Mauch MIREX 2010

function [mpe, ll] = calc_mpe_dbn(engine, evidence, break_ties)

% CALC_MPE Computes the most probable explanation of the evidence

% [mpe, ll] = calc_mpe_dbn(engine, evidence, break_ties)

%

% INPUT

% engine must support max-propagation

% evidence{i,t} is the observed value of node i in slice t, or [] if hidden

%

% OUTPUT

% mpe{i,t} is the most likely value of node i (cell array!)

% ll is the log-likelihood of the globally best assignment

%

% This currently only works when all hidden nodes are discrete

if nargin < 3, break_ties = 0; end

if break_ties

  disp('warning: break ties is ignored')

end

[engine, ll] = enter_evidence(engine, evidence, 'maximize', 1);

observed = ~isemptycell(evidence);

[ss T] = size(evidence);

scalar = 1;

N = length(evidence);

mpe = cell(ss,T);

bnet = bnet_from_engine(engine);

ns = bnet.node_sizes;

for t=1:T

  for i=1:ss

    m = marginal_nodes(engine, i, t);

    % observed nodes are all set to 1 inside the inference engine, so we must undo this

    if observed(i,t)

      mpe{i,t} = evidence{i,t};

    else

      assert(length(m.T) == ns(i));

      mpe{i,t} = argmax(m.T);

    end

  end

end