Mauch MIREX 2010

function fg = mk_fgraph_given_ev(G, node_sizes, factors, ev_CPD, evidence, varargin)

% MK_FGRAPH_GIVEN_EV Make a factor graph where each node has its own private evidence term

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

%

% G, node_sizes and factors are as in mk_fgraph, but they refer to the hidden nodes.

% ev_CPD{i} is a CPD for the i'th hidden node; this will be converted into a factor

% for node i using evidence{i}.

% We currently assume all hidden nodes are discrete, for simplicity.

%

% 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]

% ev_equiv_class - ev_equiv_class(i)=j  means evidence node i gets its params from ev_CPD{j} [1:N]

N = length(node_sizes);

nfactors = length(factors);

% default values for parameters

eclass = 1:nfactors;

ev_eclass = 1:N;

if nargin >= 6

  args = varargin;

  nargs = length(args);

  for i=1:2:nargs

    switch args{i},

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

     case 'ev_equiv_class', ev_eclass = args{i+1}; 

     otherwise,  

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

    end

  end

end

pot_type = 'd';

for x=1:N

  ev = cell(1,2); % cell 1 is the hidden parent, cell 2 is the observed child

  ev(2) = evidence(x); 

  dom = 1:2;

  F = convert_to_pot(ev_CPD{ev_eclass(x)}, pot_type, dom(:), ev);

  M = pot_to_marginal(F);

  %factors{end+1} = tabular_CPD('self', 1, 'ps', [], 'sz', node_sizes(x), 'CPT', M.T);

  factors{end+1} = mk_isolated_tabular_CPD(node_sizes(x), {'CPT', M.T});

end

E = max(eclass);

fg = mk_fgraph([G eye(N)], node_sizes, factors, 'equiv_class', [eclass E+1:E+N]);