Cosmetics.

[clueless-kmeans.git] / clusterer.h

diff --git a/clusterer.h b/clusterer.h
index 88c168a..b45f8c3 100644 (file)
--- a/clusterer.h
+++ b/clusterer.h
@@ -29,6 +29,9 @@
 
 class Clusterer {
 public:
+
+  enum { STANDARD_ASSOCIATION, STANDARD_LP_ASSOCIATION, UNINFORMATIVE_LP_ASSOCIATION };
+
   const static int max_nb_iterations = 10;
   const static scalar_t min_iteration_improvement = 0.999;
 
@@ -38,26 +41,38 @@ public:
 
   void initialize_clusters(int nb_points, scalar_t **points);
 
+  // Does the standard hard k-mean association
+
   scalar_t baseline_cluster_association(int nb_points, scalar_t **points,
                                         int nb_classes, int *labels,
                                         scalar_t **gamma);
 
+  // Does the same with an LP formulation, as a sanity check
+
   scalar_t baseline_lp_cluster_association(int nb_points, scalar_t **points,
                                            int nb_classes, int *labels,
                                            scalar_t **gamma);
 
+  // Does the association under constraints that each cluster gets
+  // associated clusters with the same class proportion as the overall
+  // training set
+
   scalar_t uninformative_lp_cluster_association(int nb_points, scalar_t **points,
                                                 int nb_classes, int *labels,
                                                 scalar_t **gamma);
 
-  void baseline_update_clusters(int nb_points, scalar_t **points, scalar_t **gamma);
+  void update_clusters(int nb_points, scalar_t **points, scalar_t **gamma);
 
 public:
   Clusterer();
   ~Clusterer();
-  void train(int nb_clusters, int dim,
+
+  void train(int mode,
+             int nb_clusters, int dim,
              int nb_points, scalar_t **points,
              int nb_classes, int *labels,
+             // This last array returns for each sample to what
+             // cluster it was associated. It can be null.
              int *cluster_associations);
 
   int cluster(scalar_t *point);