X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=clueless-kmeans.git;a=blobdiff_plain;f=clusterer.h;h=6fa538273c22c039831ed295ce5079cb9b72c323;hp=f73d6c78f557f1d8c565f4f7d37deddd2b57b0b7;hb=HEAD;hpb=ca6d045155d4c948063f49b6de8c35c0e3246e7a

diff --git a/clusterer.h b/clusterer.h
index f73d6c7..6fa5382 100644
--- a/clusterer.h
+++ b/clusterer.h
@@ -1,17 +1,17 @@
 /*
- *  clueless-kmean is a variant of k-mean which enforces balanced
+ *  clueless-kmeans is a variant of k-means which enforces balanced
  *  distribution of classes in every cluster
  *
  *  Copyright (c) 2013 Idiap Research Institute, http://www.idiap.ch/
  *  Written by Francois Fleuret <francois.fleuret@idiap.ch>
  *
- *  This file is part of clueless-kmean.
+ *  This file is part of clueless-kmeans.
  *
- *  clueless-kmean is free software: you can redistribute it and/or
+ *  clueless-kmeans is free software: you can redistribute it and/or
  *  modify it under the terms of the GNU General Public License
  *  version 3 as published by the Free Software Foundation.
  *
- *  clueless-kmean is distributed in the hope that it will be useful,
+ *  clueless-kmeans is distributed in the hope that it will be useful,
  *  but WITHOUT ANY WARRANTY; without even the implied warranty of
  *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  *  General Public License for more details.
@@ -31,29 +31,38 @@ class Clusterer {
 public:
 
   enum {
+    // Standard k-mean
     STANDARD_ASSOCIATION,
+    // Same, implemented as a LP problem for sanity check
     STANDARD_LP_ASSOCIATION,
-    UNINFORMATIVE_LP_ASSOCIATION
+    // Criterion forcing to have the same distribution of classes in
+    // all clusters
+    UNINFORMATIVE_LP_ASSOCIATION,
+    // Criterion forcing to have the same number of samples of each
+    // class in all clusters
+    UNINFORMATIVE_LP_ASSOCIATION_ABSOLUTE
   };
 
   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
+  // Standard hard k-means 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
+  // Standard k-means association implemented as an LP optimization
 
   scalar_t baseline_lp_cluster_association(int nb_points, scalar_t **points,
                                            int nb_classes, int *labels,
@@ -64,7 +73,8 @@ public:
 
   scalar_t uninformative_lp_cluster_association(int nb_points, scalar_t **points,
                                                 int nb_classes, int *labels,
-                                                scalar_t **gamma);
+                                                scalar_t **gamma,
+                                                int absolute_proportion);
 
   void update_clusters(int nb_points, scalar_t **points, scalar_t **gamma);