Most_probable_explanations

 function[MPEs] = Most_probable_explanations(bnets,func)

 Most_probable_explanations: Computes the most probable explanation and its fitness value at each
                             generation given the corresponding Bayesian network

 INPUT
 bnets{maxgen}: Cell array that stores the Bayesian networks learned at
                each generation
 func:          The fitness function used in the EDA execution


 OUTPUT
 MPEs: Cell array that stores the most probable configuration, its 
       probability values and its fitness function at each
       generation.

       MPEs{1,i}: An array with the point with highest probability at generation i
       MPEs{2,i}: The probability value for the MPE at generation i. It  
                  is an array where MPEs{2,i}(1,1) is the probability
                  in logarithmic scale and MPEs{2,i}(1,2)
                  is the original probability value. 
       MPEs{3,i}: The fitness function value for the MPE at generation i

 Example: 
 for i=1:maxgen
   bnets{i}=Cache{3,i};
 end
 [MPEs] = Most_probable_explanations(bnets, F);

 The fitness function corresponding to the most probable configurations are shown
 for i=1:maxgen
   fo_mpes(i)=MPEs{3,i};  
 end
 X=[1:maxgen];
 plot(X,fo_mpes);

 Last version 5/11/08. Carlos Echegoyen and Roberto Santana(carlos.echegoyen@ehu.es)

0001 function[MPEs] = Most_probable_explanations(bnets,func)
0002 % function[MPEs] = Most_probable_explanations(bnets,func)
0003 %
0004 % Most_probable_explanations: Computes the most probable explanation and its fitness value at each
0005 %                             generation given the corresponding Bayesian network
0006 %
0007 % INPUT
0008 % bnets{maxgen}: Cell array that stores the Bayesian networks learned at
0009 %                each generation
0010 % func:          The fitness function used in the EDA execution
0011 %
0012 %
0013 % OUTPUT
0014 % MPEs: Cell array that stores the most probable configuration, its
0015 %       probability values and its fitness function at each
0016 %       generation.
0017 %
0018 %       MPEs{1,i}: An array with the point with highest probability at generation i
0019 %       MPEs{2,i}: The probability value for the MPE at generation i. It
0020 %                  is an array where MPEs{2,i}(1,1) is the probability
0021 %                  in logarithmic scale and MPEs{2,i}(1,2)
0022 %                  is the original probability value.
0023 %       MPEs{3,i}: The fitness function value for the MPE at generation i
0024 %
0025 % Example:
0026 % for i=1:maxgen
0027 %   bnets{i}=Cache{3,i};
0028 % end
0029 % [MPEs] = Most_probable_explanations(bnets, F);
0030 %
0031 % The fitness function corresponding to the most probable configurations are shown
0032 % for i=1:maxgen
0033 %   fo_mpes(i)=MPEs{3,i};
0034 % end
0035 % X=[1:maxgen];
0036 % plot(X,fo_mpes);
0037 %
0038 % Last version 5/11/08. Carlos Echegoyen and Roberto Santana(carlos.echegoyen@ehu.es)
0039 
0040 k = size(bnets,2); % Number of net
0041 num_vars = size(bnets{1}.dnodes,2); % Number of variables
0042 
0043 for i=1:k
0044    [mpe_solution,prob_value] =  FindMPE(bnets{i});
0045     MPEs{1,i} = cell2num(mpe_solution)-1;
0046     MPEs{2,i} = prob_value;
0047     MPEs{3,i} = feval(func,MPEs{1,i});
0048 end
0049