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view 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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function CPD = update_ess2(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) % UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. % function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) % Figure out the node numbers associated with each parent dom = fmarginal.domain; self = dom(end); % by assumption old_self = dom(CPD.old_self_ndx); Fself = dom(CPD.Fself_ndx); Fbelow = dom(CPD.Fbelow_ndx); Qps = dom(CPD.Qps_ndx); Qsz = CPD.Qsz; Qpsz = CPD.Qpsz; fmarg = add_ev_to_dmarginal(fmarginal, evidence, ns); % hor_counts(old_self, Qps, self), % fmarginal(old_self, Fbelow, Fself, Qps, self) % hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not % ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) % Since any of i,j,k may be observed, we write % hor_counts(counts_ndx{:}) = fmarginal(fmarg_ndx{:}) % where e.g., counts_ndx = {1, ':', 2} if Qps is hidden but we observe old_self=1, self=2. % To create this counts_ndx, we write counts_ndx = mk_multi_ndx(3, obs_dim, obs_val) % where counts_obs_dim = [1 3], counts_obs_val = [1 2] specifies the values of dimensions 1 and 3. counts_obs_dim = []; fmarg_obs_dim = []; obs_val = []; if hidden_bitv(self) effQsz = Qsz; else effQsz = 1; counts_obs_dim = [counts_obs_dim 3]; fmarg_obs_dim = [fmarg_obs_dim 5]; obs_val = [obs_val evidence{self}]; end % 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)]. % so self = Q3(t), old_self = Q3(t-1), CPD.Qps = [1 2], Qps = [Q1(t) Q2(t)] dom = fmarginal.domain; self = dom(end); old_self = dom(1); Qps = dom(length(dom)-length(CPD.Qps):end-1); Qsz = CPD.Qsizes(CPD.d); Qpsz = prod(CPD.Qsizes(CPD.Qps)); % If some of the Q nodes are observed (which happens during supervised training) % the counts will only be non-zero in positions % consistent with the evidence. We put the computed marginal responsibilities % into the appropriate slots of the big counts array. % (Recall that observed discrete nodes only have a single effective value.) % (A more general, but much slower, way is to call add_evidence_to_dmarginal.) % We assume the F nodes are never observed. obs_self = ~hidden_bitv(self); obs_Qps = (~isempty(Qps)) & (~any(hidden_bitv(Qps))); % we assume that all or none of the Q parents are observed if obs_self self_val = evidence{self}; oldself_val = evidence{old_self}; end if obs_Qps Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); if Qps_val == 0 keyboard end end if CPD.d==1 % no Qps from above if ~CPD.F1toQ1 % no F from self % marg(Q1(t-1), F2(t-1), Q1(t)) % F2(t-1) P(Q1(t)=j | Q1(t-1)=i) % 1 delta(i,j) % 2 transprob(i,j) if obs_self hor_counts = zeros(Qsz, Qsz); hor_counts(oldself_val, self_val) = fmarginal.T(2); else marg = reshape(fmarginal.T, [Qsz 2 Qsz]); hor_counts = squeeze(marg(:,2,:)); end else % marg(Q1(t-1), F2(t-1), F1(t-1), Q1(t)) % F2(t-1) F1(t-1) P(Qd(t)=j| Qd(t-1)=i) % ------------------------------------------------------ % 1 1 delta(i,j) % 2 1 transprob(i,j) % 1 2 impossible % 2 2 startprob(j) if obs_self marg = myreshape(fmarginal.T, [1 2 2 1]); hor_counts = zeros(Qsz, Qsz); hor_counts(oldself_val, self_val) = marg(1,2,1,1); ver_counts = zeros(Qsz, 1); %ver_counts(self_val) = marg(1,2,2,1); ver_counts(self_val) = marg(1,2,2,1) + marg(1,1,2,1); else marg = reshape(fmarginal.T, [Qsz 2 2 Qsz]); hor_counts = squeeze(marg(:,2,1,:)); %ver_counts = squeeze(sum(marg(:,2,2,:),1)); % sum over i ver_counts = squeeze(sum(marg(:,2,2,:),1)) + squeeze(sum(marg(:,1,2,:),1)); % sum i,b end end % F1toQ1 else % d ~= 1 if CPD.d < CPD.D % general case % marg(Qd(t-1), Fd+1(t-1), Fd(t-1), Qps(t), Qd(t)) % Fd+1(t-1) Fd(t-1) P(Qd(t)=j| Qd(t-1)=i, Qps(t)=k) % ------------------------------------------------------ % 1 1 delta(i,j) % 2 1 transprob(i,k,j) % 1 2 impossible % 2 2 startprob(k,j) if obs_Qps & obs_self marg = myreshape(fmarginal.T, [1 2 2 1 1]); k = 1; hor_counts = zeros(Qsz, Qpsz, Qsz); hor_counts(oldself_val, Qps_val, self_val) = marg(1, 2,1, k,1); ver_counts = zeros(Qpsz, Qsz); %ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1); ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1) + marg(1, 1,2, k,1); elseif obs_Qps & ~obs_self marg = myreshape(fmarginal.T, [Qsz 2 2 1 Qsz]); k = 1; hor_counts = zeros(Qsz, Qpsz, Qsz); hor_counts(:, Qps_val, :) = marg(:, 2,1, k,:); ver_counts = zeros(Qpsz, Qsz); %ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1); ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1) + sum(marg(:, 1,2, k,:), 1); elseif ~obs_Qps & obs_self error('not yet implemented') else % everything is hidden marg = reshape(fmarginal.T, [Qsz 2 2 Qpsz Qsz]); hor_counts = squeeze(marg(:,2,1,:,:)); % i,k,j %ver_counts = squeeze(sum(marg(:,2,2,:,:),1)); % sum over i ver_counts = squeeze(sum(marg(:,2,2,:,:),1)) + squeeze(sum(marg(:,1,2,:,:),1)); % sum over i,b end else % d == D, so no F from below % marg(QD(t-1), FD(t-1), Qps(t), QD(t)) % FD(t-1) P(QD(t)=j | QD(t-1)=i, Qps(t)=k) % 1 transprob(i,k,j) % 2 startprob(k,j) if obs_Qps & obs_self marg = myreshape(fmarginal.T, [1 2 1 1]); k = 1; hor_counts = zeros(Qsz, Qpsz, Qsz); hor_counts(oldself_val, Qps_val, self_val) = marg(1, 1, k,1); ver_counts = zeros(Qpsz, Qsz); ver_counts(Qps_val, self_val) = marg(1, 2, k,1); elseif obs_Qps & ~obs_self marg = myreshape(fmarginal.T, [Qsz 2 1 Qsz]); k = 1; hor_counts = zeros(Qsz, Qpsz, Qsz); hor_counts(:, Qps_val, :) = marg(:, 1, k,:); ver_counts = zeros(Qpsz, Qsz); ver_counts(Qps_val, :) = sum(marg(:, 2, k, :), 1); elseif ~obs_Qps & obs_self error('not yet implemented') else % everything is hidden marg = reshape(fmarginal.T, [Qsz 2 Qpsz Qsz]); hor_counts = squeeze(marg(:,1,:,:)); ver_counts = squeeze(sum(marg(:,2,:,:),1)); % sum over i end end end CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); if ~isempty(CPD.sub_CPD_start) CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); end