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1 function bnet = mk_water_dbn(discrete_obs, obs_leaves)
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2 % MK_WATER_DBN
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3 % bnet = mk_water_dbn(discrete_obs, obs_leaves)
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4 %
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5 % If discrete_obs = 1 (default), the leaves are binary, else scalar Gaussians
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6 % If obs_leaves = 1, all the leaves are observed, otherwise rnd nodes are observed
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7 %
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8 % This is a model of the biological processes of a water purification plant, developed
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9 % by Finn V. Jensen, Uffe Kjærulff, Kristian G. Olesen, and Jan Pedersen.
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10 % See http://www-nt.cs.berkeley.edu/home/nir/public_html/Repository/water.htm
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11 % See also Boyen and Koller, "Tractable Inference for Complex Stochastic Processes", UAI98
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12
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13 if nargin < 1, discrete_obs = 1; end
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14 if nargin < 1, obs_leaves = 1; end
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15
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16 ss = 12;
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17 intra = zeros(ss);
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18 intra(1,9) = 1;
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19 intra(3,10) = 1;
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20 intra(4,11) = 1;
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21 intra(8,12) = 1;
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22
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23 inter = zeros(ss);
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24 inter(1, [1 3]) = 1;
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25 inter(2, [2 3 7]) = 1;
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26 inter(3, [3 4 5]) = 1;
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27 inter(4, [3 4 6]) = 1;
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28 inter(5, [3 5 6]) = 1;
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29 inter(6, [4 5 6]) = 1;
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30 inter(7, [7 8]) = 1;
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31 inter(8, [6 7 8]) = 1;
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32
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33 if obs_leaves
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34 onodes = 9:12; % leaves
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35 else
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36 onodes = [1 5 9:12]; % throw in some other nodes
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37 end
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38 hnodes = 1:8;
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39 if discrete_obs
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40 ns = 2*ones(1 ,ss);
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41 dnodes = 1:ss;
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42 else
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43 ns = [2*ones(1,length(hnodes)) 1*ones(length(onodes))];
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44 dnodes = hnodes;
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45 end
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46
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47 eclass1 = 1:12;
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48 eclass2 = [13:20 9:12];
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49 bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ...
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50 'observed', onodes);
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51 if discrete_obs
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52 for i=1:max(eclass2)
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53 bnet.CPD{i} = tabular_CPD(bnet, i);
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54 end
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55 else
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56 for i=hnodes(:)'
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57 bnet.CPD{i} = tabular_CPD(bnet, i);
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58 end
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59 for i=onodes(:)'
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60 bnet.CPD{i} = gaussian_CPD(bnet, i);
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61 end
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62 for i=hnodes(:)'+ss
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63 bnet.CPD{i} = tabular_CPD(bnet, i);
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64 end
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65 end
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66
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67
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68
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