Merge pull request #1 from ziejd2/octave_php_separate

Octave php separate

12 files changed, 776 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Entries
new file mode 100644
index 00000000..c4379581
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Entries
@@ -0,0 +1,6 @@
+/letter2num.m/1.1.1.1/Fri Nov 22 23:10:20 2002//
+/mgram1.m/1.1.1.1/Fri Nov 22 23:59:00 2002//
+/mgram2.m/1.1.1.1/Tue Nov 26 22:04:24 2002//
+/mgram3.m/1.1.1.1/Tue Nov 26 22:14:10 2002//
+/num2letter.m/1.1.1.1/Fri Nov 22 23:07:40 2002//
+D/Old////
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Repository
new file mode 100644
index 00000000..5ede715e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/dynamic/HHMM/Mgram
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Root b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Entries
new file mode 100644
index 00000000..07b688e2
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Entries
@@ -0,0 +1,2 @@
+/mgram2.m/1.1.1.1/Sat Nov 23 00:44:34 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Repository
new file mode 100644
index 00000000..ba3ae6df
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/dynamic/HHMM/Mgram/Old
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Root b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/mgram2.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/mgram2.m
new file mode 100644
index 00000000..719a3167
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/Old/mgram2.m
@@ -0,0 +1,191 @@
+% like mgram1, except we use a durational HMM instead of an HHMM2
+
+past = 0;
+
+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));
+
+% 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 
+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
+
+
+
+
+[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
+  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,:));
+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
+
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/letter2num.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/letter2num.m
new file mode 100644
index 00000000..f4e3f1d9
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/letter2num.m
@@ -0,0 +1,12 @@
+function n = letter2num(l)
+
+% map a-z to 1:26 and A-Z to 27:52
+punct_code = [32:47 58:64 91:96 123:126];
+digits_code = 48:57;
+upper_code = 65:90;
+lower_code = 97:122;
+
+c = double(l);
+n = c-96;
+ndx = find(n <= 0); % upper case
+n(ndx) = c(ndx)  - 64 + 26;
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram1.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram1.m
new file mode 100644
index 00000000..52ca472e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram1.m
@@ -0,0 +1,116 @@
+% a multigram is a degenerate 2HHMM where the bottom level HMMs emit deterministic strings
+% and the the top level abstract states are independent of each other
+% cf. HSMM/test_mgram2 
+
+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;
+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])=1;
+
+% node sizes
+ns = zeros(1,ss);
+ns(W) = nwords;
+ns(L) = D;
+ns(F) = 2;
+ns(O) = alphasize;
+
+
+% 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));
+
+% always start in state 1 for each bottom level HMM
+delta1_start = zeros(1, D);
+delta1_start(1) = 1;
+Lstart = repmat(delta1_start, nwords, 1);
+LRtrans = mk_leftright_transmat(D, 0); % 0 self loop prob
+Ltrans = repmat(LRtrans, [1 1 nwords]);
+
+% Finish in the last letter of each word
+Fprob = zeros(nwords, D, 2);
+Fprob(:,:,1)=1;
+for i=1:nwords
+  Fprob(i,length(words{i}),2)=1;
+  Fprob(i,length(words{i}),1)=0;
+end
+
+% Each state uniquely emits a letter
+Oprob = zeros(nwords, D, alphasize);
+for i=1:nwords
+  for l=1:length(words{i})
+    a = double(words{i}(l))-96;
+    Oprob(i,l,a)=1;
+  end
+end
+
+
+% Define CPDs for slice 
+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);
+bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', Oprob);
+
+% 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);
+
+evidence = cell(ss,T);
+evidence{W,1}=1;
+sample = cell2num(sample_dbn(bnet, 'length', T, 'evidence', evidence));
+str = lower(sample(4,:))
+
+engine = jtree_dbn_inf_engine(bnet);
+evidence = cell(ss,T);
+evidence(O,:) = num2cell(data);
+[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
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram2.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram2.m
new file mode 100644
index 00000000..c61f855a
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram2.m
@@ -0,0 +1,200 @@
+% 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
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram3.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram3.m
new file mode 100644
index 00000000..49228584
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/mgram3.m
@@ -0,0 +1,235 @@
+% like mgram2, except we unroll the DBN so we can use smaller
+% state spaces for the early duration nodes:
+% the state spaces are D1 in {1}, D2 in {1,2}
+
+past = 1;
+
+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;
+
+T = 3;
+dag = unroll_dbn_topology(intra, inter, T);
+
+% node sizes
+ns = zeros(1,ss);
+ns(W) = nwords;
+ns(L) = D;
+ns(F) = 2;
+ns(O) = alphasize;
+ns = repmat(ns(:), [1 T]);
+for d=1:D
+  ns(d,L)=d; % max duration
+end
+ns = ns(:);
+
+% Equiv class in brackets for D=3
+% The Lt's are not tied until t>=D, since they have different sizes.
