Port random.h & Co. from svn -r3422 + some cleanups
- gauss(double std_dev) has been remove for clarity
1.1 --- a/lemon/Makefile.am Thu Dec 20 23:46:50 2007 +0000
1.2 +++ b/lemon/Makefile.am Fri Dec 21 00:07:03 2007 +0000
1.3 @@ -7,13 +7,16 @@
1.4 lib_LTLIBRARIES += lemon/libemon.la
1.5
1.6 lemon_libemon_la_SOURCES = \
1.7 - lemon/base.cc
1.8 + lemon/base.cc \
1.9 + lemon/random.cc
1.10 +
1.11
1.12 lemon_libemon_la_CXXFLAGS = $(GLPK_CFLAGS) $(CPLEX_CFLAGS) $(SOPLEX_CXXFLAGS)
1.13 lemon_libemon_la_LDFLAGS = $(GLPK_LIBS) $(CPLEX_LIBS) $(SOPLEX_LIBS)
1.14
1.15 lemon_HEADERS += \
1.16 lemon/dim2.h \
1.17 + lemon/random.h \
1.18 lemon/list_graph.h \
1.19 lemon/tolerance.h
1.20
2.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
2.2 +++ b/lemon/random.cc Fri Dec 21 00:07:03 2007 +0000
2.3 @@ -0,0 +1,29 @@
2.4 +/* -*- C++ -*-
2.5 + *
2.6 + * This file is a part of LEMON, a generic C++ optimization library
2.7 + *
2.8 + * Copyright (C) 2003-2007
2.9 + * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
2.10 + * (Egervary Research Group on Combinatorial Optimization, EGRES).
2.11 + *
2.12 + * Permission to use, modify and distribute this software is granted
2.13 + * provided that this copyright notice appears in all copies. For
2.14 + * precise terms see the accompanying LICENSE file.
2.15 + *
2.16 + * This software is provided "AS IS" with no warranty of any kind,
2.17 + * express or implied, and with no claim as to its suitability for any
2.18 + * purpose.
2.19 + *
2.20 + */
2.21 +
2.22 +///\file
2.23 +///\brief Instantiation of the Random class.
2.24 +
2.25 +#include <lemon/random.h>
2.26 +
2.27 +namespace lemon {
2.28 + /// \brief Global random number generator instance
2.29 + ///
2.30 + /// A global mersenne twister random number generator instance
2.31 + Random rnd;
2.32 +}
3.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
3.2 +++ b/lemon/random.h Fri Dec 21 00:07:03 2007 +0000
3.3 @@ -0,0 +1,850 @@
3.4 +/* -*- C++ -*-
3.5 + *
3.6 + * This file is a part of LEMON, a generic C++ optimization library
3.7 + *
3.8 + * Copyright (C) 2003-2007
3.9 + * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
3.10 + * (Egervary Research Group on Combinatorial Optimization, EGRES).
3.11 + *
3.12 + * Permission to use, modify and distribute this software is granted
3.13 + * provided that this copyright notice appears in all copies. For
3.14 + * precise terms see the accompanying LICENSE file.
3.15 + *
3.16 + * This software is provided "AS IS" with no warranty of any kind,
3.17 + * express or implied, and with no claim as to its suitability for any
3.18 + * purpose.
3.19 + *
3.20 + */
3.21 +
3.22 +/*
3.23 + * This file contains the reimplemented version of the Mersenne Twister
3.24 + * Generator of Matsumoto and Nishimura.
3.25 + *
3.26 + * See the appropriate copyright notice below.
3.27 + *
3.28 + * Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
3.29 + * All rights reserved.
3.30 + *
3.31 + * Redistribution and use in source and binary forms, with or without
3.32 + * modification, are permitted provided that the following conditions
3.33 + * are met:
3.34 + *
3.35 + * 1. Redistributions of source code must retain the above copyright
3.36 + * notice, this list of conditions and the following disclaimer.
3.37 + *
3.38 + * 2. Redistributions in binary form must reproduce the above copyright
3.39 + * notice, this list of conditions and the following disclaimer in the
3.40 + * documentation and/or other materials provided with the distribution.
3.41 + *
3.42 + * 3. The names of its contributors may not be used to endorse or promote
3.43 + * products derived from this software without specific prior written
3.44 + * permission.
3.45 + *
3.46 + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
3.47 + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
3.48 + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
3.49 + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
3.50 + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
3.51 + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
3.52 + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
3.53 + * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
3.54 + * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
3.55 + * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
3.56 + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
3.57 + * OF THE POSSIBILITY OF SUCH DAMAGE.
3.58 + *
3.59 + *
3.60 + * Any feedback is very welcome.
