X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=clusterer.h;h=ad0c58f910bb22bdb449e8b63d4d5bf4549834c5;hb=30a7eaeed7e34e69b62d2920e074a113a6e850fc;hp=88c168a488511fc1f2d5e41f916d5b04641f7004;hpb=2455f83ba251602d5e04640067094f09f03aaa3d;p=clueless-kmeans.git

diff --git a/clusterer.h b/clusterer.h
index 88c168a..ad0c58f 100644
--- a/clusterer.h
+++ b/clusterer.h
@@ -29,35 +29,56 @@
 
 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;
+  const static scalar_t min_cluster_variance = 0.01f;
 
   int _nb_clusters;
   int _dim;
   scalar_t **_cluster_means, **_cluster_var;
 
+  scalar_t distance_to_centroid(scalar_t *x, int k);
+
   void initialize_clusters(int nb_points, scalar_t **points);
 
+  // Standard hard k-mean association
+
   scalar_t baseline_cluster_association(int nb_points, scalar_t **points,
                                         int nb_classes, int *labels,
                                         scalar_t **gamma);
 
+  // Standard k-mean association implemented as an LP optimization
+
   scalar_t baseline_lp_cluster_association(int nb_points, scalar_t **points,
                                            int nb_classes, int *labels,
                                            scalar_t **gamma);
 
+  // Association under the constraint that each cluster gets the same
+  // class proportions 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);