BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

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);