Changeset 204

Timestamp:

Nov 1, 2004, 7:13:55 PM (19 years ago)

Author:

Peter

Message:

Minor changes

Location:

trunk/src

Files:

• RegressionLinear.h (modified) (1 diff)
• RegressionNaive.cc (modified) (1 diff)
• RegressionNaive.h (modified) (2 diffs)

trunk/src/RegressionLinear.h

@@ -43 +43 @@
     /// This function computes the best-fit linear regression
     /// coefficients (k,m) of the model  \f$ y = kx + m \f$ from
-    /// vectors \a x and \a y, by minimizing \f$ \sum{(kx_i+m-x_i)^2}
+    /// vectors \a x and \a y, by minimizing \f$ \sum{(kx_i+m-y_i)^2}
     /// \f$.
     ///
     inline int fit(const gslapi::vector& x, const gslapi::vector& y) 
-      { return gsl_fit_linear_vector(x.gsl_vector_pointer(),
-                                     y.gsl_vector_pointer(), &m_, &k_, 
-                                     &cov00_, &cov01_, &cov11_, &sumsq_); }
+    { return gsl_fit_linear_vector(x.gsl_vector_pointer(), 
+                                   y.gsl_vector_pointer(), &m_, &k_, 
+                                   &cov00_, &cov01_, &cov11_, &sumsq_); }  
 
     ///
     /// This function computes the best-fit linear regression
-    /// coefficients (k,m) of the model \f$ y = kx + m \f$ from from
-    /// vectors \a x and \a y, by minimizing \f$ \sum
-    /// w_i(kx_i+m-x_i)^2 \f$.
+    /// coefficients (k,m) of the model \f$ y = kx + m \f$ from
+    /// vectors \a x and \a y, by minimizing \f$ \sum w_i(kx_i+m-y_i)^2
+    /// \f$. The weight \f$ w_i \f$ is the inverse of the variance for
+    /// \f$ y_i \f$
     ///
-    inline int fit_weighted(const gslapi::vector x, const gslapi::vector y, 
-                           const gslapi::vector w)
-      { return gsl_fit_wlinear_vector(x.gsl_vector_pointer(),
-                                      w.gsl_vector_pointer(),
-                                      y.gsl_vector_pointer(), &m_, &k_, 
-                                      &cov00_, &cov01_, &cov11_, &sumsq_); }
+    inline int fit_weighted(gslapi::vector& x,gslapi::vector& y,
+                            gslapi::vector& w)
+    { return gsl_fit_wlinear_vector(x.gsl_vector_pointer(), 
+                                    w.gsl_vector_pointer(), 
+                                    y.gsl_vector_pointer(),
+                                    &m_, &k_, 
+                                    &cov00_, &cov01_, &cov11_, &sumsq_); }  
          
   private:

trunk/src/RegressionNaive.cc

@@ -21 +21 @@
   }
 
-  int RegressionNaive::fit(const gslapi::vector& x, const gslapi::vector& y)
-  {
-    Averager a;
-    for (size_t i=0; i<y.size(); i++)
-      a.add(y(i));
-    m_=a.mean();
-    var_=a.variance();
-    return 0;
-  }
 
-  int RegressionNaive::fit_weighted(const gslapi::vector& x, 
-                                    const gslapi::vector& y,
-                                    const gslapi::vector& w)
-  {
-    WeightedAverager a;
-    for (size_t i=0; i<y.size(); i++)
-      a.add(y(i),w(i));
-    m_=a.mean();
-    var_=a.standard_error()*a.standard_error();
-    return 0;
-  }
 
 }} // of namespace cpptools and namespace theplu

trunk/src/RegressionNaive.h

@@ -6 +6 @@
 // C++ tools include
 /////////////////////
+#include "Averager.h"
 #include "Regression.h"
 #include "vector.h"
-
+#include "WeightedAverager.h"
 // Standard C++ includes
 ////////////////////////
@@ -45 +46 @@
     /// \sum{(y_i-m)^2} \f$.
     ///
-    int fit(const gslapi::vector& x, const gslapi::vector& y);
-    
+    inline int fit(const gslapi::vector& x, const gslapi::vector& y)
+    {
+      Averager a;
+      for (size_t i=0; i<y.size(); i++)
+        a.add(y(i));
+      m_=a.mean();
+      var_=a.standard_error()*a.standard_error(); 
+      return 0;
+    }
 
     ///
     /// This function computes the best-fit for the naive model \f$ y
     /// = m \f$ from vectors \a x and \a y, by minimizing \f$ \sum
-    /// w_i(y_i-m)^2 \f$. 
+    /// w_i(y_i-m)^2 \f$. The weight \f$ w_i \f$ is the inverse of the
+    /// variance for \f$ y_i \f$
     ///
-    int fit_weighted(const gslapi::vector& x, const gslapi::vector& y, 
-                     const gslapi::vector& w);
-    
+    inline int  fit_weighted(gslapi::vector x,gslapi::vector y,gslapi::vector w)
+    {
+      WeightedAverager a;
+      for (size_t i=0; i<y.size(); i++)
+        a.add(y(i), w(i));
+      m_=a.mean();
+      var_=a.standard_error()*a.standard_error(); 
+      return 0;
+    }
+
          
   private:
-    double m_;    // only parameter in naive model
-    double var_;  // variance of the parameter m_
+    double var_;
+    double m_;
+    double sumsq_;
 
   };