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annotate toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_hhmm3.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 function bnet = mk_hhmm3(varargin)
wolffd@0 2 % MK_HHMM3 Make a 3 level Hierarchical HMM
wolffd@0 3 % bnet = mk_hhmm3(...)
wolffd@0 4 %
wolffd@0 5 % 3-layer hierarchical HMM where level 1 only connects to level 2, not 3 or obs.
wolffd@0 6 % This enforces sub-models (which differ only in their Q1 index) to be shared.
wolffd@0 7 % Also, we enforce the fact that each model always starts in its initial state
wolffd@0 8 % and only finishes in its final state. However, the prob. of finishing (as opposed to
wolffd@0 9 % self-transitioning to the final state) can be learned.
wolffd@0 10 % The fact that we always finish from the same state means we do not need to condition
wolffd@0 11 % F(i) on Q(i-1), since finishing prob is indep of calling context.
wolffd@0 12 %
wolffd@0 13 % The DBN is the same as Fig 10 in my tech report.
wolffd@0 14 %
wolffd@0 15 % Q1 ----------> Q1
wolffd@0 16 % | / |
wolffd@0 17 % | / |
wolffd@0 18 % | F2 ------- |
wolffd@0 19 % | ^ \ |
wolffd@0 20 % | /| \ |
wolffd@0 21 % v | v v
wolffd@0 22 % Q2-| --------> Q2
wolffd@0 23 % /| | ^
wolffd@0 24 % / | | /|
wolffd@0 25 % | | F3 ---------/ |
wolffd@0 26 % | | ^ \ |
wolffd@0 27 % | v / v
wolffd@0 28 % | Q3 -----------> Q3
wolffd@0 29 % | |
wolffd@0 30 % \ |
wolffd@0 31 % v v
wolffd@0 32 % O
wolffd@0 33 %
wolffd@0 34 %
wolffd@0 35 % Optional arguments in name/value format [default]
wolffd@0 36 %
wolffd@0 37 % Qsizes - sizes at each level [ none ]
wolffd@0 38 % Osize - size of O node [ none ]
wolffd@0 39 % discrete_obs - 1 means O is tabular_CPD, 0 means O is gaussian_CPD [0]
wolffd@0 40 % Oargs - cell array of args to pass to the O CPD [ {} ]
wolffd@0 41 % transprob1 - transprob1(i,j) = P(Q1(t)=j|Q1(t-1)=i) ['ergodic']
wolffd@0 42 % startprob1 - startprob1(j) = P(Q1(t)=j) ['leftstart']
wolffd@0 43 % transprob2 - transprob2(i,k,j) = P(Q2(t)=j|Q2(t-1)=i,Q1(t)=k) ['leftright']
wolffd@0 44 % startprob2 - startprob2(k,j) = P(Q2(t)=j|Q1(t)=k) ['leftstart']
wolffd@0 45 % termprob2 - termprob2(j,f) = P(F2(t)=f|Q2(t)=j) ['rightstop']
wolffd@0 46 % transprob3 - transprob3(i,k,j) = P(Q3(t)=j|Q3(t-1)=i,Q2(t)=k) ['leftright']
wolffd@0 47 % startprob3 - startprob3(k,j) = P(Q3(t)=j|Q2(t)=k) ['leftstart']
wolffd@0 48 % termprob3 - termprob3(j,f) = P(F3(t)=f|Q3(t)=j) ['rightstop']
wolffd@0 49 %
wolffd@0 50 % leftstart means the model always starts in state 1.
wolffd@0 51 % rightstop means the model always finished in its last state (Qsize(d)).
wolffd@0 52 %
wolffd@0 53 % Q1:Q3 in slice 1 are of type tabular_CPD
wolffd@0 54 % Q1:Q3 in slice 2 are of type hhmmQ_CPD.
wolffd@0 55 % F2 is of type hhmmF_CPD, F3 is of type tabular_CPD.
wolffd@0 56
wolffd@0 57 ss = 6; D = 3;
wolffd@0 58 Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; obs = 6;
wolffd@0 59 Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3];
wolffd@0 60 names = {'Q1', 'Q2', 'Q3', 'F3', 'F2', 'obs'};
wolffd@0 61
wolffd@0 62 intra = zeros(ss);
wolffd@0 63 intra(Q1, Q2) = 1;
wolffd@0 64 intra(Q2, [F2 Q3 obs]) = 1;
wolffd@0 65 intra(Q3, [F3 obs]) = 1;
wolffd@0 66 intra(F3, F2) = 1;
wolffd@0 67
wolffd@0 68 inter = zeros(ss);
wolffd@0 69 inter(Q1,Q1) = 1;
wolffd@0 70 inter(Q2,Q2) = 1;
wolffd@0 71 inter(Q3,Q3) = 1;
wolffd@0 72 inter(F2,[Q1 Q2]) = 1;
wolffd@0 73 inter(F3,[Q2 Q3]) = 1;
wolffd@0 74
wolffd@0 75
wolffd@0 76 % get sizes of nodes
wolffd@0 77 args = varargin;
wolffd@0 78 nargs = length(args);
wolffd@0 79 Qsizes = [];
wolffd@0 80 Osize = 0;
wolffd@0 81 for i=1:2:nargs
wolffd@0 82 switch args{i},
wolffd@0 83 case 'Qsizes', Qsizes = args{i+1};
wolffd@0 84 case 'Osize', Osize = args{i+1};
wolffd@0 85 end
