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, 113 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/fgraph/fg_mrf1.m b/sourcecodes/bnt-master/BNT/examples/static/fgraph/fg_mrf1.m
new file mode 100644
index 00000000..2e204a60
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/fgraph/fg_mrf1.m
@@ -0,0 +1,113 @@
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+nrows = 3;
+ncols = 3;
+npixels = nrows*ncols;
+
+% we number pixels in transposed raster scan order (top to bottom, left to right)
+
+% hidden var
+HV = reshape(1:npixels, nrows, ncols);
+% observed var
+OV = reshape(1:npixels, nrows, ncols) + length(HV(:));
+
+% observed factor
+OF = reshape(1:npixels, nrows, ncols);
+% vertical edge factor VEF(i,j) is the factor for edge HV(i,j) - HV(i+1,j)
+VEF = reshape((1:(nrows-1)*ncols), nrows-1, ncols) + length(OF(:));
+% horizontal edge factor HEF(i,j) is the factor for edge HV(i,j) - HV(i,j+1)
+HEF = reshape((1:nrows*(ncols-1)), nrows, ncols-1) + length(OF(:)) + length(VEF(:));
+
+nvars = length(HV(:))+length(OV(:));
+assert(nvars == 2*npixels);
+nfac = length(OF(:)) + length(VEF(:)) + length(HEF(:));
+
+K = 2; % number of discrete values for the hidden vars
+%O = 1; % each observed pixel is a scalar
+O = 2; % each observed pixel is binary
+
+factors = cell(1,3);
+
+% hidden states generate observed 0 or 1 plus noise
+%factors{2} = cond_gauss1_kernel(K, O, 'mean', [0 1], 'cov', [0.1 0.1]);
+pnoise = 0.2;
+factors{1} = tabular_kernel([K O], [1-pnoise pnoise; pnoise 1-pnoise]);
+ofactor = 1;
+
+% encourage compatibility between neighboring vertical pixels
+factors{2} = tabular_kernel([K K], [0.8 0.2; 0.2 0.8]);
+vedge_factor = 2;
+
+%% no constraint between neighboring horizontal pixels
+%factors{3} = tabular_kernel([K K], [0.5 0.5; 0.5 0.5]);
+
+factors{3} = tabular_kernel([K K], [0.8 0.2; 0.2 0.8]);
+hedge_factor = 3;
+
+
+
+factor_ndx = zeros(1, 3);
+G = zeros(nvars, nfac);
+ns = [K*ones(1,length(HV(:))) O*ones(1,length(OV(:)))];
+
+N = length(ns);
+%cnodes = OV(:);
+cnodes = [];
+dnodes = 1:N;
+
+for i=1:nrows
+  for j=1:ncols
+    G([HV(i,j), OV(i,j)], OF(i,j)) = 1;
+    factor_ndx(OF(i,j)) = ofactor;
+
+    if i < nrows
+      G(HV(i:i+1,j), VEF(i,j)) = 1;
+      factor_ndx(VEF(i,j)) = vedge_factor;
+    end
+
+    if j < ncols
+      G(HV(i,j:j+1), HEF(i,j)) = 1;
+      factor_ndx(HEF(i,j)) = hedge_factor;
+    end
+
+  end
+end
+
+
+fg = mk_fgraph(G, ns, factors, 'discrete', dnodes, 'equiv_class', factor_ndx);
+
+if 1
+  % make image with vertical stripes
+  I = zeros(nrows, ncols);
+  for j=1:2:ncols
+    I(:,j) = 1;
+  end
+else
+  % make image with square in middle
+  I = zeros(nrows, ncols);
+  I(3:6,3:6) = 1;
+end
+
+  
+% corrupt image
+O = mod(I + (rand(nrows,ncols)> (1-pnoise)), 2);
+
+maximize = 1;
+engine = belprop_fg_inf_engine(fg, 'maximize', maximize, 'max_iter', npixels*5);
+
+evidence = cell(1, nvars);
+onodes = OV(:);
+evidence(onodes) = num2cell(O+1); % values must be in range {1,2}
+
+engine = enter_evidence(engine, evidence);
+
+for i=1:nrows
+  for j=1:ncols
+    m = marginal_nodes(engine, HV(i,j));
+    Ihat(i,j) = argmax(m.T)-1;
+  end
+end
+
+Ihat