comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/kjaerulff1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% Compare the speeds of various inference engines on the DBN in Kjaerulff
+% "dHugin: A computational system for dynamic time-sliced {B}ayesian networks",
+% Intl. J. Forecasting 11:89-111, 1995.
+%
+% The intra structure is (all arcs point downwards)
+%
+%  1 -> 2
+%   \  /
+%     3
+%     |
+%     4
+%    / \
+%   5   6
+%   \  /
+%     7
+%     |
+%     8
+%
+% The inter structure is 1->1, 4->4, 8->8
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+ss = 8;
+intra = zeros(ss);
+intra(1,[2 3])=1;
+intra(2,3)=1;
+intra(3,4)=1;
+intra(4,[5 6])=1;
+intra([5 6], 7)=1;
+intra(7,8)=1;
+
+inter = zeros(ss);
+inter(1,1)=1;
+inter(4,4)=1;
+inter(8,8)=1;
+
+ns = 2*ones(1,ss);
+onodes = 2;
+bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'eclass2', (1:ss)+ss);
+for i=1:2*ss
+  bnet.CPD{i} = tabular_CPD(bnet, i);
+end
+
+T = 4;
+
+engine = {};
+engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
+engine{end+1} = jtree_dbn_inf_engine(bnet);
+engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet));
+%engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); % observed nodes have children
+
+inf_time = cmp_inference_dbn(bnet, engine, T)
+learning_time = cmp_learning_dbn(bnet, engine, T)