comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/mgram2.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% Like a durational HMM, except we use soft evidence on the observed nodes.
+% Should give the same results as HSMM/test_mgram2.
+
+past = 1;
+% If past=1, P(Yt|Qt=j,Dt=d) = P(y_{t-d+1:t}|j)
+% If past=0, P(Yt|Qt=j,Dt=d) = P(y_{t:t+d-1}|j) - future evidence
+
+words = {'the', 't', 'h', 'e'};
+data = 'the';
+nwords = length(words);
+word_len = zeros(1, nwords);
+word_prob = normalise(ones(1,nwords));
+word_logprob = log(word_prob);
+for wi=1:nwords
+  word_len(wi)=length(words{wi});
+end
+D = max(word_len);
+
+
+alphasize = 26*2;
+data = letter2num(data);
+T = length(data);
+
+% node numbers
+W = 1; % top level state = word id
+L = 2; % bottom level state = letter position within word
+F = 3;
+O = 4;
+
+ss = 4;
+intra = zeros(ss,ss);
+intra(W,[F L O])=1;
+intra(L,[O F])=1;
+
+inter = zeros(ss,ss);
+inter(W,W)=1;
+inter(L,L)=1;
+inter(F,[W L O])=1;
+
+% node sizes
+ns = zeros(1,ss);
+ns(W) = nwords;
+ns(L) = D;
+ns(F) = 2;
+ns(O) = alphasize;
+ns2 = [ns ns];
+
+% Make the DBN
+bnet = mk_dbn(intra, inter, ns, 'observed', O);
+eclass = bnet.equiv_class;
+
+% uniform start distrib over words, uniform trans mat
+Wstart = normalise(ones(1,nwords));
+Wtrans = mk_stochastic(ones(nwords,nwords));
+%Wtrans = ones(nwords,nwords);
+
+% always start in state d = length(word) for each bottom level HMM
+Lstart = zeros(nwords, D);
+for i=1:nwords
+  l = length(words{i});
+  Lstart(i,l)=1;
+end
+
+% make downcounters
+RLtrans = mk_rightleft_transmat(D, 0); % 0 self loop prob
+Ltrans = repmat(RLtrans, [1 1 nwords]);
+
+% Finish when downcoutner = 1
+Fprob = zeros(nwords, D, 2);
+Fprob(:,1,2)=1;
+Fprob(:,2:end,1)=1;
+
+
+% Define CPDs for slice 1
+bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', Wstart);
+bnet.CPD{eclass(L,1)} = tabular_CPD(bnet, L, 'CPT', Lstart);
+bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', Fprob);
+
+
+% Define CPDs for slice 2
+bnet.CPD{eclass(W,2)} = hhmmQ_CPD(bnet, W+ss, 'Fbelow', F, 'startprob', Wstart,  'transprob', Wtrans);
+bnet.CPD{eclass(L,2)} = hhmmQ_CPD(bnet, L+ss, 'Fself', F, 'Qps', W+ss, 'startprob', Lstart, 'transprob', Ltrans);
+
+
+if 0
+% To test it is generating correctly, we create an artificial
+% observation process that capitalizes at the start of a new segment
+% Oprob(Ft-1,Qt,Dt,Yt)
+Oprob = zeros(2,nwords,D,alphasize);
+Oprob(1,1,3,letter2num('t'),1)=1;
+Oprob(1,1,2,letter2num('h'),1)=1;
+Oprob(1,1,1,letter2num('e'),1)=1;
+Oprob(2,1,3,letter2num('T'),1)=1;
+Oprob(2,1,2,letter2num('H'),1)=1;
+Oprob(2,1,1,letter2num('E'),1)=1;
+Oprob(1,2,1,letter2num('a'),1)=1;
+Oprob(2,2,1,letter2num('A'),1)=1;
+Oprob(1,3,1,letter2num('b'),1)=1;
+Oprob(2,3,1,letter2num('B'),1)=1;
+Oprob(1,4,1,letter2num('c'),1)=1;
+Oprob(2,4,1,letter2num('C'),1)=1;
+
+% Oprob1(Qt,Dt,Yt)
+Oprob1 = zeros(nwords,D,alphasize);
+Oprob1(1,3,letter2num('t'),1)=1;
+Oprob1(1,2,letter2num('h'),1)=1;
+Oprob1(1,1,letter2num('e'),1)=1;
+Oprob1(2,1,letter2num('a'),1)=1;
+Oprob1(3,1,letter2num('b'),1)=1;
+Oprob1(4,1,letter2num('c'),1)=1;
+
+bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', Oprob);
+bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', Oprob1);
+
+evidence = cell(ss,T);
+%evidence{W,1}=1;
+sample = cell2num(sample_dbn(bnet, 'length', T, 'evidence', evidence));
+str = num2letter(sample(4,:))
+end
+
+
+if 1
+
+[log_obslik, obslik, match] = mk_mgram_obslik(lower(data), words, word_len, word_prob);
+% obslik(j,t,d)
+softCPDpot = cell(ss,T);
+ens = ns;
+ens(O)=1;
+ens2 = [ens ens];
+for t=2:T
+  dom = [F W+ss L+ss O+ss];
+  % tab(Ft-1, Q2, Dt)
+  tab = ones(2, nwords, D);
+  if past
+    tab(1,:,:)=1; % if haven't finished previous word, likelihood is 1
+    %tab(2,:,:) = squeeze(obslik(:,t,:)); % otherwise likelihood of this segment
+    for d=1:min(t,D)
+      tab(2,:,d) = squeeze(obslik(:,t,d));
+    end
+  else
+    for d=1:max(1,min(D,T+1-t))
+      tab(2,:,d) = squeeze(obslik(:,t+d-1,d));
+    end
+  end
+  softCPDpot{O,t} = dpot(dom, ens2(dom), tab);
+end
+t = 1;
+dom = [W L O];
+% tab(Q2, Dt)
+tab = ones(nwords, D);
+if past
+  %tab = squeeze(obslik(:,t,:));
+  tab(:,1) = squeeze(obslik(:,t,1));
+else
+  for d=1:min(D,T-t)
+    tab(:,d) = squeeze(obslik(:,t+d-1,d));
+  end
+end
+softCPDpot{O,t} = dpot(dom, ens(dom), tab);
+
+
+%bnet.observed = [];
+% uniformative observations
+%bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', mk_stochastic(ones(2,nwords,D,alphasize)));
+%bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', mk_stochastic(ones(nwords,D,alphasize)));
+
+engine = jtree_dbn_inf_engine(bnet);
+evidence = cell(ss,T);
+% we add dummy data to O to force its effective size to be 1.
+% The actual values have already been incorporated into softCPDpot 
+evidence(O,:) = num2cell(ones(1,T));
+[engine, ll_dbn] = enter_evidence(engine, evidence, 'softCPDpot', softCPDpot);
+
+
+%evidence(F,:) = num2cell(2*ones(1,T));
+%[engine, ll_dbn] = enter_evidence(engine, evidence);
+
+
+gamma = zeros(nwords, T);
+for t=1:T
+  m = marginal_nodes(engine, [W F], t);
+  gamma(:,t) = m.T(:,2);
+end
+
+gamma
+
+xidbn = zeros(nwords, nwords);
+for t=1:T-1
+  m = marginal_nodes(engine, [W F W+ss], t);
+  xidbn = xidbn + squeeze(m.T(:,2,:));
+end
+
+% thee
+% xidbn(1,4)  = 0.9412  the->e
+% (2,3)=0.0588 t->h
+% (3,4)=0.0588 h-e
+% (4,4)=0.0588 e-e
+
+
+end