Daniel@0: function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, varargin)
Daniel@0: % CMP_LEARNING_DBN Compare a bunch of inference engines by learning a DBN
Daniel@0: % function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, exact, T, ncases, max_iter)
Daniel@0: %
Daniel@0: % engine{i} is the i'th inference engine.
Daniel@0: % time(e) = elapsed time for doing inference with engine e
Daniel@0: % CPD{e,c} is the learned CPD for eclass c in engine e
Daniel@0: % LL{e} is the learning curve for engine e
Daniel@0: % cases{i} is the i'th training case
Daniel@0: %
Daniel@0: % The list below gives optional arguments [default value in brackets].
Daniel@0: %
Daniel@0: % exact - specifies which engines do exact inference [ 1:length(engine) ]
Daniel@0: % check_ll - 1 means we check that the log-likelihoods are correct [1]
Daniel@0: % ncases - num. random training cases [2]
Daniel@0: % max_iter - max. num EM iterations [2]
Daniel@0: 
Daniel@0: % set default params
Daniel@0: exact = 1:length(engine);
Daniel@0: check_ll = 1;
Daniel@0: ncases = 2;
Daniel@0: max_iter = 2;
Daniel@0: 
Daniel@0: args = varargin;
Daniel@0: nargs = length(args);
Daniel@0: for i=1:2:nargs
Daniel@0:   switch args{i},
Daniel@0:    case 'exact', exact = args{i+1};
Daniel@0:    case 'check_ll', check_ll = args{i+1};
Daniel@0:    case 'ncases', ncases = args{i+1};
Daniel@0:    case 'max_iter', max_iter = args{i+1};
Daniel@0:    otherwise,
Daniel@0:     error(['unrecognized argument ' args{i}])
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: E = length(engine);
Daniel@0: ss = length(bnet.intra);
Daniel@0: onodes = bnet.observed;
Daniel@0: 
Daniel@0: cases = cell(1, ncases);
Daniel@0: for i=1:ncases
Daniel@0:   ev = sample_dbn(bnet, 'length', T);
Daniel@0:   cases{i} = cell(ss,T);
Daniel@0:   cases{i}(onodes,:) = ev(onodes, :);
Daniel@0: end
Daniel@0: 
Daniel@0: LL = cell(1,E);
Daniel@0: time = zeros(1,E);
Daniel@0: for i=1:E
Daniel@0:   tic
Daniel@0:   [bnet2{i}, LL{i}] = learn_params_dbn_em(engine{i}, cases, 'max_iter', max_iter);
Daniel@0:   time(i) = toc;
Daniel@0:   fprintf('engine %d took %6.4f seconds\n', i, time(i));
Daniel@0: end
Daniel@0: 
Daniel@0: ref = exact(1); % reference
Daniel@0: cmp = mysetdiff(exact, ref);
Daniel@0: if check_ll
Daniel@0:   for i=cmp(:)'
Daniel@0:     if ~approxeq(LL{ref}, LL{i})
Daniel@0:       error(['engine ' num2str(i) ' has wrong ll'])
Daniel@0:     end
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: nCPDs = length(bnet.CPD);
Daniel@0: CPD = cell(E, nCPDs);
Daniel@0: tabular = zeros(1, nCPDs);
Daniel@0: for i=1:E
Daniel@0:   temp = bnet2{i};
Daniel@0:   for c=1:nCPDs
Daniel@0:     tabular(c) = isa(temp.CPD{c}, 'tabular_CPD');
Daniel@0:     CPD{i,c} = struct(temp.CPD{c});
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: for i=cmp(:)'
Daniel@0:   for c=1:nCPDs
Daniel@0:     if tabular(c)
Daniel@0:       assert(approxeq(CPD{i,c}.CPT, CPD{ref,c}.CPT));
Daniel@0:     else
Daniel@0:       assert(approxeq(CPD{i,c}.mean, CPD{ref,c}.mean));
Daniel@0:       assert(approxeq(CPD{i,c}.cov, CPD{ref,c}.cov));
Daniel@0:       assert(approxeq(CPD{i,c}.weights, CPD{ref,c}.weights));
Daniel@0:     end
Daniel@0:   end
Daniel@0: end
Daniel@0: