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.exception.NotStrictlyPositiveException; 20 import org.apache.commons.math.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 * @version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 aot 2010) $ 35 */ 36 public abstract class AbstractRandomGenerator implements RandomGenerator { 37 38 /** 39 * Cached random normal value. The default implementation for 40 * {@link #nextGaussian} generates pairs of values and this field caches the 41 * second value so that the full algorithm is not executed for every 42 * activation. The value {@code Double.NaN} signals that there is 43 * no cached value. Use {@link #clear} to clear the cached value. 44 */ 45 private double cachedNormalDeviate = Double.NaN; 46 47 /** 48 * Construct a RandomGenerator. 49 */ 50 public AbstractRandomGenerator() { 51 super(); 52 53 } 54 55 /** 56 * Clears the cache used by the default implementation of 57 * {@link #nextGaussian}. Implemementations that do not override the 58 * default implementation of {@code nextGaussian} should call this 59 * method in the implementation of {@link #setSeed(long)} 60 */ 61 public void clear() { 62 cachedNormalDeviate = Double.NaN; 63 } 64 65 /** {@inheritDoc} */ 66 public void setSeed(int seed) { 67 setSeed((long) seed); 68 } 69 70 /** {@inheritDoc} */ 71 public void setSeed(int[] seed) { 72 // the following number is the largest prime that fits in 32 bits (it is 2^32 - 5) 73 final long prime = 4294967291l; 74 75 long combined = 0l; 76 for (int s : seed) { 77 combined = combined * prime + s; 78 } 79 setSeed(combined); 80 } 81 82 /** 83 * Sets the seed of the underyling random number generator using a 84 * {@code long} seed. Sequences of values generated starting with the 85 * same seeds should be identical. 86 * <p> 87 * Implementations that do not override the default implementation of 88 * {@code nextGaussian} should include a call to {@link #clear} in the 89 * implementation of this method.</p> 90 * 91 * @param seed the seed value 92 */ 93 public abstract void setSeed(long seed); 94 95 /** 96 * Generates random bytes and places them into a user-supplied 97 * byte array. The number of random bytes produced is equal to 98 * the length of the byte array. 99 * <p> 100 * The default implementation fills the array with bytes extracted from 101 * random integers generated using {@link #nextInt}.</p> 102 * 103 * @param bytes the non-null byte array in which to put the 104 * random bytes 105 */ 106 public void nextBytes(byte[] bytes) { 107 int bytesOut = 0; 108 while (bytesOut < bytes.length) { 109 int randInt = nextInt(); 110 for (int i = 0; i < 3; i++) { 111 if ( i > 0) { 112 randInt = randInt >> 8; 113 } 114 bytes[bytesOut++] = (byte) randInt; 115 if (bytesOut == bytes.length) { 116 return; 117 } 118 } 119 } 120 } 121 122 /** 123 * Returns the next pseudorandom, uniformly distributed {@code int} 124 * value from this random number generator's sequence. 125 * All 2<font size="-1"><sup>32</sup></font> possible {@code int} values 126 * should be produced with (approximately) equal probability. 127 * <p> 128 * The default implementation provided here returns 129 * <pre> 130 * <code>(int) (nextDouble() * Integer.MAX_VALUE)</code> 131 * </pre></p> 132 * 133 * @return the next pseudorandom, uniformly distributed {@code int} 134 * value from this random number generator's sequence 135 */ 136 public int nextInt() { 137 return (int) (nextDouble() * Integer.MAX_VALUE); 138 } 139 140 /** 141 * Returns a pseudorandom, uniformly distributed {@code int} value 142 * between 0 (inclusive) and the specified value (exclusive), drawn from 143 * this random number generator's sequence. 144 * <p> 145 * The default implementation returns 146 * <pre> 147 * <code>(int) (nextDouble() * n</code> 148 * </pre></p> 149 * 150 * @param n the bound on the random number to be returned. Must be 151 * positive. 152 * @return a pseudorandom, uniformly distributed {@code int} 153 * value between 0 (inclusive) and n (exclusive). 154 * @throws NotStrictlyPositiveException if {@code n <= 0}. 155 */ 156 public int nextInt(int n) { 157 if (n <= 0 ) { 158 throw new NotStrictlyPositiveException(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} 166 * value from this random number generator's sequence. All 167 * 2<font size="-1"><sup>64</sup></font> possible {@code long} 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} 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} 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} 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} 202 * value between {@code 0.0} and {@code 1.0} 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} 211 * value between {@code 0.0} and {@code 1.0} 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} value between {@code 0.0} and 221 * {@code 1.0} 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} value between {@code 0.0} and 228 * {@code 1.0} 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} value with mean {@code 0.0} and standard 235 * deviation {@code 1.0} 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} value with mean {@code 0.0} and 249 * standard deviation {@code 1.0} 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 = FastMath.sqrt(-2 * FastMath.log(s) / s); 268 } 269 cachedNormalDeviate = v2 * s; 270 return v1 * s; 271 } 272 } 273