3.61 + * http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
3.62 + * email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
3.63 + */
3.64 +
3.65 +#ifndef LEMON_RANDOM_H
3.66 +#define LEMON_RANDOM_H
3.67 +
3.68 +#include <algorithm>
3.69 +#include <iterator>
3.70 +#include <vector>
3.71 +
3.72 +#include <ctime>
3.73 +#include <cmath>
3.74 +
3.75 +#include <lemon/dim2.h>
3.76 +///\ingroup misc
3.77 +///\file
3.78 +///\brief Mersenne Twister random number generator
3.79 +///
3.80 +///\author Balazs Dezso
3.81 +
3.82 +namespace lemon {
3.83 +
3.84 + namespace _random_bits {
3.85 +
3.86 + template <typename _Word, int _bits = std::numeric_limits<_Word>::digits>
3.87 + struct RandomTraits {};
3.88 +
3.89 + template <typename _Word>
3.90 + struct RandomTraits<_Word, 32> {
3.91 +
3.92 + typedef _Word Word;
3.93 + static const int bits = 32;
3.94 +
3.95 + static const int length = 624;
3.96 + static const int shift = 397;
3.97 +
3.98 + static const Word mul = 0x6c078965u;
3.99 + static const Word arrayInit = 0x012BD6AAu;
3.100 + static const Word arrayMul1 = 0x0019660Du;
3.101 + static const Word arrayMul2 = 0x5D588B65u;
3.102 +
3.103 + static const Word mask = 0x9908B0DFu;
3.104 + static const Word loMask = (1u << 31) - 1;
3.105 + static const Word hiMask = ~loMask;
3.106 +
3.107 +
3.108 + static Word tempering(Word rnd) {
3.109 + rnd ^= (rnd >> 11);
3.110 + rnd ^= (rnd << 7) & 0x9D2C5680u;
3.111 + rnd ^= (rnd << 15) & 0xEFC60000u;
3.112 + rnd ^= (rnd >> 18);
3.113 + return rnd;
3.114 + }
3.115 +
3.116 + };
3.117 +
3.118 + template <typename _Word>
3.119 + struct RandomTraits<_Word, 64> {
3.120 +
3.121 + typedef _Word Word;
3.122 + static const int bits = 64;
3.123 +
3.124 + static const int length = 312;
3.125 + static const int shift = 156;
3.126 +
3.127 + static const Word mul = Word(0x5851F42Du) << 32 | Word(0x4C957F2Du);
3.128 + static const Word arrayInit = Word(0x00000000u) << 32 |Word(0x012BD6AAu);
3.129 + static const Word arrayMul1 = Word(0x369DEA0Fu) << 32 |Word(0x31A53F85u);
3.130 + static const Word arrayMul2 = Word(0x27BB2EE6u) << 32 |Word(0x87B0B0FDu);
3.131 +
3.132 + static const Word mask = Word(0xB5026F5Au) << 32 | Word(0xA96619E9u);
3.133 + static const Word loMask = (Word(1u) << 31) - 1;
3.134 + static const Word hiMask = ~loMask;
3.135 +
3.136 + static Word tempering(Word rnd) {
3.137 + rnd ^= (rnd >> 29) & (Word(0x55555555u) << 32 | Word(0x55555555u));
3.138 + rnd ^= (rnd << 17) & (Word(0x71D67FFFu) << 32 | Word(0xEDA60000u));
3.139 + rnd ^= (rnd << 37) & (Word(0xFFF7EEE0u) << 32 | Word(0x00000000u));
3.140 + rnd ^= (rnd >> 43);
3.141 + return rnd;
3.142 + }
3.143 +
3.144 + };
3.145 +
3.146 + template <typename _Word>
3.147 + class RandomCore {
3.148 + public:
3.149 +
3.150 + typedef _Word Word;
3.151 +
3.152 + private:
3.153 +
3.154 + static const int bits = RandomTraits<Word>::bits;
3.155 +
3.156 + static const int length = RandomTraits<Word>::length;
3.157 + static const int shift = RandomTraits<Word>::shift;
3.158 +
3.159 + public:
3.160 +
3.161 + void initState() {
3.162 + static const Word seedArray[4] = {
3.163 + 0x12345u, 0x23456u, 0x34567u, 0x45678u
3.164 + };
3.165 +
3.166 + initState(seedArray, seedArray + 4);
3.167 + }
3.168 +
3.169 + void initState(Word seed) {
3.170 +
3.171 + static const Word mul = RandomTraits<Word>::mul;
3.172 +
3.173 + current = state;
3.174 +
3.175 + Word *curr = state + length - 1;
3.176 + curr[0] = seed; --curr;
3.177 + for (int i = 1; i < length; ++i) {
3.178 + curr[0] = (mul * ( curr[1] ^ (curr[1] >> (bits - 2)) ) + i);
3.179 + --curr;
3.180 + }
3.181 + }
3.182 +
3.183 + template <typename Iterator>
3.184 + void initState(Iterator begin, Iterator end) {
3.185 +
3.186 + static const Word init = RandomTraits<Word>::arrayInit;
3.187 + static const Word mul1 = RandomTraits<Word>::arrayMul1;
3.188 + static const Word mul2 = RandomTraits<Word>::arrayMul2;
3.189 +
3.190 +
