Mauch MIREX 2010

function fg = mk_fgraph(G, node_sizes, factors, varargin)

% MK_FGRAPH Make a factor graph

% fg = mk_fgraph(G, node_sizes, factors, ...)

%

% A factor graph is a bipartite graph, with one side containing variables,

% and the other containing functions of (subsets of) these variables.

% For details, see "Factor Graphs and the Sum-Product Algorithm",

%  F. Kschischang and B. Frey and H-A. Loeliger,

%  IEEE Trans. Info. Theory, 2001

%

% G(i,j) = 1 if there is an arc from variable i to factor j

%

% node_sizes(i) is the number of values node i can take on,

%   or the length of node i if i is a continuous-valued vector.

%

% 'factors' is the list of factors (kernel functions)

%

% The list below gives optional arguments [default value in brackets].

% 

% equiv_class - equiv_class(i)=j  means factor node i gets its params from factors{j} [1:F]

% discrete - the list of nodes which are discrete random variables [1:N]

%

% e.g., fg = mk_fgraph(G, [2 2], {bnet.CPD{1},bnet.CPD{2}}, 'discrete', [1 2])

fg.G = G;

fg.node_sizes = node_sizes;

fg.factors = factors;

[fg.nvars fg.nfactors] = size(G);

% default values for parameters

fg.equiv_class = 1:fg.nfactors;

fg.dnodes = 1:fg.nvars;

if nargin >= 4

  args = varargin;

  nargs = length(args);

  for i=1:2:nargs

    switch args{i},

     case 'equiv_class', fg.equiv_class = args{i+1}; 

     case 'discrete',    fg.dnodes = args{i+1}; 

     otherwise,  

      error(['invalid argument name ' args{i}]);       

    end

  end

end

% so that determine_pot_type will work...

fg.utility_nodes = [];

%fg.decision_nodes = [];

%fg.chance_nodes = fg.nvars;

fg.dom = cell(1, fg.nfactors);

for f=1:fg.nfactors

  fg.dom{f} = find(G(:,f));

end

fg.dep = cell(1, fg.nvars);

for x=1:fg.nvars

  fg.dep{x} = find(G(x,:));

end

fg.cnodes = mysetdiff(1:fg.nvars, fg.dnodes);