From e26434979adc33bd949566ba7bf02dff64e80a3e Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cab.ark.cs.cmu.edu>
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 <assert.h>
-#include <math.h>
-
-#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 <stdio.h>
-
-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
--
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