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;
}
int
main(int argc, char **argv)
{
ESL_HISTOGRAM *h;
ESL_RANDOMNESS *r;
ESL_HYPEREXP *hxp;
ESL_HYPEREXP *ehxp;
int n = 20000;
double binwidth = 0.1;
int i;
double x;
double *data;
int ndata;
int k, ek, mink;
double mindiff, diff;
int opti;
int be_verbose = FALSE;
char *paramfile = NULL;
char *plotfile = NULL;
FILE *pfp = stdout;
int plot_pdf = FALSE;
int plot_logpdf = FALSE;
int plot_cdf = FALSE;
int plot_logcdf = FALSE;
int plot_surv = FALSE;
int plot_logsurv = FALSE;
int xmin_set = FALSE;
double xmin;
int xmax_set = FALSE;
double xmax;
int xstep_set = FALSE;
double xstep;
int do_fixmix = FALSE;
int status;
for (opti = 1; opti < argc && *(argv[opti]) == '-'; opti++)
{
if (strcmp(argv[opti], "-f") == 0) do_fixmix = TRUE;
else if (strcmp(argv[opti], "-i") == 0) paramfile = argv[++opti];
else if (strcmp(argv[opti], "-n") == 0) n = atoi(argv[++opti]);
else if (strcmp(argv[opti], "-o") == 0) plotfile = argv[++opti];
else if (strcmp(argv[opti], "-v") == 0) be_verbose = TRUE;
else if (strcmp(argv[opti], "-w") == 0) binwidth = atof(argv[++opti]);
else if (strcmp(argv[opti], "-C") == 0) plot_cdf = TRUE;
else if (strcmp(argv[opti], "-LC") == 0) plot_logcdf = TRUE;
else if (strcmp(argv[opti], "-P") == 0) plot_pdf = TRUE;
else if (strcmp(argv[opti], "-LP") == 0) plot_logpdf = TRUE;
else if (strcmp(argv[opti], "-S") == 0) plot_surv = TRUE;
else if (strcmp(argv[opti], "-LS") == 0) plot_logsurv = TRUE;
else if (strcmp(argv[opti], "-XL") == 0) { xmin_set = TRUE; xmin = atof(argv[++opti]); }
else if (strcmp(argv[opti], "-XH") == 0) { xmax_set = TRUE; xmax = atof(argv[++opti]); }
else if (strcmp(argv[opti], "-XS") == 0) { xstep_set = TRUE; xstep = atof(argv[++opti]); }
else esl_fatal("bad option");
}
if (paramfile != NULL)
{
status = esl_hyperexp_ReadFile(paramfile, &hxp);
if (status == eslENOTFOUND) esl_fatal("Param file %s not found", paramfile);
else if (status == eslEFORMAT) esl_fatal("Parse failed: param file %s invalid format", paramfile);
else if (status != eslOK) esl_fatal("Unusual failure opening param file %s", paramfile);
}
else
{
hxp = esl_hyperexp_Create(3);
hxp->mu = -2.0;
hxp->q[0] = 0.5; hxp->q[1] = 0.3; hxp->q[2] = 0.2;
hxp->lambda[0] = 1.0; hxp->lambda[1] = 0.3; hxp->lambda[2] = 0.1;
}
if (do_fixmix) esl_hyperexp_FixedUniformMixture(hxp); /* overrides q's above */
if (be_verbose) esl_hyperexp_Dump(stdout, hxp);
r = esl_randomness_Create(42);
h = esl_histogram_CreateFull(hxp->mu, 100., binwidth);
if (plotfile != NULL) {
if ((pfp = fopen(plotfile, "w")) == NULL)
esl_fatal("Failed to open plotfile");
}
if (! xmin_set) xmin = hxp->mu;
if (! xmax_set) xmax = hxp->mu+ 20*(1. / esl_vec_DMin(hxp->lambda, hxp->K));
if (! xstep_set) xstep = 0.1;
for (i = 0; i < n; i++)
{
x = esl_hxp_Sample(r, hxp);
esl_histogram_Add(h, x);
}
esl_histogram_GetData(h, &data, &ndata); /* get sorted data vector */
ehxp = esl_hyperexp_Create(hxp->K);
