Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/general/mk_fgraph.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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);