Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/update_ess2.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function CPD = update_ess2(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) | |
2 % UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. | |
3 % function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) | |
4 | |
5 % Figure out the node numbers associated with each parent | |
6 dom = fmarginal.domain; | |
7 self = dom(end); % by assumption | |
8 old_self = dom(CPD.old_self_ndx); | |
9 Fself = dom(CPD.Fself_ndx); | |
10 Fbelow = dom(CPD.Fbelow_ndx); | |
11 Qps = dom(CPD.Qps_ndx); | |
12 | |
13 Qsz = CPD.Qsz; | |
14 Qpsz = CPD.Qpsz; | |
15 | |
16 | |
17 fmarg = add_ev_to_dmarginal(fmarginal, evidence, ns); | |
18 | |
19 | |
20 | |
21 % hor_counts(old_self, Qps, self), | |
22 % fmarginal(old_self, Fbelow, Fself, Qps, self) | |
23 % hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not | |
24 % ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) | |
25 % Since any of i,j,k may be observed, we write | |
26 % hor_counts(counts_ndx{:}) = fmarginal(fmarg_ndx{:}) | |
27 % where e.g., counts_ndx = {1, ':', 2} if Qps is hidden but we observe old_self=1, self=2. | |
28 % To create this counts_ndx, we write counts_ndx = mk_multi_ndx(3, obs_dim, obs_val) | |
29 % where counts_obs_dim = [1 3], counts_obs_val = [1 2] specifies the values of dimensions 1 and 3. | |
30 | |
31 counts_obs_dim = []; | |
32 fmarg_obs_dim = []; | |
33 obs_val = []; | |
34 if hidden_bitv(self) | |
35 effQsz = Qsz; | |
36 else | |
37 effQsz = 1; | |
38 counts_obs_dim = [counts_obs_dim 3]; | |
39 fmarg_obs_dim = [fmarg_obs_dim 5]; | |
40 obs_val = [obs_val evidence{self}]; | |
41 end | |
42 | |
43 % e.g., D=4, d=3, Qps = all Qs above, so dom = [Q3(t-1) F4(t-1) F3(t-1) Q1(t) Q2(t) Q3(t)]. | |
44 % so self = Q3(t), old_self = Q3(t-1), CPD.Qps = [1 2], Qps = [Q1(t) Q2(t)] | |
45 dom = fmarginal.domain; | |
46 self = dom(end); | |
47 old_self = dom(1); | |
48 Qps = dom(length(dom)-length(CPD.Qps):end-1); | |
49 | |
50 Qsz = CPD.Qsizes(CPD.d); | |
51 Qpsz = prod(CPD.Qsizes(CPD.Qps)); | |
52 | |
53 % If some of the Q nodes are observed (which happens during supervised training) | |
54 % the counts will only be non-zero in positions | |
55 % consistent with the evidence. We put the computed marginal responsibilities | |
56 % into the appropriate slots of the big counts array. | |
57 % (Recall that observed discrete nodes only have a single effective value.) | |
58 % (A more general, but much slower, way is to call add_evidence_to_dmarginal.) | |
59 % We assume the F nodes are never observed. | |
60 | |
61 obs_self = ~hidden_bitv(self); | |
62 obs_Qps = (~isempty(Qps)) & (~any(hidden_bitv(Qps))); % we assume that all or none of the Q parents are observed | |
63 | |
64 if obs_self | |
65 self_val = evidence{self}; | |
66 oldself_val = evidence{old_self}; | |
67 end | |
68 | |
69 if obs_Qps | |
70 Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); | |
71 if Qps_val == 0 | |
72 keyboard | |
73 end | |
74 end | |
75 | |
76 if CPD.d==1 % no Qps from above | |
77 if ~CPD.F1toQ1 % no F from self | |
78 % marg(Q1(t-1), F2(t-1), Q1(t)) | |
79 % F2(t-1) P(Q1(t)=j | Q1(t-1)=i) | |
80 % 1 delta(i,j) | |
81 % 2 transprob(i,j) | |
82 if obs_self | |
83 hor_counts = zeros(Qsz, Qsz); | |
84 hor_counts(oldself_val, self_val) = fmarginal.T(2); | |
85 else | |
86 marg = reshape(fmarginal.T, [Qsz 2 Qsz]); | |
87 hor_counts = squeeze(marg(:,2,:)); | |
88 end | |
89 else | |
90 % marg(Q1(t-1), F2(t-1), F1(t-1), Q1(t)) | |
91 % F2(t-1) F1(t-1) P(Qd(t)=j| Qd(t-1)=i) | |