if (do_fixmix) esl_hyperexp_FixedUniformMixture(ehxp);
esl_hxp_FitGuess(data, ndata, ehxp);
if ( esl_hxp_FitComplete(data, ndata, ehxp) != eslOK) esl_fatal("Failed to fit hyperexponential");
if (be_verbose) esl_hyperexp_Dump(stdout, ehxp);
if (fabs( (ehxp->mu-hxp->mu)/hxp->mu ) > 0.01)
esl_fatal("Error in (complete) fitted mu > 1%\n");
for (ek = 0; ek < ehxp->K; ek++)
{ /* try to match each estimated lambda up to a parametric lambda */
mindiff = 1.0;
mink = -1;
for (k = 0; k < hxp->K; k++)
{
diff = fabs( (ehxp->lambda[ek] - hxp->lambda[k]) / hxp->lambda[k]);
if (diff < mindiff) {
mindiff = diff;
mink = k;
}
}
if (mindiff > 0.50)
esl_fatal("Error in (complete) fitted lambda > 50%\n");
if (fabs( (ehxp->q[ek] - hxp->q[mink]) / hxp->q[mink]) > 1.0)
esl_fatal("Error in (complete) fitted q > 2-fold%\n");
}
esl_hxp_FitGuessBinned(h, ehxp);
if ( esl_hxp_FitCompleteBinned(h, ehxp) != eslOK) esl_fatal("Failed to fit binned hyperexponential");
if (be_verbose) esl_hyperexp_Dump(stdout, ehxp);
if (fabs( (ehxp->mu-hxp->mu)/hxp->mu ) > 0.01)
esl_fatal("Error in (binned) fitted mu > 1%\n");
for (ek = 0; ek < ehxp->K; ek++)
{ /* try to match each estimated lambda up to a parametric lambda */
mindiff = 1.0;
mink = -1;
for (k = 0; k < hxp->K; k++)
{
diff = fabs( (ehxp->lambda[ek] - hxp->lambda[k]) / hxp->lambda[k]);
if (diff < mindiff) {
mindiff = diff;
mink = k;
}
}
if (mindiff > 0.50)
esl_fatal("Error in (binned) fitted lambda > 50%\n");
if (fabs( (ehxp->q[ek] - hxp->q[mink]) / hxp->q[mink]) > 1.0)
esl_fatal("Error in (binned) fitted q > 2-fold\n");
}
if (plot_pdf) esl_hxp_Plot(pfp, hxp, &esl_hxp_pdf, xmin, xmax, xstep);
if (plot_logpdf) esl_hxp_Plot(pfp, hxp, &esl_hxp_logpdf, xmin, xmax, xstep);
if (plot_cdf) esl_hxp_Plot(pfp, hxp, &esl_hxp_cdf, xmin, xmax, xstep);
if (plot_logcdf) esl_hxp_Plot(pfp, hxp, &esl_hxp_logcdf, xmin, xmax, xstep);
if (plot_surv) esl_hxp_Plot(pfp, hxp, &esl_hxp_surv, xmin, xmax, xstep);
if (plot_logsurv) esl_hxp_Plot(pfp, hxp, &esl_hxp_logsurv, xmin, xmax, xstep);
if (plotfile != NULL) fclose(pfp);
esl_histogram_Destroy(h);
esl_hyperexp_Destroy(hxp);
esl_hyperexp_Destroy(ehxp);
esl_randomness_Destroy(r);
return 0;
}
int
main(int argc, char **argv)
{
ESL_HISTOGRAM *h;
ESL_RANDOMNESS *r;
double mu = -5.0;
double lambda = 2.0;
double tau = 0.7;
int n = 10000;
double binwidth = 0.1;
double elambda, etau;
int i;
double x;
double *data;
int ndata;
int opti;
int be_verbose = FALSE;
char *plotfile = NULL;
FILE *pfp = stdout;
int plot_pdf = FALSE;
int plot_logpdf = FALSE;
int plot_cdf = FALSE;
int plot_logcdf = FALSE;
int plot_surv = FALSE;
int plot_logsurv = FALSE;
int xmin_set = FALSE;
double xmin;
int xmax_set = FALSE;
double xmax;
int xstep_set = FALSE;
double xstep;
for (opti = 1; opti < argc && *(argv[opti]) == '-'; opti++)
{
if (strcmp(argv[opti], "-m") == 0) mu = atof(argv[++opti]);
else if (strcmp(argv[opti], "-l") == 0) lambda = atof(argv[++opti]);
