diff toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/cooper_yoo.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/StructLearn/cooper_yoo.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,65 @@
+% Do the example in Cooper and Yoo, "Causal discovery from a mixture of experimental and
+% observational data", UAI 99, p120
+
+N = 2;
+dag = zeros(N);
+A = 1; B = 2;
+dag(A,B) = 1;
+ns = 2*ones(1,N);
+
+bnet0 = mk_bnet(dag, ns);
+%bnet0.CPD{A} = tabular_CPD(bnet0, A, 'unif', 1);
+bnet0.CPD{A} = tabular_CPD(bnet0, A, 'CPT', 'unif', 'prior_type', 'dirichlet');
+bnet0.CPD{B} = tabular_CPD(bnet0, B, 'CPT', 'unif', 'prior_type', 'dirichlet');
+
+samples = [2 2;
+	   2 1; 
+	   2 2;
+	   1 1;
+	   1 2;
+	   2 2;
+	   1 1;
+	   2 2;
+	   1 2;
+	   2 1;
+	   1 1];
+
+clamped = [0 0;
+	   0 0;
+	   0 0;
+	   0 0;
+	   0 0;
+	   1 0;
+	   1 0;
+	   0 1;
+	   0 1;
+	   0 1;
+	   0 1];
+
+nsamples = size(samples, 1);
+
+% sequential version
+LL = 0;
+bnet = bnet0;
+for l=1:nsamples
+  ev = num2cell(samples(l,:)');
+  manip = find(clamped(l,:)');
+  LL = LL + log_marg_lik_complete(bnet, ev, manip);
+  bnet = bayes_update_params(bnet, ev, manip);
+end
+assert(approxeq(exp(LL), 5.97e-7)) % compare with result from UAI paper
+
+
+% batch version
+cases = num2cell(samples');
+LL2 = log_marg_lik_complete(bnet0, cases, clamped');
+bnet2 = bayes_update_params(bnet0, cases, clamped');
+
+assert(approxeq(LL, LL2))
+
+for j=1:N
+  s1 = struct(bnet.CPD{j}); % violate object privacy
+  s2 = struct(bnet2.CPD{j});
+  assert(approxeq(s1.CPT, s2.CPT))
+end
+