3.191 + Word *curr = state + length - 1; --curr;
3.192 + Iterator it = begin; int cnt = 0;
3.193 + int num;
3.194 +
3.195 + initState(init);
3.196 +
3.197 + num = length > end - begin ? length : end - begin;
3.198 + while (num--) {
3.199 + curr[0] = (curr[0] ^ ((curr[1] ^ (curr[1] >> (bits - 2))) * mul1))
3.200 + + *it + cnt;
3.201 + ++it; ++cnt;
3.202 + if (it == end) {
3.203 + it = begin; cnt = 0;
3.204 + }
3.205 + if (curr == state) {
3.206 + curr = state + length - 1; curr[0] = state[0];
3.207 + }
3.208 + --curr;
3.209 + }
3.210 +
3.211 + num = length - 1; cnt = length - (curr - state) - 1;
3.212 + while (num--) {
3.213 + curr[0] = (curr[0] ^ ((curr[1] ^ (curr[1] >> (bits - 2))) * mul2))
3.214 + - cnt;
3.215 + --curr; ++cnt;
3.216 + if (curr == state) {
3.217 + curr = state + length - 1; curr[0] = state[0]; --curr;
3.218 + cnt = 1;
3.219 + }
3.220 + }
3.221 +
3.222 + state[length - 1] = Word(1) << (bits - 1);
3.223 + }
3.224 +
3.225 + void copyState(const RandomCore& other) {
3.226 + std::copy(other.state, other.state + length, state);
3.227 + current = state + (other.current - other.state);
3.228 + }
3.229 +
3.230 + Word operator()() {
3.231 + if (current == state) fillState();
3.232 + --current;
3.233 + Word rnd = *current;
3.234 + return RandomTraits<Word>::tempering(rnd);
3.235 + }
3.236 +
3.237 + private:
3.238 +
3.239 +
3.240 + void fillState() {
3.241 + static const Word mask[2] = { 0x0ul, RandomTraits<Word>::mask };
3.242 + static const Word loMask = RandomTraits<Word>::loMask;
3.243 + static const Word hiMask = RandomTraits<Word>::hiMask;
3.244 +
3.245 + current = state + length;
3.246 +
3.247 + register Word *curr = state + length - 1;
3.248 + register long num;
3.249 +
3.250 + num = length - shift;
3.251 + while (num--) {
3.252 + curr[0] = (((curr[0] & hiMask) | (curr[-1] & loMask)) >> 1) ^
3.253 + curr[- shift] ^ mask[curr[-1] & 1ul];
3.254 + --curr;
3.255 + }
3.256 + num = shift - 1;
3.257 + while (num--) {
3.258 + curr[0] = (((curr[0] & hiMask) | (curr[-1] & loMask)) >> 1) ^
3.259 + curr[length - shift] ^ mask[curr[-1] & 1ul];
3.260 + --curr;
3.261 + }
3.262 + curr[0] = (((curr[0] & hiMask) | (curr[length - 1] & loMask)) >> 1) ^
3.263 + curr[length - shift] ^ mask[curr[length - 1] & 1ul];
3.264 +
3.265 + }
3.266 +
3.267 +
3.268 + Word *current;
3.269 + Word state[length];
3.270 +
3.271 + };
3.272 +
3.273 +
3.274 + template <typename Result,
3.275 + int shift = (std::numeric_limits<Result>::digits + 1) / 2>
3.276 + struct Masker {
3.277 + static Result mask(const Result& result) {
3.278 + return Masker<Result, (shift + 1) / 2>::
3.279 + mask(static_cast<Result>(result | (result >> shift)));
3.280 + }
3.281 + };
3.282 +
3.283 + template <typename Result>
3.284 + struct Masker<Result, 1> {
3.285 + static Result mask(const Result& result) {
3.286 + return static_cast<Result>(result | (result >> 1));
3.287 + }
3.288 + };
3.289 +
3.290 + template <typename Result, typename Word,
3.291 + int rest = std::numeric_limits<Result>::digits, int shift = 0,
3.292 + bool last = rest <= std::numeric_limits<Word>::digits>
3.293 + struct IntConversion {
3.294 + static const int bits = std::numeric_limits<Word>::digits;
3.295 +
3.296 + static Result convert(RandomCore<Word>& rnd) {
3.297 + return static_cast<Result>(rnd() >> (bits - rest)) << shift;
3.298 + }
3.299 +
3.300 + };
3.301 +
3.302 + template <typename Result, typename Word, int rest, int shift>
3.303 + struct IntConversion<Result, Word, rest, shift, false> {
3.304 + static const int bits = std::numeric_limits<Word>::digits;
3.305 +
3.306 + static Result convert(RandomCore<Word>& rnd) {
3.307 + return (static_cast<Result>(rnd()) << shift) |
3.308 + IntConversion<Result, Word, rest - bits, shift + bits>::convert(rnd);
3.309 + }
3.310 + };
3.311 +
3.312 +
3.313 + template <typename Result, typename Word,
3.314 + bool one_word = (std::numeric_limits<Word>::digits <
3.315 + std::numeric_limits<Result>::digits) >
