();
189	118	int m = getNumberOfSuccesses();
190	118	int k = getSampleSize();
191
192	118	int[] domain = getDomain(n, m, k);
193	118	if(x < domain[0] || x > domain[1]){
194	28	ret = 0.0;
195		} else {
196	90	ret = probability(n, m, k, x);
197		}
198
199	118	return ret;
200		}
201
202		/**
203		* For the disbution, X, defined by the given hypergeometric distribution
204		* parameters, this method returns P(X = x).
205		*
206		* @param n the population size.
207		* @param m number of successes in the population.
208		* @param k the sample size.
209		* @param x the value at which the PMF is evaluated.
210		* @return PMF for the distribution.
211		*/
212		private double probability(int n, int m, int k, int x) {
213	5942	return Math.exp(MathUtils.binomialCoefficientLog(m, x) +
214		MathUtils.binomialCoefficientLog(n - m, k - x) -
215		MathUtils.binomialCoefficientLog(n, k));
216		}
217
218		/**
219		* Modify the number of successes.
220		* @param num the new number of successes.
221		* @throws IllegalArgumentException if <code>num</code> is negative.
222		*/
223		public void setNumberOfSuccesses(int num) {
224	38	if(num < 0){
225	2	throw new IllegalArgumentException(
226		"number of successes must be non-negative.");
227		}
228	36	numberOfSuccesses = num;
229	36	}
230
231		/**
232		* Modify the population size.
233		* @param size the new population size.
234		* @throws IllegalArgumentException if <code>size</code> is not positive.
235		*/
236		public void setPopulationSize(int size) {
237	44	if(size <= 0){
238	2	throw new IllegalArgumentException(
239		"population size must be positive.");
240		}
241	42	populationSize = size;
242	42	}
243
244		/**
245		* Modify the sample size.
246		* @param size the new sample size.
247		* @throws IllegalArgumentException if <code>size</code> is negative.
248		*/
249		public void setSampleSize(int size) {
250	40	if (size < 0) {
251	2	throw new IllegalArgumentException(
252		"sample size must be non-negative.");
253		}
254	38	sampleSize = size;
255	38	}
256
257		/**
258		* For this disbution, X, this method returns P(X &ge; x).
259		* @param x the value at which the CDF is evaluated.
260		* @return upper tail CDF for this distribution.
261		* @since 1.1
262		*/
263		public double upperCumulativeProbability(int x) {
264		double ret;
265
266	72	int n = getPopulationSize();
267	72	int m = getNumberOfSuccesses();
268	72	int k = getSampleSize();
269
270	72	int[] domain = getDomain(n, m, k);
271	72	if (x < domain[0]) {
272	0	ret = 1.0;
273	72	} else if(x > domain[1]) {
274	0	ret = 0.0;
275		} else {
276	72	ret = innerCumulativeProbability(domain[1], x, -1, n, m, k);
277		}
278
279	72	return ret;
280		}
281
282		/**
283		* For this disbution, X, this method returns P(x0 &le; X &le; x1). This
284		* probability is computed by summing the point probabilities for the values
285		* x0, x0 + 1, x0 + 2, ..., x1, in the order directed by dx.
286		* @param x0 the inclusive, lower bound
287		* @param x1 the inclusive, upper bound
288		* @param dx the direction of summation. 1 indicates summing from x0 to x1.
289		* 0 indicates summing from x1 to x0.
290		* @param n the population size.
291		* @param m number of successes in the population.
292		* @param k the sample size.
293		* @return P(x0 &le; X &le; x1).
294		*/
295		private double innerCumulativeProbability(
296		int x0, int x1, int dx, int n, int m, int k)
297		{
298	272	double ret = probability(n, m, k, x0);
299	5852	while (x0 != x1) {
300	5580	x0 += dx;
301	5580	ret += probability(n, m, k, x0);
302		}
303	272	return ret;
304		}
305		}