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 discreteif nargin < 3, break_ties = 0; endif 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  endend