Changeset 983 for applications/bdmtoolbox/mex

Timestamp:

05/25/10 22:33:29 (14 years ago)

Author:

smidl

Message:

doc - extensions

Location:

applications/bdmtoolbox/mex/mex_classes

Files:

• mexDirac.m (modified) (3 diffs)
• mexLaplaceBM.m (modified) (2 diffs)

applications/bdmtoolbox/mex/mex_classes/mexDirac.m

@@ -1 +1 @@
 classdef mexDirac < mexEpdf
     % Dirac delta probability distribution
-    properties
-        point % point of the support
+    properties                                                 % DATA structures
+        point                                                  % point of the support
     end
     methods
-        function m=mean(obj)
+        function m=mean(obj)                                   % compute mean values of the density
             m = obj.point;
         end
-        function obj=validate(obj)
+        function obj=validate(obj)                             % check if data structures are consistent
             % point should be a column
             if (size(obj.point,2)>1)
@@ -18 +18 @@
             end
         end
-        function dim=dimension(obj)
+        function dim=dimension(obj)                            % inform others about your dimensions
             dim = size(obj.point,1);
         end
-        function v=variance(obj)
+        function v=variance(obj)                               % compute variance
             v=zeros(size(obj.point));
         end
-        function l=evallog(obj,x)
+        function l=evallog(obj,x)                              % return logarithm of your density at point x
             if obj.point==x
                 l = inf;
@@ -31 +31 @@
             end
         end
-        function s=sample(obj);
+        function s=sample(obj);                                % return random sample from your density
             s = obj.point;
         end

applications/bdmtoolbox/mex/mex_classes/mexLaplaceBM.m

@@ -3 +3 @@
     % Maximum likelihood approximation of the Bayes rule is used, posterior is in the form of dirac.
     properties
-        max_window_length = 10;   % max window length
-        data_window =[];          % estimate of mu is median of observations
+        max_window_length = 10;                               % max window length (default = 10)
+        data_window =[];                                      % sliding window of data 
     end
     methods
-        function obj=validate(obj)
-            % prepare all internal objects for use
+        function obj=validate(obj)                            % prepare all internal objects for use
             obj.apost_pdf = mexDirac;
             obj.apost_pdf.point = [0;0];
-            obj.log_evidence = 0; % evidence is not computed!
+            obj.log_evidence = 0;                             % evidence is not computed!
             disp('val');
         end
 
         function dims=dimensions(obj)
-            %please fill
-            %dims = [size_of_posterior size_of_data size_of_condition]
-            dims = [2,1,0] % we have: [2d parameters, 1d observations, 0d condition]
+            %please fill: dims = [size_of_posterior size_of_data size_of_condition]
+            dims = [2,1,0]                                    % we have: [2d parameters, 1d observations, 0d condition]
         end
-        function obj=bayes(obj,dt,cond)
+        function obj=bayes(obj,dt,cond)                       % approximate bayes rule
             if size(obj.data_window,2)>=obj.max_window_length
                 obj.data_window = [dt obj.data_window(1:end-1)];
@@ -29 +27 @@
             m_hat = mean(obj.data_window);
             b_hat = sum(abs(obj.data_window-m_hat))/ size(obj.data_window,2);
-            obj.apost_pdf.point = [m_hat; b_hat];
+            obj.apost_pdf.point = [m_hat; b_hat];             % store result in psoterior pdf
         end
-        function p=epredictor(obj,cond)
+        function p=epredictor(obj,cond)                       % when predictive density is needed approximate it by Laplace with point estimates
             % return predictive density (max likelihood)
             p = mexLaplace;