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

1 files changed, 178 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m
new file mode 100644
index 00000000..41fc7380
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m
@@ -0,0 +1,178 @@
+function CPD = update_ess2(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node.
+% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv)
+
+% Figure out the node numbers associated with each parent
+dom = fmarginal.domain;
+self = dom(end); % by assumption
+old_self = dom(CPD.old_self_ndx);
+Fself = dom(CPD.Fself_ndx);
+Fbelow = dom(CPD.Fbelow_ndx);
+Qps = dom(CPD.Qps_ndx);
+
+Qsz = CPD.Qsz;
+Qpsz = CPD.Qpsz;
+
+
+fmarg = add_ev_to_dmarginal(fmarginal, evidence, ns);
+
+
+
+% hor_counts(old_self, Qps, self),
+% fmarginal(old_self, Fbelow, Fself, Qps, self)
+% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not
+% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset)
+% Since any of i,j,k may be observed, we write
+% hor_counts(counts_ndx{:}) = fmarginal(fmarg_ndx{:})
+% where e.g., counts_ndx = {1, ':', 2} if Qps is hidden but we observe old_self=1, self=2.
+% To create this counts_ndx, we write counts_ndx = mk_multi_ndx(3, obs_dim, obs_val)
+% where counts_obs_dim = [1 3], counts_obs_val = [1 2] specifies the values of dimensions 1 and 3.
+
+counts_obs_dim = [];
+fmarg_obs_dim = [];
+obs_val = []; 
+if hidden_bitv(self)
+  effQsz = Qsz;
+else
+  effQsz = 1;
+  counts_obs_dim = [counts_obs_dim 3];
+  fmarg_obs_dim = [fmarg_obs_dim 5];
+  obs_val = [obs_val evidence{self}];
+end
+  
+% e.g., D=4, d=3, Qps = all Qs above, so dom = [Q3(t-1) F4(t-1) F3(t-1) Q1(t) Q2(t) Q3(t)].
+% so self = Q3(t), old_self = Q3(t-1), CPD.Qps = [1 2], Qps = [Q1(t) Q2(t)]
+dom = fmarginal.domain;
+self = dom(end);
+old_self = dom(1);
+Qps = dom(length(dom)-length(CPD.Qps):end-1);
+
+Qsz = CPD.Qsizes(CPD.d);
+Qpsz = prod(CPD.Qsizes(CPD.Qps));
+
+% If some of the Q nodes are observed (which happens during supervised training)
+% the counts will only be non-zero in positions
+% consistent with the evidence. We put the computed marginal responsibilities
+% into the appropriate slots of the big counts array.
+% (Recall that observed discrete nodes only have a single effective value.)
+% (A more general, but much slower, way is to call add_evidence_to_dmarginal.)
+% We assume the F nodes are never observed.
+
+obs_self = ~hidden_bitv(self);
+obs_Qps = (~isempty(Qps)) & (~any(hidden_bitv(Qps))); % we assume that all or none of the Q parents are observed
+
+if obs_self
+  self_val = evidence{self};
+  oldself_val = evidence{old_self};
+end
+
+if obs_Qps
+  Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps}));
+  if Qps_val == 0
+    keyboard
+  end
+end
+
+if CPD.d==1 % no Qps from above
+  if ~CPD.F1toQ1 % no F from self
+    % marg(Q1(t-1), F2(t-1), Q1(t))                            
+    % F2(t-1) P(Q1(t)=j | Q1(t-1)=i)
+    % 1       delta(i,j)
+    % 2       transprob(i,j)
+    if obs_self
+      hor_counts = zeros(Qsz, Qsz);
+      hor_counts(oldself_val, self_val) = fmarginal.T(2);
+    else
+      marg = reshape(fmarginal.T, [Qsz 2 Qsz]);
+      hor_counts = squeeze(marg(:,2,:));
+    end
+  else
+    % marg(Q1(t-1), F2(t-1), F1(t-1), Q1(t))                            
+    % F2(t-1) F1(t-1)  P(Qd(t)=j| Qd(t-1)=i)
+    % ------------------------------------------------------
+    % 1        1         delta(i,j)
+    % 2        1         transprob(i,j)
+    % 1        2         impossible
