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

first hg version after svn

+% Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17)
+%    1
+%  / | \
+% 2  3  4
+% |  |  |
+% 5  6  7
+%  \/ \/
+%  8   9
+% where all arcs point downwards
+
+N = 9;
+dag = zeros(N,N);
+dag(1,2)=1; dag(1,3)=1; dag(1,4)=1;
+dag(2,5)=1; dag(3,6)=1; dag(4,7)=1;
+dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1;
+
+dnodes = 1:N;
+false = 1; true = 2;
+ns = 2*ones(1,N); % binary nodes
+
+onodes = [1];
+evidence = cell(1,N);
+evidence(onodes) = num2cell(1);
+bnet = mk_bnet(dag, ns, 'observed', onodes);
+% use random params
+%for i=1:N
+%  bnet.CPD{i} = tabular_CPD(bnet, i);
+%end
+bnet.CPD{1} = tabular_CPD(bnet, 1, 'sparse', 1, 'CPT', [0.8, 0.2]);
+bnet.CPD{2} = tabular_CPD(bnet, 2, 'sparse', 1, 'CPT', [1 0 0 1]);
+bnet.CPD{3} = tabular_CPD(bnet, 3, 'sparse', 1, 'CPT', [0 1 1 0]);
+bnet.CPD{4} = tabular_CPD(bnet, 4, 'sparse', 1, 'CPT', [1 1 0 0]);
+bnet.CPD{5} = tabular_CPD(bnet, 5, 'sparse', 1, 'CPT', [0 0 1 1]);
+bnet.CPD{6} = tabular_CPD(bnet, 6, 'sparse', 1, 'CPT', [1 0 0 1]);
+bnet.CPD{7} = tabular_CPD(bnet, 7, 'sparse', 1, 'CPT', [0 1 1 0]);
+bnet.CPD{8} = tabular_CPD(bnet, 8, 'sparse', 1, 'CPT', [1 1 0 0 0 0 1 1]);
+bnet.CPD{9} = tabular_CPD(bnet, 9, 'sparse', 1, 'CPT', [0 1 0 1 1 0 1 0]);
+
+engine = jtree_sparse_inf_engine(bnet);
+tic
+[engine, ll] = enter_evidence(engine, evidence);
+toc
+