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

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_minimal_qmr_bnet.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,82 @@
+function [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, leak, prior, pos, neg, pos_only)
+% MK_MINIMAL_QMR_BNET Make a QMR model which only contains the observed findings
+% [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, prior, leak, pos, neg)
+%
+% Input:
+% G(i,j) = 1 iff there is an arc from disease i to finding j
+% inhibit(i,j) = inhibition probability on i->j arc
+% leak(j) = inhibition prob. on leak->j arc
+% prior(i) = prob. disease i is on
+% pos = list of leaves that have positive observations
+% neg = list of leaves that have negative observations
+% pos_only = 1 means only include positively observed leaves in the model - the negative
+%   ones are absorbed into the prior terms
+%
+% Output:
+% bnet
+% vals is their value
+
+if pos_only
+  obs = pos;
+else
+  obs = myunion(pos, neg);
+end
+Nfindings = length(obs);
+[Ndiseases maxNfindings] = size(inhibit);
+N = Ndiseases + Nfindings;
+finding_node = Ndiseases+1:N;
+
+% j = finding_node(i) means the i'th finding node is the j'th node in the bnet
+% k = obs(i) means the i'th observed (positive) finding is the k'th finding overall
+% If all findings are observed, and posonly = 0, we have i = obs(i) for all i.
+
+%dag = sparse(N, N);
+dag = zeros(N, N);
+dag(1:Ndiseases, Ndiseases+1:N) = G(:,obs);
+
+ns = 2*ones(1,N);
+bnet = mk_bnet(dag, ns, 'observed', finding_node);
+
+CPT = cell(1, Ndiseases);
+for d=1:Ndiseases
+  CPT{d} = [1-prior(d) prior(d)];
+end
+
+if pos_only
+  % Fold in the negative evidence into the prior
+  for i=1:length(neg)
+    n = neg(i);
+    ps = parents(G,n);
+    for pi=1:length(ps)
+      p = ps(pi);
+      q = inhibit(p,n);
+      CPT{p} = CPT{p} .* [1 q];
+    end
+    % Arbitrarily attach the leak term to the first parent
+    p = ps(1);
+    q = leak(n);
+    CPT{p} = CPT{p} .* [q q];
+  end
+end
+
+for d=1:Ndiseases
+  bnet.CPD{d} = tabular_CPD(bnet, d, CPT{d}');
+end
+
+for i=1:Nfindings
+  fnode = finding_node(i);
+  fid = obs(i);
+  ps = parents(G, fid);
+  bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(fid), inhibit(ps, fid));
+end
+
+obs_nodes = finding_node;
+vals = sparse(1, maxNfindings);
+vals(pos) = 2;
+vals(neg) = 1;
+vals = full(vals(obs));
+
+
+
+
+