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

first hg version after svn

+function bnet = mk_cancer_bnet(CPD_type, p)
+% MK_CANCER_BNET Make the 'Cancer' Bayes net.
+%
+% BNET = MK_CANCER_BNET uses the noisy-or parameters specified in Fig 4a of the UAI98 paper by
+% Friedman, Murphy and Russell, "Learning the Structure of DPNs", p145.
+%
+% BNET = MK_CANCER_BNET('noisyor', p) makes each CPD a noisy-or, with probability p of
+% suppression for each parent; leaks are turned off.
+%
+% BNET = MK_CANCER_BNET('cpt', p) uses random CPT parameters drawn from a Dirichlet(p,p,...)
+% distribution. If p << 1, this is near deterministic; if p >> 1, this is near 1/k.
+% p defaults to 1.0 (uniform distribution).
+%
+% BNET = MK_CANCER_BNET('bool') makes each CPT a random boolean function.
+%
+% In all cases, the root is set to a uniform distribution.
+
+if nargin == 0
+  rnd = 0;
+else
+  rnd = 1;
+end
+
+n = 5;
+dag = zeros(n);
+dag(1,[2 3]) = 1;
+dag(2,4) = 1;
+dag(3,4) = 1;
+dag(4,5) = 1;
+
+ns = 2*ones(1,n);
+bnet = mk_bnet(dag, ns);
+    
+if ~rnd
+  bnet.CPD{1} = tabular_CPD(bnet, 1, [0.5 0.5]);
+  bnet.CPD{2} = noisyor_CPD(bnet, 2, 1.0, 1-0.9);
+  bnet.CPD{3} = noisyor_CPD(bnet, 3, 1.0, 1-0.2);
+  bnet.CPD{4} = noisyor_CPD(bnet, 4, 1.0, 1-[0.7 0.6]);
+  bnet.CPD{5} = noisyor_CPD(bnet, 5, 1.0, 1-0.5);
+else
+  switch CPD_type
+   case 'noisyor',
+    for i=1:n
+      ps = parents(dag, i);
+      bnet.CPD{i} = noisyor_CPD(bnet, i, 1.0, p*ones(1,length(ps)));
+    end
+   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(['bad CPD type ' CPD_type]);
+  end
+end
+  
+  
+