function [engine, ll, niter] = enter_evidence(engine, evidence, varargin)% ENTER_EVIDENCE Propagate evidence using belief propagation% [engine, ll, niter] = enter_evidence(engine, evidence, ...)%% The log-likelihood is not computed; ll = 0.% niter contains the number of iterations used %% The following optional arguments can be specified in the form of name/value pairs:% [default value in brackets]%% maximize - 1 means use max-product, 0 means use sum-product [0]%% e.g., engine = enter_evidence(engine, ev, 'maximize', 1)ll = 0;maximize = 0;if nargin >= 3  args = varargin;  nargs = length(args);  for i=1:2:nargs    switch args{i},     case 'maximize', maximize = args{i+1};     otherwise,      error(['invalid argument name ' args{i}]);    end  endendverbose = 0;ns = engine.fgraph.node_sizes;onodes = find(~isemptycell(evidence));hnodes = find(isemptycell(evidence));cnodes = engine.fgraph.cnodes;pot_type = determine_pot_type(engine.fgraph, onodes);% prime each local kernel with evidence (if any)nfactors = engine.fgraph.nfactors;nvars = engine.fgraph.nvars;factors = cell(1,nfactors);for f=1:nfactors  K = engine.fgraph.factors{engine.fgraph.equiv_class(f)};  factors{f} = convert_to_pot(K, pot_type, engine.fgraph.dom{f}(:), evidence);end  % initialise msgsmsg_var_to_fac = cell(nvars, nfactors);for x=1:nvars  for f=engine.fgraph.dep{x}    msg_var_to_fac{x,f} = mk_initial_pot(pot_type, x, ns, cnodes, onodes);  endendmsg_fac_to_var = cell(nfactors, nvars);dom = cell(1, nfactors);for f=1:nfactors  %hdom{f} = myintersect(engine.fgraph.dom{f}, hnodes);  dom{f} = engine.fgraph.dom{f}(:)';  for x=dom{f}    msg_fac_to_var{f,x} = mk_initial_pot(pot_type, x, ns, cnodes, onodes);    %msg_fac_to_var{f,x} = marginalize_pot(factors{f}, x);  endendconverged = 0;iter = 1;var_prod = cell(1, nvars);fac_prod = cell(1, nfactors);while ~converged & (iter <= engine.max_iter)  if verbose, fprintf('iter %d\n', iter);  end    % absorb  old_var_prod = var_prod;  for x=1:nvars    var_prod{x} = mk_initial_pot(pot_type, x, ns, cnodes, onodes);    for f=engine.fgraph.dep{x}      var_prod{x} = multiply_by_pot(var_prod{x}, msg_fac_to_var{f,x});    end  end  for f=1:nfactors    fac_prod{f} = mk_initial_pot(pot_type, dom{f}, ns, cnodes, onodes);    for x=dom{f}      fac_prod{f} = multiply_by_pot(fac_prod{f}, msg_var_to_fac{x,f});    end  end  % send msgs to neighbors  old_msg_var_to_fac = msg_var_to_fac;  old_msg_fac_to_var = msg_fac_to_var;  converged = 1;  for x=1:nvars    %if verbose, disp(['var ' num2str(x) ' sending to fac ' num2str(engine.fgraph.dep{x})]); end    for f=engine.fgraph.dep{x}      temp = divide_by_pot(var_prod{x}, old_msg_fac_to_var{f,x});      msg_var_to_fac{x,f} = normalize_pot(temp);      if ~approxeq_pot(msg_var_to_fac{x,f}, old_msg_var_to_fac{x,f}, engine.tol), converged = 0; end    end  end  for f=1:nfactors    %if verbose, disp(['fac ' num2str(f) ' sending to var ' num2str(dom{f})]); end    for x=dom{f}      temp = divide_by_pot(fac_prod{f}, old_msg_var_to_fac{x,f});      temp2 = multiply_by_pot(factors{f}, temp);      temp3 = marginalize_pot(temp2, x, maximize);      msg_fac_to_var{f,x} = normalize_pot(temp3);      if ~approxeq_pot(msg_fac_to_var{f,x}, old_msg_fac_to_var{f,x}, engine.tol), converged = 0; end    end  end  if iter==1    converged = 0;  end  iter = iter + 1;endniter = iter - 1;engine.niter = niter;for x=1:nvars  engine.marginal_nodes{x} = normalize_pot(var_prod{x});end