Changed code to avoid triggering one of visual studio's bug checking hooks (when…

Changed code to avoid triggering one of visual studio's bug checking hooks (when there isn't a bug).

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403638

dlib/manifold_regularization/graph_creation.h

@@ -151,13 +151,16 @@ namespace dlib
         // within the inner for loop we have:
         //   [begin_i, end_i) == the range in edges that contains neighbors of samples[i]
         //   [begin_j, end_j) == the range in edges that contains neighbors of samples[j]
-        for (unsigned long i = 0; i < samples.size(); ++i)
+        for (unsigned long i = 0; i+1 < samples.size(); ++i)
         {
-            begin_j = begin_i + k;
-            end_j = begin_j + k;
+            begin_j = begin_i;
+            end_j = end_i;
 
             for (unsigned long j = i+1; j < samples.size(); ++j)
             {
+                begin_j += k;
+                end_j += k;
+
                 const float dist = dist_funct(samples[i], samples[j]);
 
                 if (dist < worst_dists[i])
@@ -171,9 +174,6 @@ namespace dlib
                     *iterator_of_worst(begin_j, end_j) = sample_pair(i, j, dist);
                     worst_dists[j] = iterator_of_worst(begin_j, end_j)->distance();
                 }
-
-                begin_j += k;
-                end_j += k;
             }
 
             begin_i += k;

dlib/test/linear_manifold_regularizer.cpp

@@ -188,6 +188,54 @@ namespace
         }
 
 
+        void test_knn1()
+        {
+            std::vector<matrix<double,2,1> > samples;
+
+            matrix<double,2,1> test;
+            
+            test = 0,0;  samples.push_back(test);
+            test = 1,1;  samples.push_back(test);
+            test = 1,-1;  samples.push_back(test);
+            test = -1,1;  samples.push_back(test);
+            test = -1,-1;  samples.push_back(test);
+
+            std::vector<sample_pair> edges;
+            find_k_nearest_neighbors(samples, squared_euclidean_distance(), 1, edges);
+            DLIB_TEST(edges.size() == 4);
+
+            std::sort(edges.begin(), edges.end(), &order_by_index);
+
+            DLIB_TEST(edges[0] == sample_pair(0,1,0));
+            DLIB_TEST(edges[1] == sample_pair(0,2,0));
+            DLIB_TEST(edges[2] == sample_pair(0,3,0));
+            DLIB_TEST(edges[3] == sample_pair(0,4,0));
+
+        }
+
+        void test_knn2()
+        {
+            std::vector<matrix<double,2,1> > samples;
+
+            matrix<double,2,1> test;
+            
+            test = 1,1;  samples.push_back(test);
+            test = 1,-1;  samples.push_back(test);
+            test = -1,1;  samples.push_back(test);
+            test = -1,-1;  samples.push_back(test);
+
+            std::vector<sample_pair> edges;
+            find_k_nearest_neighbors(samples, squared_euclidean_distance(), 2, edges);
+            DLIB_TEST(edges.size() == 4);
+
+            std::sort(edges.begin(), edges.end(), &order_by_index);
+
+            DLIB_TEST(edges[0] == sample_pair(0,1,0));
+            DLIB_TEST(edges[1] == sample_pair(0,2,0));
+            DLIB_TEST(edges[2] == sample_pair(1,3,0));
+            DLIB_TEST(edges[3] == sample_pair(2,3,0));
+
+        }
 
         void perform_test (
         )
@@ -195,7 +243,10 @@ namespace
             for (int i = 0; i < 5; ++i)
             {
                 do_the_test();
+
             }
+            test_knn1();
+            test_knn2();
 
         }
     };