From 16fd4612a88e603a34f0e8a0079932ecf080bc1f Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Wed, 27 Mar 2013 12:48:50 +0100 Subject: [PATCH] Added comments in the main method. --- clusterer.cc | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/clusterer.cc b/clusterer.cc index 47d9ac3..9c5e7cb 100644 --- a/clusterer.cc +++ b/clusterer.cc @@ -167,12 +167,21 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t glp_set_prob_name(lp, "uninformative_lp_cluster_association"); glp_set_obj_dir(lp, GLP_MIN); + // We have one constraint per points and one per cluster/class + glp_add_rows(lp, nb_points + _nb_clusters * nb_classes); + // (A) For each point, the constraint is that the sum of its + // association coefficients will be equal to 1.0 + for(int n = 1; n <= nb_points; n++) { glp_set_row_bnds(lp, n, GLP_FX, 1.0, 1.0); } + // (B) For each cluster and each 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 + for(int k = 1; k <= _nb_clusters; k++) { for(int c = 1; c <= nb_classes; c++) { int row = nb_points + (k - 1) * nb_classes + c; @@ -181,6 +190,9 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t } } + // Each one of the constraints above involve a linear combination of + // all the association coefficients + glp_add_cols(lp, nb_points * _nb_clusters); for(int k = 1; k <= _nb_clusters; k++) { @@ -194,13 +206,24 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t dist += 0.5 * log(_cluster_var[k-1][d]); } + // The LP weight on this association coefficient is the distance + // (normalized) of that sample to the centroid of that cluster + glp_set_obj_coef(lp, r, dist); + + // And this association coefficient is in [0,1] + glp_set_col_bnds(lp, r, GLP_DB, 0.0, 1.0); } } int l = 1; + // We build the matrix of the LP coefficients + + // The sums of the association coefficients per points for the + // constraints (A) above. + for(int n = 1; n <= nb_points; n++) { for(int k = 1; k <= _nb_clusters; k++) { int row = n; @@ -211,6 +234,9 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t } } + // And the sums of coefficients for each pair class/cluster for + // constraint (B) above. + for(int k = 1; k <= _nb_clusters; k++) { for(int c = 1; c <= nb_classes; c++) { int row = nb_points + (k - 1) * nb_classes + c; @@ -229,8 +255,12 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t glp_load_matrix(lp, nb_coeffs, ia, ja, ar); + // Now a miracle occurs + glp_simplex(lp, NULL); + // We retrieve the result + scalar_t total_dist = glp_get_obj_val(lp); for(int k = 1; k <= _nb_clusters; k++) { -- 2.39.5