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

13 files changed, 431 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Entries
new file mode 100644
index 00000000..6550caa0
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Entries
@@ -0,0 +1,11 @@
+/belprop_loop1_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/belprop_loop1_gauss.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/belprop_loopy_cg.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/belprop_loopy_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/belprop_loopy_gauss.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/belprop_polytree_cg.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/belprop_polytree_discrete.m/1.1.1.1/Tue Oct  1 18:21:26 2002//
+/belprop_polytree_gauss.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/bp1.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/gmux1.m/1.1.1.1/Wed May 29 15:59:54 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Repository
new file mode 100644
index 00000000..f3d573bd
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/static/Belprop
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Root b/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_discrete.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_discrete.m
new file mode 100644
index 00000000..20faed5e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_discrete.m
@@ -0,0 +1,26 @@
+% Compare different loopy belief propagation algorithms on a graph with a single loop.
+% LBP should give exact results if it converges.
+
+N = 4;
+dag = zeros(N,N);
+C = 1; S = 2; R = 3; W = 4;
+dag(C,[R S]) = 1;
+dag(R,W) = 1;
+dag(S,W)=1;
+ns = 2*ones(1,N); 
+bnet = mk_bnet(dag, ns);
+for i=1:N
+  bnet.CPD{i} = tabular_CPD(bnet, i);
+end
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+engines{end+1} = belprop_fg_inf_engine(bnet_to_fgraph(bnet));
+engines{end+1} = belprop_inf_engine(bnet, 'protocol', 'parallel');
+
+% belprop_fg does not support marginal_family
+% belprop_fg and belprop do not support loglik even on discrete
+[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', 2, ...
+				   'check_ll', 0, 'singletons_only', 1, 'check_converged', 2:4);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_gauss.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_gauss.m
new file mode 100644
index 00000000..e547e44a
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loop1_gauss.m
@@ -0,0 +1,25 @@
+% Compare different loopy belief propagation algorithms on a graph with a single loop.
+% LBP should give exact results if it converges.
+
+N = 4;
+dag = zeros(N,N);
+C = 1; S = 2; R = 3; W = 4;
+dag(C,[R S]) = 1;
+dag(R,W) = 1;
+dag(S,W)=1;
+ns = 2*ones(1,N); 
+bnet = mk_bnet(dag, ns, 'discrete', []);
+for i=1:N
+  bnet.CPD{i} = gaussian_CPD(bnet, i);
+end
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel', 'max_iter', 20);
+%engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel', 'max_iter', 20, 'filename', ...
+%				  '/home/eecs/murphyk/matlab/gausspearl.txt', 'tol', 1e-5);
+
+% pearl gaussian does not compute loglik
+[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', [2], ...
+				   'check_ll', 0, 'singletons_only', 0, 'check_converged', [2]);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_cg.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_cg.m
new file mode 100644
index 00000000..01f36b03
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_cg.m
@@ -0,0 +1,22 @@
+% Same as cg1, except we assume all discretes are observed,
+% and use loopy for approximate inference.
+
+ns = 2*ones(1,9);
+F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9;
+n = 9;
+dnodes = [B F W];
+cnodes = mysetdiff(1:n, dnodes);
+
+%bnet  = mk_incinerator_bnet(ns);
+bnet  = mk_incinerator_bnet;
+
+bnet.observed = [dnodes E];
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+nengines = length(engines);
+
+
+[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'check_ll', 0, ...
+				      'singletons_only', 0, 'exact', 1, 'check_converged', 2);
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_discrete.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_discrete.m
new file mode 100644
index 00000000..c46e6d02
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_discrete.m
@@ -0,0 +1,13 @@
+% Compare different loopy belief propagation algorithms on a graph with many loops
+
+bnet = mk_asia_bnet('orig');
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+engines{end+1} = belprop_fg_inf_engine(bnet_to_fgraph(bnet));
+engines{end+1} = belprop_inf_engine(bnet, 'protocol', 'parallel');
+
+[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', [1 3 5], ...
