Added nearest_center()

dlib/svm/kkmeans.h

@@ -364,12 +364,12 @@ namespace dlib
 // ----------------------------------------------------------------------------------------
 
     template <
-        typename vector_type, 
+        typename array_type, 
         typename sample_type,
         typename alloc
         >
     void find_clusters_using_kmeans (
-        const vector_type& samples,
+        const array_type& samples,
         std::vector<sample_type, alloc>& centers,
         unsigned long max_iter = 1000
     )
@@ -461,6 +461,40 @@ namespace dlib
 
     }
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename array_type, 
+        typename EXP 
+        >
+    unsigned long nearest_center (
+        const array_type& centers,
+        const matrix_exp<EXP>& sample
+    )
+    {
+        // make sure requires clause is not broken
+        DLIB_ASSERT(centers.size() > 0 && samples.size() > 0 && is_vector(sample),
+            "\t unsigned long nearest_center()"
+            << "\n\t You have given invalid inputs to this function."
+            << "\n\t centers.size():    " << centers.size() 
+            << "\n\t sample.size():     " << sample.size() 
+            << "\n\t is_vector(sample): " << is_vector(sample) 
+            );
+
+        double best_dist = length_squared(centers[0] - sample);
+        unsigned long best_idx = 0;
+        for (unsigned long i = 1; i < centers.size(); ++i)
+        {
+            const double dist = length_squared(centers[i] - sample);
+            if (dist < best_dist)
+            {
+                best_dist = dist;
+                best_idx = i;
+            }
+        }
+        return best_idx;
+    }
+
 // ----------------------------------------------------------------------------------------
 
 }

dlib/svm/kkmeans_abstract.h

@@ -240,12 +240,12 @@ namespace dlib
 // ----------------------------------------------------------------------------------------
 
     template <
-        typename vector_type, 
+        typename array_type, 
         typename sample_type,
         typename alloc
         >
     void find_clusters_using_kmeans (
-        const vector_type& samples,
+        const array_type& samples,
         std::vector<sample_type, alloc>& centers,
         unsigned long max_iter = 1000
     );
@@ -255,7 +255,7 @@ namespace dlib
             - samples == a bunch of row or column vectors and they all must be of the
               same length.
             - centers.size() > 0
-            - vector_type == something with an interface compatible with std::vector
+            - array_type == something with an interface compatible with std::vector
               and it must contain row or column vectors capable of being stored in 
               sample_type objects
             - sample_type == a dlib::matrix capable of representing vectors
@@ -267,6 +267,30 @@ namespace dlib
               terminates.
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename array_type, 
+        typename EXP 
+        >
+    unsigned long nearest_center (
+        const array_type& centers,
+        const matrix_exp<EXP>& sample
+    );
+    /*!
+        requires
+            - centers.size() > 0
+            - sample.size() > 0
+            - is_vector(sample) == true
+            - centers must be an array of vectors such that the following expression is
+              valid: length_squared(sample - centers[0]).  (e.g. centers could be a
+              std::vector of matrix objects holding column vectors).
+        ensures
+            - returns the index that identifies the element of centers that is nearest to
+              sample.  That is, returns a number IDX such that centers[IDX] is the element
+              of centers that minimizes length(centers[IDX]-sample).
+    !*/
+
 // ----------------------------------------------------------------------------------------
 
 }