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 valuesN = length(fg.G);engine.max_iter = 2*N;engine.momentum = 0;engine.tol = 1e-3;engine.maximize = 0;% parse optional argumentsengine = set_params(engine, varargin);engine.fgraph = fg;% store results computed by enter_evidence hereengine.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 = [];