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annotate toolboxes/FullBNT-1.0.7/bnt/examples/static/gaussian2.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 % Make 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
wolffd@0 12 N = 9;
wolffd@0 13 dag = zeros(N,N);
wolffd@0 14 dag(1,2)=1; dag(1,3)=1; dag(1,4)=1;
wolffd@0 15 dag(2,5)=1; dag(3,6)=1; dag(4,7)=1;
wolffd@0 16 dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1;
wolffd@0 17
wolffd@0 18 ns = [5 4 3 2 2 1 2 2 2]; % vector-valued nodes
wolffd@0 19 %ns = ones(1,9); % scalar nodes
wolffd@0 20 dnodes = [];
wolffd@0 21
wolffd@0 22 bnet = mk_bnet(dag, ns, 'discrete', []);
wolffd@0 23 rand('state', 0);
wolffd@0 24 randn('state', 0);
wolffd@0 25 for i=1:N
wolffd@0 26 bnet.CPD{i} = gaussian_CPD(bnet, i);
wolffd@0 27 end
wolffd@0 28
wolffd@0 29 clear engine;
wolffd@0 30 engine{1} = gaussian_inf_engine(bnet);
wolffd@0 31 engine{2} = jtree_inf_engine(bnet);
wolffd@0 32
wolffd@0 33 [err, time] = cmp_inference_static(bnet, engine);
wolffd@0 34
wolffd@0 35 Nsamples = 100;
wolffd@0 36 samples = cell(N, Nsamples);
wolffd@0 37 for s=1:Nsamples
wolffd@0 38 samples(:,s) = sample_bnet(bnet);
wolffd@0 39 end
wolffd@0 40 bnet2 = learn_params(bnet, samples);