Changeset 916 for trunk/yat/classifier

Timestamp:

Sep 30, 2007, 2:50:10 AM (16 years ago)

Author:

Peter

Message:

Sorry this commit is a bit to big.

Adding a yat_assert. The yat assert are turned on by providing a
'-DYAT_DEBUG' flag to preprocessor if normal cassert is turned
on. This flag is activated for developers running configure with
--enable-debug. The motivation is that we can use these yat_asserts in
header files and the yat_asserts will be invisible to the normal user
also if he uses C-asserts.

added output operator in DataLookup2D and removed output operator in
MatrixLookup?

Removed template function add_values in Averager and weighted version

Added function to AveragerWeighted? taking iterator to four ranges.

Location:

trunk/yat/classifier

Files:

• DataLookup2D.cc (modified) (1 diff)
• DataLookup2D.h (modified) (1 diff)
• KNN.h (modified) (4 diffs)
• MatrixLookup.cc (modified) (1 diff)
• MatrixLookup.h (modified) (1 diff)
• NCC.cc (modified) (1 diff)

trunk/yat/classifier/DataLookup2D.cc

@@ -139 +139 @@
   }
 
+  std::ostream& operator<<(std::ostream& s, const DataLookup2D& m)
+  {
+    s.setf(std::ios::dec);
+    s.precision(12);
+    for(size_t i=0, j=0; i<m.rows(); i++)
+      for (j=0; j<m.columns(); j++) {
+        s << m(i,j);
+        if (j<m.columns()-1)
+          s << s.fill();
+        else if (i<m.rows()-1)
+          s << "\n";
+      }
+    return s;
+  }
+
 }}} // of namespace classifier, yat, and theplu

trunk/yat/classifier/DataLookup2D.h

@@ -175 +175 @@
   };  
   
+  ///
+  /// The output operator DataLookup2D
+  ///
+  std::ostream& operator<< (std::ostream& s, const DataLookup2D&);
+
 }}} // of namespace classifier, yat, and theplu
 

trunk/yat/classifier/KNN.h

@@ -10 +10 @@
 #include "yat/statistics/vector_distance.h"
 #include "yat/utility/matrix.h"
-
+#include "yat/utility/yat_assert.h"
+
+#include <cmath>
 #include <map>
 
@@ -119 +121 @@
 
     // matrix with training samples as rows and test samples as columns
-    utility::matrix* distances = new utility::matrix(data_.columns(),input.columns());
+    utility::matrix* distances = 
+      new utility::matrix(data_.columns(),input.columns());
       
     if(weighted_data && weighted_input) {
@@ -126 +129 @@
         for(size_t j=0; j<input.columns(); j++) {
           classifier::DataLookupWeighted1D test(*weighted_input,j,false);
+          yat_assert(training.size()==test.size());
           (*distances)(i,j)=statistics::vector_distance(training.begin(),training.end(),test.begin(),typename statistics::vector_distance_traits<Distance>::distance());
+          yat_assert(!std::isnan((*distances)(i,j)));
         }
       }
@@ -186 +191 @@
     utility::matrix* distances=calculate_distances(input);
     
-    // for each test sample (column in distances) find the closest training samples
-    prediction.clone(utility::matrix(target_.nof_classes(), input.columns(),0.0));
+    // for each test sample (column in distances) find the closest
+    // training samples
+    prediction.clone(utility::matrix(target_.nof_classes(),input.columns(),0.0));
     for(size_t sample=0;sample<distances->columns();sample++) {
       std::vector<size_t> k_index;

trunk/yat/classifier/MatrixLookup.cc

@@ -217 +217 @@
 
 
-  std::ostream& operator<<(std::ostream& s, const MatrixLookup& m)
-  {
-    s.setf(std::ios::dec);
-    s.precision(12);
-    for(size_t i=0, j=0; i<m.rows(); i++)
-      for (j=0; j<m.columns(); j++) {
-        s << m(i,j);
-        if (j<m.columns()-1)
-          s << s.fill();
-        else if (i<m.rows()-1)
-          s << "\n";
-      }
-    return s;
-  }
-
 
 

trunk/yat/classifier/MatrixLookup.h

@@ -284 +284 @@
   };  
   
-  ///
-  /// The output operator MatrixLookup
-  ///
-  std::ostream& operator<< (std::ostream& s, const MatrixLookup&);
-
 }}} // of namespace classifier, yat, and theplu
 

trunk/yat/classifier/NCC.cc

@@ -130 +130 @@
         for(size_t k=0; k<centroids_.columns();k++) {
           DataLookupWeighted1D centroid(weighted_centroids,k,false);
+
+          assert(in.size()==centroid.size());
           prediction(k,j)=(*distance_)(in.begin(),in.end(),centroid.begin());
         }