Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/general/mk_limid.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 bnet = mk_limid(dag, node_sizes, varargin) % MK_LIMID Make a limited information influence diagram % % BNET = MK_LIMID(DAG, NODE_SIZES, ...) % DAG is the adjacency matrix for a directed acyclic graph. % The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. % For decision nodes, the parents must explicitely include all nodes % on which it can depends, in contrast to the implicit no-forgetting assumption of influence diagrams. % (For details, see "Representing and solving decision problems with limited information", % Lauritzen and Nilsson, Management Science, 2001.) % % 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. % node_sizes(i) = 1 if i is a utility node. % % The list below gives optional arguments [default value in brackets]. % % chance - the list of nodes which are random variables [1:N] % decision - the list of nodes which are decision nodes [ [] ] % utility - the list of nodes which are utility nodes [ [] ] % equiv_class - equiv_class(i)=j means node i gets its params from CPD{j} [1:N] % % e.g., limid = mk_limid(dag, ns, 'chance', [1 3], 'utility', [2]) n = length(dag); % default values for parameters bnet.chance_nodes = 1:n; bnet.equiv_class = 1:n; bnet.utility_nodes = []; bnet.decision_nodes = []; bnet.dnodes = 1:n; % discrete if nargin >= 3 args = varargin; nargs = length(args); if ~isstr(args{1}) if nargs >= 1, bnet.dnodes = args{1}; end if nargs >= 2, bnet.equiv_class = args{2}; end else for i=1:2:nargs switch args{i}, case 'equiv_class', bnet.equiv_class = args{i+1}; case 'chance', bnet.chance_nodes = args{i+1}; case 'utility', bnet.utility_nodes = args{i+1}; case 'decision', bnet.decision_nodes = args{i+1}; case 'discrete', bnet.dnodes = args{i+1}; otherwise, error(['invalid argument name ' args{i}]); end end end end bnet.limid = 1; bnet.dag = dag; bnet.node_sizes = node_sizes(:)'; bnet.cnodes = mysetdiff(1:n, bnet.dnodes); % too many functions refer to cnodes to rename it to cts_nodes - % We hope it won't be confused with chance nodes! bnet.parents = cell(1,n); for i=1:n bnet.parents{i} = parents(dag, i); end E = max(bnet.equiv_class); mem = cell(1,E); for i=1:n e = bnet.equiv_class(i); mem{e} = [mem{e} i]; end bnet.members_of_equiv_class = mem; bnet.CPD = cell(1, E); % for e=1:E % i = bnet.members_of_equiv_class{e}(1); % pick arbitrary member % switch type{e} % case 'tabular', bnet.CPD{e} = tabular_CPD(bnet, i); % case 'gaussian', bnet.CPD{e} = gaussian_CPD(bnet, i); % otherwise, error(['unrecognized CPD type ' type{e}]); % end % end directed = 1; if ~acyclic(dag,directed) error('graph must be acyclic') end bnet.order = topological_sort(bnet.dag);