3.316 + struct Mapping {
3.317 + static Result map(RandomCore<Word>& rnd, const Result& bound) {
3.318 + Word max = Word(bound - 1);
3.319 + Result mask = Masker<Result>::mask(bound - 1);
3.320 + Result num;
3.321 + do {
3.322 + num = IntConversion<Result, Word>::convert(rnd) & mask;
3.323 + } while (num > max);
3.324 + return num;
3.325 + }
3.326 + };
3.327 +
3.328 + template <typename Result, typename Word>
3.329 + struct Mapping<Result, Word, false> {
3.330 + static Result map(RandomCore<Word>& rnd, const Result& bound) {
3.331 + Word max = Word(bound - 1);
3.332 + Word mask = Masker<Word, (std::numeric_limits<Result>::digits + 1) / 2>
3.333 + ::mask(max);
3.334 + Word num;
3.335 + do {
3.336 + num = rnd() & mask;
3.337 + } while (num > max);
3.338 + return num;
3.339 + }
3.340 + };
3.341 +
3.342 + template <typename Result, int exp, bool pos = (exp >= 0)>
3.343 + struct ShiftMultiplier {
3.344 + static const Result multiplier() {
3.345 + Result res = ShiftMultiplier<Result, exp / 2>::multiplier();
3.346 + res *= res;
3.347 + if ((exp & 1) == 1) res *= static_cast<Result>(2.0);
3.348 + return res;
3.349 + }
3.350 + };
3.351 +
3.352 + template <typename Result, int exp>
3.353 + struct ShiftMultiplier<Result, exp, false> {
3.354 + static const Result multiplier() {
3.355 + Result res = ShiftMultiplier<Result, exp / 2>::multiplier();
3.356 + res *= res;
3.357 + if ((exp & 1) == 1) res *= static_cast<Result>(0.5);
3.358 + return res;
3.359 + }
3.360 + };
3.361 +
3.362 + template <typename Result>
3.363 + struct ShiftMultiplier<Result, 0, true> {
3.364 + static const Result multiplier() {
3.365 + return static_cast<Result>(1.0);
3.366 + }
3.367 + };
3.368 +
3.369 + template <typename Result>
3.370 + struct ShiftMultiplier<Result, -20, true> {
3.371 + static const Result multiplier() {
3.372 + return static_cast<Result>(1.0/1048576.0);
3.373 + }
3.374 + };
3.375 +
3.376 + template <typename Result>
3.377 + struct ShiftMultiplier<Result, -32, true> {
3.378 + static const Result multiplier() {
3.379 + return static_cast<Result>(1.0/424967296.0);
3.380 + }
3.381 + };
3.382 +
3.383 + template <typename Result>
3.384 + struct ShiftMultiplier<Result, -53, true> {
3.385 + static const Result multiplier() {
3.386 + return static_cast<Result>(1.0/9007199254740992.0);
3.387 + }
3.388 + };
3.389 +
3.390 + template <typename Result>
3.391 + struct ShiftMultiplier<Result, -64, true> {
3.392 + static const Result multiplier() {
3.393 + return static_cast<Result>(1.0/18446744073709551616.0);
3.394 + }
3.395 + };
3.396 +
3.397 + template <typename Result, int exp>
3.398 + struct Shifting {
3.399 + static Result shift(const Result& result) {
3.400 + return result * ShiftMultiplier<Result, exp>::multiplier();
3.401 + }
3.402 + };
3.403 +
3.404 + template <typename Result, typename Word,
3.405 + int rest = std::numeric_limits<Result>::digits, int shift = 0,
3.406 + bool last = rest <= std::numeric_limits<Word>::digits>
3.407 + struct RealConversion{
3.408 + static const int bits = std::numeric_limits<Word>::digits;
3.409 +
3.410 + static Result convert(RandomCore<Word>& rnd) {
3.411 + return Shifting<Result, - shift - rest>::
3.412 + shift(static_cast<Result>(rnd() >> (bits - rest)));
3.413 + }
3.414 + };
3.415 +
3.416 + template <typename Result, typename Word, int rest, int shift>
3.417 + struct RealConversion<Result, Word, rest, shift, false> {
3.418 + static const int bits = std::numeric_limits<Word>::digits;
3.419 +
3.420 + static Result convert(RandomCore<Word>& rnd) {
3.421 + return Shifting<Result, - shift - bits>::
3.422 + shift(static_cast<Result>(rnd())) +
3.423 + RealConversion<Result, Word, rest-bits, shift + bits>::
3.424 + convert(rnd);
3.425 + }
3.426 + };
3.427 +
3.428 + template <typename Result, typename Word>
3.429 + struct Initializer {
3.430 +
3.431 + template <typename Iterator>
3.432 + static void init(RandomCore<Word>& rnd, Iterator begin, Iterator end) {
