fg2.m

贝叶斯算法(matlab编写) 安装,添加目录 /home/ai2/murphyk/matlab/FullBNT

% make a factor graph corresponding to an  HMM, where we absorb the evidence up front,
% and then eliminate the observed nodes.
% Compare this with not absorbing the evidence.

seed = 1;
rand('state', seed);
randn('state', seed);

T = 3;
Q = 3;
O = 2;
cts_obs = 0;
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);
big_bnet = fgraph_to_bnet(big_fg);



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);
engine{4} = belprop_fg_inf_engine(big_fg, 'max_iter', 3*T);
engine{5} = jtree_inf_engine(big_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


% on big_fg, use the original evidence
evidence = cell(1, 2*T);
evidence(onodes) = data(onodes);
tic; [engine{4}, ll(4)] = enter_evidence(engine{4}, evidence); toc


% on big_bnet, we must add evidence to the dummy nodes
V = big_fg.nvars;
assert(V == 2*T);
dummy = V+1:V+big_fg.nfactors;
N = max(dummy);
evidence = cell(1, N);
evidence(onodes) = data(onodes);
evidence(dummy) = {1};
tic; [engine{5}, ll(5)] = enter_evidence(engine{5}, 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)