comparison toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/belprop_mrf2_inf_engine.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function engine = belprop_mrf2_inf_engine(mrf2, varargin) 
+% BELPROP_MRF2_INF_ENGINE Belief propagation for MRFs with discrete pairwise potentials
+% engine = belprop_mrf2_inf_engine(mrf2, ...)
+%
+% This is like belprop_inf_engine, except it is designed for mrf2, so is much faster.
+%
+% [ ... ] = belprop_mrf2_inf_engine(..., 'param1',val1, 'param2',val2, ...)
+% allows you to specify optional parameters as name/value pairs.
+% Parameters modifying behavior of enter_evidence are below [default value in brackets]
+%
+% max_iter - max. num. iterations [ 5*nnodes]
+% momentum - weight assigned to old message in convex combination
+%            (useful for damping oscillations) [0]
+% tol      - tolerance used to assess convergence [1e-3]
+% verbose - 1 means print error at every iteration [0]
+%
+% Parameters can be changed later using set_params 
+
+
+% The advantages of pairwise potentials are
+% (1) we can compute messages using vector-matrix multiplication
+% (2) we can easily specify the parameters: one potential per edge
+% In contrast, potentials on larger cliques are more complicated to deal with.
+
+
+nnodes = length(mrf2.adj_mat);
+
+[engine.max_iter, engine.momentum, engine.tol, engine.verbose] = ...
+    process_options(varargin, 'max_iter', [], 'momentum', 0, 'tol', 1e-3, ...
+		   'verbose', 0);
+
+if isempty(engine.max_iter) % no user supplied value, so compute default
+  engine.max_iter = 5*nnodes;
+  %if acyclic(mrf2.adj_mat, 0) --- can be very slow!
+  %  engine.max_iter = nnodes;
+  %else
+  %  engine.max_iter = 5*nnodes;
+  %end
+end
+
+engine.bel = cell(1, nnodes); % store results of enter_evidence here
+engine.mrf2 = mrf2;
+
+engine = class(engine, 'belprop_mrf2_inf_engine');
+
+