3.433 + std::vector<Word> ws;
3.434 + for (Iterator it = begin; it != end; ++it) {
3.435 + ws.push_back(Word(*it));
3.436 + }
3.437 + rnd.initState(ws.begin(), ws.end());
3.438 + }
3.439 +
3.440 + static void init(RandomCore<Word>& rnd, Result seed) {
3.441 + rnd.initState(seed);
3.442 + }
3.443 + };
3.444 +
3.445 + template <typename Word>
3.446 + struct BoolConversion {
3.447 + static bool convert(RandomCore<Word>& rnd) {
3.448 + return (rnd() & 1) == 1;
3.449 + }
3.450 + };
3.451 +
3.452 + template <typename Word>
3.453 + struct BoolProducer {
3.454 + Word buffer;
3.455 + int num;
3.456 +
3.457 + BoolProducer() : num(0) {}
3.458 +
3.459 + bool convert(RandomCore<Word>& rnd) {
3.460 + if (num == 0) {
3.461 + buffer = rnd();
3.462 + num = RandomTraits<Word>::bits;
3.463 + }
3.464 + bool r = (buffer & 1);
3.465 + buffer >>= 1;
3.466 + --num;
3.467 + return r;
3.468 + }
3.469 + };
3.470 +
3.471 + }
3.472 +
3.473 + /// \ingroup misc
3.474 + ///
3.475 + /// \brief Mersenne Twister random number generator
3.476 + ///
3.477 + /// The Mersenne Twister is a twisted generalized feedback
3.478 + /// shift-register generator of Matsumoto and Nishimura. The period
3.479 + /// of this generator is \f$ 2^{19937} - 1 \f$ and it is
3.480 + /// equi-distributed in 623 dimensions for 32-bit numbers. The time
3.481 + /// performance of this generator is comparable to the commonly used
3.482 + /// generators.
3.483 + ///
3.484 + /// This implementation is specialized for both 32-bit and 64-bit
3.485 + /// architectures. The generators differ sligthly in the
3.486 + /// initialization and generation phase so they produce two
3.487 + /// completly different sequences.
3.488 + ///
3.489 + /// The generator gives back random numbers of serveral types. To
3.490 + /// get a random number from a range of a floating point type you
3.491 + /// can use one form of the \c operator() or the \c real() member
3.492 + /// function. If you want to get random number from the {0, 1, ...,
3.493 + /// n-1} integer range use the \c operator[] or the \c integer()
3.494 + /// method. And to get random number from the whole range of an
3.495 + /// integer type you can use the argumentless \c integer() or \c
3.496 + /// uinteger() functions. After all you can get random bool with
3.497 + /// equal chance of true and false or given probability of true
3.498 + /// result with the \c boolean() member functions.
3.499 + ///
3.500 + ///\code
3.501 + /// // The commented code is identical to the other
3.502 + /// double a = rnd(); // [0.0, 1.0)
3.503 + /// // double a = rnd.real(); // [0.0, 1.0)
3.504 + /// double b = rnd(100.0); // [0.0, 100.0)
3.505 + /// // double b = rnd.real(100.0); // [0.0, 100.0)
3.506 + /// double c = rnd(1.0, 2.0); // [1.0, 2.0)
3.507 + /// // double c = rnd.real(1.0, 2.0); // [1.0, 2.0)
3.508 + /// int d = rnd[100000]; // 0..99999
3.509 + /// // int d = rnd.integer(100000); // 0..99999
3.510 + /// int e = rnd[6] + 1; // 1..6
3.511 + /// // int e = rnd.integer(1, 1 + 6); // 1..6
3.512 + /// int b = rnd.uinteger<int>(); // 0 .. 2^31 - 1
3.513 + /// int c = rnd.integer<int>(); // - 2^31 .. 2^31 - 1
3.514 + /// bool g = rnd.boolean(); // P(g = true) = 0.5
3.515 + /// bool h = rnd.boolean(0.8); // P(h = true) = 0.8
3.516 + ///\endcode
3.517 + ///
3.518 + /// The lemon provides a global instance of the random number
3.519 + /// generator which name is \ref lemon::rnd "rnd". Usually it is a
3.520 + /// good programming convenience to use this global generator to get
3.521 + /// random numbers.
3.522 + ///
3.523 + /// \author Balazs Dezso
3.524 + class Random {
3.525 + private:
3.526 +
3.527 + // architecture word
3.528 + typedef unsigned long Word;
3.529 +
3.530 + _random_bits::RandomCore<Word> core;
3.531 + _random_bits::BoolProducer<Word> bool_producer;
3.532 +
3.533 +
3.534 + public:
3.535 +
3.536 + /// \brief Constructor
3.537 + ///
3.538 + /// Constructor with constant seeding.