else if (strcmp(argv[opti], "-n") == 0) n = atoi(argv[++opti]);
else if (strcmp(argv[opti], "-o") == 0) plotfile = argv[++opti];
else if (strcmp(argv[opti], "-t") == 0) tau = atof(argv[++opti]);
else if (strcmp(argv[opti], "-v") == 0) be_verbose = TRUE;
else if (strcmp(argv[opti], "-w") == 0) binwidth = atof(argv[++opti]);
else if (strcmp(argv[opti], "-C") == 0) plot_cdf = TRUE;
else if (strcmp(argv[opti], "-LC") == 0) plot_logcdf = TRUE;
else if (strcmp(argv[opti], "-P") == 0) plot_pdf = TRUE;
else if (strcmp(argv[opti], "-LP") == 0) plot_logpdf = TRUE;
else if (strcmp(argv[opti], "-S") == 0) plot_surv = TRUE;
else if (strcmp(argv[opti], "-LS") == 0) plot_logsurv = TRUE;
else if (strcmp(argv[opti], "-XL") == 0) { xmin_set = TRUE; xmin = atof(argv[++opti]); }
else if (strcmp(argv[opti], "-XH") == 0) { xmax_set = TRUE; xmax = atof(argv[++opti]); }
else if (strcmp(argv[opti], "-XS") == 0) { xstep_set = TRUE; xstep = atof(argv[++opti]); }
else ESL_EXCEPTION(eslEINVAL, "bad option");
}
if (be_verbose)
printf("Parametric: mu = %f lambda = %f tau = %f\n", mu, lambda, tau);
r = esl_randomness_Create(0);
h = esl_histogram_CreateFull(mu, 100., binwidth);
if (plotfile != NULL) {
if ((pfp = fopen(plotfile, "w")) == NULL)
ESL_EXCEPTION(eslFAIL, "Failed to open plotfile");
}
if (! xmin_set) xmin = mu;
if (! xmax_set) xmax = mu+40*(1./lambda);
if (! xstep_set) xstep = 0.1;
for (i = 0; i < n; i++)
{
x = esl_gam_Sample(r, mu, lambda, tau);
esl_histogram_Add(h, x);
}
esl_histogram_GetData(h, &data, &ndata);
esl_gam_FitComplete(data, ndata, mu, &elambda, &etau);
if (be_verbose)
printf("Complete data fit: mu = %f lambda = %f tau = %f\n",
mu, elambda, etau);
if (fabs( (elambda-lambda)/lambda ) > 0.10)
ESL_EXCEPTION(eslFAIL, "Error in (complete) fitted lambda > 10%\n");
if (fabs( (etau-tau)/tau ) > 0.10)
ESL_EXCEPTION(eslFAIL, "Error in (complete) fitted tau > 10%\n");
if (plot_pdf) esl_gam_Plot(pfp, mu, lambda, tau, &esl_gam_pdf, xmin, xmax, xstep);
if (plot_logpdf) esl_gam_Plot(pfp, mu, lambda, tau, &esl_gam_logpdf, xmin, xmax, xstep);
if (plot_cdf) esl_gam_Plot(pfp, mu, lambda, tau, &esl_gam_cdf, xmin, xmax, xstep);
if (plot_logcdf) esl_gam_Plot(pfp, mu, lambda, tau, &esl_gam_logcdf, xmin, xmax, xstep);
if (plot_surv) esl_gam_Plot(pfp, mu, lambda, tau, &esl_gam_surv, xmin, xmax, xstep);
if (plot_logsurv) esl_gam_Plot(pfp, mu, lambda, tau, &esl_gam_logsurv, xmin, xmax, xstep);
if (plotfile != NULL) fclose(pfp);
esl_randomness_Destroy(r);
esl_histogram_Destroy(h);
return 0;
}
int
main(int argc, char **argv)
{
ESL_GETOPTS *go = esl_getopts_CreateDefaultApp(options, 0, argc, argv, banner, usage);
double mu = esl_opt_GetReal(go, "-m");
double lambda = esl_opt_GetReal(go, "-l");
double tau = esl_opt_GetReal(go, "-t");
ESL_RANDOMNESS *r = esl_randomness_Create(esl_opt_GetInteger(go, "-s"));
ESL_HISTOGRAM *h = esl_histogram_CreateFull(mu, 100., esl_opt_GetReal(go, "-w"));;
