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Diffstat (limited to 'gsl-1.9/randist/binomial_tpe.c')
-rw-r--r-- | gsl-1.9/randist/binomial_tpe.c | 381 |
1 files changed, 381 insertions, 0 deletions
diff --git a/gsl-1.9/randist/binomial_tpe.c b/gsl-1.9/randist/binomial_tpe.c new file mode 100644 index 0000000..40af697 --- /dev/null +++ b/gsl-1.9/randist/binomial_tpe.c @@ -0,0 +1,381 @@ +/* randist/binomial_tpe.c + * + * Copyright (C) 1996-2003 James Theiler, Brian Gough + * + * This program is free software; you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation; either version 2 of the License, or (at + * your option) any later version. + * + * This program is distributed in the hope that it will be useful, but + * WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program; if not, write to the Free Software + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. + */ + +#include <config.h> +#include <math.h> +#include <gsl/gsl_rng.h> +#include <gsl/gsl_randist.h> +#include <gsl/gsl_pow_int.h> +#include <gsl/gsl_sf_gamma.h> + +/* The binomial distribution has the form, + + f(x) = n!/(x!(n-x)!) * p^x (1-p)^(n-x) for integer 0 <= x <= n + = 0 otherwise + + This implementation follows the public domain ranlib function + "ignbin", the bulk of which is the BTPE (Binomial Triangle + Parallelogram Exponential) algorithm introduced in + Kachitvichyanukul and Schmeiser[1]. It has been translated to use + modern C coding standards. + + If n is small and/or p is near 0 or near 1 (specifically, if + n*min(p,1-p) < SMALL_MEAN), then a different algorithm, called + BINV, is used which has an average runtime that scales linearly + with n*min(p,1-p). + + But for larger problems, the BTPE algorithm takes the form of two + functions b(x) and t(x) -- "bottom" and "top" -- for which b(x) < + f(x)/f(M) < t(x), with M = floor(n*p+p). b(x) defines a triangular + region, and t(x) includes a parallelogram and two tails. Details + (including a nice drawing) are in the paper. + + [1] Kachitvichyanukul, V. and Schmeiser, B. W. Binomial Random + Variate Generation. Communications of the ACM, 31, 2 (February, + 1988) 216. + + Note, Bruce Schmeiser (personal communication) points out that if + you want very fast binomial deviates, and you are happy with + approximate results, and/or n and n*p are both large, then you can + just use gaussian estimates: mean=n*p, variance=n*p*(1-p). + + This implementation by James Theiler, April 2003, after obtaining + permission -- and some good advice -- from Drs. Kachitvichyanukul + and Schmeiser to use their code as a starting point, and then doing + a little bit of tweaking. + + Additional polishing for GSL coding standards by Brian Gough. */ + +#define SMALL_MEAN 14 /* If n*p < SMALL_MEAN then use BINV + algorithm. The ranlib + implementation used cutoff=30; but + on my computer 14 works better */ + +#define BINV_CUTOFF 110 /* In BINV, do not permit ix too large */ + +#define FAR_FROM_MEAN 20 /* If ix-n*p is larger than this, then + use the "squeeze" algorithm. + Ranlib used 20, and this seems to + be the best choice on my machine as + well */ + +#define LNFACT(x) gsl_sf_lnfact(x) + +inline static double +Stirling (double y1) +{ + double y2 = y1 * y1; + double s = + (13860.0 - + (462.0 - (132.0 - (99.0 - 140.0 / y2) / y2) / y2) / y2) / y1 / 166320.0; + return s; +} + +unsigned int +gsl_ran_binomial_tpe (const gsl_rng * rng, double p, unsigned int n) +{ + return gsl_ran_binomial (rng, p, n); +} + +unsigned int +gsl_ran_binomial (const gsl_rng * rng, double p, unsigned int n) +{ + int ix; /* return value */ + int flipped = 0; + double q, s, np; + + if (n == 0) + return 0; + + if (p > 0.5) + { + p = 1.0 - p; /* work with small p */ + flipped = 1; + } + + q = 1 - p; + s = p / q; + np = n * p; + + /* Inverse cdf logic for small mean (BINV in K+S) */ + + if (np < SMALL_MEAN) + { + double f0 = gsl_pow_int (q, n); /* f(x), starting with x=0 */ + + while (1) + { + /* This while(1) loop will almost certainly only loop once; but + * if u=1 to within a few epsilons of machine precision, then it + * is possible for roundoff to prevent the main loop over ix to + * achieve its proper value. following the ranlib implementation, + * we introduce a check for that situation, and when it occurs, + * we just try again. + */ + + double f = f0; + double u = gsl_rng_uniform (rng); + + for (ix = 0; ix <= BINV_CUTOFF; ++ix) + { + if (u < f) + goto Finish; + u -= f; + /* Use recursion f(x+1) = f(x)*[(n-x)/(x+1)]*[p/(1-p)] */ + f *= s * (n - ix) / (ix + 1); + } + + /* It should be the case that the 'goto Finish' was encountered + * before this point was ever reached. But if we have reached + * this point, then roundoff has prevented u from decreasing + * all the way to zero. This can happen only if the initial u + * was very nearly equal to 1, which is a rare situation. In + * that rare situation, we just try again. + * + * Note, following the ranlib implementation, we loop ix only to + * a hardcoded value of SMALL_MEAN_LARGE_N=110; we could have + * looped to n, and 99.99...% of the time it won't matter. This + * choice, I think is a little more robust against the rare + * roundoff error. If n>LARGE_N, then it is technically + * possible for ix>LARGE_N, but it is astronomically rare, and + * if ix is that large, it is more likely due to roundoff than + * probability, so better to nip it at LARGE_N than to take a + * chance that roundoff will somehow conspire to produce an even + * larger (and more improbable) ix. If n<LARGE_N, then once + * ix=n, f=0, and the loop will continue until ix=LARGE_N. + */ + } + } + else + { + /* For n >= SMALL_MEAN, we invoke the BTPE algorithm */ + + int k; + + double ffm = np + p; /* ffm = n*p+p */ + int m = (int) ffm; /* m = int floor[n*p+p] */ + double fm = m; /* fm = double m; */ + double xm = fm + 0.5; /* xm = half integer mean (tip of triangle) */ + double npq = np * q; /* npq = n*p*q */ + + /* Compute cumulative area of tri, para, exp tails */ + + /* p1: radius of triangle region; since height=1, also: area of region */ + /* p2: p1 + area of parallelogram region */ + /* p3: p2 + area of left tail */ + /* p4: p3 + area of right tail */ + /* pi/p4: probability of i'th area (i=1,2,3,4) */ + + /* Note: magic numbers 2.195, 4.6, 0.134, 20.5, 15.3 */ + /* These magic numbers are not adjustable...at least not easily! */ + + double p1 = floor (2.195 * sqrt (npq) - 4.6 * q) + 0.5; + + /* xl, xr: left and right edges of triangle */ + double xl = xm - p1; + double xr = xm + p1; + + /* Parameter of exponential tails */ + /* Left tail: t(x) = c*exp(-lambda_l*[xl - (x+0.5)]) */ + /* Right tail: t(x) = c*exp(-lambda_r*[(x+0.5) - xr]) */ + + double c = 0.134 + 20.5 / (15.3 + fm); + double p2 = p1 * (1.0 + c + c); + + double al = (ffm - xl) / (ffm - xl * p); + double lambda_l = al * (1.0 + 0.5 * al); + double ar = (xr - ffm) / (xr * q); + double lambda_r = ar * (1.0 + 0.5 * ar); + double p3 = p2 + c / lambda_l; + double p4 = p3 + c / lambda_r; + + double var, accept; + double u, v; /* random variates */ + + TryAgain: + + /* generate random variates, u specifies which region: Tri, Par, Tail */ + u = gsl_rng_uniform (rng) * p4; + v = gsl_rng_uniform (rng); + + if (u <= p1) + { + /* Triangular region */ + ix = (int) (xm - p1 * v + u); + goto Finish; + } + else if (u <= p2) + { + /* Parallelogram region */ + double x = xl + (u - p1) / c; + v = v * c + 1.0 - fabs (x - xm) / p1; + if (v > 1.0 || v <= 0.0) + goto TryAgain; + ix = (int) x; + } + else if (u <= p3) + { + /* Left tail */ + ix = (int) (xl + log (v) / lambda_l); + if (ix < 0) + goto TryAgain; + v *= ((u - p2) * lambda_l); + } + else + { + /* Right tail */ + ix = (int) (xr - log (v) / lambda_r); + if (ix > (double) n) + goto TryAgain; + v *= ((u - p3) * lambda_r); + } + + /* At this point, the goal is to test whether v <= f(x)/f(m) + * + * v <= f(x)/f(m) = (m!