+% W1 and W2 are not tied since they have different parent sets.
+
+% W1 (1)  W2 (5) W3 (5) W4 (5)
+% L1 (2)  L2 (6) L3 (7) L4 (7)
+% F1 (3)  F2 (3) F3 (4) F3 (4)
+% O1 (4)  O2 (4) O2 (4) O4 (4)
+
+% Since we are not learning, we can dispense with tying
+
+% Make the bnet
+Wnodes = unroll_set(W, ss, T);
+Lnodes = unroll_set(L, ss, T);
+Fnodes = unroll_set(F, ss, T);
+Onodes = unroll_set(O, ss, T);
+
+bnet = mk_bnet(dag, ns);
+eclass = bnet.equiv_class;
+
+% uniform start distrib over words, uniform trans mat
+Wstart = normalise(ones(1,nwords));
+Wtrans = mk_stochastic(ones(nwords,nwords));
+bnet.CPD{eclass(Wnodes(1))} = tabular_CPD(bnet, Wnodes(1), 'CPT', Wstart);
+for t=2:T
+bnet.CPD{eclass(Wnodes(t))} = hhmmQ_CPD(bnet, Wnodes(t), 'Fbelow', Fnodes(t-1), ...
+					'startprob', Wstart,  'transprob', Wtrans);
+end
+
+% always start in state d = length(word) for each bottom level HMM
+% and then count down
+% make downcounters
+RLtrans = mk_rightleft_transmat(D, 0); % 0 self loop prob
+Ltrans = repmat(RLtrans, [1 1 nwords]);
+
+for t=1:T
+  Lstart = zeros(nwords, min(t,D));
+  for i=1:nwords
+    l = length(words{i});
+    Lstart(i,l)=1;
+    if d==1
+      bnet.CPD{eclass(Lnodes(1))} = tabular_CPD(bnet, Lnodes(1), 'CPT', Lstart);
+    else
+      bnet.CPD{eclass(Lnodes(t))} = hhmmQ_CPD(bnet, Lnodes(t), 'Fself', Fnodes(t-1), 'Qps', Wnodes(t), ...
+					      'startprob', Lstart, 'transprob', Ltrans);
+    end
+  end
+end
+
+
+% Finish when downcoutner = 1
+Fprob = zeros(nwords, D, 2);
+Fprob(:,1,2)=1;
+Fprob(:,2:end,1)=1;
+
+
+% Define CPDs for slice 
+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
+
+
+
+
+[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
+
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/num2letter.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/num2letter.m
new file mode 100644
index 00000000..139b81fa
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Mgram/num2letter.m
@@ -0,0 +1,10 @@
+function l = num2letter(n)
+
+% map 1:26 to a-z and 27:52 to A-Z
+punct_code = [32:47 58:64 91:96 123:126];
+digits_code = 48:57;
+upper_code = 65:90;
+lower_code = 97:122;
+
+letters = [char(lower_code) char(upper_code)];
+l = letters(n);