3.539 + Random() { core.initState(); }
3.540 +
3.541 + /// \brief Constructor
3.542 + ///
3.543 + /// Constructor with seed. The current number type will be converted
3.544 + /// to the architecture word type.
3.545 + template <typename Number>
3.546 + Random(Number seed) {
3.547 + _random_bits::Initializer<Number, Word>::init(core, seed);
3.548 + }
3.549 +
3.550 + /// \brief Constructor
3.551 + ///
3.552 + /// Constructor with array seeding. The given range should contain
3.553 + /// any number type and the numbers will be converted to the
3.554 + /// architecture word type.
3.555 + template <typename Iterator>
3.556 + Random(Iterator begin, Iterator end) {
3.557 + typedef typename std::iterator_traits<Iterator>::value_type Number;
3.558 + _random_bits::Initializer<Number, Word>::init(core, begin, end);
3.559 + }
3.560 +
3.561 + /// \brief Copy constructor
3.562 + ///
3.563 + /// Copy constructor. The generated sequence will be identical to
3.564 + /// the other sequence. It can be used to save the current state
3.565 + /// of the generator and later use it to generate the same
3.566 + /// sequence.
3.567 + Random(const Random& other) {
3.568 + core.copyState(other.core);
3.569 + }
3.570 +
3.571 + /// \brief Assign operator
3.572 + ///
3.573 + /// Assign operator. The generated sequence will be identical to
3.574 + /// the other sequence. It can be used to save the current state
3.575 + /// of the generator and later use it to generate the same
3.576 + /// sequence.
3.577 + Random& operator=(const Random& other) {
3.578 + if (&other != this) {
3.579 + core.copyState(other.core);
3.580 + }
3.581 + return *this;
3.582 + }
3.583 +
3.584 + /// \brief Returns a random real number from the range [0, 1)
3.585 + ///
3.586 + /// It returns a random real number from the range [0, 1). The
3.587 + /// default Number type is double.
3.588 + template <typename Number>
3.589 + Number real() {
3.590 + return _random_bits::RealConversion<Number, Word>::convert(core);
3.591 + }
3.592 +
3.593 + double real() {
3.594 + return real<double>();
3.595 + }
3.596 +
3.597 + /// \brief Returns a random real number the range [0, b)
3.598 + ///
3.599 + /// It returns a random real number from the range [0, b).
3.600 + template <typename Number>
3.601 + Number real(Number b) {
3.602 + return real<Number>() * b;
3.603 + }
3.604 +
3.605 + /// \brief Returns a random real number from the range [a, b)
3.606 + ///
3.607 + /// It returns a random real number from the range [a, b).
3.608 + template <typename Number>
3.609 + Number real(Number a, Number b) {
3.610 + return real<Number>() * (b - a) + a;
3.611 + }
3.612 +
3.613 + /// \brief Returns a random real number from the range [0, 1)
3.614 + ///
3.615 + /// It returns a random double from the range [0, 1).
3.616 + double operator()() {
3.617 + return real<double>();
3.618 + }
3.619 +
3.620 + /// \brief Returns a random real number from the range [0, b)
3.621 + ///
3.622 + /// It returns a random real number from the range [0, b).
3.623 + template <typename Number>
3.624 + Number operator()(Number b) {
3.625 + return real<Number>() * b;
3.626 + }
3.627 +
3.628 + /// \brief Returns a random real number from the range [a, b)
3.629 + ///
3.630 + /// It returns a random real number from the range [a, b).
3.631 + template <typename Number>
3.632 + Number operator()(Number a, Number b) {
3.633 + return real<Number>() * (b - a) + a;
3.634 + }
3.635 +
3.636 + /// \brief Returns a random integer from a range
3.637 + ///
3.638 + /// It returns a random integer from the range {0, 1, ..., b - 1}.
3.639 + template <typename Number>
3.640 + Number integer(Number b) {
3.641 + return _random_bits::Mapping<Number, Word>::map(core, b);
3.642 + }
3.643 +
3.644 + /// \brief Returns a random integer from a range
3.645 + ///
3.646 + /// It returns a random integer from the range {a, a + 1, ..., b - 1}.
3.647 + template <typename Number>
3.648 + Number integer(Number a, Number b) {
3.649 + return _random_bits::Mapping<Number, Word>::map(core, b - a) + a;
3.650 + }
3.651 +
3.652 + /// \brief Returns a random integer from a range
3.653 + ///
3.654 + /// It returns a random integer from the range {0, 1, ..., b - 1}.
3.655 + template <typename Number>
3.656 + Number operator[](Number b) {
3.657 + return _random_bits::Mapping<Number, Word>::map(core, b);
3.658 + }
3.659 +
3.660 + /// \brief Returns a random non-negative integer
3.661 + ///
3.662 + /// It returns a random non-negative integer uniformly from the
3.663 + /// whole range of the current \c Number type. The default result
3.664 + /// type of this function is unsigned int.
