Mercurial > hg > camir-aes2014
view 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
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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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');