Changeset 3614

Timestamp:

Feb 6, 2017, 2:37:33 AM (6 years ago)

Author:

Peter

Message:

refs #867 and #882. Interface for Negative Binomiual and Poisson regression

Location:

trunk/yat/regression

Files:

• Makefile.am (modified) (3 diffs)
• MultiDimensional.h (modified) (4 diffs)
• Multivariate.cc (added)
• Multivariate.h (added)
• NegativeBinomial.h (added)
• Poisson.h (added)

trunk/yat/regression/Makefile.am

@@ -29 +29 @@
   yat/regression/LinearInterpolation.cc yat/regression/LinearWeighted.cc  \
   yat/regression/Local.cc yat/regression/MultiDimensional.cc \
-  yat/regression/MultiDimensionalWeighted.cc yat/regression/Naive.cc    \
+  yat/regression/MultiDimensionalWeighted.cc \
+  yat/regression/Multivariate.cc \
+  yat/regression/Naive.cc   \
   yat/regression/NaiveWeighted.cc yat/regression/OneDimensional.cc \
   yat/regression/OneDimensionalWeighted.cc        \
@@ -51 +53 @@
   $(srcdir)/yat/regression/MultiDimensional.h \
   $(srcdir)/yat/regression/MultiDimensionalWeighted.h \
+  $(srcdir)/yat/regression/Multivariate.h \
   $(srcdir)/yat/regression/Naive.h \
   $(srcdir)/yat/regression/NaiveWeighted.h \
+  $(srcdir)/yat/regression/NegativeBinomial.h \
   $(srcdir)/yat/regression/OneDimensional.h \
   $(srcdir)/yat/regression/OneDimensionalWeighted.h \
@@ -58 +62 @@
   $(srcdir)/yat/regression/PolynomialInterpolation.h \
   $(srcdir)/yat/regression/PolynomialWeighted.h \
+  $(srcdir)/yat/regression/Poisson.h \
   $(srcdir)/yat/regression/TukeyBiweight.h

trunk/yat/regression/MultiDimensional.h

@@ -5 +5 @@
 
 /*
-  Copyright (C) 2005, 2006, 2007, 2008 Jari Häkkinen, Peter Johansson
   Copyright (C) 2009 Peter Johansson
 
@@ -24 +23 @@
 */
 
+#include "Multivariate.h"
+
 #include "yat/utility/Matrix.h"
 #include "yat/utility/Vector.h"
@@ -33 +34 @@
 namespace regression {
 
-  ///
-  /// @brief MultiDimesional fitting.
-  ///
-  class MultiDimensional
+  /**
+     \brief Linear MultiDimesional regression
+  */
+  class MultiDimensional : public Multivariate
   {
   public:
@@ -53 +54 @@
        \brief covariance of parameters
 
-       The covariance of fit parameters are calculated as \f$ \sigma^2
-       (X'X)^{-1} \f$ where \f$ \sigma^2 \f$ is the variance of the of
-       the error terms.
+       The covariance of fit parameters is calculated as \f$ \sigma^2
+       (X'X)^{-1} \f$ where \f$ \sigma^2\f$ is the variance of error
+       residuals.
     */
     const utility::Matrix& covariance(void) const;