2 * This file is part of the GROMACS molecular simulation package.
4 * Copyright (c) 1991-2000, University of Groningen, The Netherlands.
5 * Copyright (c) 2001-2008, The GROMACS development team.
6 * Copyright (c) 2010,2014, by the GROMACS development team, led by
7 * Mark Abraham, David van der Spoel, Berk Hess, and Erik Lindahl,
8 * and including many others, as listed in the AUTHORS file in the
9 * top-level source directory and at http://www.gromacs.org.
11 * GROMACS is free software; you can redistribute it and/or
12 * modify it under the terms of the GNU Lesser General Public License
13 * as published by the Free Software Foundation; either version 2.1
14 * of the License, or (at your option) any later version.
16 * GROMACS is distributed in the hope that it will be useful,
17 * but WITHOUT ANY WARRANTY; without even the implied warranty of
18 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
19 * Lesser General Public License for more details.
21 * You should have received a copy of the GNU Lesser General Public
22 * License along with GROMACS; if not, see
23 * http://www.gnu.org/licenses, or write to the Free Software Foundation,
24 * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
26 * If you want to redistribute modifications to GROMACS, please
27 * consider that scientific software is very special. Version
28 * control is crucial - bugs must be traceable. We will be happy to
29 * consider code for inclusion in the official distribution, but
30 * derived work must not be called official GROMACS. Details are found
31 * in the README & COPYING files - if they are missing, get the
32 * official version at http://www.gromacs.org.
34 * To help us fund GROMACS development, we humbly ask that you cite
35 * the research papers on the package. Check out http://www.gromacs.org.
38 #ifndef _GMX_RANDOM_H_
39 #define _GMX_RANDOM_H_
42 #include "types/simple.h"
48 /*! \brief Abstract datatype for a random number generator
50 * This is a handle to the full state of a random number generator.
51 * You can not access anything inside the gmx_rng structure outside this
54 typedef struct gmx_rng *
58 /*! \brief Returns the size of the RNG integer data structure
60 * Returns the size of the RNG integer data structure.
67 /*! \brief Create a new RNG, seeded from a single integer.
69 * If you dont want to pick a seed, just call it as
70 * rng=gmx_rng_init(gmx_rng_make_seed()) to seed it from
71 * the system time or a random device.
73 * \param seed Random seed, unsigned 32-bit integer.
75 * \return Reference to a random number generator, or NULL if there was an
81 gmx_rng_init(unsigned int seed);
84 /*! \brief Generate a 'random' RNG seed.
86 * This routine tries to get a seed from /dev/random if present,
87 * and if not it uses time-of-day and process id to generate one.
89 * \return 32-bit unsigned integer random seed.
91 * Tip: If you use this in your code, it is a good idea to write the
92 * returned random seed to a logfile, so you can recreate the exact sequence
93 * of random number if you need to reproduce your run later for one reason
99 gmx_rng_make_seed(void);
102 /*! \brief Initialize a RNG with 624 integers (>32 bits of entropy).
104 * The Mersenne twister RNG used in Gromacs has an extremely long period,
105 * but when you only initialize it with a 32-bit integer there are only
106 * 2^32 different possible sequences of number - much less than the generator
109 * If you really need the full entropy, this routine makes it possible to
110 * initialize the RNG with up to 624 32-bit integers, which will give you
111 * up to 2^19968 bits of entropy.
113 * \param seed Array of unsigned integers to form a seed
114 * \param seed_length Number of integers in the array, up to 624 are used.
116 * \return Reference to a random number generator, or NULL if there was an
122 gmx_rng_init_array(unsigned int seed[],
126 /*! \brief Release resources of a RNG
128 * This routine destroys a random number generator and releases all
129 * resources allocated by it.
131 * \param rng Handle to random number generator previously returned by
132 * gmx_rng_init() or gmx_rng_init_array().
134 * \threadsafe Function itself is threadsafe, but you should only destroy a
135 * certain RNG once (i.e. from one thread).
138 gmx_rng_destroy(gmx_rng_t rng);
141 /*! \brief Get the state of a RNG
143 * This routine stores the random state in mt and mti, mt should have
144 * a size of at least 624, mt of 1.
