From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Tue, 2 Oct 2012 00:19:43 -0400 Subject: cdec cleanup, remove bayesian stuff, parsing stuff --- gi/pyp-topics/src/gammadist.c | 247 ------------------------------------------ 1 file changed, 247 deletions(-) delete mode 100644 gi/pyp-topics/src/gammadist.c (limited to 'gi/pyp-topics/src/gammadist.c') diff --git a/gi/pyp-topics/src/gammadist.c b/gi/pyp-topics/src/gammadist.c deleted file mode 100644 index 4e260db8..00000000 --- a/gi/pyp-topics/src/gammadist.c +++ /dev/null @@ -1,247 +0,0 @@ -/* gammadist.c -- computes probability of samples under / produces samples from a Gamma distribution - * - * Mark Johnson, 22nd March 2008 - * - * WARNING: you need to set the flag -std=c99 to compile - * - * gammavariate() was translated from random.py in Python library - * - * The Gamma distribution is: - * - * Gamma(x | alpha, beta) = pow(x/beta, alpha-1) * exp(-x/beta) / (gamma(alpha)*beta) - * - * shape parameter alpha > 0 (also called c), scale parameter beta > 0 (also called s); - * mean is alpha*beta, variance is alpha*beta**2 - * - * Note that many parameterizations of the Gamma function are in terms of an _inverse_ - * scale parameter beta, which is the inverse of the beta given here. - * - * To define a main() that tests the routines, uncomment the following #define: - */ -/* #define GAMMATEST */ - -#include -#include - -#include "gammadist.h" -#include "mt19937ar.h" - -/* gammadist() returns the probability density of x under a Gamma(alpha,beta) - * distribution - */ - -long double gammadist(long double x, long double alpha, long double beta) { - assert(alpha > 0); - assert(beta > 0); - return pow(x/beta, alpha-1) * exp(-x/beta) / (tgamma(alpha)*beta); -} - -/* lgammadist() returns the log probability density of x under a Gamma(alpha,beta) - * distribution - */ - -long double lgammadist(long double x, long double alpha, long double beta) { - assert(alpha > 0); - assert(beta > 0); - return (alpha-1)*log(x) - alpha*log(beta) - x/beta - lgamma(alpha); -} - -/* This definition of gammavariate is from Python code in - * the Python random module. - */ - -long double gammavariate(long double alpha, long double beta) { - - assert(alpha > 0); - assert(beta > 0); - - if (alpha > 1.0) { - - /* Uses R.C.H. Cheng, "The generation of Gamma variables with - non-integral shape parameters", Applied Statistics, (1977), 26, - No. 1, p71-74 */ - - long double ainv = sqrt(2.0 * alpha - 1.0); - long double bbb = alpha - log(4.0); - long double ccc = alpha + ainv; - - while (1) { - long double u1 = mt_genrand_real3(); - if (u1 > 1e-7 || u1 < 0.9999999) { - long double u2 = 1.0 - mt_genrand_real3(); - long double v = log(u1/(1.0-u1))/ainv; - long double x = alpha*exp(v); - long double z = u1*u1*u2; - long double r = bbb+ccc*v-x; - if (r + (1.0+log(4.5)) - 4.5*z >= 0.0 || r >= log(z)) - return x * beta; - } - } - } - else if (alpha == 1.0) { - long double u = mt_genrand_real3(); - while (u <= 1e-7) - u = mt_genrand_real3(); - return -log(u) * beta; - } - else { - /* alpha is between 0 and 1 (exclusive) - Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle */ - - while (1) { - long double u = mt_genrand_real3(); - long double b = (exp(1) + alpha)/exp(1); - long double p = b*u; - long double x = (p <= 1.0) ? pow(p, 1.0/alpha) : -log((b-p)/alpha); - long double u1 = mt_genrand_real3(); - if (! (((p <= 1.0) && (u1 > exp(-x))) || - ((p > 1.0) && (u1 > pow(x, alpha - 1.0))))) - return x * beta; - } - } -} - -/* betadist() returns the probability density of x under a