X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=mtp.cc;h=7f55e0a8a060cafb96d0e961dbeb1ec7dd5e5fc0;hb=4d9b58034ce82094c233b61da247e11a584ec0bd;hp=cd1290c767d480713c0e701630837a537807fc91;hpb=ae55303c805d340b8c4a3d080ecdadb41c84a9ec;p=mtp.git

diff --git a/mtp.cc b/mtp.cc
index cd1290c..7f55e0a 100644
--- a/mtp.cc
+++ b/mtp.cc
@@ -27,21 +27,29 @@ using namespace std;
 
 //////////////////////////////////////////////////////////////////////
 
-scalar_t detection_score(int true_label, scalar_t flip_noise) {
-  if((true_label > 0) == (drand48() < flip_noise)) {
-    return   1.0 + 0.2 * (drand48() - 0.5);
+scalar_t detection_score(scalar_t a, scalar_t b, scalar_t score_noise, scalar_t flip_noise) {
+  if(drand48() > flip_noise) {
+    return a + score_noise * (2.0 * drand48() - 1.0);
   } else {
-    return - 1.0 + 0.2 * (drand48() - 0.5);
+    return b + score_noise * (2.0 * drand48() - 1.0);
   }
 }
 
 int main(int argc, char **argv) {
-  int nb_locations = 6;
-  int nb_time_steps = 5;
+  int nb_locations = 7;
+  int nb_time_steps = 8;
   int motion_amplitude = 1;
 
   Tracker *tracker = new Tracker(nb_time_steps, nb_locations);
 
+  // We define the spatial structures by stating what are the possible
+  // motions of targets, and what are the entrances and the
+  // exits.
+
+  // Here our example is a 1D space with motions from any location to
+  // any location less than motion_amplitude away, entrance at
+  // location 0 and exit at location nb_locations-1.
+
   for(int l = 0; l < nb_locations; l++) {
     for(int k = 0; k < nb_locations; k++) {
       tracker->allowed_motion[l][k] = abs(l - k) <= motion_amplitude;
@@ -50,42 +58,63 @@ int main(int argc, char **argv) {
     tracker->exits[nb_locations - 1] = 1;
   }
 
+  // We construct the graph corresponding to this structure
+
   tracker->build_graph();
 
-  // We generate synthetic detection scores at location
-  // nb_locations/2, with 5% false detection (FP or FN)
+  // 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;
+
+  // We first put a background noise, with negative scores at every
+  // location.
 
   for(int t = 0; t < nb_time_steps; t++) {
     for(int l = 0; l < nb_locations; l++) {
-      tracker->detection_score[t][l] = detection_score(-1, 0.95);
+      tracker->detection_scores[t][l] = detection_score(-1.0, 1.0, score_noise, flip_noise);
     }
   }
 
+  // Then we two targets with the typical local minimum:
+  //
+  // * Target A moves from location 0 to the middle, stays there for a
+  //   while, and comes back, and is strongly detected on the first
+  //   half
+  //
+  // * Target B moves from location nb_locations-1 to the middle, stay
+  //   there for a while, and comes back, and is strongly detected on
+  //   the second half
+
+  int la, lb; // Target locations
+  scalar_t sa, sb; // Target detection scores
   for(int t = 0; t < nb_time_steps; t++) {
-    tracker->detection_score[t][nb_locations/2] = detection_score(1, 0.95);
+    if(t < nb_time_steps/2) {
+      la = t;
+      lb = nb_locations - 1 - t;
+      sa = detection_score(10.0, -1.0, score_noise, flip_noise);
+      sb = detection_score( 1.0, -1.0, score_noise, flip_noise);
+    } else {
+      la = nb_time_steps - 1 - t;
+      lb = t - nb_time_steps + nb_locations;
+      sa = detection_score( 1.0, -1.0, score_noise, flip_noise);
+      sb = detection_score(10.0, -1.0, score_noise, flip_noise);
+    }
+
+    if(la > nb_locations/2 - 1) la = nb_locations/2 - 1;
+    if(lb < nb_locations/2 + 1) lb = nb_locations/2 + 1;
+
+    tracker->detection_scores[t][la] = sa;
+    tracker->detection_scores[t][lb] = sb;
   }
 
-  // Puts two target with the typical local minimum (i.e. the optimal
-  // single path would track the first target on the first half and
-  // the second on the second half, while the optimal two paths would
-  // each follow one of the target properly)
-
-  // for(int t = 0; t < nb_time_steps; t++) {
-    // int a = nb_time_steps/2 - abs(t - nb_time_steps/2);
-    // int b = nb_locations - 1 - a;
-    // if(a > nb_locations/2 - 1) a = nb_locations/2 - 1;
-    // if(b < nb_locations/2 + 1) b = nb_locations/2 + 1;
-    // if(t < nb_time_steps/2) {
-      // tracker->detection_score[t][a] = 10.0;
-      // tracker->detection_score[t][b] = 1.0;
-    // } else {
-      // tracker->detection_score[t][a] = 1.0;
-      // tracker->detection_score[t][b] = 10.0;
-    // }
-  // }
+  // Does the tracking per se
 
   tracker->track();
 
+  // Prints the detected trajectories
+
   for(int t = 0; t < tracker->nb_trajectories(); t++) {
     cout << "TRAJECTORY "
          << t
@@ -97,6 +126,9 @@ int main(int argc, char **argv) {
     cout << endl;
   }
 
+  // Save the underlying graph in the dot format, with occupied edges
+  // marked in bold.
+
   {
     ofstream dot("graph.dot");
     tracker->print_graph_dot(&dot);