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

4 files changed, 195 insertions, 0 deletions

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
+
+