3.665 + template <typename Number>
3.666 + Number uinteger() {
3.667 + return _random_bits::IntConversion<Number, Word>::convert(core);
3.668 + }
3.669 +
3.670 + unsigned int uinteger() {
3.671 + return uinteger<unsigned int>();
3.672 + }
3.673 +
3.674 + /// \brief Returns a random integer
3.675 + ///
3.676 + /// It returns a random integer uniformly from the whole range of
3.677 + /// the current \c Number type. The default result type of this
3.678 + /// function is int.
3.679 + template <typename Number>
3.680 + Number integer() {
3.681 + static const int nb = std::numeric_limits<Number>::digits +
3.682 + (std::numeric_limits<Number>::is_signed ? 1 : 0);
3.683 + return _random_bits::IntConversion<Number, Word, nb>::convert(core);
3.684 + }
3.685 +
3.686 + int integer() {
3.687 + return integer<int>();
3.688 + }
3.689 +
3.690 + /// \brief Returns a random bool
3.691 + ///
3.692 + /// It returns a random bool. The generator holds a buffer for
3.693 + /// random bits. Every time when it become empty the generator makes
3.694 + /// a new random word and fill the buffer up.
3.695 + bool boolean() {
3.696 + return bool_producer.convert(core);
3.697 + }
3.698 +
3.699 + ///\name Nonuniform distributions
3.700 + ///
3.701 +
3.702 + ///@{
3.703 +
3.704 + /// \brief Returns a random bool
3.705 + ///
3.706 + /// It returns a random bool with given probability of true result
3.707 + bool boolean(double p) {
3.708 + return operator()() < p;
3.709 + }
3.710 +
3.711 + /// Standard Gauss distribution
3.712 +
3.713 + /// Standard Gauss distribution.
3.714 + /// \note The Cartesian form of the Box-Muller
3.715 + /// transformation is used to generate a random normal distribution.
3.716 + /// \todo Consider using the "ziggurat" method instead.
3.717 + double gauss()
3.718 + {
3.719 + double V1,V2,S;
3.720 + do {
3.721 + V1=2*real<double>()-1;
3.722 + V2=2*real<double>()-1;
3.723 + S=V1*V1+V2*V2;
3.724 + } while(S>=1);
3.725 + return std::sqrt(-2*std::log(S)/S)*V1;
3.726 + }
3.727 + /// Gauss distribution with given mean and standard deviation
3.728 +
3.729 + /// \sa gauss()
3.730 + ///
3.731 + double gauss(double mean,double std_dev)
3.732 + {
3.733 + return gauss()*std_dev+mean;
3.734 + }
3.735 +
3.736 + /// Exponential distribution with given mean
3.737 +
3.738 + /// This function generates an exponential distribution random number
3.739 + /// with mean <tt>1/lambda</tt>.
3.740 + ///
3.741 + double exponential(double lambda=1.0)
3.742 + {
3.743 + return -std::log(real<double>())/lambda;
3.744 + }
3.745 +
3.746 + /// Gamma distribution with given integer shape
3.747 +
3.748 + /// This function generates a gamma distribution random number.
3.749 + ///
3.750 + ///\param k shape parameter (<tt>k>0</tt> integer)
3.751 + double gamma(int k)
3.752 + {
3.753 + double s = 0;
3.754 + for(int i=0;i<k;i++) s-=std::log(1.0-real<double>());
3.755 + return s;
3.756 + }
3.757 +
3.758 + /// Gamma distribution with given shape and scale parameter
3.759 +
3.760 + /// This function generates a gamma distribution random number.
3.761 + ///
3.762 + ///\param k shape parameter (<tt>k>0</tt>)
3.763 + ///\param theta scale parameter
3.764 + ///
3.765 + double gamma(double k,double theta=1.0)
3.766 + {
3.767 + double xi,nu;
3.768 + const double delta = k-std::floor(k);
3.769 + const double v0=M_E/(M_E-delta);
3.770 + do {
3.771 + double V0=1.0-real<double>();
3.772 + double V1=1.0-real<double>();
3.773 + double V2=1.0-real<double>();
3.774 + if(V2<=v0)
3.775 + {
3.776 + xi=std::pow(V1,1.0/delta);
3.777 + nu=V0*std::pow(xi,delta-1.0);
3.778 + }
3.779 + else
3.780 + {
3.781 + xi=1.0-std::log(V1);
3.782 + nu=V0*std::exp(-xi);
3.783 + }
3.784 + } while(nu>std::pow(xi,delta-1.0)*std::exp(-xi));
3.785 + return theta*(xi-gamma(int(std::floor(k))));
3.786 + }
3.787 +
3.788 +
3.789 + ///@}
3.790 +
3.791 + ///\name Two dimensional distributions
3.792 + ///
3.793 +
3.794 + ///@{
3.795 +
3.796 + /// Uniform distribution on the full unit circle.
