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, 73 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m
new file mode 100644
index 00000000..0c15580e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m
@@ -0,0 +1,73 @@
+function CPD = hhmmF_CPD(bnet, self, Qself, Fbelow, varargin)
+% HHMMF_CPD Make the CPD for an F node in a hierarchical HMM
+% CPD = hhmmF_CPD(bnet, self, Qself,  Fbelow, ...)
+%
+%        Qps
+%          \
+%           \
+%           Fself
+%         /   |
+%        /    |
+%       Qself Fbelow
+%
+% We assume nodes are ordered (numbered) as follows: Qps, Q, Fbelow, F
+% All nodes numbers should be from slice 1.
+%
+% If Fbelow if missing, this becomes a regular tabular_CPD.
+% Qps may be omitted.
+%
+% optional args [defaults]
+% 
+% Qps - node numbers.
+% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state wp 0.9]
+%
+% hhmmF_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc.
+%
+% We create an isolated tabular_CPD with no F parent to learn termprob
+% so we can avail of e.g., entropic or Dirichlet priors.
+%
+% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01.
+
+
+
+Qps = [];
+% get parents
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'Qps', Qps = varargin{i+1}; 
+  end
+end
+
+ns = bnet.node_sizes(:);
+Qsz = ns(Qself);
+Qpsz = prod(ns(Qps));
+CPD.Qsz = Qsz;
+CPD.Qpsz = Qpsz;
+
+ps = parents(bnet.dag, self);
+CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps);
+CPD.Qps_ndx = find_equiv_posns(Qps, ps);
+CPD.Qself_ndx = find_equiv_posns(Qself, ps);
+
+% set default arguments
+p = 0.9;
+%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz
+termprob = zeros(Qpsz, Qsz, 2);
+termprob(:, Qsz, 2) = p; 
+termprob(:, Qsz, 1) = 1-p; 
+termprob(:, 1:(Qsz-1), 1) = 1; 
+    
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'termprob', termprob = varargin{i+1}; 
+  end
+end
+
+CPD.sub_CPD_term = mk_isolated_tabular_CPD([Qpsz Qsz 2], {'CPT', termprob});
+S = struct(CPD.sub_CPD_term);
+CPD.termprob = S.CPT;
+
+CPD = class(CPD, 'hhmmF_CPD', tabular_CPD(bnet, self));
+
+CPD = update_CPT(CPD);
+