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annotate toolboxes/FullBNT-1.0.7/bnt/examples/static/discrete3.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 % Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17)
wolffd@0 2 % 1
wolffd@0 3 % / | \
wolffd@0 4 % 2 3 4
wolffd@0 5 % | | |
wolffd@0 6 % 5 6 7
wolffd@0 7 % \/ \/
wolffd@0 8 % 8 9
wolffd@0 9 % where all arcs point downwards
wolffd@0 10
wolffd@0 11 N = 9;
wolffd@0 12 dag = zeros(N,N);
wolffd@0 13 dag(1,2)=1; dag(1,3)=1; dag(1,4)=1;
wolffd@0 14 dag(2,5)=1; dag(3,6)=1; dag(4,7)=1;
wolffd@0 15 dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1;
wolffd@0 16
wolffd@0 17 dnodes = 1:N;
wolffd@0 18 false = 1; true = 2;
wolffd@0 19 ns = 2*ones(1,N); % binary nodes
wolffd@0 20
wolffd@0 21 onodes = [1];
wolffd@0 22 evidence = cell(1,N);
wolffd@0 23 evidence(onodes) = num2cell(1);
wolffd@0 24 bnet = mk_bnet(dag, ns, 'observed', onodes);
wolffd@0 25 % use random params
wolffd@0 26 %for i=1:N
wolffd@0 27 % bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0 28 %end
wolffd@0 29 bnet.CPD{1} = tabular_CPD(bnet, 1, 'sparse', 1, 'CPT', [0.8, 0.2]);
wolffd@0 30 bnet.CPD{2} = tabular_CPD(bnet, 2, 'sparse', 1, 'CPT', [1 0 0 1]);
wolffd@0 31 bnet.CPD{3} = tabular_CPD(bnet, 3, 'sparse', 1, 'CPT', [0 1 1 0]);
wolffd@0 32 bnet.CPD{4} = tabular_CPD(bnet, 4, 'sparse', 1, 'CPT', [1 1 0 0]);
wolffd@0 33 bnet.CPD{5} = tabular_CPD(bnet, 5, 'sparse', 1, 'CPT', [0 0 1 1]);
wolffd@0 34 bnet.CPD{6} = tabular_CPD(bnet, 6, 'sparse', 1, 'CPT', [1 0 0 1]);
wolffd@0 35 bnet.CPD{7} = tabular_CPD(bnet, 7, 'sparse', 1, 'CPT', [0 1 1 0]);
wolffd@0 36 bnet.CPD{8} = tabular_CPD(bnet, 8, 'sparse', 1, 'CPT', [1 1 0 0 0 0 1 1]);
wolffd@0 37 bnet.CPD{9} = tabular_CPD(bnet, 9, 'sparse', 1, 'CPT', [0 1 0 1 1 0 1 0]);
wolffd@0 38
wolffd@0 39 engine = jtree_sparse_inf_engine(bnet);
wolffd@0 40 tic
wolffd@0 41 [engine, ll] = enter_evidence(engine, evidence);
wolffd@0 42 toc
wolffd@0 43