Changeset 1050 for trunk/yat/classifier

Timestamp:

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

Author:

Peter

Message:

Simplifying distance structure

Location:

trunk/yat/classifier

Files:

• IGP.h (modified) (3 diffs)
• KNN.h (modified) (5 diffs)
• NCC.h (modified) (4 diffs)

trunk/yat/classifier/IGP.h

@@ -30 +30 @@
 #include "yat/utility/vector.h"
 #include "yat/utility/yat_assert.h"
-#include "yat/statistics/distance.h"
 
 #include <cmath>
@@ -71 +70 @@
   private:
     utility::vector igp_;
+    Distance distance_;
 
     const MatrixLookup& matrix_;
@@ -94 +94 @@
       for(u_int j=0; j<target_.size(); j++) {           
         DataLookup1D b(matrix_,j,false);
-        double dist=statistics::
-          distance(a.begin,a.end(),b.begin(),
-                          statistics::distance_traits<Distance>::distace());
+        double dist=distance_(a.begin, a.end(), b.begin());
         if(j!=i && dist<mindist) {
           mindist=dist;

trunk/yat/classifier/KNN.h

@@ -25 +25 @@
 */
 
+#include "DataLookup1D.h"
 #include "DataLookupWeighted1D.h"
 #include "MatrixLookup.h"
@@ -30 +31 @@
 #include "SupervisedClassifier.h"
 #include "Target.h"
-#include "yat/statistics/distance.h"
 #include "yat/utility/matrix.h"
 #include "yat/utility/yat_assert.h"
@@ -113 +113 @@
     u_int k_;
 
+    Distance distance_;
     ///
     /// Calculates the distances between a data set and the training
@@ -156 +157 @@
         for(size_t j=0; j<test.columns(); j++) {
           classifier::DataLookup1D test(*test_unweighted,j,false);
-          (*distances)(i,j) =
-            statistics::distance(classifier::DataLookup1D(data_,
-                                                          i,false).begin(),
-                                 classifier::DataLookup1D(data_,
-                                                          i,false).end(),
-                                 test.begin(), 
-                                 typename statistics::
-                                 distance_traits<Distance>::distance());
+          classifier::DataLookup1D tmp(data_,i,false);
+          (*distances)(i,j) = distance_(tmp.begin(), tmp.end(), test.begin());
           utility::yat_assert<std::runtime_error>(!std::isnan((*distances)(i,j)));
         }
@@ -180 +175 @@
             classifier::DataLookupWeighted1D test(*test_weighted,j,false);
             utility::yat_assert<std::runtime_error>(training.size()==test.size());
-            (*distances)(i,j) =
-              statistics::distance(training.begin(),training.end(),
-                                   test.begin(), typename statistics::distance_traits<Distance>::distance());
+            (*distances)(i,j) = distance_(training.begin(), training.end(),
+                                          test.begin());
             utility::yat_assert<std::runtime_error>(!std::isnan((*distances)(i,j)));
           }

trunk/yat/classifier/NCC.h

@@ -37 +37 @@
 #include "yat/statistics/Averager.h"
 #include "yat/statistics/AveragerWeighted.h"
-#include "yat/statistics/distance.h"
-
 #include "yat/utility/Iterator.h"
 #include "yat/utility/IteratorWeighted.h"
@@ -112 +110 @@
     utility::matrix* centroids_;
     bool centroids_nan_;
+    Distance distance_;
 
     // data_ has to be of type DataLookup2D to accomodate both
@@ -270 +269 @@
         DataLookup1D centroid(unweighted_centroids,k,false);            
         utility::yat_assert<std::runtime_error>(in.size()==centroid.size());
-        prediction(k,j)=statistics::
-          distance(in.begin(),in.end(),centroid.begin(),
-                   typename statistics::distance_traits<Distance>::distance());
+        prediction(k,j) = distance_(in.begin(), in.end(), centroid.begin());
       }
     }
@@ -287 +284 @@
         DataLookupWeighted1D centroid(weighted_centroids,k,false);
         utility::yat_assert<std::runtime_error>(in.size()==centroid.size());
-        prediction(k,j)=statistics::
-          distance(in.begin(),in.end(),centroid.begin(),
-                   typename statistics::distance_traits<Distance>::distance());
+        prediction(k,j) = distance_(in.begin(), in.end(), centroid.begin());
       }
     }