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annotate toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/hhmm_jtree_clqs.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 % Find out how big the cliques are in an HHMM as a function of depth
wolffd@0 2 % (This is how we get the complexity bound of O(D K^{1.5D}).)
wolffd@0 3
wolffd@0 4 if 0
wolffd@0 5 Qsize = [];
wolffd@0 6 Fsize = [];
wolffd@0 7 Nclqs = [];
wolffd@0 8 end
wolffd@0 9
wolffd@0 10 ds = 1:15;
wolffd@0 11
wolffd@0 12 for d = ds
wolffd@0 13 allQ = 1;
wolffd@0 14 [intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(d, allQ);
wolffd@0 15
wolffd@0 16 N = length(intra);
wolffd@0 17 ns = 2*ones(1,N);
wolffd@0 18
wolffd@0 19 bnet = mk_dbn(intra, inter, ns);
wolffd@0 20 for i=1:N
wolffd@0 21 bnet.CPD{i} = tabular_CPD(bnet, i);
wolffd@0 22 end
wolffd@0 23
wolffd@0 24 if 0
wolffd@0 25 T = 5;
wolffd@0 26 dag = unroll_dbn_topology(intra, inter, T);
wolffd@0 27 engine = jtree_unrolled_dbn_inf_engine(bnet, T, 'constrained', 1);
wolffd@0 28 S = struct(engine);
wolffd@0 29 S1 = struct(S.sub_engine);
wolffd@0 30 end
wolffd@0 31
wolffd@0 32 engine = jtree_dbn_inf_engine(bnet);
wolffd@0 33 S = struct(engine);
wolffd@0 34 J = S.jtree_struct;
wolffd@0 35
wolffd@0 36 ss = 2*d+1;
wolffd@0 37 Qnodes2 = Qnodes + ss;
wolffd@0 38 QQnodes = [Qnodes Qnodes2];
wolffd@0 39
wolffd@0 40 % find out how many Q nodes in each clique, and how many F nodes
wolffd@0 41 C = length(J.cliques);
wolffd@0 42 Nclqs(d) = 0;
wolffd@0 43 for c=1:C
wolffd@0 44 Qsize(c,d) = length(myintersect(J.cliques{c}, QQnodes));
wolffd@0 45 Fsize(c,d) = length(myintersect(J.cliques{c}, Fnodes));
wolffd@0 46 if length(J.cliques{c}) > 1 % exclude observed leaves
wolffd@0 47 Nclqs(d) = Nclqs(d) + 1;
wolffd@0 48 end
wolffd@0 49 end
wolffd@0 50 %pred_max_Qsize(d) = ceil(d+(d+1)/2);
wolffd@0 51 pred_max_Qsize(d) = ceil(1.5*d);
wolffd@0 52
wolffd@0 53 fprintf('d=%d\n', d);
wolffd@0 54 %fprintf('D=%d, max F = %d. max Q = %d, pred max Q = %d\n', ...
wolffd@0 55 % D, max(Fsize), max(Qsize), ceil(D+(D+1)/2));
wolffd@0 56
wolffd@0 57 %histc(Qsize,1:max(Qsize)) % how many of each size?
