int
main(int argc, char **argv)
{
ESL_RANDOMNESS *r; /* source of random numbers */
ESL_HISTOGRAM *h; /* histogram to store the data */
ESL_HYPEREXP *hxp; /* hyperexponential to sample from */
ESL_HYPEREXP *ehxp; /* estimated hyperexponential */
double x; /* sampled data point */
int n = 100000; /* number of samples */
double *data;
int ndata;
int i;
hxp = esl_hyperexp_Create(3);
hxp->mu = -2.0;
hxp->q[0] = 0.6; hxp->q[1] = 0.3; hxp->q[2] = 0.1;
hxp->lambda[0] = 1.0; hxp->lambda[1] = 0.3; hxp->lambda[2] = 0.1;
r = esl_randomness_Create(0);
h = esl_histogram_CreateFull(hxp->mu, 100, 1.0);
for (i = 0; i < n; i++)
{
x = esl_hxp_Sample(r, hxp);
esl_histogram_Add(h, x);
}
esl_histogram_GetData(h, &data, &ndata);
/* Plot the empirical (sampled) and expected survivals */
esl_histogram_PlotSurvival(stdout, h);
esl_hxp_Plot(stdout, hxp, &esl_hxp_surv, h->xmin, h->xmax, 0.1);
/* ML fit to complete data, and plot fitted survival curve */
ehxp = esl_hyperexp_Create(3);
esl_hxp_FitGuess(data, ndata, ehxp);
esl_hxp_FitComplete(data, ndata, ehxp);
esl_hxp_Plot(stdout, ehxp, &esl_hxp_surv, h->xmin, h->xmax, 0.1);
/* ML fit to binned data, plot fitted survival curve */
esl_hxp_FitGuessBinned(h, ehxp);
esl_hxp_FitCompleteBinned(h, ehxp);
esl_hxp_Plot(stdout, ehxp, &esl_hxp_surv, h->xmin, h->xmax, 0.1);
esl_randomness_Destroy(r);
esl_histogram_Destroy(h);
esl_hyperexp_Destroy(hxp);
esl_hyperexp_Destroy(ehxp);
return 0;
}
int
main(int argc, char **argv)
{
double mu = -50.0;
double lambda = 2.5;
double tau = 0.7;
ESL_HISTOGRAM *h = esl_histogram_CreateFull(mu, 100., 0.1);
ESL_RANDOMNESS *r = esl_randomness_Create(0);
int n = 10000;
double *data;
int ndata;
double emu, elam, etau;
int i;
double x;
for (i = 0; i < n; i++)
{
x = esl_sxp_Sample(r, mu, lambda, tau);
esl_histogram_Add(h, x);
}
esl_histogram_GetData(h, &data, &ndata);
/* Plot the empirical (sampled) and expected survivals */
esl_histogram_PlotSurvival(stdout, h);
esl_sxp_Plot(stdout, mu, lambda, tau,
&esl_sxp_surv, h->xmin, h->xmax, 0.1);
/* ML fit to complete data, and plot fitted survival curve */
esl_sxp_FitComplete(data, ndata, &emu, &elam, &etau);
esl_sxp_Plot(stdout, emu, elam, etau,
&esl_sxp_surv, h->xmin, h->xmax, 0.1);
/* ML fit to binned data, plot fitted survival curve */
esl_sxp_FitCompleteBinned(h, &emu, &elam, &etau);
esl_sxp_Plot(stdout, emu, elam, etau,
&esl_sxp_surv, h->xmin, h->xmax, 0.1);
esl_randomness_Destroy(r);
esl_histogram_Destroy(h);
return 0;
}
static int
output_result(ESL_GETOPTS *go, struct cfg_s *cfg, char *errbuf, P7_HMM *hmm, double *scores, int *alilens)
{
ESL_HISTOGRAM *h = NULL;
int i;
double tailp;
double x10;
double mu, lambda, E10;
double mufix, E10fix;
double mufix2, E10fix2;
double E10p;
double almean, alvar; /* alignment length mean and variance (optional output) */
double pmu, plambda;
int status;
/* fetch statistical params from HMM for expected distribution */
if (esl_opt_GetBoolean(go, "--vit")) { pmu = hmm->evparam[p7_VMU]; plambda = hmm->evparam[p7_VLAMBDA]; }
else if (esl_opt_GetBoolean(go, "--msv")) { pmu = hmm->evparam[p7_MMU]; plambda = hmm->evparam[p7_MLAMBDA]; }
else if (esl_opt_GetBoolean(go, "--fwd")) { pmu = hmm->evparam[p7_FTAU]; plambda = hmm->evparam[p7_FLAMBDA]; }
/* Optional output of scores/alignment lengths: */
if (cfg->xfp) fwrite(scores, sizeof(double), cfg->N, cfg->xfp);
if (cfg->alfp) for (i = 0; i < cfg->N; i++) fprintf(cfg->alfp, "%d %.3f\n", alilens[i], scores[i]);
if (esl_opt_GetBoolean(go, "-v")) for (i = 0; i < cfg->N; i++) printf("%.3f\n", scores[i]);
/* optional "filter power" data file: <hmm name> <# seqs <= P threshold> <fraction of seqs <= P threshold> */
if (cfg->ffp) output_filter_power(go, cfg, errbuf, hmm, scores);
/* Count the scores into a histogram object. */
if ((h = esl_histogram_CreateFull(-50., 50., 0.2)) == NULL) ESL_XFAIL(eslEMEM, errbuf, "allocation failed");
for (i = 0; i < cfg->N; i++) esl_histogram_Add(h, scores[i]);
/* For viterbi, MSV, and hybrid, fit data to a Gumbel, either with known lambda or estimated lambda. */
