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 package org.apache.commons.math.random;
18
19 import org.apache.commons.math.MathRuntimeException;
20
21 /**
22 * Abstract class implementing the {@link RandomGenerator} interface.
23 * Default implementations for all methods other than {@link #nextDouble()} and
24 * {@link #setSeed(long)} are provided.
25 * <p>
26 * All data generation methods are based on <code>nextDouble().</code>
27 * Concrete implementations <strong>must</strong> override
28 * this method and <strong>should</strong> provide better / more
29 * performant implementations of the other methods if the underlying PRNG
30 * supplies them.</p>
31 *
32 * @since 1.1
33 * @version $Revision: 796543 $ $Date: 2009-07-21 17:32:38 -0400 (Tue, 21 Jul 2009) $
34 */
35 public abstract class AbstractRandomGenerator implements RandomGenerator {
36
37 /**
38 * Cached random normal value. The default implementation for
39 * {@link #nextGaussian} generates pairs of values and this field caches the
40 * second value so that the full algorithm is not executed for every
41 * activation. The value <code>Double.NaN</code> signals that there is
42 * no cached value. Use {@link #clear} to clear the cached value.
43 */
44 private double cachedNormalDeviate = Double.NaN;
45
46 /**
47 * Construct a RandomGenerator.
48 */
49 public AbstractRandomGenerator() {
50 super();
51
52 }
53
54 /**
55 * Clears the cache used by the default implementation of
56 * {@link #nextGaussian}. Implemementations that do not override the
57 * default implementation of <code>nextGaussian</code> should call this
58 * method in the implementation of {@link #setSeed(long)}
59 */
60 public void clear() {
61 cachedNormalDeviate = Double.NaN;
62 }
63
64 /** {@inheritDoc} */
65 public void setSeed(int seed) {
66 setSeed((long) seed);
67 }
68
69 /** {@inheritDoc} */
70 public void setSeed(int[] seed) {
71 // the following number is the largest prime that fits in 32 bits (it is 2^32 - 5)
72 final long prime = 4294967291l;
73
74 long combined = 0l;
75 for (int s : seed) {
76 combined = combined * prime + s;
77 }
78 setSeed(combined);
79 }
80
81 /**
82 * Sets the seed of the underyling random number generator using a
83 * <code>long</code> seed. Sequences of values generated starting with the
84 * same seeds should be identical.
85 * <p>
86 * Implementations that do not override the default implementation of
87 * <code>nextGaussian</code> should include a call to {@link #clear} in the
88 * implementation of this method.</p>
89 *
90 * @param seed the seed value
91 */
92 public abstract void setSeed(long seed);
93
94 /**
95 * Generates random bytes and places them into a user-supplied
96 * byte array. The number of random bytes produced is equal to
97 * the length of the byte array.
98 * <p>
99 * The default implementation fills the array with bytes extracted from
100 * random integers generated using {@link #nextInt}.</p>
101 *
102 * @param bytes the non-null byte array in which to put the
103 * random bytes
104 */
105 public void nextBytes(byte[] bytes) {
106 int bytesOut = 0;
107 while (bytesOut < bytes.length) {
108 int randInt = nextInt();
109 for (int i = 0; i < 3; i++) {
110 if ( i > 0) {
111 randInt = randInt >> 8;
112 }
113 bytes[bytesOut++] = (byte) randInt;
114 if (bytesOut == bytes.length) {
115 return;
116 }
117 }
118 }
119 }
120
121 /**
122 * Returns the next pseudorandom, uniformly distributed <code>int</code>
123 * value from this random number generator's sequence.
124 * All 2<font size="-1"><sup>32</sup></font> possible <tt>int</tt> values
125 * should be produced with (approximately) equal probability.
126 * <p>
127 * The default implementation provided here returns
128 * <pre>
129 * <code>(int) (nextDouble() * Integer.MAX_VALUE)</code>
130 * </pre></p>
131 *
132 * @return the next pseudorandom, uniformly distributed <code>int</code>
133 * value from this random number generator's sequence
134 */
135 public int nextInt() {
136 return (int) (nextDouble() * Integer.MAX_VALUE);
137 }
138
139 /**
140 * Returns a pseudorandom, uniformly distributed <tt>int</tt> value
141 * between 0 (inclusive) and the specified value (exclusive), drawn from
142 * this random number generator's sequence.
143 * <p>
144 * The default implementation returns
145 * <pre>
146 * <code>(int) (nextDouble() * n</code>
147 * </pre></p>
148 *
149 * @param n the bound on the random number to be returned. Must be
150 * positive.
