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, 111 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/mk_nbrs_of_digraph_broken.m b/sourcecodes/bnt-master/graph/mk_nbrs_of_digraph_broken.m
new file mode 100644
index 00000000..8d43afcb
--- /dev/null
+++ b/sourcecodes/bnt-master/graph/mk_nbrs_of_digraph_broken.m
@@ -0,0 +1,111 @@
+function [Gs, op, nodes] = mk_nbrs_of_digraph(G0)
+% MK_NBRS_OF_DIGRAPH Make all digraphs that differ from G0 by a single edge deletion, addition or reversal
+% [Gs, op, nodes] = mk_nbrs_of_digraph(G0)
+%
+% Gs(:,:,i) is the i'th neighbor
+% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. 
+% nodes(i,1:2) are the head and tail of the operated-on arc.
+
+debug = 0; % the vectorized version is about 3 to 10 times faster
+
+n = length(G0);
+[I,J] = find(G0); % I(k), J(k) is the k'th edge
+E = length(I); % num edges present in G0
+
+% SINGLE EDGE DELETIONS
+
+Grep = repmat(G0(:), 1, E); % each column is a copy of G0
+% edge_ndx(k) is the scalar location of the k'th edge 
+edge_ndx = find(G0);
+% edge_ndx = subv2ind([n n], [I J]); % equivalent
+% We set (ndx(k), k) to 0 for k=1:E in Grep
+ndx = subv2ind(size(Grep), [edge_ndx(:) (1:E)']);
+G1 = Grep;
+G1(ndx) = 0;
+Gdel = reshape(G1, [n n E]);
+
+
+% if debug
+% % Non-vectorized version
+% ctr = 1;
+% for e=1:E
+%   i = I(e); j = J(e);
+%   Gdel2(:,:,ctr) = G0;
+%   Gdel2(i,j,ctr) = 0;
+%   ctr = ctr + 1;
+% end
+% assert(isequal(Gdel, Gdel2));
+% end
+
+
+% SINGLE EDGE REVERSALS
+
+% rev_edge_ndx(k) is the scalar location of the k'th reversed edge
+%rev_edge_ndx = find(G0'); % different order to edge_ndx, which is bad
+rev_edge_ndx = subv2ind([n n], [J I]);
+% We set (rev_edge_ndx(k), k) to 1 for k=1:E in G1
+% We have already deleted i->j in the previous step
+ndx = subv2ind(size(Grep), [rev_edge_ndx(:) (1:E)']);
+G1(ndx) = 1;
+Grev = reshape(G1, [n n E]);
+
+% if debug
+% % Non-vectorized version
+% ctr = 1;
+% for e=1:E
+%   i = I(e); j = J(e);
+%   Grev2(:,:,ctr) = G0;
+%   Grev2(i,j,ctr) = 0;
+%   Grev2(j,i,ctr) = 1;
+%   ctr = ctr + 1;
+% end
+% assert(isequal(Grev, Grev2));
+% end
+
+
+% SINGLE EDGE ADDITIONS
+
+Gbar = ~G0; % Gbar(i,j)=1 iff there is no i->j edge in G0
+Gbar = setdiag(Gbar, 0); % turn off self loops
+[Ibar,Jbar] = find(Gbar); 
+
+bar_edge_ndx = find(Gbar);
+Ebar = length(Ibar); % num edges present in Gbar
+Grep = repmat(G0(:), 1, Ebar); % each column is a copy of G0
+ndx = subv2ind(size(Grep), [bar_edge_ndx(:) (1:Ebar)']);
+Grep(ndx) = 1;
+Gadd = reshape(Grep, [n n Ebar]);
+
+% if debug
+% % Non-vectorized version
+% ctr = 1;
+% for e=1:length(Ibar)
+%   i = Ibar(e); j = Jbar(e);
+%   Gadd2(:,:,ctr) = G0;
+%   Gadd2(i,j,ctr) = 1;
+%   ctr = ctr + 1;
+% end
+% assert(isequal(Gadd, Gadd2));
+% end
+
+
+Gs = cat(3, Gdel, Grev, Gadd);
+
+nodes = [I J;
+	 I J;
+	 Ibar Jbar];
+
+op = cell(1, E+E+Ebar);
+op(1:E) = {'del'};
+op(E+1:2*E) = {'rev'};
+op(2*E+1:end) = {'add'};
+
+
+% numeric output:
+% op(i) = 1, 2, or 3, if the i'th neighbor was created by adding, deleting or reversing an arc.
+
+ADD = 1;
+DEL = 2;
+REV = 3;
+
+%op = [repmat(DEL, 1, E) repmat(REV, 1, E) repmat(ADD, 1, Ebar)];