}
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]);
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++) {
int col = n + nb_points * (k - 1);
- // The LP weight on this association coefficient for the global
- // loss is the normalized distance of that sample to the
- // centroid of that cluster
+ // The LP weight on the gammas for the global loss is the
+ // normalized distance of that sample to the centroid of that
+ // cluster
glp_set_obj_coef(lp, col, distance_to_centroid(points[n-1], k-1));
}
}
- // The (B) constraints: for each point, the sum of its association
- // coefficients is equal to 1.0
+ // The (B) constraints: for each point, the sum of its gamma is
+ // equal to 1.0
for(int n = 1; n <= nb_points; n++) {
int row = n;
}
// The (C) constraints: For each pair cluster/class, the sum of the
- // association coefficient to this cluster for this class is equal
- // to the number of sample of that class, divided by the number of
- // clusters
+ // gammas for this cluster and this class is equal to the number of
+ // sample of that class, divided by the number of clusters
for(int k = 1; k <= _nb_clusters; k++) {
for(int c = 1; c <= nb_classes; c++) {