Mauch MIREX 2010

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