Added README.md

[clueless-kmeans.git] / clusterer.h

diff --git a/clusterer.h b/clusterer.h
index 065dd3f..6fa5382 100644 (file)
--- 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.
@@ -30,38 +30,51 @@
 class Clusterer {
 public:
 
-  enum { STANDARD_ASSOCIATION, STANDARD_LP_ASSOCIATION, UNINFORMATIVE_LP_ASSOCIATION };
+  enum {
+    // Standard k-mean
+    STANDARD_ASSOCIATION,
+    // Same, implemented as a LP problem for sanity check
+    STANDARD_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);
 
-  // Does the 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);
 
-  // Does the same with an LP formulation, as a sanity check
+  // 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,
                                            scalar_t **gamma);
 
-  // Does the association under constraints that each cluster gets
-  // associated clusters with the same class proportion as the overall
-  // training set
+  // 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);
+                                                scalar_t **gamma,
+                                                int absolute_proportion);
 
   void update_clusters(int nb_points, scalar_t **points, scalar_t **gamma);