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1 function bnet = mk_ideker_bnet(CPD_type, p)
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2 % MK_IDEKER_BNET Make the Bayes net in the PSB'00 paper by Ideker, Thorsson and Karp.
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3 %
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4 % BNET = MK_IDEKER_BNET uses the boolean functions specified in the paper
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5 % "Discovery of regulatory interactions through perturbation: inference and experimental design",
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6 % Pacific Symp. on Biocomputing, 2000.
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7 %
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8 % BNET = MK_IDEKER_BNET('root') uses the above boolean functions, but puts a uniform
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9 % distribution on the root nodes.
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10 %
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11 % BNET = MK_IDEKER_BNET('cpt', p) uses random parameters drawn from a Dirichlet(p,p,...)
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12 % distribution. If p << 1, this is nearly deterministic; if p >> 1, this is nearly uniform.
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13 %
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14 % BNET = MK_IDEKER_BNET('bool') makes each CPT a random boolean function.
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15 %
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16 % BNET = MK_IDEKER_BNET('orig') is the same as MK_IDEKER_BNET.
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17
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18
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19 if nargin == 0
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20 CPD_type = 'orig';
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21 end
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22
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23 n = 4;
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24 dag = zeros(n);
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25 dag(1,3)=1;
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26 dag(2,[3 4])=1;
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27 dag(3,4)=1;
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28 ns = 2*ones(1,n);
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29 bnet = mk_bnet(dag, ns);
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30
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31 switch CPD_type
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32 case 'orig',
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33 bnet.CPD{1} = tabular_CPD(bnet, 1, [0 1]);
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34 bnet.CPD{2} = tabular_CPD(bnet, 2, [0 1]);
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35 bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
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36 bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
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37 case 'root',
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38 bnet.CPD{1} = tabular_CPD(bnet, 1, [0.5 0.5]);
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39 bnet.CPD{2} = tabular_CPD(bnet, 2, [0.5 0.5]);
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40 bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
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41 bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
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42 case 'bool',
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43 for i=1:n
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44 bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd');
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45 end
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46 case 'cpt',
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47 for i=1:n
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48 bnet.CPD{i} = tabular_CPD(bnet, i, p);
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49 end
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50 otherwise,
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51 error(['unknown type ' CPD_type]);
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52 end
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