double
estimate_ml_t(log_like_function_t *log_like, double t[],
const size_t n_pts, const double tolerance, bsm_t* model,
bool* success)
{
*success = false;
size_t iter = 0;
const point_t *max_pt;
double *l = malloc(sizeof(double) * n_pts);
/* We allocate an extra point for scratch */
point_t *points = malloc(sizeof(point_t) * (n_pts + 1));
evaluate_ll(log_like, t, n_pts, points);
/* First, classify points */
monotonicity_t m = monotonicity(points, n_pts);
/* If the function is no longer monotonic, start over */
if(m != NON_MONOTONIC) {
free(points);
size_t n = N_DEFAULT_START;
point_t *start_pts = malloc(sizeof(point_t) * n);
evaluate_ll(log_like, DEFAULT_START, n, start_pts);
points = select_points(log_like, start_pts, &n, DEFAULT_MAX_POINTS);
if(points == NULL) {
free(l);
*success = false;
return NAN;
}
free(start_pts);
m = monotonicity(points, n);
if(m == MONO_DEC) {
double ml_t = points[0].t;
*success = true;
free(points);
free(l);
return ml_t;
} else if (m == MONO_INC) {
*success = false;
free(points);
free(l);
return NAN;
}
assert(n >= n_pts);
if(n > n_pts) {
/* Subset to top n_pts */
subset_points(points, n, n_pts);
}
/* Allocate an extra point for scratch */
points = realloc(points, sizeof(point_t) * (n_pts + 1));
}
max_pt = max_point(points, n_pts);
double ml_t = max_pt->t;
/* Re-fit */
lcfit_bsm_rescale(max_pt->t, max_pt->ll, model);
blit_points_to_arrays(points, n_pts, t, l);
lcfit_fit_bsm(n_pts, t, l, model);
for(iter = 0; iter < MAX_ITERS; iter++) {
ml_t = lcfit_bsm_ml_t(model);
if(isnan(ml_t)) {
*success = false;
break;
}
/* convergence check */
if(fabs(ml_t - max_pt->t) <= tolerance) {
*success = true;
break;
}
/* Check for nonsensical ml_t - if the value is outside the bracketed
* window, give up. */
size_t max_idx = max_pt - points;
if(ml_t < points[max_idx - 1].t || ml_t > points[max_idx + 1].t) {
*success = false;
ml_t = NAN;
break;
}
/* Add ml_t estimate */
if(ml_t < 0) {
*success = true;
ml_t = 1e-8;
break;
}
points[n_pts].t = ml_t;
points[n_pts].ll = log_like->fn(ml_t, log_like->args);
ml_likelihood_calls++;
/* Retain top n_pts by log-likelihood */
sort_by_t(points, n_pts + 1);
if(monotonicity(points, n_pts + 1) != NON_MONOTONIC) {
*success = false;
ml_t = NAN;
break;
}
subset_points(points, n_pts + 1, n_pts);
blit_points_to_arrays(points, n_pts, t, l);
lcfit_fit_bsm(n_pts, t, l, model);
max_pt = max_point(points, n_pts);
}
if(iter == MAX_ITERS)
*success = false;
free(l);
free(points);
return ml_t;
}
double
estimate_ml_t(log_like_function_t *log_like, const double* t,
size_t n_pts, const double tolerance, bsm_t* model,
bool* success, const double min_t, const double max_t)
{
*success = false;
point_t *starting_pts = malloc(sizeof(point_t) * n_pts);
evaluate_ll(log_like, t, n_pts, starting_pts);
const size_t orig_n_pts = n_pts;
point_t* points = select_points(log_like, starting_pts, &n_pts,
DEFAULT_MAX_POINTS, min_t, max_t);
free(starting_pts);
if (points == NULL) {
fprintf(stderr, "ERROR: select_points returned NULL\n");
*success = false;
return NAN;
}
curve_type_t curvature = classify_curve(points, n_pts);
if (!(curvature == CRV_ENC_MAXIMA || curvature == CRV_MONO_DEC)) {
fprintf(stderr, "ERROR: "
