diff toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/belprop_inf_engine_nostr.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/belprop_inf_engine_nostr.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,31 @@
+function engine = belprop_inf_engine(fg, max_iter, momentum, tol, maximize)
+
+if nargin < 2, max_iter = length(fg.G); end
+if nargin < 3, momentum = 0; end
+if nargin < 4, tol = 1e-3; end
+if nargin < 5, maximize = 0; end
+
+engine.fgraph = fg;
+engine.max_iter = max_iter;
+engine.momentum = momentum;
+engine.tol = tol;
+engine.maximize = maximize;
+
+% store results computed by enter_evidence here
+ndoms = length(fg.doms);
+nvars = length(fg.vars);
+engine.marginal_domains = cell(1, ndoms);
+
+% to compute the marginal on each variable, we need to know which domain to marginalize
+% so we represent each domain as a bit vector, and compute its (pre-evidence) weight
+engine.dom_weight = [];
+
+% engine.dom_bitv = sparse(ndoms, nvars);
+% ns = fg.node_sizes;
+% for i=1:ndoms
+%   engine.dom_bitv(i, fg.doms{i}) = 1;
+%   engine.dom_weight(i) = prod(ns(fg.doms{i}));
+% end
+
+
+engine = class(engine, 'belprop_inf_engine');