92 % ------------------------------------------------------ | |
93 % 1 1 delta(i,j) | |
94 % 2 1 transprob(i,j) | |
95 % 1 2 impossible | |
96 % 2 2 startprob(j) | |
97 if obs_self | |
98 marg = myreshape(fmarginal.T, [1 2 2 1]); | |
99 hor_counts = zeros(Qsz, Qsz); | |
100 hor_counts(oldself_val, self_val) = marg(1,2,1,1); | |
101 ver_counts = zeros(Qsz, 1); | |
102 %ver_counts(self_val) = marg(1,2,2,1); | |
103 ver_counts(self_val) = marg(1,2,2,1) + marg(1,1,2,1); | |
104 else | |
105 marg = reshape(fmarginal.T, [Qsz 2 2 Qsz]); | |
106 hor_counts = squeeze(marg(:,2,1,:)); | |
107 %ver_counts = squeeze(sum(marg(:,2,2,:),1)); % sum over i | |
108 ver_counts = squeeze(sum(marg(:,2,2,:),1)) + squeeze(sum(marg(:,1,2,:),1)); % sum i,b | |
109 end | |
110 end % F1toQ1 | |
111 else % d ~= 1 | |
112 if CPD.d < CPD.D % general case | |
113 % marg(Qd(t-1), Fd+1(t-1), Fd(t-1), Qps(t), Qd(t)) | |
114 % Fd+1(t-1) Fd(t-1) P(Qd(t)=j| Qd(t-1)=i, Qps(t)=k) | |
115 % ------------------------------------------------------ | |
116 % 1 1 delta(i,j) | |
117 % 2 1 transprob(i,k,j) | |
118 % 1 2 impossible | |
119 % 2 2 startprob(k,j) | |
120 if obs_Qps & obs_self | |
121 marg = myreshape(fmarginal.T, [1 2 2 1 1]); | |
122 k = 1; | |
123 hor_counts = zeros(Qsz, Qpsz, Qsz); | |
124 hor_counts(oldself_val, Qps_val, self_val) = marg(1, 2,1, k,1); | |
125 ver_counts = zeros(Qpsz, Qsz); | |
126 %ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1); | |
127 ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1) + marg(1, 1,2, k,1); | |
128 elseif obs_Qps & ~obs_self | |
129 marg = myreshape(fmarginal.T, [Qsz 2 2 1 Qsz]); | |
130 k = 1; | |
131 hor_counts = zeros(Qsz, Qpsz, Qsz); | |
132 hor_counts(:, Qps_val, :) = marg(:, 2,1, k,:); | |
133 ver_counts = zeros(Qpsz, Qsz); | |
134 %ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1); | |
135 ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1) + sum(marg(:, 1,2, k,:), 1); | |
136 elseif ~obs_Qps & obs_self | |
137 error('not yet implemented') | |
138 else % everything is hidden | |
139 marg = reshape(fmarginal.T, [Qsz 2 2 Qpsz Qsz]); | |
140 hor_counts = squeeze(marg(:,2,1,:,:)); % i,k,j | |
141 %ver_counts = squeeze(sum(marg(:,2,2,:,:),1)); % sum over i | |
142 ver_counts = squeeze(sum(marg(:,2,2,:,:),1)) + squeeze(sum(marg(:,1,2,:,:),1)); % sum over i,b | |
143 end | |
144 else % d == D, so no F from below | |
145 % marg(QD(t-1), FD(t-1), Qps(t), QD(t)) | |
146 % FD(t-1) P(QD(t)=j | QD(t-1)=i, Qps(t)=k) | |
147 % 1 transprob(i,k,j) | |
148 % 2 startprob(k,j) | |
149 if obs_Qps & obs_self | |
150 marg = myreshape(fmarginal.T, [1 2 1 1]); | |
151 k = 1; | |
152 hor_counts = zeros(Qsz, Qpsz, Qsz); | |
153 hor_counts(oldself_val, Qps_val, self_val) = marg(1, 1, k,1); | |
154 ver_counts = zeros(Qpsz, Qsz); | |
155 ver_counts(Qps_val, self_val) = marg(1, 2, k,1); | |
156 elseif obs_Qps & ~obs_self | |
157 marg = myreshape(fmarginal.T, [Qsz 2 1 Qsz]); | |
158 k = 1; | |
159 hor_counts = zeros(Qsz, Qpsz, Qsz); | |
160 hor_counts(:, Qps_val, :) = marg(:, 1, k,:); | |
161 ver_counts = zeros(Qpsz, Qsz); | |
162 ver_counts(Qps_val, :) = sum(marg(:, 2, k, :), 1); | |
163 elseif ~obs_Qps & obs_self | |
164 error('not yet implemented') | |
165 else % everything is hidden | |
166 marg = reshape(fmarginal.T, [Qsz 2 Qpsz Qsz]); | |
167 hor_counts = squeeze(marg(:,1,:,:)); | |
168 ver_counts = squeeze(sum(marg(:,2,:,:),1)); % sum over i | |
169 end | |
170 end | |
171 end | |
172 | |
173 CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); | |
174 | |
175 if ~isempty(CPD.sub_CPD_start) | |
176 CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); | |
177 end | |
178 |