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/* evd_test.c
* SRE, Wed Nov 12 11:17:27 1997 [St. Louis]
*
* Test driver for EVD distribution support in histogram.c
* Generates random EVD samples; fits them; checks fitted mu, lambda
* against parametric mu, lambda. If they differ badly, calls Die().
* If OK, returns EXIT_SUCCESS.
*
* CVS $Id: evd_test.c,v 1.7 2003/04/14 16:00:17 eddy Exp $
*/
#include "config.h"
#include <stdio.h>
#include <string.h>
#include <time.h>
#include <math.h>
#include "structs.h"
#include "funcs.h"
#include "globals.h"
#include "squid.h"
static char banner[] = "\
evd_test : testing of EVD code in histogram.c";
static char usage[] = "\
Usage: testdriver [-options]\n\
Available options are:\n\
-h : help; display this usage info\n\
-c <x> : censor data below <x>\n\
-e <n> : sample <n> times from EVD\n\
-g <n> : add <n> Gaussian samples of \"noise\"\n\
-n <n> : set number of trials to <n>\n\
-s <n> : set random seed to <n>\n\
-v : be verbose (default is to simply exit with status 1 or 0)\n\
";
static char experts[] = "\
--xmgr <file> : save graphical data to <file>\n\
--hist : fit to histogram instead of raw samples\n\
--loglog <file> : save log log regression line to <file>\n\
--regress : do old-style linear regression fit, not ML\n\
--mu <x> : set EVD mu to <x>\n\
--lambda <x> : set EVD lambda to <x>\n\
--mean <x> : set Gaussian mean to <x>\n\
--sd <x> : set Gaussian std. dev. to <x>\n\
\n";
static struct opt_s OPTIONS[] = {
{ "-h", TRUE, sqdARG_NONE },
{ "-c", TRUE, sqdARG_FLOAT },
{ "-e", TRUE, sqdARG_INT },
{ "-g", TRUE, sqdARG_INT },
{ "-n", TRUE, sqdARG_INT },
{ "-s", TRUE, sqdARG_INT },
{ "-v", TRUE, sqdARG_NONE },
{ "--xmgr", FALSE, sqdARG_STRING},
{ "--hist", FALSE, sqdARG_NONE},
{ "--loglog", FALSE, sqdARG_STRING},
{ "--regress",FALSE, sqdARG_NONE},
{ "--mu", FALSE, sqdARG_FLOAT},
{ "--lambda", FALSE, sqdARG_FLOAT},
{ "--mean", FALSE, sqdARG_FLOAT},
{ "--sd", FALSE, sqdARG_FLOAT},
};
#define NOPTIONS (sizeof(OPTIONS) / sizeof(struct opt_s))
int
main(int argc, char **argv)
{
struct histogram_s *h; /* histogram structure */
int ntrials; /* number of different fits */
int be_verbose; /* option: TRUE to show output */
int seed; /* option: random number seed */
int nevd; /* # of samples from EVD */
float mu; /* EVD mu parameter */
float lambda; /* EVD lambda parameter */
int ngauss; /* # of samples from Gaussian */
float mean; /* Gaussian "noise" mean */
float sd; /* Gaussian "noise" std. dev. */
float x; /* a random sample */
int i, idx;
float *val; /* array of samples */
float mlmu; /* estimate of mu */
float mllambda; /* estimate of lambda */
char *xmgrfile; /* output file for XMGR graph data */
char *logfile; /* output file for regression line */
FILE *xmgrfp; /* open output file */
FILE *logfp; /* open log log file */
int do_ml; /* TRUE to do a max likelihood fit */
int fit_hist; /* TRUE to fit histogram instead of samples */
int censoring; /* TRUE to left-censor the data */
float censorlevel; /* value to censor at */
char *optname; /* name of option found by Getopt() */
char *optarg; /* argument found by Getopt() */
int optind; /* index in argv[] */
/***********************************************
* Parse command line
***********************************************/
be_verbose = FALSE;
seed = (int) time ((time_t *) NULL);
ntrials = 1;
nevd = 1000;
mu = -20.0;
lambda = 0.4;
ngauss = 0;
mean = 20.;
sd = 20.;
xmgrfile = NULL;
logfile = NULL;
xmgrfp = NULL;
logfp = NULL;
do_ml = TRUE;
censoring = FALSE;
censorlevel= 0.;
fit_hist = FALSE;
while (Getopt(argc, argv, OPTIONS, NOPTIONS, usage,
&optind, &optname, &optarg)) {
if (strcmp(optname, "-e") == 0) { nevd = atoi(optarg); }
else if (strcmp(optname, "-c") == 0) { censoring = TRUE;
censorlevel= atof(optarg); }
else if (strcmp(optname, "-g") == 0) { ngauss = atoi(optarg); }
else if (strcmp(optname, "-n") == 0) { ntrials = atoi(optarg); }
else if (strcmp(optname, "-s") == 0) { seed = atoi(optarg); }
else if (strcmp(optname, "-v") == 0) { be_verbose = TRUE; }
else if (strcmp(optname, "--xmgr") == 0) { xmgrfile = optarg; }
else if (strcmp(optname, "--hist") == 0) { fit_hist = TRUE; }
