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% Try to learn a 3 level HHMM similar to mk_square_hhmm
% from hand-drawn squares.
% Because startprob should be shared for t=1:T,
% but in the DBN is shared for t=2:T, we train using a single long sequence.
discrete_obs = 0;
supervised = 1;
obs_finalF2 = 0;
% It is not possible to observe F2 if we learn
% because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume
% the F nodes are always hidden (for speed).
% However, for generating, we might want to set the final F2=true
% to force all subroutines to finish.
seed = 1;
rand('state', seed);
randn('state', seed);
bnet = mk_square_hhmm(discrete_obs, 0);
ss = 6;
Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6;
Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3];
Qsizes = [2 4 1];
if supervised
bnet.observed = [Q1 Q2 Onode];
else
bnet.observed = [Onode];
end
if obs_finalF2
engine = jtree_dbn_inf_engine(bnet);
% can't use ndx version because sometimes F2 is hidden, sometimes observed
error('can''t observe F when learning')
else
if supervised
engine = jtree_ndx_dbn_inf_engine(bnet);
else
engine = jtree_hmm_inf_engine(bnet);
end
end
load 'square4_cases' % cases{seq}{i,t} for i=1:ss
%plot_square_hhmm(cases{1})
%long_seq = cat(2, cases{:});
train_cases = cases(1:2);
long_seq = cat(2, train_cases{:});
if ~supervised
T = size(long_seq,2);
for t=1:T
long_seq{Q1,t} = [];
long_seq{Q2,t} = [];
end
end
[bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 2);
eclass = bnet2.equiv_class;
CPDO=struct(bnet2.CPD{eclass(Onode,1)});
mu = CPDO.mean;
Sigma = CPDO.cov;
CPDO_full = CPDO;
% force diagonal covs after training
for k=1:size(Sigma,3)
Sigma(:,:,k) = diag(diag(Sigma(:,:,k)));
end
bnet2.CPD{6} = set_fields(bnet.CPD{6}, 'cov', Sigma);
if 0
% visualize each model by concatenating means for each model for nsteps in a row
nsteps = 5;
ev = cell(ss, nsteps*prod(Qsizes(2:3)));
t = 1;
for q2=1:Qsizes(2)
for q3=1:Qsizes(3)
for i=1:nsteps
ev{Onode,t} = mu(:,q2,q3);
ev{Q2,t} = q2;
t = t + 1;
end
end
end
plot_square_hhmm(ev)
end
% bnet3 is the same as the learned model, except we will use it in testing mode
if supervised
bnet3 = bnet2;
bnet3.observed = [Onode];
engine3 = hmm_inf_engine(bnet3);
%engine3 = jtree_ndx_dbn_inf_engine(bnet3);
else
bnet3 = bnet2;
engine3 = engine2;
end
if 0
% segment whole sequence
mpe = calc_mpe_dbn(engine3, long_seq);
pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []);
end
% segment each sequence
test_cases = cases(3:4);
for i=1:2
ev = test_cases{i};
T = size(ev, 2);
for t=1:T
ev{Q1,t} = [];
ev{Q2,t} = [];
end
%mpe = calc_mpe_dbn(engine3, ev);
mpe = find_mpe(engine3, ev)
subplot(1,2,i)
plot_square_hhmm(mpe)
%pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []);
q1s = cell2num(mpe(Q1,:));
h = hist(q1s, 1:Qsizes(1));
map_q1 = argmax(h);
str = sprintf('test seq %d is of type %d\n', i, map_q1);
title(str)
end
if 0
% Estimate gotten by couting transitions in the labelled data
% Note that a self transition shouldnt count if F2=off.
Q2ev = cell2num(ev(Q2,:));
Q2a = Q2ev(1:end-1);
Q2b = Q2ev(2:end);
counts = compute_counts([Q2a; Q2b], [4 4]);
end
eclass = bnet2.equiv_class;
CPDQ1=struct(bnet2.CPD{eclass(Q1,2)});
CPDQ2=struct(bnet2.CPD{eclass(Q2,2)});
CPDQ3=struct(bnet2.CPD{eclass(Q3,2)});
CPDF2=struct(bnet2.CPD{eclass(F2,1)});
CPDF3=struct(bnet2.CPD{eclass(F3,1)});
A=add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2));
squeeze(A(:,1,:));
CPDQ2.startprob;
if 0
S=struct(CPDF2.sub_CPD_term);
S.nsamples
reshape(S.counts, [2 4 2])
end
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