+    % 2        2         startprob(j)
+    if obs_self
+      marg = myreshape(fmarginal.T, [1 2 2 1]);
+      hor_counts = zeros(Qsz, Qsz);
+      hor_counts(oldself_val, self_val) = marg(1,2,1,1);
+      ver_counts = zeros(Qsz, 1);
+      %ver_counts(self_val) = marg(1,2,2,1);
+      ver_counts(self_val) = marg(1,2,2,1) + marg(1,1,2,1);
+    else
+      marg = reshape(fmarginal.T, [Qsz 2 2 Qsz]);
+      hor_counts = squeeze(marg(:,2,1,:));
+      %ver_counts = squeeze(sum(marg(:,2,2,:),1)); % sum over i
+      ver_counts = squeeze(sum(marg(:,2,2,:),1)) + squeeze(sum(marg(:,1,2,:),1)); % sum i,b
+    end
+  end % F1toQ1
+else % d ~= 1
+  if CPD.d < CPD.D % general case
+    % marg(Qd(t-1), Fd+1(t-1), Fd(t-1), Qps(t), Qd(t))                            
+    % Fd+1(t-1) Fd(t-1)  P(Qd(t)=j| Qd(t-1)=i, Qps(t)=k)
+    % ------------------------------------------------------
+    % 1        1         delta(i,j)
+    % 2        1         transprob(i,k,j)
+    % 1        2         impossible
+    % 2        2         startprob(k,j)
+    if obs_Qps & obs_self
+      marg = myreshape(fmarginal.T, [1 2 2 1 1]);
+      k = 1;
+      hor_counts = zeros(Qsz, Qpsz, Qsz);
+      hor_counts(oldself_val, Qps_val, self_val) = marg(1, 2,1, k,1);
+      ver_counts = zeros(Qpsz, Qsz);
+      %ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1);
+      ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1) + marg(1, 1,2, k,1);
+    elseif obs_Qps & ~obs_self
+      marg = myreshape(fmarginal.T, [Qsz 2 2 1 Qsz]);
+      k = 1;
+      hor_counts = zeros(Qsz, Qpsz, Qsz);
+      hor_counts(:, Qps_val, :) = marg(:, 2,1, k,:);
+      ver_counts = zeros(Qpsz, Qsz);
+      %ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1);
+      ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1) + sum(marg(:, 1,2, k,:), 1);
+    elseif ~obs_Qps & obs_self
+      error('not yet implemented')
+    else % everything is hidden
+      marg = reshape(fmarginal.T, [Qsz 2 2 Qpsz Qsz]);
+      hor_counts = squeeze(marg(:,2,1,:,:)); % i,k,j
+      %ver_counts = squeeze(sum(marg(:,2,2,:,:),1)); % sum over i
+      ver_counts = squeeze(sum(marg(:,2,2,:,:),1)) + squeeze(sum(marg(:,1,2,:,:),1)); % sum over i,b
+    end
+  else % d == D, so no F from below
+    % marg(QD(t-1), FD(t-1), Qps(t), QD(t))                            
+    % FD(t-1) P(QD(t)=j | QD(t-1)=i, Qps(t)=k)
+    % 1      transprob(i,k,j) 
+    % 2      startprob(k,j)
+    if obs_Qps & obs_self
+      marg = myreshape(fmarginal.T, [1 2 1 1]);
+      k = 1;
+      hor_counts = zeros(Qsz, Qpsz, Qsz);
+      hor_counts(oldself_val, Qps_val, self_val) = marg(1, 1, k,1);
+      ver_counts = zeros(Qpsz, Qsz);
+      ver_counts(Qps_val, self_val) = marg(1, 2, k,1);
+    elseif obs_Qps & ~obs_self
+      marg = myreshape(fmarginal.T, [Qsz 2 1 Qsz]);
+      k = 1;
+      hor_counts = zeros(Qsz, Qpsz, Qsz);
+      hor_counts(:, Qps_val, :) = marg(:, 1, k,:);
+      ver_counts = zeros(Qpsz, Qsz);
+      ver_counts(Qps_val, :) = sum(marg(:, 2, k, :), 1);
+    elseif ~obs_Qps & obs_self
+      error('not yet implemented')
+    else % everything is hidden
+      marg = reshape(fmarginal.T, [Qsz 2 Qpsz Qsz]);
+      hor_counts = squeeze(marg(:,1,:,:));
+      ver_counts = squeeze(sum(marg(:,2,:,:),1)); % sum over i
+    end
+  end
+end
+
+CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts);
+
+if ~isempty(CPD.sub_CPD_start)
+  CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts);
+end
+