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, 132 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m
new file mode 100644
index 00000000..6f289602
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m
@@ -0,0 +1,132 @@
+function CPD = hhmmQ_CPD(bnet, self, varargin)
+% HHMMQ_CPD Make the CPD for a Q node in a hierarchical HMM
+% CPD = hhmmQ_CPD(bnet, self, ...)
+%
+%  Fself(t-1)   Qps(t)
+%           \    |
+%            \   v
+%  Qold(t-1) ->  Q(t)
+%            /
+%           /
+%  Fbelow(t-1) 
+%
+% Let ss = slice size = num. nodes per slice.
+% This node is Q(t), and has mandatory parents Qold(t-1) (assumed to be numbered Q(t)-ss)
+% and optional parents Fbelow, Fself, Qps.
+% We require parents to be ordered (numbered) as follows:
+% Qold, Fbelow, Fself, Qps, Q.
+%
+% If Fself=2, we use the transition matrix, else we use the prior matrix.
+% If Fself node is omitted (eg. top level), we always use the transition matrix.
+% If Fbelow=2, we may change state, otherwise we must stay in the same state.
+% If Fbelow node is omitted (eg., bottom level), we may change state at every step.
+% If Qps (Q parents) are specified, all parameters are conditioned on their joint value.
+% We may choose any subset of nodes to condition on, as long as they as numbered lower than self.
+%
+% optional args [defaults]
+%
+% Fself - node number <= ss
+% Fbelow  - node number  <= ss
+% Qps - node numbers (all <= 2*ss) - uses 2TBN indexing
+% transprob - transprob(i,k,j) = prob transition from i to j given Qps = k ['leftright']
+% selfprob  - prob of a transition from i to i given Qps=k [0.1]
+% startprob - startprob(k,j) = prob start in j given Qps = k ['leftstart']
+% startargs - other args to be passed to the sub tabular_CPD for learning startprob
+% transargs - other args will be passed to the sub tabular_CPD for learning transprob
+% fullstartprob - 1 means startprob depends on Q(t-1) [0]
+% hhmmQ_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc.
+%
+% We create isolated tabular_CPDs with no F parents to learn transprob/startprob
+% so we can avail of e.g., entropic or Dirichlet priors.
+% In the future, we will be able to represent the transprob using a tree_CPD.
+%
+% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01.
+
+
+ss = bnet.nnodes_per_slice;
+ns = bnet.node_sizes(:);
+
+% set default arguments
+Fself = [];
+Fbelow = [];
+Qps = [];
+startprob = 'leftstart';
+transprob = 'leftright';
+startargs = {};
+transargs = {};
+selfprob = 0.1;
+fullstartprob = 0;
+
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'Fself', Fself = varargin{i+1};
+   case 'Fbelow', Fbelow = varargin{i+1};
+   case 'Qps', Qps = varargin{i+1};
+   case 'transprob', transprob = varargin{i+1}; 
+   case 'selfprob',  selfprob = varargin{i+1}; 
+   case 'startprob', startprob = varargin{i+1}; 
+   case 'startargs', startargs = varargin{i+1}; 
+   case 'transargs', transargs = varargin{i+1}; 
+   case 'fullstartprob', fullstartprob = varargin{i+1}; 
+  end
+end
+
+CPD.fullstartprob = fullstartprob;
+
+ps = parents(bnet.dag, self);
+ndsz = ns(:)';
+CPD.dom_sz = [ndsz(ps) ns(self)];
+CPD.Fself_ndx = find_equiv_posns(Fself, ps);
+CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps);
+%CPD.Qps_ndx = find_equiv_posns(Qps+ss, ps);
+CPD.Qps_ndx = find_equiv_posns(Qps, ps);
+old_self = self-ss;
+CPD.old_self_ndx = find_equiv_posns(old_self, ps);
+
+Qps = ps(CPD.Qps_ndx);
+CPD.Qsz = ns(self);
+CPD.Qpsz = prod(ns(Qps));
+CPD.Qpsizes = ns(Qps);
+Qsz = CPD.Qsz;
+Qpsz = CPD.Qpsz;
+
+if strcmp(transprob, 'leftright')
+  LR = mk_leftright_transmat(Qsz, selfprob);
+  transprob = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j)
+  transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j)
+end
+transargs{end+1} = 'CPT';
+transargs{end+1} = transprob;
+CPD.sub_CPD_trans = mk_isolated_tabular_CPD(ns([old_self Qps self]), transargs);
+S = struct(CPD.sub_CPD_trans);
+%CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]);
+CPD.transprob = S.CPT;
+
+
+if strcmp(startprob, 'leftstart')
+  startprob = zeros(Qpsz, Qsz);
+  startprob(:,1) = 1;
+end
+if isempty(CPD.Fself_ndx)
+  CPD.sub_CPD_start = [];
+  CPD.startprob = [];
+else
+  startargs{end+1} = 'CPT';
+  startargs{end+1} = startprob;
+  if CPD.fullstartprob
+    CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([self Qps self]), startargs);
+    S = struct(CPD.sub_CPD_start);
+    %CPD.startprob = myreshape(S.CPT, [Qsz Qpsz Qsz]);
+    CPD.startprob = S.CPT;
+  else
+    CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([Qps self]), startargs);
+    S = struct(CPD.sub_CPD_start);
+    %CPD.startprob = myreshape(S.CPT, [CPD.Qpsizes Qsz]);
+    CPD.startprob = S.CPT;
+  end
+end
+
+CPD = class(CPD, 'hhmmQ_CPD', tabular_CPD(bnet, self));
+
+CPD = update_CPT(CPD);
+