BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 153 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/limids/pigs1.m b/sourcecodes/bnt-master/BNT/examples/limids/pigs1.m
new file mode 100644
index 00000000..6a56ad04
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/limids/pigs1.m
@@ -0,0 +1,153 @@
+% pigs model from Lauritzen and Nilsson, 2001
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+% we number nodes down and to the right
+h = [1 5 9 13];
+t = [2 6 10];
+d = [3 7 11];
+u = [4 8 12 14];
+
+N = 14;
+dag = zeros(N);
+
+% causal arcs
+for i=1:3
+  dag(h(i), [t(i) h(i+1)]) = 1;
+  dag(d(i), [u(i) h(i+1)]) = 1;
+end
+dag(h(4), u(4)) = 1;
+
+% information arcs
+fig = 2;
+switch fig
+ case 0,
+  % no info arcs
+ case 1,
+   % no-forgetting policy (figure 1)
+   for i=1:3
+     dag(t(i), d(i:3)) = 1;
+   end
+ case 2,
+  % reactive policy (figure 2)
+  for i=1:3
+    dag(t(i), d(i)) = 1;
+  end
+ case 7,
+  % omniscient policy (figure 7: di has access to hidden state h(i-1))
+  dag(t(1), d(1)) = 1;
+  for i=2:3
+    %dag([h(i-1) t(i-1) d(i-1)], d(i)) = 1;
+    dag([h(i-1) d(i-1)], d(i)) = 1; % t(i-1) is redundant given h(i-1)
+  end
+end
+
+
+ns = 2*ones(1,N);
+ns(u) = 1;
+
+% parameter tying
+params = ones(1,N);
+uparam = 1;
+final_uparam = 2;
+tparam = 3;
+h1_param = 4;
+hparam = 5;
+dparams = 6:8;
+
+params(u(1:3)) = uparam;
+params(u(4)) = final_uparam;
+params(t) = tparam;
+params(h(1)) = h1_param;
+params(h(2:end)) = hparam;
+params(d) = dparams;
+
+limid = mk_limid(dag, ns, 'chance', [h t], 'decision', d, 'utility', u, 'equiv_class', params);
+
+% h = 1 means healthy, h = 2 means diseased
+% d = 1 means don't treat, d = 2 means treat
+% t = 1 means test shows healthy, t = 2 means test shows diseased
+
+if 0
+  % use random params
+  limid.CPD{final_uparam} = tabular_utility_node(limid, u(4));
+  limid.CPD{uparam} = tabular_utility_node(limid, u(1));
+  limid.CPD{tparam} = tabular_CPD(limid, t(1));
+  limid.CPD{h1_param} = tabular_CPD(limid, h(1));
+  limid.CPD{hparam} = tabular_CPD(limid, h(2));
+else
+  limid.CPD{final_uparam} = tabular_utility_node(limid, u(4), [1000 300]);
+  limid.CPD{uparam} = tabular_utility_node(limid, u(1), [0 -100]); % costs have negative utility!
+  
+  % h  P(t=1) P(t=2)
+  % 1  0.9   0.1
+  % 2  0.2   0.8
+  limid.CPD{tparam} = tabular_CPD(limid, t(1), [0.9 0.2 0.1 0.8]);
+  
+  % P(h1)
+  limid.CPD{h1_param} = tabular_CPD(limid, h(1), [0.9 0.1]);
+  
+  % hi di P(hj=1) P(hj=2),  j = i+1, i=1:3
+  % 1  1  0.8     0.2
+  % 2  1  0.1     0.9
+  % 1  2  0.9     0.1
+  % 2  2  0.5     0.5
+  limid.CPD{hparam} = tabular_CPD(limid, h(2), [0.8 0.1 0.9 0.5 0.2 0.9 0.1 0.5]);
+end
+
+% Decision nodes get assigned uniform policies by default
+for i=1:3
+  limid.CPD{dparams(i)} = tabular_decision_node(limid, d(i));
+end
+
+
+fname = '/home/cs/murphyk/matlab/Misc/loopybel.txt';
+
+engines = {};
+engines{end+1} = global_joint_inf_engine(limid);
+engines{end+1} = jtree_limid_inf_engine(limid);
+%engines{end+1} = belprop_inf_engine(limid, 'max_iter', 1*N, 'filename', fname, 'tol', 1e-3);
+
+exact = [1 2];
+%approx = 3;
+approx = [];
+
+max_iter = 1;
+order = d(end:-1:1);
+%order = d(1:end);
+
+NE = length(engines);
+MEU = zeros(1, NE);
+niter = zeros(1, NE);
+strategy = cell(1, NE);
+for e=1:NE
+  [strategy{e}, MEU(e), niter(e)] = solve_limid(engines{e}, 'max_iter', max_iter, 'order',  order);
+end
+MEU
+
+% check results match those in the paper (p. 22)
+direct_policy = eye(2); % treat iff test is positive
+never_policy = [1 0; 1 0]; % never treat
+tol = 1e-0; % results in paper are reported to 0dp
+for e=exact(:)'
+  switch fig
+   case 2, % reactive policy
+    assert(approxeq(MEU(e), 727, tol));
+    assert(approxeq(strategy{e}{d(1)}(:), never_policy(:)))
+    assert(approxeq(strategy{e}{d(2)}(:), direct_policy(:)))
+    assert(approxeq(strategy{e}{d(3)}(:), direct_policy(:)))
+   case 1, assert(approxeq(MEU(e), 729, tol));
+   case 7, assert(approxeq(MEU(e), 732, tol));
+  end
+end
+
+
+for e=approx(:)'
+  for i=1:3
+    approxeq(strategy{exact(1)}{d(i)}, strategy{e}{d(i)})
+    dispcpt(strategy{e}{d(i)})
+  end
+end
+