Changeset 529

Timestamp:

Mar 1, 2006, 3:03:27 PM (16 years ago)

Author:

Markus Ringnér

Message:

Added score for signal-to-noise ratio (Golub score). Fixed documentation. Improved weighted version of tScore

Location:

trunk/lib/statistics

Files:

• Makefile.am (modified) (2 diffs)
• SNR.cc (added)
• SNR.h (added)
• Score.cc (modified) (1 diff)
• Score.h (modified) (2 diffs)
• tScore.cc (modified) (2 diffs)
• tScore.h (modified) (1 diff)

trunk/lib/statistics/Makefile.am

@@ -15 +15 @@
   MultiDimensional.cc Naive.cc NaiveWeighted.cc OneDimensional.cc\
   Pearson.cc PearsonDistance.cc \
-  Polynomial.cc ROC.cc Score.cc tScore.cc utility.cc \
+  Polynomial.cc ROC.cc Score.cc tScore.cc SNR.cc utility.cc \
   WilcoxonFoldChange.cc
 
@@ -28 +28 @@
   Local.h MultiDimensional.h Naive.h NaiveWeighted.h OneDimensional.h \
   OneDimensionalWeighted.h Pearson.h  PearsonDistance.h \
-  Polynomial.h ROC.h Score.h tScore.h \
+  Polynomial.h ROC.h Score.h SNR.h tScore.h \
   utility.h WilcoxonFoldChange.h

trunk/lib/statistics/Score.cc

@@ -7 +7 @@
 
   Score::Score(bool absolute)
-    : absolute_(absolute)
+    : absolute_(absolute), weighted_(false)
   {
   }

trunk/lib/statistics/Score.h

@@ -79 +79 @@
     /// @param value vector of the values
     /// @param weight vector of accompanied weight to the values
-    /// @train_set defining which values to use (number of values used
-    /// in the calculation is equal to size of \a train_set)
     ///
     virtual double 
@@ -99 +97 @@
     /// @param value vector of the values
     /// @param weight vector of accompanied weight to the values
-    /// @train_set defining which values to use (number of values used
-    /// in the calculation is equal to size of \a train_set)
     ///
     inline double 

trunk/lib/statistics/tScore.cc

@@ -50 +50 @@
     statistics::AveragerWeighted positive;
     statistics::AveragerWeighted negative;
-    dof_=target.size()-2;
     for(size_t i=0; i<target.size(); i++){
       if (target.binary(i))
@@ -60 +59 @@
     double s2=(positive.sum_xx_centered()+negative.sum_xx_centered())/
       (positive.n()+negative.n()-2);
-    t_=diff/sqrt(s2*(1.0/positive.sum_w()+1.0/negative.sum_w()));
+    t_=diff/sqrt(s2*(1.0/positive.sum_w()+1.0/negative.sum_w()));   
     if (t_<0 && absolute_)
       t_=-t_;
+
+    if(positive.n()==0 || negative.n()==0 ||
+       positive.sum_w()==0 || positive.sum_w()==0)
+      t_=0;
+    dof_=target.size()-2;
      
     return t_;

trunk/lib/statistics/tScore.h

@@ -42 +42 @@
     ///
     /// Weighted version of t-Score @return t-score if absolute=true
-    /// absolute value of t-score is returned
+    /// absolute value of t-score is returned. 
+    /// @todo Peter: This is probably 
+    /// not implemented as it should be!
     ///
     double score(const classifier::Target& target,