Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/cmp_learning_dbn.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, varargin) % CMP_LEARNING_DBN Compare a bunch of inference engines by learning a DBN % function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, exact, T, ncases, max_iter) % % engine{i} is the i'th inference engine. % time(e) = elapsed time for doing inference with engine e % CPD{e,c} is the learned CPD for eclass c in engine e % LL{e} is the learning curve for engine e % cases{i} is the i'th training case % % The list below gives optional arguments [default value in brackets]. % % exact - specifies which engines do exact inference [ 1:length(engine) ] % check_ll - 1 means we check that the log-likelihoods are correct [1] % ncases - num. random training cases [2] % max_iter - max. num EM iterations [2] % set default params exact = 1:length(engine); check_ll = 1; ncases = 2; max_iter = 2; args = varargin; nargs = length(args); for i=1:2:nargs switch args{i}, case 'exact', exact = args{i+1}; case 'check_ll', check_ll = args{i+1}; case 'ncases', ncases = args{i+1}; case 'max_iter', max_iter = args{i+1}; otherwise, error(['unrecognized argument ' args{i}]) end end E = length(engine); ss = length(bnet.intra); onodes = bnet.observed; cases = cell(1, ncases); for i=1:ncases ev = sample_dbn(bnet, 'length', T); cases{i} = cell(ss,T); cases{i}(onodes,:) = ev(onodes, :); end LL = cell(1,E); time = zeros(1,E); for i=1:E tic [bnet2{i}, LL{i}] = learn_params_dbn_em(engine{i}, cases, 'max_iter', max_iter); time(i) = toc; fprintf('engine %d took %6.4f seconds\n', i, time(i)); end ref = exact(1); % reference cmp = mysetdiff(exact, ref); if check_ll for i=cmp(:)' if ~approxeq(LL{ref}, LL{i}) error(['engine ' num2str(i) ' has wrong ll']) end end end nCPDs = length(bnet.CPD); CPD = cell(E, nCPDs); tabular = zeros(1, nCPDs); for i=1:E temp = bnet2{i}; for c=1:nCPDs tabular(c) = isa(temp.CPD{c}, 'tabular_CPD'); CPD{i,c} = struct(temp.CPD{c}); end end for i=cmp(:)' for c=1:nCPDs if tabular(c) assert(approxeq(CPD{i,c}.CPT, CPD{ref,c}.CPT)); else assert(approxeq(CPD{i,c}.mean, CPD{ref,c}.mean)); assert(approxeq(CPD{i,c}.cov, CPD{ref,c}.cov)); assert(approxeq(CPD{i,c}.weights, CPD{ref,c}.weights)); end end end