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1 function [strategy, MEU, niter] = solve_limid(engine, varargin)
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2 % SOLVE_LIMID Find the (locally) optimal strategy for a LIMID
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3 % [strategy, MEU, niter] = solve_limid(inf_engine, ...)
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4 %
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5 % strategy{d} = stochastic policy for node d (a decision node)
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6 % MEU = maximum expected utility
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7 % niter = num iterations used
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8 %
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9 % The following optional arguments can be specified in the form of name/value pairs:
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10 % [default in brackets]
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11 %
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12 % max_iter - max. num. iterations [ 1 ]
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13 % tol - tolerance required of consecutive MEU values, used to assess convergence [1e-3]
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14 % order - order in which decision nodes are optimized [ reverse numerical order ]
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15 %
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16 % e.g., solve_limid(engine, 'tol', 1e-2, 'max_iter', 10)
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17
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18 bnet = bnet_from_engine(engine);
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19
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20 % default values
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21 max_iter = 1;
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22 tol = 1e-3;
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23 D = bnet.decision_nodes;
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24 order = D(end:-1:1);
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25
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26 args = varargin;
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27 nargs = length(args);
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28 for i=1:2:nargs
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29 switch args{i},
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30 case 'max_iter', max_iter = args{i+1};
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31 case 'tol', tol = args{i+1};
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32 case 'order', order = args{i+1};
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33 otherwise,
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34 error(['invalid argument name ' args{i}]);
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35 end
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36 end
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37
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38 CPDs = bnet.CPD;
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39 ns = bnet.node_sizes;
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40 N = length(ns);
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41 evidence = cell(1,N);
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42 strategy = cell(1, N);
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43
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44 iter = 1;
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45 converged = 0;
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46 oldMEU = 0;
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47 while ~converged & (iter <= max_iter)
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48 for d=order(:)'
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49 engine = enter_evidence(engine, evidence, 'exclude', d);
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50 [m, pot] = marginal_family(engine, d);
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51 %pot = marginal_family_pot(engine, d);
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52 [policy, score] = upot_to_opt_policy(pot);
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53 e = bnet.equiv_class(d);
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54 CPDs{e} = set_fields(CPDs{e}, 'policy', policy);
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55 engine = update_engine(engine, CPDs);
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56 strategy{d} = policy;
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57 end
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58 engine = enter_evidence(engine, evidence);
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59 [m, pot] = marginal_nodes(engine, []);
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60 %pot = marginal_family_pot(engine, []);
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61 [dummy, MEU] = upot_to_opt_policy(pot);
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62 if approxeq(MEU, oldMEU, tol)
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63 converged = 1;
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64 end
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65 oldMEU = MEU;
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66 iter = iter + 1;
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67 end
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68 niter = iter - 1;
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