jens
6e85f72992
git-svn-id: http://moon:8086/svn/software/trunk/libsrc/noise@1 b431acfa-c32f-4a4a-93f1-934dc6c82436
81 lines
2.1 KiB
C
Executable File
81 lines
2.1 KiB
C
Executable File
// --------------------------------------------------------------
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// --------------------------------------------------------------
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#include <string.h>
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#include <stdlib.h>
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#include <time.h>
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#include <math.h>
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#include "noise.h"
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// --------------------------------------------------------------
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// internal funcs
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// --------------------------------------------------------------
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#define PM_IA 16807
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#define PM_IM 2147483647
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#define PM_AM (1.0/PM_IM)
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#define PM_IQ 127773
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#define PM_IR 2836
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#define PM_MASK 123459876
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// 'Minimal' random number generator of Park and Miller. Returns a uniform random deviate
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// between 0.0 and 1.0. Set or resetidumto any integer value (except the unlikely value MASK)
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// to initialize the sequence; idummust not be altered between calls for successive deviates in
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// a sequence.
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double ran0(long *idum)
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{
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long k;
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double ans;
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*idum ^= PM_MASK; // XORing with MASKallows use of zero and other
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k=(*idum)/PM_IQ; // simple bit patterns for idum.
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*idum=PM_IA*(*idum-k*PM_IQ)-PM_IR *k; // Compute idum=(IA*idum) % IM without over-
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// flows by Schrage’s method.
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if (*idum < 0)
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*idum += PM_IM;
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ans=PM_AM*(*idum); // Convert idumto a floating result.
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*idum ^= PM_MASK; // Unmask before return.
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return ans;
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}
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// --------------------------------------------------------------
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// Exported functions
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// --------------------------------------------------------------
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void Noise_Init(noise_gen_t *pObj, long seed)
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{
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pObj->state = seed;
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}
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void Noise_Free(noise_gen_t *pObj)
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{
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}
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double Noise_Uniform(noise_gen_t *pObj, double mu)
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{
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return ran0(&pObj->state) + mu -0.5;
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}
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double Noise_Gaussian(noise_gen_t *pObj, double mu, double sigma)
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{
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double U1, U2, V1, V2, S, Y;
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do
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{
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U1 = Noise_Uniform(pObj, 0.5); // U1=[0,1]
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U2 = Noise_Uniform(pObj, 0.5); // U2=[0,1]
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V1 = 2 * U1 - 1; // V1=[-1,1]
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V2 = 2 * U2 - 1; // V2=[-1,1]
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S = V1 * V1 + V2 * V2;
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} while (S >= 1);
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// X = sqrt(-2 * log(S) / S) * V1;
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Y = sqrt(-2 * log(S) / S) * V2;
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Y = mu + sigma*Y;
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return Y;
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}
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