151 * @return a pseudorandom, uniformly distributed <tt>int</tt>
152 * value between 0 (inclusive) and n (exclusive).
153 * @throws IllegalArgumentException if n is not positive.
154 */
155 public int nextInt(int n) {
156 if (n <= 0 ) {
157 throw MathRuntimeException.createIllegalArgumentException(
158 "upper bound must be positive ({0})", n);
159 }
160 int result = (int) (nextDouble() * n);
161 return result < n ? result : n - 1;
162 }
163
164 /**
165 * Returns the next pseudorandom, uniformly distributed <code>long</code>
166 * value from this random number generator's sequence. All
167 * 2<font size="-1"><sup>64</sup></font> possible <tt>long</tt> values
168 * should be produced with (approximately) equal probability.
169 * <p>
170 * The default implementation returns
171 * <pre>
172 * <code>(long) (nextDouble() * Long.MAX_VALUE)</code>
173 * </pre></p>
174 *
175 * @return the next pseudorandom, uniformly distributed <code>long</code>
176 *value from this random number generator's sequence
177 */
178 public long nextLong() {
179 return (long) (nextDouble() * Long.MAX_VALUE);
180 }
181
182 /**
183 * Returns the next pseudorandom, uniformly distributed
184 * <code>boolean</code> value from this random number generator's
185 * sequence.
186 * <p>
187 * The default implementation returns
188 * <pre>
189 * <code>nextDouble() <= 0.5</code>
190 * </pre></p>
191 *
192 * @return the next pseudorandom, uniformly distributed
193 * <code>boolean</code> value from this random number generator's
194 * sequence
195 */
196 public boolean nextBoolean() {
197 return nextDouble() <= 0.5;
198 }
199
200 /**
201 * Returns the next pseudorandom, uniformly distributed <code>float</code>
202 * value between <code>0.0</code> and <code>1.0</code> from this random
203 * number generator's sequence.
204 * <p>
205 * The default implementation returns
206 * <pre>
207 * <code>(float) nextDouble() </code>
208 * </pre></p>
209 *
210 * @return the next pseudorandom, uniformly distributed <code>float</code>
211 * value between <code>0.0</code> and <code>1.0</code> from this
212 * random number generator's sequence
213 */
214 public float nextFloat() {
215 return (float) nextDouble();
216 }
217
218 /**
219 * Returns the next pseudorandom, uniformly distributed
220 * <code>double</code> value between <code>0.0</code> and
221 * <code>1.0</code> from this random number generator's sequence.
222 * <p>
223 * This method provides the underlying source of random data used by the
224 * other methods.</p>
225 *
226 * @return the next pseudorandom, uniformly distributed
227 * <code>double</code> value between <code>0.0</code> and
228 * <code>1.0</code> from this random number generator's sequence
229 */
230 public abstract double nextDouble();
231
232 /**
233 * Returns the next pseudorandom, Gaussian ("normally") distributed
234 * <code>double</code> value with mean <code>0.0</code> and standard
235 * deviation <code>1.0</code> from this random number generator's sequence.
236 * <p>
237 * The default implementation uses the <em>Polar Method</em>
238 * due to G.E.P. Box, M.E. Muller and G. Marsaglia, as described in
239 * D. Knuth, <u>The Art of Computer Programming</u>, 3.4.1C.</p>
240 * <p>
241 * The algorithm generates a pair of independent random values. One of
242 * these is cached for reuse, so the full algorithm is not executed on each
243 * activation. Implementations that do not override this method should
244 * make sure to call {@link #clear} to clear the cached value in the
245 * implementation of {@link #setSeed(long)}.</p>
246 *
247 * @return the next pseudorandom, Gaussian ("normally") distributed
248 * <code>double</code> value with mean <code>0.0</code> and
249 * standard deviation <code>1.0</code> from this random number
250 * generator's sequence
251 */
252 public double nextGaussian() {
253 if (!Double.isNaN(cachedNormalDeviate)) {
254 double dev = cachedNormalDeviate;
255 cachedNormalDeviate = Double.NaN;
256 return dev;
257 }
258 double v1 = 0;
259 double v2 = 0;
260 double s = 1;
261 while (s >=1 ) {
262 v1 = 2 * nextDouble() - 1;
263 v2 = 2 * nextDouble() - 1;
264 s = v1 * v1 + v2 * v2;
265 }
266 if (s != 0) {
267 s = Math.sqrt(-2 * Math.log(s) / s);
268 }
269 cachedNormalDeviate = v2 * s;
270 return v1 * s;
271 }
272 }