Changeset 493

Timestamp:

Jan 9, 2006, 3:01:40 PM (17 years ago)

Author:

Peter

Message:

fixed typo in tScore and added make_classifier in SVM

Location:

trunk/lib

Files:

• classifier/SVM.cc (modified) (3 diffs)
• classifier/SVM.h (modified) (4 diffs)
• statistics/tScore.cc (modified) (1 diff)

trunk/lib/classifier/SVM.cc

@@ -4 +4 @@
 #include <c++_tools/classifier/SVM.h>
 
-#include <c++_tools/classifier/KernelLookup.h>
+#include <c++_tools/classifier/DataLookup2D.h>
 #include <c++_tools/gslapi/matrix.h>
 #include <c++_tools/gslapi/vector.h>
@@ -137 +137 @@
   }
 
-  SVM::SVM(const KernelLookup& kernel, const Target& target)
+  SVM::SVM(const DataLookup2D& kernel, const Target& target)
     : SupervisedClassifier(kernel,target),
       alpha_(target.size(),0),
@@ -150 +150 @@
       tolerance_(0.00000001)
   {
+  }
+
+  SupervisedClassifier* SVM::make_classifier(const DataLookup2D& data, 
+                                             const Target& target) const
+  {
+    SVM* sc = new SVM(data,target);
+    //Copy those variables possible to modify from outside
+    return sc;
   }
 

trunk/lib/classifier/SVM.h

@@ -108 +108 @@
     /// the SVM is no longer defined.
     ///
-    SVM(const KernelLookup&, const Target&);
+    SVM(const DataLookup2D&, const Target&);
+
+    ///
+    /// @todo doc
+    ///
+    SupervisedClassifier* 
+    make_classifier(const DataLookup2D&, const Target&) const;
 
     ///
@@ -152 +158 @@
     { trained_=false; alpha_=gslapi::vector(target_.size(),0); }
 
-    ///
-    /// Training the SVM following Platt's SMO, with Keerti's
-    /// modifacation. Minimizing \f$ \frac{1}{2}\sum
-    /// y_iy_j\alpha_i\alpha_j(K_{ij}+\frac{1}{C_i}\delta_{ij}) \f$,
-    /// which corresponds to minimizing \f$ \sum w_i^2+\sum C_i\xi_i^2
-    /// \f$. 
+    ///
+    /// Training the SVM following Platt's SMO, with Keerti's
+    /// modifacation. Minimizing \f$ \frac{1}{2}\sum
+    /// y_iy_j\alpha_i\alpha_j(K_{ij}+\frac{1}{C_i}\delta_{ij}) \f$,
+    /// which corresponds to minimizing \f$ \sum w_i^2+\sum C_i\xi_i^2
+    /// \f$. 
     ///
     bool train();
@@ -169 +175 @@
     SVM(const SVM&);
          
-    ///
-    /// Default constructor (not implemented)
-    ///
-    SVM(void);
-
     ///
     /// Calculates bounds for alpha2
@@ -207 +208 @@
     double bias_;
     double C_inverse_;
-    const KernelLookup& kernel_; 
+    const DataLookup2D& kernel_; 
     unsigned long int max_epochs_;
     gslapi::vector output_;

trunk/lib/statistics/tScore.cc

@@ -58 +58 @@
     double diff = positive.mean() - negative.mean();
     double s2=(positive.sum_xx_centered()+negative.sum_xx_centered())/
-      (positive.n()+negative.n()-2;
+      (positive.n()+negative.n()-2);
     t_=diff/sqrt(s2*(1.0/positive.sum_w()+1.0/negative.sum_w()));
     assert(0);