wolffd@0 86 end
wolffd@0 87 if isempty(Qsizes), error('must specify Qsizes'); end
wolffd@0 88 if Osize==0, error('must specify Osize'); end
wolffd@0 89
wolffd@0 90 % set default params
wolffd@0 91 discrete_obs = 0;
wolffd@0 92 Oargs = {};
wolffd@0 93 startprob1 = 'ergodic';
wolffd@0 94 startprob2 = 'leftstart';
wolffd@0 95 startprob3 = 'leftstart';
wolffd@0 96 transprob1 = 'ergodic';
wolffd@0 97 transprob2 = 'leftright';
wolffd@0 98 transprob3 = 'leftright';
wolffd@0 99 termprob2 = 'rightstop';
wolffd@0 100 termprob3 = 'rightstop';
wolffd@0 101
wolffd@0 102
wolffd@0 103 for i=1:2:nargs
wolffd@0 104 switch args{i},
wolffd@0 105 case 'discrete_obs', discrete_obs = args{i+1};
wolffd@0 106 case 'Oargs', Oargs = args{i+1};
wolffd@0 107 case 'Q1args', Q1args = args{i+1};
wolffd@0 108 case 'Q2args', Q2args = args{i+1};
wolffd@0 109 case 'Q3args', Q3args = args{i+1};
wolffd@0 110 case 'F2args', F2args = args{i+1};
wolffd@0 111 case 'F3args', F3args = args{i+1};
wolffd@0 112 end
wolffd@0 113 end
wolffd@0 114
wolffd@0 115
wolffd@0 116 ns = zeros(1,ss);
wolffd@0 117 ns(Qnodes) = Qsizes;
wolffd@0 118 ns(obs) = Osize;
wolffd@0 119 ns(Fnodes) = 2;
wolffd@0 120
wolffd@0 121 dnodes = [Qnodes Fnodes];
wolffd@0 122 if discrete_obs
wolffd@0 123 dnodes = [dnodes obs];
wolffd@0 124 end
wolffd@0 125 onodes = [obs];
wolffd@0 126
wolffd@0 127 bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'discrete', dnodes, 'names', names);
wolffd@0 128 eclass = bnet.equiv_class;
wolffd@0 129
wolffd@0 130 if strcmp(startprob1, 'ergodic')
wolffd@0 131 startprob1 = normalise(ones(1,ns(Q1)));
wolffd@0 132 end
wolffd@0 133 if strcmp(startprob2, 'leftstart')
wolffd@0 134 startprob2 = zeros(ns(Q1), ns(Q2));
wolffd@0 135 starpbrob2(:, 1) = 1.0;
wolffd@0 136 end
wolffd@0 137 if strcmp(startprob3, 'leftstart')
wolffd@0 138 startprob3 = zeros(ns(Q2), ns(Q3));
wolffd@0 139 starpbrob3(:, 1) = 1.0;
wolffd@0 140 end
wolffd@0 141
wolffd@0 142 if strcmp(termprob2, 'rightstop')
wolffd@0 143 p = 0.9;
wolffd@0 144 termprob2 = zeros(Qsize(2),2);
wolffd@0 145 termprob2(:, 2) = p;
wolffd@0 146 termprob2(:, 1) = 1-p;
wolffd@0 147 termprob2(1:(Qsize(2)-1), 1) = 1;
wolffd@0 148 end
wolffd@0 149 if strcmp(termprob3, 'rightstop')
wolffd@0 150 p = 0.9;
wolffd@0 151 termprob3 = zeros(Qsize(3),2);
wolffd@0 152 termprob3(:, 2) = p;
wolffd@0 153 termprob3(:, 1) = 1-p;
wolffd@0 154 termprob3(1:(Qsize(3)-1), 1) = 1;
wolffd@0 155 end
wolffd@0 156
wolffd@0 157
wolffd@0 158 % SLICE 1
wolffd@0 159
wolffd@0 160 % We clamp untied nodes in the first slice, since their params can't be estimated
wolffd@0 161 % from just one sequence
wolffd@0 162
wolffd@0 163 bnet.CPD{eclass(Q1,1)} = tabular_CPD(bnet, Q1, 'CPT', startprob1, 'adjustable', 0);
wolffd@0 164 bnet.CPD{eclass(Q2,1)} = tabular_CPD(bnet, Q2, 'CPT', startprob2, 'adjustable', 0);
wolffd@0 165 bnet.CPD{eclass(Q3,1)} = tabular_CPD(bnet, Q3, 'CPT', startprob3, 'adjustable', 0);
wolffd@0 166
wolffd@0 167 bnet.CPD{eclass(F2,1)} = hhmmF_CPD(bnet, F2, Qnodes, 2, D, 'termprob', termprob2);
wolffd@0 168 bnet.CPD{eclass(F3,1)} = tabular_CPD(bnet, F3, 'CPT', termprob3);
wolffd@0 169
wolffd@0 170 if discrete_obs
wolffd@0 171 bnet.CPD{eclass(obs,1)} = tabular_CPD(bnet, obs, Oargs{:});
wolffd@0 172 else
wolffd@0 173 bnet.CPD{eclass(obs,1)} = gaussian_CPD(bnet, obs, Oargs{:});
wolffd@0 174 end
wolffd@0 175
wolffd@0 176 % SLICE 2
wolffd@0 177
wolffd@0 178 bnet.CPD{eclass(Q1,2)} = hhmmQ_CPD(bnet, Q1+ss, Qnodes, 1, D, 'transprob', transprob1, 'startprob', startprob1);
wolffd@0 179 bnet.CPD{eclass(Q2,2)} = hhmmQ_CPD(bnet, Q2+ss, Qnodes, 2, D, 'transprob', transprob2, 'startprob', startprob2);
wolffd@0 180 bnet.CPD{eclass(Q3,2)} = hhmmQ_CPD(bnet, Q3+ss, Qnodes, 3, D, 'transprob', transprob3, 'startprob', startprob3);
wolffd@0 181