int n = esl_opt_GetInteger(go, "-n");
int be_verbose = esl_opt_GetBoolean(go, "-v");
char *plotfile = esl_opt_GetString(go, "-o");
FILE *pfp = stdout;
int plot_pdf = esl_opt_GetBoolean(go, "--P");
int plot_logpdf = esl_opt_GetBoolean(go, "--LP");
int plot_cdf = esl_opt_GetBoolean(go, "--C");
int plot_logcdf = esl_opt_GetBoolean(go, "--LC");
int plot_surv = esl_opt_GetBoolean(go, "--S");
int plot_logsurv = esl_opt_GetBoolean(go, "--LS");
double xmin = esl_opt_IsOn(go, "--XL") ? esl_opt_GetReal(go, "--XL") : mu;
double xmax = esl_opt_IsOn(go, "--XH") ? esl_opt_GetReal(go, "--XH") : mu+40*(1./lambda);
double xstep = esl_opt_IsOn(go, "--XS") ? esl_opt_GetReal(go, "--XS") : 0.1;
double emu, elambda, etau;
int i;
double x;
double *data;
int ndata;
if (be_verbose)
printf("Parametric: mu = %f lambda = %f tau = %f\n", mu, lambda, tau);
if (plotfile != NULL) {
if ((pfp = fopen(plotfile, "w")) == NULL)
esl_fatal("Failed to open plotfile");
}
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);
esl_sxp_FitComplete(data, ndata, &emu, &elambda, &etau);
if (be_verbose)
printf("Complete data fit: mu = %f lambda = %f tau = %f\n",
emu, elambda, etau);
if (fabs( (emu-mu)/mu ) > 0.01) esl_fatal("Error in (complete) fitted mu > 1%\n");
if (fabs( (elambda-lambda)/lambda ) > 0.10) esl_fatal("Error in (complete) fitted lambda > 10%\n");
if (fabs( (etau-tau)/tau ) > 0.10) esl_fatal("Error in (complete) fitted tau > 10%\n");
esl_sxp_FitCompleteBinned(h, &emu, &elambda, &etau);
if (be_verbose)
printf("Binned data fit: mu = %f lambda = %f tau = %f\n",
emu, elambda, etau);
if (fabs( (emu-mu)/mu ) > 0.01) esl_fatal("Error in (binned) fitted mu > 1%\n");
if (fabs( (elambda-lambda)/lambda ) > 0.10) esl_fatal("Error in (binned) fitted lambda > 10%\n");
if (fabs( (etau-tau)/tau ) > 0.10) esl_fatal("Error in (binned) fitted tau > 10%\n");
if (plot_pdf) esl_sxp_Plot(pfp, mu, lambda, tau, &esl_sxp_pdf, xmin, xmax, xstep);
if (plot_logpdf) esl_sxp_Plot(pfp, mu, lambda, tau, &esl_sxp_logpdf, xmin, xmax, xstep);
if (plot_cdf) esl_sxp_Plot(pfp, mu, lambda, tau, &esl_sxp_cdf, xmin, xmax, xstep);
if (plot_logcdf) esl_sxp_Plot(pfp, mu, lambda, tau, &esl_sxp_logcdf, xmin, xmax, xstep);
if (plot_surv) esl_sxp_Plot(pfp, mu, lambda, tau, &esl_sxp_surv, xmin, xmax, xstep);
if (plot_logsurv) esl_sxp_Plot(pfp, mu, lambda, tau, &esl_sxp_logsurv, xmin, xmax, xstep);
if (plotfile != NULL) fclose(pfp);
esl_histogram_Destroy(h);
esl_randomness_Destroy(r);
esl_getopts_Destroy(go);
return 0;
}
int
main(int argc, char **argv)
{
ESL_GETOPTS *go = esl_getopts_CreateDefaultApp(options, 0, argc, argv, banner, usage);
ESL_RANDOMNESS *rng = esl_randomness_Create(esl_opt_GetInteger(go, "-s"));
double mu = esl_opt_GetReal (go, "-m");
double lambda = esl_opt_GetReal (go, "-l");
double tau = esl_opt_GetReal (go, "-t");
int n = esl_opt_GetInteger(go, "-n");
double binwidth = esl_opt_GetReal (go, "-w");
int plot_cdf = esl_opt_GetBoolean(go, "--cdf");