(n-m)! / (x!(n-x)!)) * (p/q)^{x-m} + * + */ + + /* Here is a direct test using logarithms. It is a little + * slower than the various "squeezing" computations below, but + * if things are working, it should give exactly the same answer + * (given the same random number seed). */ + +#ifdef DIRECT + var = log (v); + + accept = + LNFACT (m) + LNFACT (n - m) - LNFACT (ix) - LNFACT (n - ix) + + (ix - m) * log (p / q); + +#else /* SQUEEZE METHOD */ + + /* More efficient determination of whether v < f(x)/f(M) */ + + k = abs (ix - m); + + if (k <= FAR_FROM_MEAN) + { + /* + * If ix near m (ie, |ix-m|<FAR_FROM_MEAN), then do + * explicit evaluation using recursion relation for f(x) + */ + double g = (n + 1) * s; + double f = 1.0; + + var = v; + + if (m < ix) + { + int i; + for (i = m + 1; i <= ix; i++) + { + f *= (g / i - s); + } + } + else if (m > ix) + { + int i; + for (i = ix + 1; i <= m; i++) + { + f /= (g / i - s); + } + } + + accept = f; + } + else + { + /* If ix is far from the mean m: k=ABS(ix-m) large */ + + var = log (v); + + if (k < npq / 2 - 1) + { + /* "Squeeze" using upper and lower bounds on + * log(f(x)) The squeeze condition was derived + * under the condition k < npq/2-1 */ + double amaxp = + k / npq * ((k * (k / 3.0 + 0.625) + (1.0 / 6.0)) / npq + 0.5); + double ynorm = -(k * k / (2.0 * npq)); + if (var < ynorm - amaxp) + goto Finish; + if (var > ynorm + amaxp) + goto TryAgain; + } + + /* Now, again: do the test log(v) vs. log f(x)/f(M) */ + +#if USE_EXACT + /* This is equivalent to the above, but is a little (~20%) slower */ + /* There are five log's vs three above, maybe that's it? */ + + accept = LNFACT (m) + LNFACT (n - m) + - LNFACT (ix) - LNFACT (n - ix) + (ix - m) * log (p / q); + +#else /* USE STIRLING */ + /* The "#define Stirling" above corresponds to the first five + * terms in asymptoic formula for + * log Gamma (y) - (y-0.5)log(y) + y - 0.5 log(2*pi); + * See Abramowitz and Stegun, eq 6.1.40 + */ + + /* Note below: two Stirling's are added, and two are + * subtracted. In both K+S, and in the ranlib + * implementation, all four are added. I (jt) believe that + * is a mistake -- this has been confirmed by personal + * correspondence w/ Dr. Kachitvichyanukul. Note, however, + * the corrections are so small, that I couldn't find an + * example where it made a difference that could be + * observed, let alone tested. In fact, define'ing Stirling + * to be zero gave identical results!! In practice, alv is + * O(1), ranging 0 to -10 or so, while the Stirling + * correction is typically O(10^{-5}) ...setting the + * correction to zero gives about a 2% performance boost; + * might as well keep it just to be pendantic. */ + + { + double x1 = ix + 1.0; + double w1 = n - ix + 1.0; + double f1 = fm + 1.0; + double z1 = n + 1.0 - fm; + + accept = xm * log (f1 / x1) + (n - m + 0.5) * log (z1 / w1) + + (ix - m) * log (w1 * p / (x1 * q)) + + Stirling (f1) + Stirling (z1) - Stirling (x1) - Stirling (w1); + } +#endif +#endif + } + + + if (var <= accept) + { + goto Finish; + } + else + { + goto TryAgain; + } + } + +Finish: + + return (flipped) ? (n - ix) : (unsigned int)ix; +} |