wolffd@0 58 end % next d
wolffd@0 59
wolffd@0 60
wolffd@0 61 Q = 2;
wolffd@0 62 pred_mass = ds.*(Q.^ds) + Q.^(ceil(1.5 * ds))
wolffd@0 63 pred_mass2 = Q.^(ceil(1.5 * ds))
wolffd@0 64
wolffd@0 65 for d=ds
wolffd@0 66 mass(d) = 0;
wolffd@0 67 for c=1:C
wolffd@0 68 mass(d) = mass(d) + Q^Qsize(c,d);
wolffd@0 69 end
wolffd@0 70 end
wolffd@0 71
wolffd@0 72
wolffd@0 73 if 0
wolffd@0 74 %plot(ds, max(Qsize), 'o-', ds, pred_max_Qsize, '*--');
wolffd@0 75 %plot(ds, max(Qsize), 'o-', ds, 1.5*ds, '*--');
wolffd@0 76 %plot(ds, mass, 'o-', ds, pred_mass, '*--');
wolffd@0 77 D = 15;
wolffd@0 78 %plot(ds(1:D), mass(1:D), 'bo-', ds(1:D), pred_mass(1:D), 'g*--', ds(1:D), pred_mass2(1:D), 'k+-.');
wolffd@0 79 plot(ds(1:D), log(mass(1:D)), 'bo-', ds(1:D), log(pred_mass(1:D)), 'g*--', ds(1:D), log(pred_mass2(1:D)), 'k+-.');
wolffd@0 80
wolffd@0 81 grid on
wolffd@0 82 xlabel('depth of hierarchy')
wolffd@0 83 title('max num Q nodes in any clique vs. depth')
wolffd@0 84 legend('actual', 'predicted')
wolffd@0 85
wolffd@0 86 %previewfig(gcf, 'width', 3, 'height', 1.5, 'color', 'bw');
wolffd@0 87 %exportfig(gcf, '/home/cs/murphyk/WP/ConferencePapers/HHMM/clqsize2.eps', ...
wolffd@0 88 % 'width', 3, 'height', 1.5, 'color', 'bw');
wolffd@0 89
wolffd@0 90 end
wolffd@0 91
wolffd@0 92
wolffd@0 93 if 0
wolffd@0 94 for d=ds
wolffd@0 95 effnumclqs(d) = length(find(Qsize(:,d)>0));
wolffd@0 96 end
wolffd@0 97 ds = 1:10;
wolffd@0 98 Qs = 2:10;
wolffd@0 99 maxC = size(Qsize, 1);
wolffd@0 100 cost = [];
wolffd@0 101 cost_bound = [];
wolffd@0 102 for qi=1:length(Qs)
wolffd@0 103 Q = Qs(qi);
wolffd@0 104 for d=ds
wolffd@0 105 cost(d,qi) = 0;
wolffd@0 106 for c=1:maxC
wolffd@0 107 if length(Qsize(c,d) > 0) % this clique contains Q nodes
wolffd@0 108 cost(d,qi) = cost(d,qi) + Q^Qsize(c,d)*2^Fsize(c,d);
wolffd@0 109 end
wolffd@0 110 end
wolffd@0 111 %cost_bound(d,qi) = effnumclqs(d) * 8 * Q^(max(Qsize(:,d)));
wolffd@0 112 cost_bound(d,qi) = (effnumclqs(d)*8) + Q^(max(Qsize(:,d)));
wolffd@0 113 end
wolffd@0 114 end
wolffd@0 115
wolffd@0 116 qi=2; plot(ds, cost(:,qi), 'o-', ds, cost_bound(:,qi), '*--');
wolffd@0 117 end
wolffd@0 118
wolffd@0 119
wolffd@0 120 if 0
wolffd@0 121 % convert numbers in cliques into names
wolffd@0 122 for d=1:D
wolffd@0 123 Fdecode(Fnodes(d)) = d;
wolffd@0 124 end
wolffd@0 125 for c=8:15
wolffd@0 126 clqs = J.cliques{c};
wolffd@0 127 fprintf('clique %d: ', c);
wolffd@0 128 for k=clqs
wolffd@0 129 if myismember(k, Qnodes)
wolffd@0 130 fprintf('Q%d ', k)
wolffd@0 131 elseif myismember(k, Fnodes)
wolffd@0 132 fprintf('F%d ', Fdecode(k))
wolffd@0 133 elseif isequal(k, Onode)
wolffd@0 134 fprintf('O ')
wolffd@0 135 elseif myismember(k, Qnodes2)
wolffd@0 136 fprintf('Q%d* ', k-ss)
wolffd@0 137 else
wolffd@0 138 error(['unrecognized node ' k])
wolffd@0 139 end
wolffd@0 140 end
wolffd@0 141 fprintf('\n');
wolffd@0 142 end
wolffd@0 143 end