comparison toolboxes/FullBNT-1.0.7/bnt/general/dbn_to_hmm.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [startprob, transprob, obsprob] = dbn_to_hmm(bnet)
+% DBN_TO_HMM % Convert DBN params to HMM params
+% [startprob, transprob, obsprob] = dbn_to_hmm(bnet, onodes)
+% startprob(i)
+% transprob(i,j)
+% obsprob{k}.big_CPT(i,o) if k'th observed node is discrete
+% obsprob{k}.big_mu(:,i), .big_Sigma(:,:,i) if k'th observed node is Gaussian
+% Big means the domain contains all the hidden discrete nodes, not just the parents.
+
+% Called by constructor and by update_engine
+
+ss = length(bnet.intra);
+onodes = bnet.observed;
+hnodes = mysetdiff(1:ss, onodes);
+evidence = cell(ss, 2);
+ns = bnet.node_sizes(:);
+Qh = prod(ns(hnodes));
+tmp = dpot_to_table(compute_joint_pot(bnet, hnodes, evidence));
+startprob = reshape(tmp, Qh, 1);
+
+tmp = dpot_to_table(compute_joint_pot(bnet, hnodes+ss, evidence, [hnodes hnodes+ss]));
+transprob = mk_stochastic(reshape(tmp, Qh, Qh));
+
+% P(o|ps) is used by mk_hmm_obs_lik_vec for a single time slice
+% P(o|h) (the big version), where h = all hidden nodes, is used by enter_evidence
+
+obsprob = cell(1, length(onodes));
+for i=1:length(onodes)
+  o = onodes(i);
+  if bnet.auto_regressive(o)
+    % We assume the parents of this node are all the hidden nodes in the slice,
+    % so the params already are "big". Also, we assume we regress only on our old selves.
+    % slice 1
+    e = bnet.equiv_class(o);
+    CPD = struct(bnet.CPD{e});
+    O = ns(o);
+    ps = bnet.parents{o};
+    Qps = prod(ns(ps));
+    obsprob{i}.big_mu0 = reshape(CPD.mean, [O Qps]);
+    obsprob{i}.big_Sigma0 = reshape(CPD.cov, [O O Qps]);
+
+    % slice t>1
+    e = bnet.equiv_class(o+ss);
+    CPD = struct(bnet.CPD{e});
+    O = ns(o);
+    dps = mysetdiff(bnet.parents{o+ss}, o);
+    Qdps = prod(ns(dps));
+    obsprob{i}.big_mu = reshape(CPD.mean, [O Qdps]);
+    obsprob{i}.big_Sigma = reshape(CPD.cov, [O O Qdps]);
+    obsprob{i}.big_W = reshape(CPD.weights, [O O Qdps]);
+  else
+    e = bnet.equiv_class(o+ss);
+    CPD = struct(bnet.CPD{e});
+    O = ns(o);
+    ps = bnet.parents{o};
+    Qps = prod(ns(ps));
+    % We make a big potential, replicating the params if necessary
+    % e.g., for a 2 chain coupled HMM, mu(:,Q1) becomes mu(:,Q1,Q2)
+    bigpot = pot_to_marginal(compute_joint_pot(bnet, onodes(i), evidence, [hnodes onodes(i)]));
+
+    if myismember(o, bnet.dnodes)
+      obsprob{i}.CPT = reshape(CPD.CPT, [Qps O]);
+      obsprob{i}.big_CPT = reshape(bigpot.T, Qh, O); 
+    else
+      obsprob{i}.big_mu = bigpot.mu;
+      obsprob{i}.big_Sigma = bigpot.Sigma;
+      
+      if 1
+      obsprob{i}.mu = reshape(CPD.mean, [O Qps]);
+      C = reshape(CPD.cov, [O O Qps]);
+      obsprob{i}.Sigma = C;
+      d = size(obsprob{i}.mu, 1);
+      for j=1:Qps
+	obsprob{i}.inv_Sigma(:,:,j) = inv(C(:,:,j));
+	obsprob{i}.denom(j) = (2*pi)^(d/2)*sqrt(abs(det(C(:,:,j))));
+      end
+      end
+      
+    end % if discrete
+  end % if ar
+end % for