Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/gibbs_sampling_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 = gibbs_sampling_inf_engine(bnet, varargin) % GIBBS_SAMPLING_INF_ENGINE % % engine = gibbs_sampling_inf_engine(bnet, ...) % % Optional parameters [default in brackets] % 'burnin' - How long before you start using the samples [100]. % 'gap' - how often you use the samples in the estimate [1]. % 'T' - number of samples [1000] % i.e, number of node flips (so, for % example if there are 10 nodes in the bnet, and T is 1000, each % node will get flipped 100 times (assuming a deterministic schedule)) % The total running time is proportional to burnin + T*gap. % % 'order' - if the sampling schedule is deterministic, use this % parameter to specify the order in which nodes are sampled. % Order is allowed to include multiple copies of nodes, which is % useful if you want to, say, focus sampling on particular nodes. % Default is to use a deterministic schedule that goes through the % nodes in order. % % 'sampling_dist' - when using a stochastic sampling method, at % each step the node to sample is chosen according to this % distribution (may be unnormalized) % % The sampling_dist and order parameters shouldn't both be used, % and this will cause an assert. % % % Written by "Bhaskara Marthi" <bhaskara@cs.berkeley.edu> Feb 02. engine.burnin = 100; engine.gap = 1; engine.T = 1000; use_default_order = 1; engine.deterministic = 1; engine.order = {}; engine.sampling_dist = {}; if nargin >= 2 args = varargin; nargs = length(args); for i = 1:2:nargs switch args{i} case 'burnin' engine.burnin = args{i+1}; case 'gap' engine.gap = args{i+1}; case 'T' engine.T = args{i+1}; case 'order' assert (use_default_order); use_default_order = 0; engine.order = args{i+1}; case 'sampling_dist' assert (use_default_order); use_default_order = 0; engine.deterministic = 0; engine.sampling_dist = args{i+1}; otherwise error(['unrecognized parameter to gibbs_sampling_inf_engine']); end end end engine.slice_size = size(bnet.dag, 2); if (use_default_order) engine.order = 1:engine.slice_size; end engine.hnodes = []; engine.onodes = []; engine.evidence = []; engine.state = []; engine.marginal_counts = {}; % Precompute the strides for each CPT engine.strides = compute_strides(bnet); % Precompute graphical information engine.families = compute_families(bnet); engine.children = compute_children(bnet); % For convenience, store the CPTs as tables rather than objects engine.CPT = get_cpts(bnet); engine = class(engine, 'gibbs_sampling_inf_engine', inf_engine(bnet));