Made it so the kkmeans train function can take any kind of

vector container.  Not just dlib::matrix.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402375

dlib/svm/kkmeans.h

@@ -96,8 +96,64 @@ namespace dlib
             return centers.size();
         }
 
-        template <typename matrix_type, typename matrix_type2>
+        template <typename T, typename U>
         void train (
+            const T& samples,
+            const U& initial_centers,
+            long max_iter = 1000000
+        )
+        {
+            do_train(vector_to_matrix(samples),vector_to_matrix(initial_centers),max_iter);
+        }
+
+        unsigned long operator() (
+            const sample_type& sample
+        ) const
+        {
+            unsigned long label = 0;
+            scalar_type best_score = (*centers[0])(sample);
+
+            // figure out which center the given sample is closest too
+            for (unsigned long i = 1; i < centers.size(); ++i)
+            {
+                scalar_type temp = (*centers[i])(sample);
+                if (temp < best_score)
+                {
+                    label = i;
+                    best_score = temp;
+                }
+            }
+
+            return label;
+        }
+
+        void swap (
+            kkmeans& item
+        )
+        {
+            centers.swap(item.centers);
+            kc.swap(item.kc);
+            assignments.swap(item.assignments);
+        }
+
+        friend void serialize(const kkmeans& item, std::ostream& out)
+        {
+            serialize(item.centers, out);
+            serialize(item.kc, out);
+            serialize(item.assignments, out);
+        }
+
+        friend void deserialize(kkmeans& item, std::istream& in)
+        {
+            deserialize(item.centers, in);
+            deserialize(item.kc, in);
+            deserialize(item.assignments, in);
+        }
+
+    private:
+
+        template <typename matrix_type, typename matrix_type2>
+        void do_train (
             const matrix_type& samples,
             const matrix_type2& initial_centers,
             long max_iter = 1000000
@@ -175,52 +231,6 @@ namespace dlib
 
         }
 
-        unsigned long operator() (
-            const sample_type& sample
-        ) const
-        {
-            unsigned long label = 0;
-            scalar_type best_score = (*centers[0])(sample);
-
-            // figure out which center the given sample is closest too
-            for (unsigned long i = 1; i < centers.size(); ++i)
-            {
-                scalar_type temp = (*centers[i])(sample);
-                if (temp < best_score)
-                {
-                    label = i;
-                    best_score = temp;
-                }
-            }
-
-            return label;
-        }
-
-        void swap (
-            kkmeans& item
-        )
-        {
-            centers.swap(item.centers);
-            kc.swap(item.kc);
-            assignments.swap(item.assignments);
-        }
-
-        friend void serialize(const kkmeans& item, std::ostream& out)
-        {
-            serialize(item.centers, out);
-            serialize(item.kc, out);
-            serialize(item.assignments, out);
-        }
-
-        friend void deserialize(kkmeans& item, std::istream& in)
-        {
-            deserialize(item.centers, in);
-            deserialize(item.kc, in);
-            deserialize(item.assignments, in);
-        }
-
-    private:
-
         typename array<scoped_ptr<kcentroid<kernel_type> > >::expand_1b_c centers;
         kcentroid<kernel_type> kc;