source: src/main/java/agents/anac/y2019/harddealer/math3/random/AbstractRandomGenerator.java

Last change on this file was 204, checked in by Katsuhide Fujita, 5 years ago

Fixed errors of ANAC2019 agents
  • Property svn:executable set to *
File size: 9.5 KB
Line 
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 */
17package agents.anac.y2019.harddealer.math3.random;
18
19import agents.anac.y2019.harddealer.math3.exception.NotStrictlyPositiveException;
20import agents.anac.y2019.harddealer.math3.util.FastMath;
21
22/**
23 * Abstract class implementing the {@link RandomGenerator} interface.
24 * Default implementations for all methods other than {@link #nextDouble()} and
25 * {@link #setSeed(long)} are provided.
26 * <p>
27 * All data generation methods are based on {@code code nextDouble()}.
28 * Concrete implementations <strong>must</strong> override
29 * this method and <strong>should</strong> provide better / more
30 * performant implementations of the other methods if the underlying PRNG
31 * supplies them.</p>
32 *
33 * @since 1.1
34 */
35public 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} 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}. Implementations that do not override the
57 * default implementation of {@code nextGaussian} 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 underlying random number generator using a
83 * {@code long} 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} 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 >>= 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}
123 * value from this random number generator's sequence.
124 * All 2<font size="-1"><sup>32</sup></font> possible {@code int} 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}
133 * value from this random number generator's sequence
134 */
135 public int nextInt() {
136 return (int) ((2d * nextDouble() - 1d) * Integer.MAX_VALUE);
137 }
138
139 /**
140 * Returns a pseudorandom, uniformly distributed {@code int} 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 {@code int}
152 * value between 0 (inclusive) and n (exclusive).
153 * @throws NotStrictlyPositiveException if {@code n <= 0}.
154 */
155 public int nextInt(int n) {
156 if (n <= 0 ) {
157 throw new NotStrictlyPositiveException(n);
158 }
159 int result = (int) (nextDouble() * n);
160 return result < n ? result : n - 1;
161 }
162
163 /**
164 * Returns the next pseudorandom, uniformly distributed {@code long}
165 * value from this random number generator's sequence. All
166 * 2<font size="-1"><sup>64</sup></font> possible {@code long} values
167 * should be produced with (approximately) equal probability.
168 * <p>
169 * The default implementation returns
170 * <pre>
171 * <code>(long) (nextDouble() * Long.MAX_VALUE)</code>
172 * </pre></p>
173 *
174 * @return the next pseudorandom, uniformly distributed {@code long}
175 *value from this random number generator's sequence
176 */
177 public long nextLong() {
178 return (long) ((2d * nextDouble() - 1d) * Long.MAX_VALUE);
179 }
180
181 /**
182 * Returns the next pseudorandom, uniformly distributed
183 * {@code boolean} value from this random number generator's
184 * sequence.
185 * <p>
186 * The default implementation returns
187 * <pre>
188 * <code>nextDouble() <= 0.5</code>
189 * </pre></p>
190 *
191 * @return the next pseudorandom, uniformly distributed
192 * {@code boolean} value from this random number generator's
193 * sequence
194 */
195 public boolean nextBoolean() {
196 return nextDouble() <= 0.5;
197 }
198
199 /**
200 * Returns the next pseudorandom, uniformly distributed {@code float}
201 * value between {@code 0.0} and {@code 1.0} from this random
202 * number generator's sequence.
203 * <p>
204 * The default implementation returns
205 * <pre>
206 * <code>(float) nextDouble() </code>
207 * </pre></p>
208 *
209 * @return the next pseudorandom, uniformly distributed {@code float}
210 * value between {@code 0.0} and {@code 1.0} from this
211 * random number generator's sequence
212 */
213 public float nextFloat() {
214 return (float) nextDouble();
215 }
216
217 /**
218 * Returns the next pseudorandom, uniformly distributed
219 * {@code double} value between {@code 0.0} and
220 * {@code 1.0} from this random number generator's sequence.
221 * <p>
222 * This method provides the underlying source of random data used by the
223 * other methods.</p>
224 *
225 * @return the next pseudorandom, uniformly distributed
226 * {@code double} value between {@code 0.0} and
227 * {@code 1.0} from this random number generator's sequence
228 */
229 public abstract double nextDouble();
230
231 /**
232 * Returns the next pseudorandom, Gaussian ("normally") distributed
233 * {@code double} value with mean {@code 0.0} and standard
234 * deviation {@code 1.0} from this random number generator's sequence.
235 * <p>
236 * The default implementation uses the <em>Polar Method</em>
237 * due to G.E.P. Box, M.E. Muller and G. Marsaglia, as described in
238 * D. Knuth, <u>The Art of Computer Programming</u>, 3.4.1C.</p>
239 * <p>
240 * The algorithm generates a pair of independent random values. One of
241 * these is cached for reuse, so the full algorithm is not executed on each
242 * activation. Implementations that do not override this method should
243 * make sure to call {@link #clear} to clear the cached value in the
244 * implementation of {@link #setSeed(long)}.</p>
245 *
246 * @return the next pseudorandom, Gaussian ("normally") distributed
247 * {@code double} value with mean {@code 0.0} and
248 * standard deviation {@code 1.0} from this random number
249 * generator's sequence
250 */
251 public double nextGaussian() {
252 if (!Double.isNaN(cachedNormalDeviate)) {
253 double dev = cachedNormalDeviate;
254 cachedNormalDeviate = Double.NaN;
255 return dev;
256 }
257 double v1 = 0;
258 double v2 = 0;
259 double s = 1;
260 while (s >=1 ) {
261 v1 = 2 * nextDouble() - 1;
262 v2 = 2 * nextDouble() - 1;
263 s = v1 * v1 + v2 * v2;
264 }
265 if (s != 0) {
266 s = FastMath.sqrt(-2 * FastMath.log(s) / s);
267 }
268 cachedNormalDeviate = v2 * s;
269 return v1 * s;
270 }
271}
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