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, 59 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/mk_2D_lattice.m b/sourcecodes/bnt-master/graph/mk_2D_lattice.m
new file mode 100644
index 00000000..a9873260
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+++ b/sourcecodes/bnt-master/graph/mk_2D_lattice.m
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+function G = mk_2D_lattice(nrows, ncols, con)
+% MK_2D_LATTICE Return adjacency matrix for nearest neighbor connected 2D lattice
+% G = mk_2D_lattice(nrows, ncols, con)
+% G(k1, k2) = 1 iff k1=(i1,j1) is a neighbor of k2=(i2,j2) 
+% (Two pixels are neighbors if their Euclidean distance is less than r.)
+% Default connectivity = 4.
+%
+% WE ASSUME NO WRAP AROUND. 
+%
+% This is the neighborhood as a function of con:
+%
+%  con=4,r=1  con=8,r=sqrt(2)   con=12,r=2   con=24,r=sqrt(8)
+%  nn            2nd order        4th order
+%                                  x         x x x x x
+%    x          x x x            x x x       x x x x x
+%  x o x        x o x          x x o x x     x x o x x
+%    x          x x x            x x x       x x x x x
+%                                  x         x x x x x
+%
+% Examples:
+% Consider a 3x4 grid
+%  1  4  7  10
+%  2  5  8  11
+%  3  6  9  12
+%  
+% 4-connected:
+% G=mk_2D_lattice(3,4,4);
+% find(G(1,:)) = [2 4]
+% find(G(5,:)) = [2 4 6 8]
+%
+% 8-connected:
+% G=mk_2D_lattice(3,4,8);
+% find(G(1,:)) = [2 4 5]
+% find(G(5,:)) = [1 2 3 4 6 7 8 9]
+
+% meshgrid trick due to Temu Gautama (temu@neuro.kuleuven.ac.be)
+
+if nargin < 3, con = 4; end
+
+switch con,
+ case 4, r = 1;
+ case 8, r = sqrt(2);
+ case 12, r = 2;
+ case 24, r = sqrt(8);
+ otherwise, error(['unrecognized connectivity ' num2str(con)])
+end
+
+
+npixels = nrows*ncols;
+
+[x y]=meshgrid(1:ncols, 1:nrows);
+M = [x(:) y(:)];
+M1 = repmat(reshape(M',[1 2 npixels]),[npixels 1 1]);
+M2 = repmat(M,[1 1 npixels]);
+%D = squeeze(sum(abs(M1-M2),2)); % Manhattan distance
+M3 = M1-M2;
+D = sqrt(squeeze(M3(:,1,:)) .^2 + squeeze(M3(:,2,:)) .^2); % Euclidean distance
+G = reshape(D <= r,npixels,npixels);
+G = setdiag(G, 0);