if (esl_opt_GetBoolean(go, "--vit") || esl_opt_GetBoolean(go, "--msv"))
{
esl_histogram_GetRank(h, 10, &x10);
tailp = 1.0;
/* mu, lambda, E10 fields are for ML Gumbel fit to the observed data */
if (esl_gumbel_FitComplete(scores, cfg->N, &mu, &lambda) != eslOK) esl_fatal("gumbel complete data fit failed");
E10 = cfg->N * esl_gumbel_surv(x10, mu, lambda);
/* mufix, E10fix fields: assume lambda = log2; fit an ML mu to the data */
if (esl_gumbel_FitCompleteLoc(scores, cfg->N, 0.693147, &mufix) != eslOK) esl_fatal("gumbel mu- (location-)only data fit failed for lambda = log2");
E10fix = cfg->N * esl_gumbel_surv(x10, mufix, 0.693147);
/* mufix2, E10fix2 fields: assume H3's own lambda estimate; fit ML mu */
if (esl_gumbel_FitCompleteLoc(scores, cfg->N, plambda, &mufix2) != eslOK) esl_fatal("gumbel mu- (location-)only data fit failed for fitted lambda");
E10fix2 = cfg->N * esl_gumbel_surv(x10, mufix2, plambda);
/* pmu, plambda, E10p: use H3 expectation estimates (pmu, plambda) */
E10p = cfg->N * esl_gumbel_surv(x10, pmu, plambda);
fprintf(cfg->ofp, "%-20s %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f",
hmm->name, tailp, mu, lambda, E10, mufix, E10fix, mufix2, E10fix2, pmu, plambda, E10p);
if (esl_opt_GetBoolean(go, "-a")) {
esl_stats_IMean(alilens, cfg->N, &almean, &alvar);
fprintf(cfg->ofp, " %8.4f %8.4f\n", almean, sqrt(alvar));
} else
fprintf(cfg->ofp, "\n");
if (cfg->survfp != NULL) {
double xmax = esl_opt_IsOn(go, "--xmax") ? esl_opt_GetReal(go, "--xmax") : h->xmax + 5.;
esl_histogram_PlotSurvival(cfg->survfp, h);
esl_gumbel_Plot(cfg->survfp, pmu, plambda, esl_gumbel_surv, h->xmin - 5., xmax, 0.1);
esl_gumbel_Plot(cfg->survfp, mu, lambda, esl_gumbel_surv, h->xmin - 5., xmax, 0.1);
esl_gumbel_Plot(cfg->survfp, mufix, 0.693147, esl_gumbel_surv, h->xmin - 5., xmax, 0.1);
}
if (cfg->efp != NULL) {
double x;
fprintf(cfg->efp, "# %s\n", hmm->name);
for (i = 1; i <= 1000 && i <= cfg->N; i++) {
esl_histogram_GetRank(h, i, &x);
fprintf(cfg->efp, "%d %g\n", i, cfg->N * esl_gumbel_surv(x, pmu, plambda));
}
fprintf(cfg->efp, "&\n");
}
}
/* For Forward, fit tail to exponential tails, for a range of tail mass choices. */
else if (esl_opt_GetBoolean(go, "--fwd"))
{
double tmin = esl_opt_GetReal(go, "--tmin");
double tmax = esl_opt_GetReal(go, "--tmax");
double tpoints = (double) esl_opt_GetInteger(go, "--tpoints");
int do_linear = esl_opt_GetBoolean(go, "--tlinear");
double *xv;
double tau;
int n;
esl_histogram_GetRank(h, 10, &x10);
tailp = tmin;
do {
if (tailp > 1.0) tailp = 1.0;
esl_histogram_GetTailByMass(h, tailp, &xv, &n, NULL);
if (esl_exp_FitComplete(xv, n, &mu, &lambda) != eslOK) esl_fatal("exponential fit failed");
E10 = cfg->N * tailp * esl_exp_surv(x10, mu, lambda);
mufix = mu;
E10fix = cfg->N * tailp * esl_exp_surv(x10, mu, 0.693147);
E10p = cfg->N * esl_exp_surv(x10, pmu, plambda); /* the pmu is relative to a P=1.0 tail origin. */
tau = mu + log(tailp) / lambda;
fprintf(cfg->ofp, "%-20s %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\n",
hmm->name, tailp, mu, lambda, E10, mufix, E10fix, pmu, plambda, E10p);
if (tpoints == 1) break;
else if (do_linear) tailp += (tmax-tmin) / (tpoints-1);
else tailp *= exp(log(tmax/tmin) / (tpoints-1));
} while (tailp <= tmax+1e-7);
if (cfg->survfp)
{
double xmax = esl_opt_IsOn(go, "--xmax") ? esl_opt_GetReal(go, "--xmax") : h->xmax + 5.;
esl_histogram_PlotSurvival(cfg->survfp, h);
esl_exp_Plot(cfg->survfp, pmu, plambda, esl_exp_surv, pmu, xmax, 0.1);
esl_exp_Plot(cfg->survfp, tau, lambda, esl_exp_surv, tau, xmax, 0.1);
esl_exp_Plot(cfg->survfp, tau, 0.693147, esl_exp_surv, tau, xmax, 0.1);
}
if (cfg->efp != NULL) {
double x;
fprintf(cfg->efp, "# %s\n", hmm->name);
for (i = 1; i <= 1000 && i <= cfg->N; i++) {
esl_histogram_GetRank(h, i, &x);
fprintf(cfg->efp, "%d %g\n", i, cfg->N * esl_exp_surv(x, pmu, plambda));
}
fprintf(cfg->efp, "&\n");
}
}
/* fallthrough: both normal, error cases execute same cleanup code */
status = eslOK;
ERROR:
esl_histogram_Destroy(h);
return status;
}