146 * \param rng Handle to random number generator previously returned by
147 * gmx_rng_init() or gmx_rng_init_array().
150 gmx_rng_get_state(gmx_rng_t rng, unsigned int *mt, int *mti);
153 /*! \brief Set the state of a RNG
155 * This routine sets the random state from mt and mti, mt should have
156 * a size of at least 624.
158 * \param rng Handle to random number generator previously returned by
159 * gmx_rng_init() or gmx_rng_init_array().
162 gmx_rng_set_state(gmx_rng_t rng, unsigned int *mt, int mti);
165 /*! \brief Random 32-bit integer from a uniform distribution
167 * This routine returns a random integer from the random number generator
168 * provided, and updates the state of that RNG.
170 * \param rng Handle to random number generator previously returned by
171 * gmx_rng_init() or gmx_rng_init_array().
173 * \return 32-bit unsigned integer from a uniform distribution.
175 * \threadsafe Function yes, input data no. You should not call this function
176 * from two different threads using the same RNG handle at the
177 * same time. For performance reasons we cannot lock the handle
178 * with a mutex every time we need a random number - that would
179 * slow the routine down a factor 2-5. There are two simple
180 * solutions: either use a mutex and lock it before calling
181 * the function, or use a separate RNG handle for each thread.
184 gmx_rng_uniform_uint32(gmx_rng_t rng);
187 /*! \brief Random gmx_real_t 0<=x<1 from a uniform distribution
189 * This routine returns a random floating-point number from the
190 * random number generator provided, and updates the state of that RNG.
192 * \param rng Handle to random number generator previously returned by
193 * gmx_rng_init() or gmx_rng_init_array().
195 * \return floating-point number 0<=x<1 from a uniform distribution.
197 * \threadsafe Function yes, input data no. You should not call this function
198 * from two different threads using the same RNG handle at the
199 * same time. For performance reasons we cannot lock the handle
200 * with a mutex every time we need a random number - that would
201 * slow the routine down a factor 2-5. There are two simple
202 * solutions: either use a mutex and lock it before calling
203 * the function, or use a separate RNG handle for each thread.
206 gmx_rng_uniform_real(gmx_rng_t rng);
209 /*! \brief Random gmx_real_t from a gaussian distribution
211 * This routine returns a random floating-point number from the
212 * random number generator provided, and updates the state of that RNG.
214 * The Box-Muller algorithm is used to provide gaussian random numbers. This
215 * is not the fastest known algorithm for gaussian numbers, but in contrast
216 * to the alternatives it is very well studied and you can trust the returned
217 * random numbers to have good properties and no correlations.
219 * \param rng Handle to random number generator previously returned by
220 * gmx_rng_init() or gmx_rng_init_array().
222 * \return Gaussian random floating-point number with average 0.0 and
223 * standard deviation 1.0. You can get any average/mean you want
224 * by first multiplying with the desired average and then adding
225 * the average you want.
227 * \threadsafe Function yes, input data no. You should not call this function
228 * from two different threads using the same RNG handle at the
229 * same time. For performance reasons we cannot lock the handle
230 * with a mutex every time we need a random number - that would
231 * slow the routine down a factor 2-5. There are two simple
232 * solutions: either use a mutex and lock it before calling
233 * the function, or use a separate RNG handle for each thread.
235 * It works perfectly to mix calls to get uniform and gaussian random numbers
236 * from the same generator, but since it will affect the sequence of returned
237 * numbers it is probably better to use separate random number generator
241 gmx_rng_gaussian_real(gmx_rng_t rng);
245 /* Return a new gaussian random number with expectation value
246 * 0.0 and standard deviation 1.0. This routine uses a table
247 * lookup for maximum speed.
249 * WARNING: The lookup table is 16k by default, which means
250 * the granularity of the random numbers is coarser
251 * than what you get from gmx_rng_gauss_real().
252 * In most cases this is no problem whatsoever,
253 * and it is particularly true for BD/SD integration.
254 * Note that you will NEVER get any really extreme
255 * numbers: the maximum absolute value returned is
261 gmx_rng_gaussian_table(gmx_rng_t rng);
267 #endif /* _GMX_RANDOM_H_ */