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random_mt.cpp
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Fri, Oct 18, 17:24

random_mt.cpp
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/* ----------------------------------------------------------------------
LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator
http://lammps.sandia.gov, Sandia National Laboratories
Steve Plimpton, sjplimp@sandia.gov
Copyright (2003) Sandia Corporation. Under the terms of Contract
DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains
certain rights in this software. This software is distributed under
the GNU General Public License.
See the README file in the top-level LAMMPS directory.
------------------------------------------------------------------------- */
// Mersenne Twister (MT19937) pseudo random number generator:
// M. Matsumoto & T. Nishimura,
// ACM Transactions on Modeling and Computer Simulation,
// vol. 8, no. 1, 1998, pp. 3-30.
//
// Uses the Marsaglia RNG in RanMars to generate the initial seeds
#include "math.h"
#include "random_mt.h"
#include "random_mars.h"
#include "math_inline.h"
#include "error.h"
using namespace LAMMPS_NS;
#define MT_A 0x9908B0DF
#define MT_B 0x9D2C5680
#define MT_C 0xEFC60000
/* ---------------------------------------------------------------------- */
RanMT::RanMT(LAMMPS *lmp, int seed) : Pointers(lmp)
{
int i;
const uint32_t f = 1812433253UL;
_save = _second = 0;
if (seed <= 0 || seed > 900000000)
error->one(FLERR,"Invalid seed for Mersenne Twister random # generator");
// start minimal initialization
_m[0] = seed;
_idx = MT_N-1;
for (i=1; i < MT_N; ++i)
_m[i] = (f * (_m[i-1] ^ (_m[i-1] >> 30)) + i);
// to seed the RNG some more using a second RNG
RanMars rng(lmp,seed);
for (i=0; i < MT_N-1; ++i)
_m[i+1] = (_m[i+1] ^ ((_m[i] ^ (_m[i] >> 30)) * 1664525UL))
+ (uint32_t) (rng.uniform()* (1U<<31)) + i;
_m[0] = _m[MT_N-1];
for (i=0; i < MT_N-1; ++i)
_m[i+1] = (_m[i+1] ^ ((_m[i] ^ (_m[i] >> 30)) * 1566083941UL))-i-1;
_m[0] = 0x80000000UL;
// randomize one more turn
_idx = 0;
for (i=0; i < MT_N-1; ++i) _randomize();
}
/* ----------------------------------------------------------------------
grab 32bits of randomness
------------------------------------------------------------------------- */
uint32_t RanMT::_randomize() {
uint32_t r;
if (_idx >= MT_N) {
// fill the entire status array with new data in one sweep
const uint32_t LMASK = (1LU << MT_R) - 1; // Lower MT_R bits
const uint32_t UMASK = 0xFFFFFFFF << MT_R; // Upper (32 - MT_R) bits
static const uint32_t magic[2] = {0, MT_A};
const int diff = MT_N-MT_M;
int i;
for (i=0; i < diff; ++i) {
r = (_m[i] & UMASK) | (_m[i+1] & LMASK);
_m[i] = _m[i+MT_M] ^ (r >> 1) ^ magic[r & 1];}
for (i=diff; i < MT_N-1; ++i) {
r = (_m[i] & UMASK) | (_m[i+1] & LMASK);
_m[i] = _m[i-diff] ^ (r >> 1) ^ magic[r & 1];}
r = (_m[MT_N-1] & UMASK) | (_m[0] & LMASK);
_m[MT_N-1] = _m[MT_M-1] ^ (r >> 1) ^ magic[r & 1];
_idx = 0;
}
r = _m[_idx++];
r ^= r >> MT_U;
r ^= (r << MT_S) & MT_B;
r ^= (r << MT_T) & MT_C;
r ^= r >> MT_L;
return r;
}
/* ----------------------------------------------------------------------
uniform distributed RN. just grab a 32bit integer and convert to double
------------------------------------------------------------------------- */
static const double conv_u32int = 1.0 / (256.0*256.0*256.0*256.0);
double RanMT::uniform()
{
double uni = (double) _randomize();
return uni*conv_u32int;
}
/* ----------------------------------------------------------------------
gaussian distributed RNG
------------------------------------------------------------------------- */
double RanMT::gaussian()
{
double first,v1,v2,rsq,fac;
if (!_save) {
int again = 1;
while (again) {
v1 = 2.0*uniform()-1.0;
v2 = 2.0*uniform()-1.0;
rsq = v1*v1 + v2*v2;
if (rsq < 1.0 && rsq != 0.0) again = 0;
}
// fac = sqrt(-2.0*log(rsq)/rsq);
fac = MathInline::sqrtlgx2byx(rsq);
_second = v1*fac;
first = v2*fac;
_save = 1;
} else {
first = _second;
_save = 0;
}
return first;
}

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