Changeset 149

Timestamp:

Sep 9, 2004, 3:35:13 PM (19 years ago)

Author:

Peter

Message:

modified score function

Location:

trunk/src

Files:

• Pearson.cc (modified) (4 diffs)
• Pearson.h (modified) (6 diffs)

trunk/src/Pearson.cc

@@ -42 +42 @@
     else{
       t = sqrt(nof_samples_ - 2)*abs(r_) /sqrt(1-r_*r_);
-      p = gsl_cdf_tdist_Q (t, nof_samples_ -2 );
+      p = 2*gsl_cdf_tdist_Q (t, nof_samples_ -2 );
       return p;
     }
@@ -63 +63 @@
     r_ = a.correlation();
     weighted_=false;
+    r_=abs(r_);
     return r_;
   } 
@@ -95 +96 @@
            sqrt( (x.mul_elements(w)*x)*(y.mul_elements(w)*y)) );
     weighted_=true;
+
+    r_=abs(r_);
     return r_;
   } 
@@ -103 +106 @@
                         std::vector<size_t>());
   {
-    r_ = score(vec1, vec2, w1.mul_elements(w2),train_set);
-    return r_;
+    return = score(vec1, vec2, w1.mul_elements(w2),train_set);
+    
   }
 

trunk/src/Pearson.h

@@ -15 +15 @@
 //#include <vector>
 
+
+//Peter, should add a function to get the sign of the correlation
 namespace theplu {
 namespace cpptools {  
@@ -37 +39 @@
     
     ///
-    /// \f$ \frac{\sum_i(x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum_i
+    /// \f$ \frac{\vert \sum_i(x_i-\bar{x})(y_i-\bar{y})\vert }{\sqrt{\sum_i
     /// (x_i-\bar{x})^2\sum_i (x_i-\bar{x})^2}}\f$. 
-    /// @return Pearson correlation.
+    /// @return absolute value of Pearson correlation.
     ///
     double score(const gslapi::vector&, const gslapi::vector&,  
@@ -45 +47 @@
 
     ///
-    /// \f$ \frac{\sum_iw_i(x_i-\bar{x})(y_i-\bar{y})}
+    /// \f$ \frac{\vert \sum_iw_i(x_i-\bar{x})(y_i-\bar{y})\vert }
     /// {\sqrt{\sum_iw_i(x_i-\bar{x})^2\sum_iw_i(x_i-\bar{x})^2}}\f$,
     /// where \f$m_x = \frac{\sum w_ix_i}{\sum w_i}\f$ and \f$m_x =
     /// \frac{\sum w_ix_i}{\sum w_i}\f$. This expression is chosen to
     /// get a correlation equal to unity when \a x and \a y are
-    /// equal. @return Weighted version of Pearson correlation.
+    /// equal. @return absolute value of weighted version of Pearson
+    /// correlation.
     ///
     double score(const gslapi::vector& x, const gslapi::vector& y,  
@@ -57 +60 @@
 
     ///
-    /// \f$ \frac{\sum_iw^x_iw^y_i(x_i-m_x)(y_i-m_y)}
+    /// \f$ \frac{\vert \sum_iw^x_iw^y_i(x_i-m_x)(y_i-m_y)\vert }
     /// {\sqrt{\sum_iw^x_iw^y_i(x_i-m_x)^2 ///
     /// \sum_iw^x_iw^y_i(y_i-m_y)^2}}\f$, where \f$m_x = \frac{\sum
@@ -63 +66 @@
     /// w_ix_i}{\sum w_i}\f$. This expression is chosen to get a
     /// correlation equal to unity when \a x and \a y are
-    /// equal. @return Weighted version of Pearson correlation.
+    /// equal. @return absolute value of weighted version of Pearson
+    /// correlation.
     ///
     double score(const gslapi::vector& x, const gslapi::vector& y,  
@@ -74 +78 @@
     /// correlation is zero (and the data is Gaussian). Note that this
     /// function can only be used together with the unweighted
-    /// score. @return one-sided p-value
+    /// score. @return two-sided p-value
     ///
     double p_value();