comparison toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg_mrf1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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