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1 seed = 0;
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2 rand('state', seed);
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3 randn('state', seed);
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4
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5 nrows = 3;
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6 ncols = 3;
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7 npixels = nrows*ncols;
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8
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9 % we number pixels in transposed raster scan order (top to bottom, left to right)
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10
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11 % hidden var
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12 HV = reshape(1:npixels, nrows, ncols);
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13 % observed var
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14 OV = reshape(1:npixels, nrows, ncols) + length(HV(:));
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15
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16 % observed factor
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17 OF = reshape(1:npixels, nrows, ncols);
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18 % vertical edge factor VEF(i,j) is the factor for edge HV(i,j) - HV(i+1,j)
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19 VEF = reshape((1:(nrows-1)*ncols), nrows-1, ncols) + length(OF(:));
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20 % horizontal edge factor HEF(i,j) is the factor for edge HV(i,j) - HV(i,j+1)
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21 HEF = reshape((1:nrows*(ncols-1)), nrows, ncols-1) + length(OF(:)) + length(VEF(:));
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22
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23 nvars = length(HV(:))+length(OV(:));
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24 assert(nvars == 2*npixels);
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25 nfac = length(OF(:)) + length(VEF(:)) + length(HEF(:));
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26
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27 K = 2; % number of discrete values for the hidden vars
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28 %O = 1; % each observed pixel is a scalar
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29 O = 2; % each observed pixel is binary
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30
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31 factors = cell(1,3);
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32
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33 % hidden states generate observed 0 or 1 plus noise
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34 %factors{2} = cond_gauss1_kernel(K, O, 'mean', [0 1], 'cov', [0.1 0.1]);
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35 pnoise = 0.2;
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36 factors{1} = tabular_kernel([K O], [1-pnoise pnoise; pnoise 1-pnoise]);
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37 ofactor = 1;
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38
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39 % encourage compatibility between neighboring vertical pixels
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40 factors{2} = tabular_kernel([K K], [0.8 0.2; 0.2 0.8]);
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41 vedge_factor = 2;
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42
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43 %% no constraint between neighboring horizontal pixels
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44 %factors{3} = tabular_kernel([K K], [0.5 0.5; 0.5 0.5]);
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45
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46 factors{3} = tabular_kernel([K K], [0.8 0.2; 0.2 0.8]);
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47 hedge_factor = 3;
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48
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49
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50
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51 factor_ndx = zeros(1, 3);
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52 G = zeros(nvars, nfac);
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53 ns = [K*ones(1,length(HV(:))) O*ones(1,length(OV(:)))];
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54
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55 N = length(ns);
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56 %cnodes = OV(:);
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57 cnodes = [];
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58 dnodes = 1:N;
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59
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60 for i=1:nrows
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61 for j=1:ncols
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62 G([HV(i,j), OV(i,j)], OF(i,j)) = 1;
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63 factor_ndx(OF(i,j)) = ofactor;
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64
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65 if i < nrows
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66 G(HV(i:i+1,j), VEF(i,j)) = 1;
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67 factor_ndx(VEF(i,j)) = vedge_factor;
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68 end
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69
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70 if j < ncols
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71 G(HV(i,j:j+1), HEF(i,j)) = 1;
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72 factor_ndx(HEF(i,j)) = hedge_factor;
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73 end
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74
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75 end
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76 end
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77
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78
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79 fg = mk_fgraph(G, ns, factors, 'discrete', dnodes, 'equiv_class', factor_ndx);
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80
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81 if 1
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82 % make image with vertical stripes
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83 I = zeros(nrows, ncols);
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84 for j=1:2:ncols
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85 I(:,j) = 1;
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86 end
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87 else
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88 % make image with square in middle
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89 I = zeros(nrows, ncols);
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90 I(3:6,3:6) = 1;
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91 end
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92
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93
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94 % corrupt image
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95 O = mod(I + (rand(nrows,ncols)> (1-pnoise)), 2);
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96
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97 maximize = 1;
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98 engine = belprop_fg_inf_engine(fg, 'maximize', maximize, 'max_iter', npixels*5);
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99
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100 evidence = cell(1, nvars);
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101 onodes = OV(:);
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102 evidence(onodes) = num2cell(O+1); % values must be in range {1,2}
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103
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104 engine = enter_evidence(engine, evidence);
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105
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106 for i=1:nrows
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107 for j=1:ncols
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108 m = marginal_nodes(engine, HV(i,j));
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109 Ihat(i,j) = argmax(m.T)-1;
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110 end
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111 end
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112
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113 Ihat
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