scalar_t noisy_score(scalar_t true_score, scalar_t erroneous_score,
scalar_t score_noise, scalar_t flip_noise) {
if(drand48() < flip_noise) {
- return erroneous_score + score_noise * (2.0 * drand48() - 1.0);
+ return erroneous_score + score_noise * (2.0f * scalar_t(drand48()) - 1.0f);
} else {
- return true_score + score_noise * (2.0 * drand48() - 1.0);
+ return true_score + score_noise * (2.0f * scalar_t(drand48()) - 1.0f);
}
}
// Then, we specify for every location and time step what is the
// detection score there.
- scalar_t flip_noise = 0.05;
- scalar_t score_noise = 0.0;
+ scalar_t flip_noise = 0.05f;
+ scalar_t score_noise = 0.0f;
// We first put a background noise, with negative scores at every
// location.
<< " starting at " << tracker->trajectory_entrance_time(t)
<< ", duration " << tracker->trajectory_duration(t)
<< ", score " << tracker->trajectory_score(t)
- << ", through nodes ";
+ << ", through locations";
for(int u = 0; u < tracker->trajectory_duration(t); u++) {
cout << " " << tracker->trajectory_location(t, u);
}