Cosmetics.

diff --git a/clusterer.cc b/clusterer.cc
index 5418341..d4a70a6 100644 (file)
--- a/clusterer.cc
+++ b/clusterer.cc
@@ -35,8 +35,6 @@ Clusterer::~Clusterer() {
 }
 
 scalar_t Clusterer::distance_to_centroid(scalar_t *x, int k) {
-  // We take the variance into account + the normalization term. This
-  // is between k-mean and EM with a diagonal covariance
   scalar_t dist = 0;
   for(int d = 0; d < _dim; d++) {
     dist += sq(_cluster_means[k][d] - x[d]) / (2 * _cluster_var[k][d]);
@@ -208,14 +206,17 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t
 
   glp_add_cols(lp, nb_points * _nb_clusters);
 
+  // The column for gamma[n][k] point 1<=n<=nb_points and cluster
+  // 1<=k<=_nb_clusters is nb_points * (k - 1) + n;
+
   // The constraints (A) will be expressed by putting directly bounds
   // on the variables (i.e. one per column). So we need one row per
   // (B) constraint, and one per (C) constraint.
 
   glp_add_rows(lp, nb_points + _nb_clusters * nb_classes);
 
-  // First, we set the weights for the objective function, and the
-  // constraint on the individual gammas
+  // First, we set the weights for the objective function, and the (A)
+  // constraints on the individual gammas
 
   for(int k = 1; k <= _nb_clusters; k++) {
     for(int n = 1; n <= nb_points; n++) {