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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]);
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