Beta(alpha,beta) - * distribution. - */ - -long double betadist(long double x, long double alpha, long double beta) { - assert(x >= 0); - assert(x <= 1); - assert(alpha > 0); - assert(beta > 0); - return pow(x,alpha-1)*pow(1-x,beta-1)*tgamma(alpha+beta)/(tgamma(alpha)*tgamma(beta)); -} - -/* lbetadist() returns the log probability density of x under a Beta(alpha,beta) - * distribution. - */ - -long double lbetadist(long double x, long double alpha, long double beta) { - assert(x > 0); - assert(x < 1); - assert(alpha > 0); - assert(beta > 0); - return (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta); -} - -/* betavariate() generates a sample from a Beta distribution with - * parameters alpha and beta. - * - * 0 < alpha < 1, 0 < beta < 1, mean is alpha/(alpha+beta) - */ - -long double betavariate(long double alpha, long double beta) { - long double x = gammavariate(alpha, 1); - long double y = gammavariate(beta, 1); - return x/(x+y); -} - -#ifdef GAMMATEST -#include - -int main(int argc, char **argv) { - int iteration, niterations = 1000; - - for (iteration = 0; iteration < niterations; ++iteration) { - long double alpha = 100*mt_genrand_real3(); - long double gv = gammavariate(alpha, 1); - long double pgv = gammadist(gv, alpha, 1); - long double pgvl = exp(lgammadist(gv, alpha, 1)); - fprintf(stderr, "iteration = %d, gammavariate(%lg,1) = %lg, gammadist(%lg,%lg,1) = %lg, exp(lgammadist(%lg,%lg,1) = %lg\n", - iteration, alpha, gv, gv, alpha, pgv, gv, alpha, pgvl); - } - return 0; -} - -#endif /* GAMMATEST */ - - -/* Other routines I tried, but which weren't as good as the ones above */ - -#if 0 - -/*! gammavariate() returns samples from a Gamma distribution - *! where alpha is the shape parameter and beta is the scale - *! parameter, using the algorithm described on p. 94 of - *! Gentle (1998) Random Number Generation and Monte Carlo Methods, - *! Springer. - */ - -long double gammavariate(long double alpha) { - - assert(alpha > 0); - - if (alpha > 1.0) { - while (1) { - long double u1 = mt_genrand_real3(); - long double u2 = mt_genrand_real3(); - long double v = (alpha - 1/(6*alpha))*u1/(alpha-1)*u2; - if (2*(u2-1)/(alpha-1) + v + 1/v <= 2 - || 2*log(u2)/(alpha-1) - log(v) + v <= 1) - return (alpha-1)*v; - } - } else if (alpha < 1.0) { - while (1) { - long double t = 0.07 + 0.75*sqrt(1-alpha); - long double b = alpha + exp(-t)*alpha/t; - long double u1 = mt_genrand_real3(); - long double u2 = mt_genrand_real3(); - long double v = b*u1; - if (v <= 1) { - long double x = t*pow(v, 1/alpha); - if (u2 <= (2 - x)/(2 + x)) - return x; - if (u2 <= exp(-x)) - return x; - } - else { - long double x = log(t*(b-v)/alpha); - long double y = x/t; - if (u2*(alpha + y*(1-alpha)) <= 1) - return x; - if (u2 <= pow(y,alpha-1)) - return x; - } - } - } - else - return -log(mt_genrand_real3()); -} - - -/*! gammavariate() returns a deviate distributed as a gamma - *! distribution of order alpha, beta, i.e., a waiting time to the alpha'th - *! event in a Poisson process of unit mean. - *! - *! Code from Numerical Recipes - */ - -long double nr_gammavariate(long double ia) { - int j; - long double am,e,s,v1,v2,x,y; - assert(ia > 0); - if (ia < 10) { - x=1.0; - for (j=1;j<=ia;j++) - x *= mt_genrand_real3(); - x = -log(x); - } else { - do { - do { - do { - v1=mt_genrand_real3(); - v2=2.0*mt_genrand_real3()-1.0; - } while (v1*v1+v2*v2 > 1.0); - y=v2/v1; - am=ia-1; - s=sqrt(2.0*am+1.0); - x=s*y+am; - } while (x <= 0.0); - e=(1.0+y*y)*exp(am*log(x/am)-s*y); - } while (mt_genrand_real3() > e); - } - return x; -} - -#endif -- cgit v1.2.3