Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/general/mk_bnet.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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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +function bnet = mk_bnet(dag, node_sizes, varargin) +% MK_BNET Make a Bayesian network. +% +% BNET = MK_BNET(DAG, NODE_SIZES, ...) makes a graphical model with an arc from i to j iff DAG(i,j) = 1. +% Thus DAG is the adjacency matrix for a directed acyclic graph. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% +% 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. +% +% Below are the names of optional arguments [and their default value in brackets]. +% Pass as 'PropertyName1', PropertyValue1, 'PropertyName2', PropertyValue2, ... +% +% discrete - the list of nodes which are discrete random variables [1:N] +% equiv_class - equiv_class(i)=j means node i gets its params from CPD{j} [1:N] +% observed - the list of nodes which will definitely be observed in every case [ [] ] +% 'names' - a cell array of strings to be associated with nodes 1:n [{}] +% This creates an associative array, so you write e.g. +% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42' +% assuming names = { 'foo', 'bar', ...}. +% +% e.g., bnet = mk_bnet(dag, ns, 'discrete', [1 3]) +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% bnet = mk_bnet(dag, node_sizes, discrete_nodes, equiv_class) + +n = length(dag); + +% default values for parameters +bnet.equiv_class = 1:n; +bnet.dnodes = 1:n; % discrete +bnet.observed = []; +bnet.names = {}; + +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 'discrete', bnet.dnodes = args{i+1}; + case 'observed', bnet.observed = args{i+1}; + case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n)); + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +bnet.observed = sort(bnet.observed); % for comparing sets +bnet.hidden = mysetdiff(1:n, bnet.observed(:)'); +bnet.hidden_bitv = zeros(1,n); +bnet.hidden_bitv(bnet.hidden) = 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); + +bnet.rep_of_eclass = zeros(1,E); +for e=1:E + mems = bnet.members_of_equiv_class{e}; + bnet.rep_of_eclass(e) = mems(1); +end + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + +bnet.order = topological_sort(bnet.dag);