3.797 + dim2::Point<double> disc()
3.798 + {
3.799 + double V1,V2;
3.800 + do {
3.801 + V1=2*real<double>()-1;
3.802 + V2=2*real<double>()-1;
3.803 +
3.804 + } while(V1*V1+V2*V2>=1);
3.805 + return dim2::Point<double>(V1,V2);
3.806 + }
3.807 + /// A kind of two dimensional Gauss distribution
3.808 +
3.809 + /// This function provides a turning symmetric two-dimensional distribution.
3.810 + /// Both coordinates are of standard normal distribution, but they are not
3.811 + /// independent.
3.812 + ///
3.813 + /// \note The coordinates are the two random variables provided by
3.814 + /// the Box-Muller method.
3.815 + dim2::Point<double> gauss2()
3.816 + {
3.817 + double V1,V2,S;
3.818 + do {
3.819 + V1=2*real<double>()-1;
3.820 + V2=2*real<double>()-1;
3.821 + S=V1*V1+V2*V2;
3.822 + } while(S>=1);
3.823 + double W=std::sqrt(-2*std::log(S)/S);
3.824 + return dim2::Point<double>(W*V1,W*V2);
3.825 + }
3.826 + /// A kind of two dimensional exponential distribution
3.827 +
3.828 + /// This function provides a turning symmetric two-dimensional distribution.
3.829 + /// The x-coordinate is of conditionally exponential distribution
3.830 + /// with the condition that x is positive and y=0. If x is negative and
3.831 + /// y=0 then, -x is of exponential distribution. The same is true for the
3.832 + /// y-coordinate.
3.833 + dim2::Point<double> exponential2()
3.834 + {
3.835 + double V1,V2,S;
3.836 + do {
3.837 + V1=2*real<double>()-1;
3.838 + V2=2*real<double>()-1;
3.839 + S=V1*V1+V2*V2;
3.840 + } while(S>=1);
3.841 + double W=-std::log(S)/S;
3.842 + return dim2::Point<double>(W*V1,W*V2);
3.843 + }
3.844 +
3.845 + ///@}
3.846 + };
3.847 +
3.848 +
3.849 + extern Random rnd;
3.850 +
3.851 +}
3.852 +
3.853 +#endif
4.1 --- a/test/Makefile.am Thu Dec 20 23:46:50 2007 +0000
4.2 +++ b/test/Makefile.am Fri Dec 21 00:07:03 2007 +0000
4.3 @@ -3,15 +3,17 @@
4.4
4.5 noinst_HEADERS += \
4.6 test/test_tools.h
4.7 -
4.8 +
4.9 check_PROGRAMS += \
4.10 test/dim_test \
4.11 + test/random_test \
4.12 test/test_tools_fail \
4.13 test/test_tools_pass
4.14 -
4.15 +
4.16 TESTS += $(check_PROGRAMS)
4.17 XFAIL_TESTS += test/test_tools_fail$(EXEEXT)
4.18
4.19 test_dim_test_SOURCES = test/dim_test.cc
4.20 +test_random_test_SOURCES = test/random_test.cc
4.21 test_test_tools_fail_SOURCES = test/test_tools_fail.cc
4.22 test_test_tools_pass_SOURCES = test/test_tools_pass.cc
5.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
5.2 +++ b/test/random_test.cc Fri Dec 21 00:07:03 2007 +0000
5.3 @@ -0,0 +1,36 @@
5.4 +/* -*- C++ -*-
5.5 + *
5.6 + * This file is a part of LEMON, a generic C++ optimization library
5.7 + *
5.8 + * Copyright (C) 2003-2007
5.9 + * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
5.10 + * (Egervary Research Group on Combinatorial Optimization, EGRES).
5.11 + *
5.12 + * Permission to use, modify and distribute this software is granted
5.13 + * provided that this copyright notice appears in all copies. For
5.14 + * precise terms see the accompanying LICENSE file.
5.15 + *
5.16 + * This software is provided "AS IS" with no warranty of any kind,
5.17 + * express or implied, and with no claim as to its suitability for any
5.18 + * purpose.
5.19 + *
5.20 + */
5.21 +
5.22 +#include <lemon/random.h>
5.23 +#include "test_tools.h"
5.24 +
5.25 +///\file \brief Test cases for random.h
5.26 +///
5.27 +///\todo To be extended
5.28 +///
5.29 +
5.30 +int main()
5.31 +{
5.32 + double a=lemon::rnd();
5.33 + check(a<1.0&&a>0.0,"This should be in [0,1)");
5.34 + a=lemon::rnd.gauss();
5.35 + a=lemon::rnd.gamma(3.45,0);
5.36 + a=lemon::rnd.gamma(4);
5.37 + //Does gamma work with integer k?
5.38 + a=lemon::rnd.gamma(4.0,0);
5.39 +}