Changeset 1040 for applications/bdmtoolbox/mex

Timestamp:

06/04/10 14:26:02 (14 years ago)

Author:

smidl

Message:

Doxygen documentation of bdmtoolbox

Files:

• applications/bdmtoolbox/mex/mex_classes/mexEpdf.m (modified) (3 diffs)

applications/bdmtoolbox/mex/mex_classes/mexEpdf.m

@@ +1 @@
+%> @file mexEpdf.m
+%> @brief File mappring root class of epdf from BDM
+% ======================================================================
+%> @brief Abstract class of unconditional probability density function (epdf) 
+%
+%> This class provides a bridge between bdm::epdf and Matlab
+% ======================================================================
 classdef mexEpdf
     properties
-            rv=RV; %empty description add some valid RV when needed
+            %> Description of random variable (see definitiopn of RV)
+            rv=RV; 
     end
     methods
+             %> Function returning mean value of this epdf
         function m=mean(p)
             error('define how to compute mean')
         end
+             %> This function is called before using the object. It should check consistency of the properties and fill default values.
         function validate(p)
             error('check if the density is consistent')
         end
+             %> Tell the world around it dimension of the random variable
         function dim=dimension(p)
             error('return dimension of the density')
         end
+             %> Function returning variance of this epdf
         function v=variance(p)
             error('define how to compute mean')
         end
+             %> Function returning logarithm of likelihood function in point x
         function l=evallog(p,x)
             error('define how to evaluate log of this density at point x')
         end
+             %> Function returning a signle sample from this density
          function l=sample(p)
             error('define how to sample from this density')
@@ -25 +39 @@
         %%% default functions -- no need to redefine %%%
         
+             %> Function returning logarithm of NON-normalized likelihood function in point x (speed optimization)
         function l=evallog_nn(p,x)
             % define how to evaluate non-normalized log of this density at point x
@@ -30 +45 @@
             l=evallog(p,x);
         end
+
                   function r=get_rv(p)
                         r=p.rv;
           end
+             %> Function returning a matrix of n samples from this density, 
           function m = samplemat(obj, n)
               m = zeros(obj.dimension, n);