+				   'check_ll', 0, 'singletons_only', 1, 'check_converged', 2:4);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_gauss.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_gauss.m
new file mode 100644
index 00000000..a9924aed
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_loopy_gauss.m
@@ -0,0 +1,12 @@
+% Compare different loopy belief propagation algorithms on a graph with many loops
+% If LBP converges, the means should be exact
+
+bnet = mk_asia_bnet('gauss');
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+
+[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', [1 3 5], ...
+				   'check_ll', 0, 'singletons_only', 0, 'check_converged', 2);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_cg.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_cg.m
new file mode 100644
index 00000000..70aa03da
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_cg.m
@@ -0,0 +1,33 @@
+% Inference on a conditional Gaussian model
+
+% Make the following polytree, where all arcs point down
+
+% 1   2
+%  \ /
+%   3
+%  / \
+% 4   5
+
+N = 5;
+dag = zeros(N,N);
+dag(1,3) = 1;
+dag(2,3) = 1;
+dag(3, [4 5]) = 1;
+
+ns = [2 1 2 1 2];
+
+dnodes = 1;
+%onodes = [1 5];
+bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', dnodes);
+
+bnet.CPD{1} = tabular_CPD(bnet, 1);
+for i=2:N
+  bnet.CPD{i} = gaussian_CPD(bnet, i);
+end
+
+engine = {};
+engine{end+1} = jtree_inf_engine(bnet);
+engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+
+[time, engine] = cmp_inference_static(bnet, engine, 'maximize', 0, 'check_ll', 0, ...
+				      'singletons_only', 0, 'observed', [1 3]);
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_discrete.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_discrete.m
new file mode 100644
index 00000000..d8a3a229
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_discrete.m
@@ -0,0 +1,38 @@
+% Make the following polytree, where all arcs point down
+
+% 1   2
+%  \ /
+%   3
+%  / \
+% 4   5
+
+N = 5;
+dag = zeros(N,N);
+dag(1,3) = 1;
+dag(2,3) = 1;
+dag(3, [4 5]) = 1;
+
+ns = 2*ones(1,N); % binary nodes
+
+onodes = [1 5];
+
+bnet = mk_bnet(dag, ns, 'observed', onodes);
+
+if 0
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+end
+
+for i=1:N
+  %bnet.CPD{i} = tabular_CPD(bnet, i);
+  bnet.CPD{i} = noisyor_CPD(bnet, i);
+end
+
+engine = {};
+engine{end+1} = jtree_inf_engine(bnet);
+engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'tree');
+engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+
+[err, time] = cmp_inference_static(bnet, engine, 'maximize', 0, 'check_ll', 1);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_gauss.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_gauss.m
new file mode 100644
index 00000000..1823c107
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/belprop_polytree_gauss.m
@@ -0,0 +1,135 @@
+% Do the example from Satnam Alag's PhD thesis, UCB ME dept 1996 p46
+
+% Make the following polytree, where all arcs point down
+
+% 1   2
+%  \ /
+%   3
+%  / \
+% 4   5
+
+N = 5;
+dag = zeros(N,N);
+dag(1,3) = 1;
+dag(2,3) = 1;
+dag(3, [4 5]) = 1;
+
+ns = [2 1 2 1 2];
+
+bnet = mk_bnet(dag, ns, 'discrete', []);
+
+bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', [1 0]', 'cov', [4 1; 1 4]);
+bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', 1, 'cov', 1);
+B1 = [1 2; 1 0]; B2 = [2 1]';
+bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', [0 0]', 'cov', [2 1; 1 1], ...
