function [mpe, ll] = calc_mpe(engine, evidence, break_ties)% CALC_MPE Computes the most probable explanation of the evidence% [mpe, ll] = calc_mpe_given_inf_engine(engine, evidence, break_ties)%% INPUT% engine must support max-propagation% evidence{i} is the observed value of node i, or [] if hidden% break_ties is optional. If 1, we will force ties to be broken consistently%  by calling enter_evidence N times.%% OUTPUT% mpe{i} 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; end[engine, ll] = enter_evidence(engine, evidence, 'maximize', 1);observed = ~isemptycell(evidence);if 0 % fgraphs don't support bnet_from_engineonodes = find(observed);bnet = bnet_from_engine(engine);pot_type = determine_pot_type(bnet, onodes);assert(pot_type == 'd');endscalar = 1;evidence = evidence(:); % hack to handle unrolled DBNsN = length(evidence);mpe = cell(1,N);for i=1:N  m = marginal_nodes(engine, i);  % observed nodes are all set to 1 inside the inference engine, so we must undo this  if observed(i)    mpe{i} = evidence{i};  else    mpe{i} = argmax(m.T);    % Bug fix by Ron Zohar, 8/15/01    % If there are ties, we must break them as follows (see Jensen96, p106)    if break_ties      evidence{i} = mpe{i};                                   [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1);      end  end  if length(mpe{i}) > 1, scalar = 0; endendif nargout >= 2  bnet = bnet_from_engine(engine);  ll = log_lik_complete(bnet, mpe(:));endif 0 % scalar  mpe = cell2num(mpe);end