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_limid.m b/sourcecodes/bnt-master/BNT/general/mk_limid.m
new file mode 100644
index 00000000..3c10ed81
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+++ b/sourcecodes/bnt-master/BNT/general/mk_limid.m
@@ -0,0 +1,93 @@
+function bnet = mk_limid(dag, node_sizes, varargin)
+% MK_LIMID Make a limited information influence diagram
+%
+% BNET = MK_LIMID(DAG, NODE_SIZES, ...) 
+% DAG is the adjacency matrix for a directed acyclic graph.
+% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
+% For decision nodes, the parents must explicitely include all nodes
+% on which it can depends, in contrast to the implicit no-forgetting assumption of influence diagrams.
+% (For details, see "Representing and solving decision problems with limited information",
+%    Lauritzen and Nilsson, Management Science, 2001.)
+%
+% 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.
+% 
+% The list below gives optional arguments [default value in brackets].
+% 
+% chance   - the list of nodes which are random variables [1:N]
+% decision - the list of nodes which are decision nodes [ [] ]
+% utility  - the list of nodes which are utility nodes [ [] ]
+% equiv_class - equiv_class(i)=j  means node i gets its params from CPD{j} [1:N]
+%
+% e.g., limid = mk_limid(dag, ns, 'chance', [1 3], 'utility', [2])
+
+n = length(dag);
+
+% default values for parameters
+bnet.chance_nodes = 1:n;
+bnet.equiv_class = 1:n;
+bnet.utility_nodes = [];
+bnet.decision_nodes = [];
+bnet.dnodes = 1:n; % discrete 
+
+if nargin >= 3
+  args = varargin;
+  nargs = length(args);
+  if ~isstr(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 'chance',      bnet.chance_nodes = args{i+1}; 
+       case 'utility',     bnet.utility_nodes = args{i+1}; 
+       case 'decision',    bnet.decision_nodes = args{i+1}; 
+       case 'discrete',    bnet.dnodes = args{i+1}; 
+        otherwise,  
+	error(['invalid argument name ' args{i}]);       
+      end
+    end
+  end
+end
+ 
+bnet.limid = 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);
+
+% for e=1:E
+%   i = bnet.members_of_equiv_class{e}(1); % pick arbitrary member
+%   switch type{e}
+%     case 'tabular',  bnet.CPD{e} = tabular_CPD(bnet, i);
+%     case 'gaussian', bnet.CPD{e} = gaussian_CPD(bnet, i);
+%     otherwise, error(['unrecognized CPD type ' type{e}]);
+%   end
+% end
+
+directed = 1;
+if ~acyclic(dag,directed)
+  error('graph must be acyclic')
+end
+
+bnet.order = topological_sort(bnet.dag);