Changeset 1050 for trunk/test

Timestamp:

Feb 7, 2008, 7:47:34 PM (15 years ago)

Author:

Peter

Message:

Simplifying distance structure

Location:

trunk/test

Files:

• knn_test.cc (modified) (1 diff)
• ncc_test.cc (modified) (3 diffs)
• vector_distance_test.cc (modified) (6 diffs)

trunk/test/knn_test.cc

@@ -66 +66 @@
 
   classifier::MatrixLookupWeighted dataviewweighted(data,weights);
-  classifier::KNN<statistics::pearson_distance_tag> knn(dataviewweighted,targets);
+  classifier::KNN<statistics::PearsonDistance> knn(dataviewweighted,targets);
   *error << "training KNN" << std::endl;
   knn.train();

trunk/test/ncc_test.cc

@@ -78 +78 @@
   vec[3]="bjds";
   classifier::Target target(vec);
-  classifier::NCC<statistics::pearson_distance_tag> ncctmp(ml,target);
+  classifier::NCC<statistics::EuclideanDistance> ncctmp(ml,target);
   *error << "training...\n";
   ncctmp.train();
@@ -100 +100 @@
   classifier::Target target1(vec1);
 
-  classifier::NCC<statistics::euclidean_distance_tag> ncc1(ml1,target1);
+  classifier::NCC<statistics::EuclideanDistance> ncc1(ml1,target1);
   ncc1.train();
   utility::matrix prediction1;
@@ -150 +150 @@
       
   classifier::MatrixLookupWeighted dataviewweighted(data,weights);
-  classifier::NCC<statistics::pearson_distance_tag> ncc(dataviewweighted,targets);
+  classifier::NCC<statistics::EuclideanDistance> ncc(dataviewweighted,targets);
   *error << "training...\n";
   ncc.train();

trunk/test/vector_distance_test.cc

@@ -58 +58 @@
   
   double tolerance=1e-4;
-  
-  double dist=statistics::distance(a.begin(),a.end(),b.begin(),
-                                          statistics::euclidean_distance_tag());
+  statistics::EuclideanDistance eucl_dist;
+  double dist=eucl_dist(a.begin(),a.end(),b.begin());
   if(fabs(dist-2.23607)>tolerance) {
     *error << "Error in unweighted Euclidean distance " << std::endl;
@@ -66 +65 @@
   }
   
-  dist=statistics::distance(a.begin(),a.end(),b.begin(),
-                                   statistics::pearson_distance_tag()); 
+  statistics::PearsonDistance pear_dist;
+  dist=pear_dist(a.begin(),a.end(),b.begin());
   if(fabs(dist-1.5)>tolerance) {
     *error << "Error in unweighted Pearson distance " << std::endl;
@@ -85 +84 @@
   classifier::DataLookupWeighted1D bw(mw,1,true);
   
-  dist=statistics::distance(aw.begin(),aw.end(),bw.begin(),
-                                   statistics::euclidean_distance_tag());
+  dist=eucl_dist(aw.begin(),aw.end(),bw.begin());
   
   if(fabs(dist-sqrt(6))>tolerance) {
@@ -93 +91 @@
   }
   
-  dist=statistics::distance(aw.begin(),aw.end(),bw.begin(),
-                                   statistics::pearson_distance_tag());
+  dist=pear_dist(aw.begin(),aw.end(),bw.begin());
   
   if(fabs(dist-2)>tolerance) {
@@ -108 +105 @@
   sb[2] = 1;
   
-  dist=statistics::distance(sa.begin(),sa.end(),sb.begin(),
-                                   statistics::euclidean_distance_tag()); 
+  dist=eucl_dist(sa.begin(),sa.end(),sb.begin()); 
   if(fabs(dist-2.23607)>tolerance) {
     *error << "Error in distance for std::vector " << std::endl;
@@ -118 +114 @@
   std::list<double> la;
   std::copy(sa.begin(),sa.end(),std::back_inserter<std::list<double> >(la));
-  dist=statistics::distance(la.begin(),la.end(),sb.begin(),
-                                   statistics::euclidean_distance_tag()); 
+  dist=eucl_dist(la.begin(),la.end(),sb.begin());
   if(fabs(dist-2.23607)>tolerance) {
     *error << "Error in distance for std::list " << std::endl;