📄 belprop_gdl_inf_engine.m
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function engine = belprop_gdl_inf_engine(gdl, varargin) % BELPROP_GDL_INF_ENGINE Make a belief propagation inference engine for a GDL graph% engine = belprop_gdl_inf_engine(gdl_graph, ...)%% If the GDL graph is a tree, this will give exact results.%% The following optional arguments can be specified in the form of name/value pairs:% [default in brackets]% e.g., engine = belprop_inf_engine(gdl, 'tol', 1e-2, 'max_iter', 10)%% protocol - 'tree' means send messages up then down the tree,% 'parallel' means use synchronous updates ['parallel']% 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]engine = init_fields;engine = class(engine, 'belprop_gdl_inf_engine');% set default paramsN = length(gdl.G);engine.protocol = 'parallel';engine.max_iter = 2*N;engine.momentum = 0;engine.tol = 1e-3;engine.maximize = 0;engine = set_params(engine, varargin);engine.gdl = gdl;if strcmp(engine.protocol, 'tree') % Make a rooted tree, so there is a fixed message passing order. root = N; [engine.tree, engine.preorder, engine.postorder, height, cyclic] = mk_rooted_tree(gdl.G, root); assert(~cyclic);end% store results computed by enter_evidence herendoms = length(gdl.doms);nvars = length(gdl.vars);engine.marginal_domains = cell(1, ndoms);% to compute the marginal on each variable, we need to know which domain to marginalize% and we want to choose the lightest. We compute the weight once we have seen the evidence.engine.dom_weight = [];engine.evidence = [];%%%%%%%%%function engine = init_fields()engine.protocol = [];engine.gdl = [];engine.max_iter = [];engine.momentum = [];engine.tol = [];engine.maximize = [];engine.marginal_domains = [];engine.evidence = [];engine.tree = [];engine.preorder = [];engine.postorder = [];engine.dom_weight = [];
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