"points don't enclose a maximum and aren't decreasing\n");
free(points);
*success = false;
return NAN;
}
/* From here on, curvature is CRV_ENC_MAXIMA or CRV_MONO_DEC, and
* thus ml_t is zero or positive (but not infinite). */
assert(n_pts >= orig_n_pts);
if (n_pts > orig_n_pts) {
/* Subset to top orig_n_pts */
subset_points(points, n_pts, orig_n_pts);
n_pts = orig_n_pts;
}
assert(points[0].t >= min_t);
assert(points[n_pts - 1].t <= max_t);
#ifdef LCFIT_AUTO_VERBOSE
fprintf(stderr, "starting iterative fit\n");
fprintf(stderr, "starting points: ");
print_points(stderr, points, n_pts);
fprintf(stderr, "\n");
#endif /* LCFIT_AUTO_VERBOSE */
/* Allocate an extra point for scratch */
points = realloc(points, sizeof(point_t) * (n_pts + 1));
size_t iter = 0;
const point_t* max_pt = NULL;
double ml_t = 0.0;
double prev_t = 0.0;
for (iter = 0; iter < MAX_ITERS; iter++) {
max_pt = max_point(points, n_pts);
/* Re-fit */
lcfit_bsm_rescale(max_pt->t, max_pt->ll, model);
double* tbuf = malloc(sizeof(double) * n_pts);
double* lbuf = malloc(sizeof(double) * n_pts);
blit_points_to_arrays(points, n_pts, tbuf, lbuf);
double* wbuf = malloc(sizeof(double) * n_pts);
double alpha = (double) iter / (MAX_ITERS - 1);
for (size_t i = 0; i < n_pts; ++i) {
wbuf[i] = exp(alpha * (lbuf[i] - max_pt->ll));
}
#ifdef LCFIT_AUTO_VERBOSE
fprintf(stderr, "weights: ");
for (size_t i = 0; i < n_pts; ++i) {
fprintf(stderr, "%g ", wbuf[i]);
}
fprintf(stderr, "\n");
#endif /* LCFIT_AUTO_VERBOSE */
lcfit_fit_bsm_weight(n_pts, tbuf, lbuf, wbuf, model, 250);
free(tbuf);
free(lbuf);
free(wbuf);
ml_t = lcfit_bsm_ml_t(model);
if (isnan(ml_t)) {
fprintf(stderr, "ERROR: "
"lcfit_bsm_ml_t returned NaN"
", model = { %.3f, %.3f, %.6f, %.6f }\n",
model->c, model->m, model->r, model->b);
*success = false;
break;
}
if (curvature == CRV_ENC_MAXIMA) {
/* Stop if the modeled maximum likelihood branch length is
* within tolerance of the empirical maximum. */
if (rel_err(max_pt->t, ml_t) <= tolerance) {
*success = true;
break;
}
}
double next_t = bound_point(ml_t, points, n_pts, min_t, max_t);
/* Stop if the next sample point is within tolerance of the
* previous sample point. */
if (rel_err(prev_t, next_t) <= tolerance) {
*success = true;
break;
}
points[n_pts].t = next_t;
points[n_pts].ll = log_like->fn(next_t, log_like->args);
prev_t = next_t;
sort_by_t(points, n_pts + 1);
curvature = classify_curve(points, n_pts + 1);
if (!(curvature == CRV_ENC_MAXIMA || curvature == CRV_MONO_DEC)) {
fprintf(stderr, "ERROR: "
"after iteration points don't enclose a maximum "
"and aren't decreasing\n");
*success = false;
break;
}
++n_pts;
points = realloc(points, sizeof(point_t) * (n_pts + 1));
#ifdef LCFIT_AUTO_VERBOSE
fprintf(stderr, "current points: ");
print_points(stderr, points, n_pts);
fprintf(stderr, "\n");
#endif /* LCFIT_AUTO_VERBOSE */
}
if (iter == MAX_ITERS) {
fprintf(stderr, "WARNING: maximum number of iterations reached\n");
}
free(points);
#ifdef LCFIT_AUTO_VERBOSE
fprintf(stderr, "ending iterative fit after %zu iteration(s)\n", iter);
#endif /* LCFIT_AUTO_VERBOSE */
if (ml_t < min_t) {
ml_t = min_t;
} else if (ml_t > max_t) {
ml_t = max_t;
}
return ml_t;
}