📄 hxrand.cpp
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/*
*
* (c) Copyright 1989 OPEN SOFTWARE FOUNDATION, INC.
* (c) Copyright 1989 HEWLETT-PACKARD COMPANY
* (c) Copyright 1989 DIGITAL EQUIPMENT CORPORATION
* To anyone who acknowledges that this file is provided "AS IS"
* without any express or implied warranty:
* permission to use, copy, modify, and distribute this
* file for any purpose is hereby granted without fee, provided that
* the above copyright notices and this notice appears in all source
* code copies, and that none of the names of Open Software
* Foundation, Inc., Hewlett-Packard Company, or Digital Equipment
* Corporation be used in advertising or publicity pertaining to
* distribution of the software without specific, written prior
* permission. Neither Open Software Foundation, Inc., Hewlett-
* Packard Company, nor Digital Equipment Corporation makes any
* representations about the suitability of this software for any
* purpose.
*
*/
/****************************************************************************
**
** U U I D T R U E R A N D O M N U M B E R G E N E R A T O R
**
*****************************************************************************
**
** This random number generator (RNG) was found in the ALGORITHMS Notesfile.
**
** (Note 16.7, July 7, 1989 by Robert (RDVAX::)Gries, Cambridge Research Lab,
** Computational Quality Group)
**
** It is really a "Multiple Prime Random Number Generator" (MPRNG) and is
** completely discussed in reference #1 (see below).
**
** References:
** 1) "The Multiple Prime Random Number Generator" by Alexander Hass
** pp. 368 to 381 in ACM Transactions on Mathematical Software,
** December, 1987
** 2) "The Art of Computer Programming: Seminumerical Algorithms
** (vol 2)" by Donald E. Knuth, pp. 39 to 113.
**
** A summary of the notesfile entry follows:
**
** Gries discusses the two RNG's available for ULTRIX-C. The default RNG
** uses a Linear Congruential Method (very popular) and the second RNG uses
** a technique known as a linear feedback shift register.
**
** The first (default) RNG suffers from bit-cycles (patterns/repetition),
** ie. it's "not that random."
**
** While the second RNG passes all the emperical tests, there are "states"
** that become "stable", albeit contrived.
**
** Gries then presents the MPRNG and says that it passes all emperical
** tests listed in reference #2. In addition, the number of calls to the
** MPRNG before a sequence of bit position repeats appears to have a normal
** distribution.
**
** Note (mbs): I have coded the Gries's MPRNG with the same constants that
** he used in his paper. I have no way of knowing whether they are "ideal"
** for the range of numbers we are dealing with.
**
****************************************************************************/
#include "hxtypes.h"
#include "hxrand.h"
#include "hxtick.h"
#ifdef _WINDOWS
#include <windows.h>
#endif
#include "hxheap.h"
#ifdef _DEBUG
#undef HX_THIS_FILE
static const char HX_THIS_FILE[] = __FILE__;
#endif
/*
* optimal/recommended starting values according to the reference
*/
#define RAND_M_INIT 971;
#define RAND_IA_INIT 11113;
#define RAND_IB_INIT 104322;
#define RAND_IRAND_INIT 4181;
CMultiplePrimeRandom::CMultiplePrimeRandom()
{
m_rand_m = RAND_M_INIT;
m_rand_ia = RAND_IA_INIT;
m_rand_ib = RAND_IB_INIT;
m_rand_irand = RAND_IRAND_INIT;
SetSeed(HX_GET_TICKCOUNT());
}
CMultiplePrimeRandom::CMultiplePrimeRandom(ULONG32 seed)
{
m_rand_m = RAND_M_INIT;
m_rand_ia = RAND_IA_INIT;
m_rand_ib = RAND_IB_INIT;
m_rand_irand = RAND_IRAND_INIT;
SetSeed(seed);
}
void CMultiplePrimeRandom::SetSeed(ULONG32 seed)
{
// seed the generator
m_rand_irand += seed;
}
ULONG32 CMultiplePrimeRandom::GetRandomNumber(void)
{
m_rand_m += 7;
m_rand_ia += 1907;
m_rand_ib += 73939;
if (m_rand_m >= 9973) m_rand_m -= 9871;
if (m_rand_ia >= 99991) m_rand_ia -= 89989;
if (m_rand_ib >= 224729) m_rand_ib -= 96233;
m_rand_irand = (m_rand_irand * m_rand_m) + m_rand_ia + m_rand_ib;
return m_rand_irand;
}
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