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
---
 .../static/@pearl_inf_engine/enter_evidence.m      | 153 +++++++++++++++++++++
 1 file changed, 153 insertions(+)
 create mode 100644 sourcecodes/bnt-master/BNT/inference/static/@pearl_inf_engine/enter_evidence.m

(limited to 'sourcecodes/bnt-master/BNT/inference/static/@pearl_inf_engine/enter_evidence.m')

diff --git a/sourcecodes/bnt-master/BNT/inference/static/@pearl_inf_engine/enter_evidence.m b/sourcecodes/bnt-master/BNT/inference/static/@pearl_inf_engine/enter_evidence.m
new file mode 100644
index 00000000..65e45b15
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@pearl_inf_engine/enter_evidence.m
@@ -0,0 +1,153 @@
+function [engine, loglik, niter] = enter_evidence(engine, evidence, varargin)
+% ENTER_EVIDENCE Add the specified evidence to the network (pearl)
+% [engine, loglik, num_iter] = enter_evidence(engine, evidence, ...)
+% evidence{i} = [] if 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 pa irs:
+% [default value in brackets]
+%
+% maximize - if 1, does max-product instead of sum-product [0]
+% 'filename' -  msgs will be printed to this file, so you can assess convergence while it runs [engine.filename]
+%
+% e.g., engine = enter_evidence(engine, ev, 'maximize', 1)
+%     
+% For discrete nodes, loglik is the negative Bethe free energy evaluated at the final beliefs.
+% For Gaussian nodes, loglik is currently always 0.
+%
+% 'num_iter' returns the number of iterations used.
+
+maximize = 0;
+filename = engine.filename;
+
+% parse optional params
+args = varargin;
+nargs = length(args);
+if nargs > 0
+  for i=1:2:nargs
+    switch args{i},
+     case 'maximize', maximize = args{i+1};
+     case 'filename', filename = args{i+1};
+     otherwise,
+      error(['invalid argument name ' args{i}]);
+    end
+  end
+end
+    
+
+if maximize
+  error('can''t handle max-prop yet')
+end
+
+engine.maximize = maximize;
+engine.filename = filename;
+engine.bel = []; % reset if necessary
+
+bnet = bnet_from_engine(engine);
+N = length(bnet.dag);
+ns = bnet.node_sizes(:);
+
+observed_bitv = ~isemptycell(evidence);
+disconnected = find(engine.disconnected_nodes_bitv);
+if ~all(observed_bitv(disconnected))
+  error(['The following discrete nodes must be observed: ' num2str(disconnected)])
+end
+msg = init_pearl_msgs(engine.msg_type, engine.msg_dag, ns, evidence);
+
+niter = 1;
+switch engine.protocol
+ case 'parallel', [msg, niter] = parallel_protocol(engine, evidence, msg);
+ case 'tree', msg = tree_protocol(engine, evidence, msg);
+ otherwise,
+  error(['unrecognized protocol ' engine.protocol])
+end
+engine.niter = niter;
+
+engine.marginal = cell(1,N);
+nodes = find(~engine.disconnected_nodes_bitv);
+for n=nodes(:)'
+  engine.marginal{n} = compute_bel(engine.msg_type, msg{n}.pi, msg{n}.lambda);
+end
+
+engine.evidence = evidence; % needed by marginal_nodes and marginal_family
+engine.msg = msg;  % needed by marginal_family
+
+if (nargout >= 2)
+  if (engine.msg_type == 'd')
+    loglik = bethe_free_energy(engine, evidence);
+  else
+    loglik = 0;
+  end
+end
+
+
+
+%%%%%%%%%%%
+
+function msg =  init_pearl_msgs(msg_type, dag, ns, evidence)
+% INIT_MSGS Initialize the lambda/pi message and state vectors
+% msg =  init_msgs(dag, ns, evidence)
+%
+
+N = length(dag);
+msg = cell(1,N);
+observed = ~isemptycell(evidence);
+lam_msg = 1;
+
+for n=1:N
+  ps = parents(dag, n);
+  msg{n}.pi_from_parent = cell(1, length(ps));
+  for i=1:length(ps)
+    p = ps(i);
+    msg{n}.pi_from_parent{i} = mk_msg(msg_type, ns(p));
+  end
+  
+  cs = children(dag, n);
+  msg{n}.lambda_from_child = cell(1, length(cs));
+  for i=1:length(cs)
+    c = cs(i);
+    msg{n}.lambda_from_child{i} = mk_msg(msg_type, ns(n), lam_msg);
+  end
+
+  msg{n}.lambda = mk_msg(msg_type, ns(n), lam_msg);
+  msg{n}.pi = mk_msg(msg_type, ns(n));
+  
+  if observed(n)
+    msg{n}.lambda_from_self = mk_msg_with_evidence(msg_type, ns(n), evidence{n});
+  else
+    msg{n}.lambda_from_self = mk_msg(msg_type, ns(n), lam_msg);
+  end
+end
+
+
+
+%%%%%%%%%
+
+function msg =  mk_msg(msg_type, sz, is_lambda_msg)
+
+if nargin < 3, is_lambda_msg = 0; end
+
+switch msg_type
+ case 'd', msg = ones(sz, 1);
+ case 'g', 
+  if is_lambda_msg
+    msg.precision = zeros(sz, sz);
+    msg.info_state = zeros(sz, 1);
+  else
+    msg.Sigma = zeros(sz, sz);
+    msg.mu = zeros(sz,1);
+  end
+end
+
+%%%%%%%%%%%%
+
+function msg = mk_msg_with_evidence(msg_type, sz, val)
+
+switch msg_type
+ case 'd',
+  msg = zeros(sz, 1);
+  msg(val) = 1;
+ case 'g',
+  %msg.observed_val = val(:);
+  msg.precision = inf;
+  msg.mu = val(:);
+end
-- 
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