root/applications/bdmtoolbox/mex/mex_classes/mexEpdf.m @ 1440

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1%> @file mexEpdf.m
2%> @brief File mappring root class of epdf from BDM
3% ======================================================================
4%> @brief Abstract class of unconditional probability density function (epdf)
5%
6%> This class provides a bridge between bdm::epdf and Matlab
7% ======================================================================
8classdef mexEpdf
9    properties
10            %> Description of random variable (see definitiopn of RV)
11            rv=RV;
12    end
13    methods
14             %> Function returning mean value of this epdf
15        function m=mean(p)
16            error('define how to compute mean')
17        end
18             %> This function is called before using the object. It should check consistency of the properties and fill default values.
19        function validate(p)
20            error('check if the density is consistent')
21        end
22             %> Tell the world around it dimension of the random variable
23        function dim=dimension(p)
24            error('return dimension of the density')
25        end
26             %> Function returning variance of this epdf
27        function v=variance(p)
28            error('define how to compute mean')
29        end
30             %> Function returning logarithm of likelihood function in point x
31        function l=evallog(p,x)
32            error('define how to evaluate log of this density at point x')
33        end
34             %> Function returning a signle sample from this density
35         function l=sample(p)
36            error('define how to sample from this density')
37        end
38       
39        %%% default functions -- no need to redefine %%%
40       
41             %> Function returning logarithm of NON-normalized likelihood function in point x (speed optimization)
42        function l=evallog_nn(p,x)
43            % define how to evaluate non-normalized log of this density at point x
44            % makes sense if faster than normalized
45            l=evallog(p,x);
46        end
47
48                  function r=get_rv(p)
49                        r=p.rv;
50          end
51             %> Function returning a matrix of n samples from this density,
52          function m = samplemat(obj, n)
53              m = zeros(obj.dimension, n);
54              for i=1:n
55                  m(:,i) = obj.sample;
56              end
57          end
58    end
59end
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