int plot_logcdf = esl_opt_GetBoolean(go, "--logcdf");
int plot_pdf = esl_opt_GetBoolean(go, "--pdf");
int plot_logpdf = esl_opt_GetBoolean(go, "--logpdf");
int plot_surv = esl_opt_GetBoolean(go, "--surv");
int plot_logsurv = esl_opt_GetBoolean(go, "--logsurv");
int be_verbose = esl_opt_GetBoolean(go, "-v");
char *plotfile = esl_opt_GetString (go, "-o");
ESL_HISTOGRAM *h = NULL;
int xmin_set = esl_opt_IsOn(go, "--xL");
double xmin = xmin_set ? esl_opt_GetReal(go, "--xL") : mu;
int xmax_set = esl_opt_IsOn(go, "--xH");
double xmax = xmax_set ? esl_opt_GetReal(go, "--xH") : mu+40*(1./lambda);
int xstep_set = esl_opt_IsOn(go, "--xH");
double xstep = xstep_set ? esl_opt_GetReal(go, "--xS") : 0.1;
FILE *pfp = stdout;
double emu, elambda, etau;
int i;
double x;
double *data;
int ndata;
fprintf(stderr, "## %s\n", argv[0]);
fprintf(stderr, "# rng seed = %" PRIu32 "\n", esl_randomness_GetSeed(rng));
if (be_verbose) printf("Parametric: mu = %f lambda = %f tau = %f\n", mu, lambda, tau);
h = esl_histogram_CreateFull(mu, 100., binwidth);
if (plotfile && (pfp = fopen(plotfile, "w")) == NULL) ESL_EXCEPTION(eslFAIL, "Failed to open plotfile");
for (i = 0; i < n; i++)
{
x = esl_wei_Sample(rng, mu, lambda, tau);
esl_histogram_Add(h, x);
}
esl_histogram_GetData(h, &data, &ndata);
esl_wei_FitComplete(data, ndata, &emu, &elambda, &etau);
if (be_verbose) printf("Complete data fit: mu = %f lambda = %f tau = %f\n", emu, elambda, etau);
if (fabs( (emu-mu)/mu ) > 0.01) ESL_EXCEPTION(eslFAIL, "Error in (complete) fitted mu > 1%\n");
if (fabs( (elambda-lambda)/lambda ) > 0.10) ESL_EXCEPTION(eslFAIL, "Error in (complete) fitted lambda > 10%\n");
if (fabs( (etau-tau)/tau ) > 0.10) ESL_EXCEPTION(eslFAIL, "Error in (complete) fitted tau > 10%\n");
esl_wei_FitCompleteBinned(h, &emu, &elambda, &etau);
if (be_verbose) printf("Binned data fit: mu = %f lambda = %f tau = %f\n", emu, elambda, etau);
if (fabs( (emu-mu)/mu ) > 0.01) ESL_EXCEPTION(eslFAIL, "Error in (binned) fitted mu > 1%\n");
if (fabs( (elambda-lambda)/lambda ) > 0.10) ESL_EXCEPTION(eslFAIL, "Error in (binned) fitted lambda > 10%\n");
if (fabs( (etau-tau)/tau ) > 0.10) ESL_EXCEPTION(eslFAIL, "Error in (binned) fitted lambda > 10%\n");
if (plot_pdf) esl_wei_Plot(pfp, mu, lambda, tau, &esl_wei_pdf, xmin, xmax, xstep);
if (plot_logpdf) esl_wei_Plot(pfp, mu, lambda, tau, &esl_wei_logpdf, xmin, xmax, xstep);
if (plot_cdf) esl_wei_Plot(pfp, mu, lambda, tau, &esl_wei_cdf, xmin, xmax, xstep);
if (plot_logcdf) esl_wei_Plot(pfp, mu, lambda, tau, &esl_wei_logcdf, xmin, xmax, xstep);
if (plot_surv) esl_wei_Plot(pfp, mu, lambda, tau, &esl_wei_surv, xmin, xmax, xstep);
if (plot_logsurv) esl_wei_Plot(pfp, mu, lambda, tau, &esl_wei_logsurv, xmin, xmax, xstep);
if (plotfile) fclose(pfp);
esl_histogram_Destroy(h);
esl_randomness_Destroy(rng);
esl_getopts_Destroy(go);
fprintf(stderr, "# status = ok\n");
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;
}