+			   'weights', [B1 B2]);
+H1 = [1 1];
+bnet.CPD{4} = gaussian_CPD(bnet, 4, 'mean', 0, 'cov', 1, 'weights', H1);
+H2 = [1 0; 1 1];
+bnet.CPD{5} = gaussian_CPD(bnet, 5, 'mean', [0 0]', 'cov', eye(2), 'weights', H2);
+
+engine = {};
+engine{end+1} = jtree_inf_engine(bnet);
+engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'tree');
+engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+E = length(engine);
+
+if 1
+% no evidence
+evidence = cell(1,N);
+ll = zeros(1,E);
+for e=1:E
+  [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence);
+  add_ev = 1;
+  m = marginal_nodes(engine{e}, 3, add_ev);
+  assert(approxeq(m.mu, [3 2]'))
+  assert(approxeq(m.Sigma, [30 9; 9 6]))
+
+  m = marginal_nodes(engine{e}, 4, add_ev);
+  assert(approxeq(m.mu, 5))
+  assert(approxeq(m.Sigma, 55))
+
+  m = marginal_nodes(engine{e}, 5, add_ev);
+  assert(approxeq(m.mu, [3 5]'))
+  assert(approxeq(m.Sigma, [31 39; 39 55]))
+end
+end
+
+if 1
+% evidence on leaf 5
+evidence = cell(1,N);
+evidence{5} = [5 5]';
+for e=1:E
+  [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence);
+  add_ev = 1;
+  m = marginal_nodes(engine{e}, 3, add_ev);
+  assert(approxeq(m.mu, [4.4022 1.0217]'))
+  assert(approxeq(m.Sigma, [0.7011 -0.4891; -0.4891 1.1087]))
+
+  m = marginal_nodes(engine{e}, 4, add_ev);
+  assert(approxeq(m.mu, 5.4239))
+  assert(approxeq(m.Sigma, 1.8315))
+
+  m = marginal_nodes(engine{e}, 1, add_ev);
+  assert(approxeq(m.mu, [0.3478 1.1413]'))
+  assert(approxeq(m.Sigma, [1.8261 -0.1957; -0.1957 1.0924]))
+
+  m = marginal_nodes(engine{e}, 2, add_ev);
+  assert(approxeq(m.mu, 0.9239))
+  assert(approxeq(m.Sigma, 0.8315))
+
+  m = marginal_nodes(engine{e}, 5, add_ev);
+  assert(approxeq(m.mu, evidence{5}))
+  assert(approxeq(m.Sigma, zeros(2)))
+end
+end
+
+if 1
+% evidence on leaf 4 (non-info-state version is uninvertible)
+evidence = cell(1,N);
+evidence{4} = 10;
+for e=1:E
+  [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence);
+  add_ev = 1;
+  m = marginal_nodes(engine{e}, 3, add_ev);
+  assert(approxeq(m.mu, [6.5455 3.3636]'))
+  assert(approxeq(m.Sigma, [2.3455 -1.6364; -1.6364 1.9091]))
+
+  m = marginal_nodes(engine{e}, 5, add_ev);
+  assert(approxeq(m.mu, [6.5455 9.9091]'))
+  assert(approxeq(m.Sigma, [3.3455 0.7091; 0.7091 1.9818]))
+
+  m = marginal_nodes(engine{e}, 1, add_ev);
+  assert(approxeq(m.mu, [1.9091 0.9091]'))
+  assert(approxeq(m.Sigma, [2.1818 -0.8182; -0.8182 2.1818]))
+
+  m = marginal_nodes(engine{e}, 2, add_ev);
+  assert(approxeq(m.mu, 1.2727))
+  assert(approxeq(m.Sigma, 0.8364))
+end
+end
+
+
+if 1
+% evidence on leaves 4,5 and root 2
+evidence = cell(1,N);
+evidence{2} = 0;
+evidence{4} = 10;
+evidence{5} = [5 5]';
+for e=1:E
+  [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence);
+  add_ev = 1;
+  m = marginal_nodes(engine{e}, 3, add_ev);
+  assert(approxeq(m.mu, [4.9964 2.4444]'));
+  assert(approxeq(m.Sigma, [0.6738 -0.5556; -0.5556 0.8889]));
+
+  m = marginal_nodes(engine{e}, 1, add_ev);
+  assert(approxeq(m.mu, [2.2043 1.2151]'));
+  assert(approxeq(m.Sigma, [1.2903 -0.4839; -0.4839 0.8065]));
+end
+end
+
+if 1
+  [time, engine] = cmp_inference_static(bnet, engine, 'maximize', 0, 'check_ll', 0, ...
