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, 93 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/general/mk_bnet.m b/sourcecodes/bnt-master/BNT/general/mk_bnet.m
new file mode 100644
index 00000000..37560ac2
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/general/mk_bnet.m
@@ -0,0 +1,93 @@
+function bnet = mk_bnet(dag, node_sizes, varargin)
+% MK_BNET Make a Bayesian network.
+%
+% BNET = MK_BNET(DAG, NODE_SIZES, ...) makes a graphical model with an arc from i to j iff DAG(i,j) = 1.
+% Thus DAG is the adjacency matrix for a directed acyclic graph.
+% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
+%
+% node_sizes(i) is the number of values node i can take on,
+%   or the length of node i if i is a continuous-valued vector.
+% node_sizes(i) = 1 if i is a utility node.
+% 
+% Below are the names of optional arguments [and their default value in brackets].
+% Pass as 'PropertyName1', PropertyValue1, 'PropertyName2', PropertyValue2, ...
+% 
+% discrete - the list of nodes which are discrete random variables [1:N]
+% equiv_class - equiv_class(i)=j  means node i gets its params from CPD{j} [1:N]
+% observed - the list of nodes which will definitely be observed in every case [ [] ]
+% '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', ...}.
+%
+% e.g., bnet = mk_bnet(dag, ns, 'discrete', [1 3])
+%
+% For backwards compatibility with BNT2, you can also specify the parameters in the following order
+%   bnet = mk_bnet(dag, node_sizes, discrete_nodes, equiv_class)
+
+n = length(dag);
+
+% default values for parameters
+bnet.equiv_class = 1:n;
+bnet.dnodes = 1:n; % discrete 
+bnet.observed = [];
+bnet.names = {};
+
+if nargin >= 3
+  args = varargin;
+  nargs = length(args);
+  if ~ischar(args{1})
+    if nargs >= 1, bnet.dnodes = args{1}; end
+    if nargs >= 2, bnet.equiv_class = args{2}; end
+  else    
+    for i=1:2:nargs
+      switch args{i},
+       case 'equiv_class', bnet.equiv_class = args{i+1}; 
+       case 'discrete',    bnet.dnodes = args{i+1}; 
+       case 'observed',    bnet.observed = args{i+1}; 
+       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
+bnet.hidden = mysetdiff(1:n, bnet.observed(:)');
+bnet.hidden_bitv = zeros(1,n);
+bnet.hidden_bitv(bnet.hidden) = 1;
+bnet.dag = dag;
+bnet.node_sizes = node_sizes(:)';
+
+bnet.cnodes = mysetdiff(1:n, bnet.dnodes);
+% too many functions refer to cnodes to rename it to cts_nodes - 
+% We hope it won't be confused with chance nodes!
+
+bnet.parents = cell(1,n);
+for i=1:n
+  bnet.parents{i} = parents(dag, i);
+end
+
+E = max(bnet.equiv_class);
+mem = cell(1,E);
+for i=1:n
+  e = bnet.equiv_class(i);
+  mem{e} = [mem{e} i];
+end
+bnet.members_of_equiv_class = mem;
+
+bnet.CPD = cell(1, E);
+
+bnet.rep_of_eclass = zeros(1,E);
+for e=1:E
+  mems = bnet.members_of_equiv_class{e};
+  bnet.rep_of_eclass(e) = mems(1);
+end
+
+directed = 1;
+if ~acyclic(dag,directed)
+  error('graph must be acyclic')
+end
+
+bnet.order = topological_sort(bnet.dag);