Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg3.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
---|---|
date | Tue, 10 Feb 2015 15:05:51 +0000 |
parents | |
children |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +% make a factor graph corresponding to an HMM with Gaussian outputs, where we absorb the +% evidence up front + +seed = 1; +rand('state', seed); +randn('state', seed); + +T = 3; +Q = 3; +O = 2; +cts_obs = 1; +param_tying = 1; +bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying); +N = 2*T; +onodes = bnet.observed; +hnodes = mysetdiff(1:N, onodes); + +data = sample_bnet(bnet); + +init_factor = bnet.CPD{1}; +obs_factor = bnet.CPD{3}; +edge_factor = bnet.CPD{2}; % trans matrix + +nfactors = T; +nvars = T; % hidden only +G = zeros(nvars, nfactors); +G(1,1) = 1; +for t=1:T-1 + G(t:t+1, t+1)=1; +end + +node_sizes = Q*ones(1,T); + +% We tie params as follows: +% the first hidden node use init_factor (number 1) +% all hidden nodes on the backbone use edge_factor (number 2) +% all observed nodes use the same factor, namely obs_factor + +small_fg = mk_fgraph_given_ev(G, node_sizes, {init_factor, edge_factor}, {obs_factor}, data(onodes), ... + 'equiv_class', [1 2*ones(1,T-1)], 'ev_equiv_class', ones(1,T)); + +small_bnet = fgraph_to_bnet(small_fg); + +% don't pre-process evidence +% big_fg = bnet_to_fgraph(bnet); % can't handle Gaussian node + + +engine = {}; +engine{1} = jtree_inf_engine(bnet); +engine{2} = belprop_fg_inf_engine(small_fg, 'max_iter', 2*T); +engine{3} = jtree_inf_engine(small_bnet); +nengines = length(engine); + + +% on BN, use the original evidence +evidence = cell(1, 2*T); +evidence(onodes) = data(onodes); +tic; [engine{1}, ll(1)] = enter_evidence(engine{1}, evidence); toc + + +% on small_fg, we have already included the evidence +evidence = cell(1,T); +tic; [engine{2}, ll(2)] = enter_evidence(engine{2}, evidence); toc + + +% on small_bnet, we must add evidence to the dummy nodes +V = small_fg.nvars; +dummy = V+1:V+small_fg.nfactors; +N = max(dummy); +evidence = cell(1, N); +evidence(dummy) = {1}; +tic; [engine{3}, ll(3)] = enter_evidence(engine{3}, evidence); toc + + + +marg = zeros(T, nengines, Q); % marg(t,e,:) +for t=1:T + for e=1:nengines + m = marginal_nodes(engine{e}, t); + marg(t,e,:) = m.T; + end +end +marg(:,:,1)