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, 150 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/jtree_clq_test.m b/sourcecodes/bnt-master/BNT/examples/dynamic/jtree_clq_test.m
new file mode 100644
index 00000000..647a2763
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+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/jtree_clq_test.m
@@ -0,0 +1,150 @@
+% Construct various DBNs and examine their clique structure.
+% This was used to generate various figures in chap 3-4 of my thesis.
+
+% Examine the cliques in the unrolled mildew net
+
+%dbn = mk_mildew_dbn;
+dbn = mk_chmm(4);
+ss = dbn.nnodes_per_slice;
+T = 7;
+N = ss*T;
+bnet = dbn_to_bnet(dbn, T);
+
+constrained = 0;
+if constrained
+  stages = num2cell(unroll_set(1:ss, ss, T), 1);
+else
+  stages = { 1:N; };
+end
+clusters = {};
+%[jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges] = ...
+%    dag_to_jtree(bnet, bnet.observed, stages, clusters);
+[jtree, root, cliques] =  graph_to_jtree(moralize(bnet.dag), ones(1,N), stages, clusters);
+
+flip=1;
+clf;[dummyx, dummyy, h] = draw_dbn(dbn.intra, dbn.inter, flip, T, -1);
+dir = '/home/eecs/murphyk/WP/Thesis/Figures/Inf/MildewUnrolled';
+mk_ps_from_clqs(dbn, T, cliques, [])
+%mk_collage_from_clqs(dir, cliques)
+
+
+% Examine the cliques in the cascade DBN
+
+% A-A
+%  \
+% B B
+%  \
+% C C
+%  \
+% D D
+ss = 4;
+intra = zeros(ss);
+inter = zeros(ss);
+inter(1, [1 2])=1;
+for i=2:ss-1
+  inter(i,i+1)=1;
+end
+
+
+% 2 coupled HMMs 1,3  and 2,4
+ss = 4;
+intra = zeros(ss);
+inter = zeros(ss); % no persistent edges
+%inter = diag(ones(ss,1)); % persitence edges
+inter(1,3)=1; inter(3,1)=1;
+inter(2,4)=1; inter(4,2)=1;
+
+%bnet = mk_fhmm(3);
+bnet = mk_chmm(4);
+intra = bnet.intra;
+inter = bnet.inter;
+
+clqs = compute_minimal_interface(intra, inter);
+celldisp(clqs)
+
+
+
+
+% A A
+%  \
+% B B
+%  \
+% C C
+%  \
+% D-D
+ss = 4;
+intra = zeros(ss);
+inter = zeros(ss);
+for i=1:ss-1
+  inter(i,i+1)=1;
+end
+inter(4,4)=1;
+
+
+
+ns = 2*ones(1,ss);
+dbn = mk_dbn(intra, inter, ns);
+for i=2*ss
+  dbn.CPD{i} = tabular_CPD(bnet, i);
+end
+
+T = 4;
+N = ss*T;
+bnet = dbn_to_bnet(dbn, T);
+
+constrained = 1;
+if constrained
+  % elim first 3 slices first in any order
+  stages = {1:12, 13:16};
+  %stages = num2cell(unroll_set(1:ss, ss, T), 1);
+else
+  stages = { 1:N; };
+end
+clusters = {};
+%[jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges] = ...
+%    dag_to_jtree(bnet, bnet.observed, stages, clusters);
+[jtree, root, cliques] =  graph_to_jtree(moralize(bnet.dag), ones(1,N), stages, clusters);
+
+
+
+
+
+% Examine the cliques in the 1.5 slice DBN
+
+%dbn = mk_mildew_dbn;
+dbn = mk_water_dbn;
+%dbn = mk_bat_dbn;
+ss = dbn.nnodes_per_slice;
+int = compute_fwd_interface(dbn);
+bnet15 = mk_slice_and_half_dbn(dbn, int);
+N = length(bnet15.dag);
+stages = {1:N};
+
+% bat
+%cl1 = [16 17 19 7 14];
+%cl2 = [27 25 21 23 20];
+%clusters = {cl1, cl2, cl1+ss, cl2+ss};
+
+% water
+%cl1 = 1:2; cl2 = 3:6; cl3 = 7:8;
+%clusters = {cl1, cl2, cl3, cl1+ss, cl2+ss, cl3+ss};
+
+%clusters = {};
+clusters = {int, int+ss};
+%[jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges] = ...
+%    dag_to_jtree(bnet15, bnet.observed, stages, clusters);
+[jtree, root, cliques] =  graph_to_jtree(moralize(bnet15.dag), ones(1,N), stages, clusters);
+
+clq_len = [];
+for c=1:length(cliques)
+  clq_len(c) = length(cliques{c});
+end
+hist(clq_len, 1:max(clq_len));
+h=hist(clq_len, 1:max(clq_len));
+axis([1 max(clq_len)+1 0 max(h)+1])
+xlabel('clique size','fontsize',16)
+ylabel('number','fontsize',16)
+
+
+
+