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

first hg version after svn

+function engine = belprop_fg_inf_engine(fg, varargin) 
+% BELPROP_FG_INF_ENGINE Make a belief propagation inference engine for factor graphs
+% engine = belprop_fg_inf_engine(factor_graph, ...)
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default in brackets]
+% e.g., engine = belprop_inf_engine(fg, 'tol', 1e-2, 'max_iter', 10)
+%
+% max_iter - max. num. iterations [ 2*num_nodes ]
+% momentum - weight assigned to old message in convex combination (useful for damping oscillations) [0]
+% tol - tolerance used to assess convergence [1e-3]
+% maximize - 1 means use max-product, 0 means use sum-product [0]
+%
+% This uses potential objects, like belprop_inf_engine, and hence is quite slow.
+
+engine = init_fields;
+engine = class(engine, 'belprop_fg_inf_engine');
+
+% set params to default values
+N = length(fg.G);
+engine.max_iter = 2*N;
+engine.momentum = 0;
+engine.tol = 1e-3;
+engine.maximize = 0;
+
+% parse optional arguments
+engine = set_params(engine, varargin);
+
+engine.fgraph = fg;
+
+% store results computed by enter_evidence here
+engine.marginal_nodes = cell(1, fg.nvars);
+engine.evidence = [];
+
+
+%%%%%%%%%%%%
+
+function engine = init_fields()
+
+engine.fgraph = [];
+engine.max_iter = [];
+engine.momentum = [];
+engine.tol = [];
+engine.maximize = [];
+engine.marginal_nodes = [];
+engine.evidence = [];
+engine.niter = [];