else if (strcmp(optname, "--loglog") == 0) { logfile = optarg; }
else if (strcmp(optname, "--regress")== 0) { do_ml = FALSE; }
else if (strcmp(optname, "--mu") == 0) { mu = atof(optarg); }
else if (strcmp(optname, "--lambda") == 0) { lambda = atof(optarg); }
else if (strcmp(optname, "--mean") == 0) { mean = atof(optarg); }
else if (strcmp(optname, "--sd") == 0) { sd = atof(optarg); }
else if (strcmp(optname, "-h") == 0) {
HMMERBanner(stdout, banner);
puts(usage);
puts(experts);
exit(0);
}
}
if (argc - optind != 0)
Die("Incorrect number of arguments.\n%s\n", usage);
sre_srandom(seed);
/****************************************************************
* Print options
****************************************************************/
if (be_verbose)
{
puts("--------------------------------------------------------");
printf("EVD samples = %d\n", nevd);
printf("mu, lambda = %f, %f\n", mu, lambda);
if (ngauss > 0) {
printf("Gaussian noise = %d\n", ngauss);
printf("mean, sd = %f, %f\n", mean, sd);
}
if (censoring) printf("pre-censoring = ON, at %f\n", censorlevel);
printf("total trials = %d\n", ntrials);
printf("random seed = %d\n", seed);
printf("fit method = %s\n", do_ml ? "ML" : "linear regression");
printf("fit is to = %s\n", fit_hist ? "histogram" : "list");
puts("--------------------------------------------------------");
}
if (xmgrfile != NULL)
if ((xmgrfp = fopen(xmgrfile, "w")) == NULL)
Die("Failed to open output file %s", xmgrfile);
if (logfile != NULL)
if ((logfp = fopen(logfile, "w")) == NULL)
Die("Failed to open output file %s", logfile);
/* Generate random EVD "signal" (and Gaussian "noise")
* samples and put them in the histogram
*/
while (ntrials--)
{
val = MallocOrDie(sizeof(double) * (nevd+ngauss));
h = AllocHistogram(-20, 20, 10);
/* EVD signal */
idx = 0;
for (i = 0; i < nevd; i++)
{
x = EVDrandom(mu, lambda);
if (! censoring || x > censorlevel)
{
AddToHistogram(h, x);
val[idx] = x;
idx++;
}
}
/* Gaussian noise */
for (; i < nevd + ngauss; i++)
{
x = Gaussrandom(mean, sd);
if (! censoring || x > censorlevel)
{
AddToHistogram(h, x);
val[idx] = x;
idx++;
}
}
if (do_ml)
{
if (censoring)
{
if (be_verbose)
printf("I have censored the data at %f: %d observed, %d censored\n", censorlevel, idx, (nevd+ngauss)-idx);
EVDCensoredFit(val, NULL, idx,
(nevd+ngauss)-idx, censorlevel,
&mlmu, &mllambda);
ExtremeValueSetHistogram(h, (float) mlmu, (float) mllambda,
censorlevel, h->highscore, 1);
}
else
{
if (fit_hist)
{
ExtremeValueFitHistogram(h, TRUE, 20.);
}
else
{
EVDMaxLikelyFit(val, NULL, idx, &mlmu, &mllambda);
ExtremeValueSetHistogram(h, (float) mlmu, (float) mllambda,
h->lowscore, h->highscore, 2);
}
}
}
else
EVDBasicFit(h);
if (be_verbose) {
printf("%f\tmu\n", h->param[EVD_MU]);
printf("%f\tlambda\n", h->param[EVD_LAMBDA]);
printf("%f\t%% error on mu\n",
fabs(100. * (h->param[EVD_MU] - mu) / mu));
printf("%f\t%% error on lambda\n",
fabs(100. * (h->param[EVD_LAMBDA] - lambda) / lambda));
printf("%f\tchi-squared P value\n", h->chip);
}
if (xmgrfp != NULL) PrintXMGRHistogram(xmgrfp, h);
/* if (xmgrfp != NULL) PrintXMGRDistribution(xmgrfp, h); */
if (logfp != NULL) PrintXMGRRegressionLine(logfp, h);
/* Generate the expected lines: sets 5,7 of xmgrfile (manually delete 4,6)
* set 3 of loglogfile (manually delete 2)
*/
ExtremeValueSetHistogram(h, mu, lambda, h->lowscore, h->highscore, 0);
if (xmgrfp != NULL) PrintXMGRHistogram(xmgrfp, h);
/* if (xmgrfp != NULL) PrintXMGRDistribution(xmgrfp, h); */
if (logfp != NULL) PrintXMGRRegressionLine(logfp, h);
/* Do the internal test.
* Criterion: on a 1000 sample EVD of u = -40 and lambda = 0.4,
* estimate u to within +/- 2 and lambda to within +/- 0.05.
*/
if (fabs(h->param[EVD_MU] - mu) > 2.)
Die("evd_test: tolerance to mu exceeded (%f)",
fabs(h->param[EVD_MU] - mu));
if (fabs(h->param[EVD_LAMBDA] - lambda) > 0.05)
Die("evd_test: tolerance to lambda exceeded (%f)",
fabs(h->param[EVD_LAMBDA] - lambda));
FreeHistogram(h);
free(val);
}
if (xmgrfp != NULL) fclose(xmgrfp);
if (logfp != NULL) fclose(logfp);
return EXIT_SUCCESS;
}
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