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, 133 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/general/mk_dbn.m b/sourcecodes/bnt-master/BNT/general/mk_dbn.m
new file mode 100644
index 00000000..82859bc3
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+++ b/sourcecodes/bnt-master/BNT/general/mk_dbn.m
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+function bnet = mk_dbn(intra, inter, node_sizes, varargin)
+% MK_DBN Make a Dynamic Bayesian Network.
+%
+% BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs
+% from i in slice t to j in slice t iff intra(i,j) = 1, and 
+% from i in slice t to j in slice t+1 iff inter(i,j) = 1,
+% for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1.
+% node_sizes(i) is the number of values node i can take on.
+% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
+% See also mk_bnet.
+%
+% Optional arguments [default in brackets]
+% 'discrete' - list of discrete nodes [1:n]
+% 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ]
+% 'eclass1' - equiv class for slice 1 [1:n]
+% 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1]
+%    equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j},
+%    i.e., nodes i and j have tied parameters.
+% 'intra1' - topology of first slice, if different from others
+% 'names' - a cell array of strings to be associated with nodes 1:n [{}]
+%    This creates an associative array, so you write e.g.
+%     'evidence(bnet.names{'bar'}) = 42' instead of  'evidence(2} = 42' 
+%     assuming names = { 'foo', 'bar', ...}.
+%    
+% For backwards compatibility with BNT2, arguments can also be specified as follows
+%   bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1)
+%
+% After calling this function, you must specify the parameters (conditional probability
+% distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc.
+
+
+n = length(intra);
+ss = n;
+bnet.nnodes_per_slice = ss;
+bnet.intra = intra;
+bnet.inter = inter;
+bnet.intra1 = intra;
+dag = zeros(2*n);
+dag(1:n,1:n) = bnet.intra1;
+dag(1:n,(1:n)+n) = bnet.inter;
+dag((1:n)+n,(1:n)+n) = bnet.intra;
+bnet.dag = dag;
+bnet.names = {};
+
+directed = 1;
+if ~acyclic(dag,directed)
+  error('graph must be acyclic')
+end
+
+
+bnet.eclass1 = 1:n;
+%bnet.eclass2 = (1:n)+n;
+bnet.eclass2 = bnet.eclass1;
+for i=1:ss
+  if isequal(parents(dag, i+ss), parents(dag, i)+ss)
+    %fprintf('%d has isomorphic parents, eclass %d\n', i, bnet.eclass2(i))
+  else
+    bnet.eclass2(i) = max(bnet.eclass2) + 1;
+    %fprintf('%d has non isomorphic parents, eclass %d\n', i, bnet.eclass2(i))
+  end
+end
+
+dnodes = 1:n;
+bnet.observed = [];
+
+if nargin >= 4
+  args = varargin;
+  nargs = length(args);
+  if ~isstr(args{1})
+    if nargs >= 1, dnodes = args{1}; end
+    if nargs >= 2, bnet.eclass1 = args{2}; end
+    if nargs >= 3, bnet.eclass2 = args{3}; end
+    if nargs >= 4, bnet.intra1 = args{4}; end
+  else
+    for i=1:2:nargs
+      switch args{i},
+       case 'discrete', dnodes = args{i+1}; 
+       case 'observed', bnet.observed = args{i+1}; 
+       case 'eclass1',  bnet.eclass1 = args{i+1}; 
+       case 'eclass2',  bnet.eclass2 = args{i+1}; 
+       case 'intra1',  bnet.intra1 = args{i+1}; 
+       %case 'ar_hmm',  bnet.ar_hmm = args{i+1};  % should check topology
+       case 'names',  bnet.names = assocarray(args{i+1}, num2cell(1:n)); 
+       otherwise,  
+	error(['invalid argument name ' args{i}]);       
+      end
+    end
+  end
+end
+
+
+bnet.observed = sort(bnet.observed); % for comparing sets
+ns = node_sizes;
+bnet.node_sizes_slice = ns(:)';
+bnet.node_sizes = [ns(:) ns(:)];
+
+cnodes = mysetdiff(1:n, dnodes);
+bnet.dnodes_slice = dnodes;
+bnet.cnodes_slice = cnodes;
+bnet.dnodes = [dnodes dnodes+n];
+bnet.cnodes = [cnodes cnodes+n];
+
+bnet.equiv_class = [bnet.eclass1(:) bnet.eclass2(:)];
+bnet.CPD = cell(1,max(bnet.equiv_class(:)));
+eclass = bnet.equiv_class(:);
+E = max(eclass);
+bnet.rep_of_eclass = zeros(1,E);
+for e=1:E
+  mems = find(eclass==e);
+  bnet.rep_of_eclass(e) = mems(1);
+end
+
+ss = n;
+onodes = bnet.observed;
+hnodes = mysetdiff(1:ss, onodes);
+bnet.hidden_bitv = zeros(1,2*ss);
+bnet.hidden_bitv(hnodes) = 1;
+bnet.hidden_bitv(hnodes+ss) = 1;
+
+bnet.parents = cell(1, 2*ss);
+for i=1:ss
+  bnet.parents{i} = parents(bnet.dag, i);
+  bnet.parents{i+ss} = parents(bnet.dag, i+ss);
+end
+
+bnet.auto_regressive = zeros(1,ss);
+% ar(i)=1 means (observed) node i depends on i in the  previous slice
+for o=bnet.observed(:)'
+  if any(bnet.parents{o+ss} <= ss)
+    bnet.auto_regressive(o) = 1;
+  end
+end
+