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, 135 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/mk_2D_lattice_slow.m b/sourcecodes/bnt-master/graph/mk_2D_lattice_slow.m
new file mode 100644
index 00000000..0c646d80
--- /dev/null
+++ b/sourcecodes/bnt-master/graph/mk_2D_lattice_slow.m
@@ -0,0 +1,135 @@
+function G = mk_2D_lattice_slow(nrows, ncols, wrap_around) 
+% MK_2D_LATTICE Return adjacency matrix for 4-nearest neighbor connected 2D lattice
+% G = mk_2D_lattice(nrows, ncols, wrap_around)
+% G(k1, k2) = 1 iff k1=(i1,j1) is connected to k2=(i2,j2)
+%
+% If wrap_around = 1, we use toroidal boundary conditions (default = 0)
+%
+% Nodes are assumed numbered as in the following 3x3 lattice
+%   1 4 7
+%   2 5 8 
+%   3 6 9
+%
+% e.g., G = mk_2D_lattice(3, 3, 0) returns
+%   0 1 0 1 0 0 0 0 0 
+%   1 0 1 0 1 0 0 0 0 
+%   0 1 0 0 0 1 0 0 0 
+%   1 0 0 0 1 0 1 0 0 
+%   0 1 0 1 0 1 0 1 0 
+%   0 0 1 0 1 0 0 0 1 
+%   0 0 0 1 0 0 0 1 0 
+%   0 0 0 0 1 0 1 0 1 
+%   0 0 0 0 0 1 0 1 0 
+% so find(G(5,:)) = [2 4 6 8] 
+% but find(G(1,:)) = [2 4]
+%
+% Using wrap around, G = mk_2D_lattice(3, 3, 1), we get
+%   0 1 1 1 0 0 1 0 0 
+%   1 0 1 0 1 0 0 1 0 
+%   1 1 0 0 0 1 0 0 1 
+%   1 0 0 0 1 1 1 0 0 
+%   0 1 0 1 0 1 0 1 0 
+%   0 0 1 1 1 0 0 0 1 
+%   1 0 0 1 0 0 0 1 1 
+%   0 1 0 0 1 0 1 0 1 
+%   0 0 1 0 0 1 1 1 0 
+% so find(G(5,:)) = [2 4 6 8] 
+% and find(G(1,:)) = [2 3 4 7]
+
+if nargin < 3, wrap_around = 0; end
+
+% M contains the number of each cell e.g.
+%   1 4 7
+%   2 5 8 
+%   3 6 9
+% North neighbors (assuming wrap around) are
+%   3 6 9
+%   1 4 7
+%   2 5 8
+% Without wrap around, they are
+%   1 4 7
+%   1 4 7
+%   2 5 8
+% The first row is arbitrary, since pixels at the top have no north neighbor.
+
+if nrows==1
+  G = zeros(1, ncols);
+  for i=1:ncols-1
+    G(i,i+1) = 1;
+    G(i+1,i) = 1;
+  end
+  if wrap_around
+    G(1,ncols) = 1;
+    G(ncols,1) = 1;
+  end
+  return;
+end
+
+  
+npixels = nrows*ncols;
+
+N = 1; E = 2; S = 3; W = 4;
+if wrap_around
+  rows{N} = [nrows 1:nrows-1]; cols{N} = 1:ncols;
+  rows{E} = 1:nrows; cols{E} = [2:ncols 1];
+  rows{S} = [2:nrows 1]; cols{S} = 1:ncols;
+  rows{W} = 1:nrows; cols{W} = [ncols 1:ncols-1];
+else
+  rows{N} = [1 1:nrows-1]; cols{N} = 1:ncols;
+  rows{E} = 1:nrows; cols{E} = [1 1:ncols-1];
+  rows{S} = [2:nrows nrows]; cols{S} = 1:ncols;
+  rows{W} = 1:nrows; cols{W} = [2:ncols ncols];
+end
+
+M = reshape(1:npixels, [nrows ncols]);
+nbrs = cell(1, 4);
+for i=1:4
+  nbrs{i} = M(rows{i}, cols{i});
+end
+
+
+G = zeros(npixels, npixels);
+if wrap_around
+  for i=1:4
+    if 0
+      % naive
+      for p=1:npixels
+	G(p, nbrs{i}(p)) = 1;
+      end
+    else
+      % vectorized
+      ndx2 = sub2ind([npixels npixels], 1:npixels, nbrs{i}(:)');
+      G(ndx2) = 1;
+    end
+  end
+else
+  i = N;
+  mask = ones(nrows, ncols);
+  mask(1,:) = 0; % pixels in row 1 have no nbr to the north
+  ndx = find(mask);
+  ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx));
+  G(ndx2) = 1;
+
+  i = E;
+  mask = ones(nrows, ncols);
+  mask(:,ncols) = 0;
+  ndx = find(mask);
+  ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx));
+  G(ndx2) = 1;
+
+  i = S;
+  mask = ones(nrows, ncols);
+  mask(nrows,:)=0;
+  ndx = find(mask);
+  ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx));
+  G(ndx2) = 1;
+  
+  i = W;
+  mask = ones(nrows, ncols);
+  mask(:,1)=0;
+  ndx = find(mask);
+  ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx));
+  G(ndx2) = 1;
+end
+
+G = setdiag(G, 0);