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

first hg version after svn

+function bnet = mk_ideker_bnet(CPD_type, p)
+% MK_IDEKER_BNET Make the Bayes net in the PSB'00 paper by Ideker, Thorsson and Karp.
+%
+% BNET = MK_IDEKER_BNET uses the boolean functions specified in the paper 
+% "Discovery of regulatory interactions through perturbation: inference and experimental design",
+% Pacific Symp. on Biocomputing, 2000.
+% 
+% BNET = MK_IDEKER_BNET('root') uses the above boolean functions, but puts a uniform
+% distribution on the root nodes.
+%
+% BNET = MK_IDEKER_BNET('cpt', p) uses random parameters drawn from a Dirichlet(p,p,...)
+% distribution. If p << 1, this is nearly deterministic; if p >> 1, this is nearly uniform.
+% 
+% BNET = MK_IDEKER_BNET('bool') makes each CPT a random boolean function.
+%
+% BNET = MK_IDEKER_BNET('orig') is the same as MK_IDEKER_BNET.
+
+
+if nargin == 0
+  CPD_type = 'orig';
+end
+
+n = 4;
+dag = zeros(n);
+dag(1,3)=1;
+dag(2,[3 4])=1;
+dag(3,4)=1;
+ns = 2*ones(1,n);
+bnet = mk_bnet(dag, ns);
+
+switch CPD_type
+ case 'orig',
+  bnet.CPD{1} = tabular_CPD(bnet, 1, [0 1]);
+  bnet.CPD{2} = tabular_CPD(bnet, 2, [0 1]);
+  bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
+  bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
+ case 'root',
+  bnet.CPD{1} = tabular_CPD(bnet, 1, [0.5 0.5]);
+  bnet.CPD{2} = tabular_CPD(bnet, 2, [0.5 0.5]);
+  bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)'));
+  bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)'));
+ case 'bool',
+  for i=1:n
+    bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd');
+  end
+ case 'cpt',
+  for i=1:n
+    bnet.CPD{i} = tabular_CPD(bnet, i, p);
+  end
+ otherwise,
+  error(['unknown type ' CPD_type]);
+end