+				     'singletons_only', 0, 'observed', [1 3 5]);
+end
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/bp1.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/bp1.m
new file mode 100644
index 00000000..93cba443
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/bp1.m
@@ -0,0 +1,24 @@
+% Compare different loopy belief propagation algorithms on a graph with a single loop.
+% LBP should give exact results if it converges.
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+N = 2;
+dag = zeros(N,N);
+dag(1,2)=1;
+ns = ones(1,N); 
+bnet = mk_bnet(dag, ns, 'discrete', []);
+for i=1:N
+  %bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', 0);
+  bnet.CPD{i} = gaussian_CPD(bnet, i);
+end
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'tree');
+
+[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1:2, 'observed', [2], ...
+				   'check_ll', 0, 'singletons_only', 1, 'check_converged', []);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Belprop/gmux1.m b/sourcecodes/bnt-master/BNT/examples/static/Belprop/gmux1.m
new file mode 100644
index 00000000..21846b68
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Belprop/gmux1.m
@@ -0,0 +1,90 @@
+% Test gmux.
+% The following model, where Y is a gmux node,
+% and M is set to 1, should be equivalent to X1 -> Y
+%
+% X1 Xn M
+% \ |  /
+%   Y
+
+n = 3;
+N = n+2;
+Xs = 1:n;
+M = n+1; 
+Y = n+2;
+dag = zeros(N,N);
+dag([Xs M], Y)=1; 
+
+dnodes = M;
+ns = zeros(1, N);
+sz = 2;
+ns(Xs) = sz;
+ns(M) = n;
+ns(Y) = sz;
+
+bnet = mk_bnet(dag, ns, 'discrete', M, 'observed', [M Y]);
+
+psz = ns(Xs(1));
+selfsz = ns(Y);
+
+W = randn(selfsz, psz);
+mu = randn(selfsz, 1);
+Sigma = eye(selfsz, selfsz);
+
+bnet.CPD{M} = root_CPD(bnet, M);
+for i=Xs(:)'
+  bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', zeros(psz, 1), 'cov', eye(psz, psz));
+end
+bnet.CPD{Y} = gmux_CPD(bnet, Y, 'mean', mu, 'weights', W, 'cov', Sigma);
+  
+evidence = cell(1,N);
+yval = randn(selfsz, 1);
+evidence{Y} = yval;
+m = 2;
+%notm = not(m-1)+1; % only valid for n=2
+notm = mysetdiff(1:n, m);
+evidence{M} = m;
+
+engines = {};
+engines{end+1} = jtree_inf_engine(bnet);
+engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel');
+
+for e=1:length(engines)
+  engines{e} = enter_evidence(engines{e}, evidence);
+  mXm{e} = marginal_nodes(engines{e}, Xs(m));
+
+  % Since M=m, only Xm was updated.
+  % Hence the posterior on Xnotm should equal the prior.
+  for i=notm(:)'
+    mXnotm = marginal_nodes(engines{e}, Xs(i));
+    assert(approxeq(mXnotm.mu, zeros(psz,1)))
+    assert(approxeq(mXnotm.Sigma, eye(psz, psz)))
+  end
+end
+
+% Check that all engines give the same posterior
+for e=2:length(engines)
+  assert(approxeq(mXm{e}.mu, mXm{1}.mu))
+  assert(approxeq(mXm{e}.Sigma, mXm{1}.Sigma))
+end
+
+
+% Compute the correct posterior by building Xm -> Y
+
+N = 2;
+dag = zeros(N,N);
+dag(1, 2)=1;
+ns = [psz selfsz];
+bnet = mk_bnet(dag, ns, 'discrete', [], 'observed', 2);
+
+bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', zeros(psz, 1), 'cov', eye(psz, psz));
+bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu, 'cov', Sigma, 'weights', W);
+
+jengine  = jtree_inf_engine(bnet);
+evidence = {[], yval};
+jengine = enter_evidence(jengine, evidence); % apply Bayes rule to invert the arc
+mX = marginal_nodes(jengine, 1);
+
+for e=1:length(engines)
+  assert(approxeq(mX.mu, mXm{e}.mu))
+  assert(approxeq(mX.Sigma, mXm{e}.Sigma))
+end