From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: 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
---
 .../SLP/learning/@jtree_inf_engine2/find_mpe.m     | 71 ++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m

(limited to 'sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m')

diff --git a/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m
new file mode 100644
index 00000000..8a46c1ed
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/@jtree_inf_engine2/find_mpe.m
@@ -0,0 +1,71 @@
+function mpe = find_mpe(engine, evidence, varargin)
+% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes)
+% function mpe = find_mpe(engine, evidence,...)
+%
+% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default value in brackets]
+%
+% soft    - a cell array of soft/virtual evidence;
+%           soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ]
+%
+
+bnet = bnet_from_engine(engine);
+ns = bnet.node_sizes(:);
+N = length(bnet.dag);
+
+engine.evidence = evidence;
+  
+% set default params
+exclude = [];
+soft_evidence = cell(1,N);
+
+% parse optional params
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'soft',    soft_evidence = args{i+1}; 
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end
+engine.maximize = 1;
+
+onodes = find(~isemptycell(evidence));
+hnodes = find(isemptycell(evidence));
+pot_type = determine_pot_type(bnet, onodes);
+ if strcmp(pot_type, 'cg')
+  check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag);
+end
+
+hard_nodes = 1:N;
+soft_nodes = find(~isemptycell(soft_evidence));
+S = length(soft_nodes);
+if S > 0
+  assert(pot_type == 'd');
+  assert(mysubset(soft_nodes, bnet.dnodes));
+end
+ 
+% Evaluate CPDs with evidence, and convert to potentials  
+pot = cell(1, N+S);
+for n=1:N
+  fam = family(bnet.dag, n);
+  e = bnet.equiv_class(n);
+  if isempty(bnet.CPD{e})
+    error(['must define CPD ' num2str(e)])
+  else
+    pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence);
+  end
+end
+
+for i=1:S
+  n = soft_nodes(i);
+  pot{N+i} = dpot(n, ns(n), soft_evidence{n});
+end
+clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); 
+
+[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes);
+[clpot, seppot] = collect_evidence(engine, clpot, seppot);
+mpe = find_max_config(engine, clpot, seppot, evidence); % instead of distribute evidence
-- 
cgit 1.4.1