diff 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

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/gibbs_sampling_inf_engine.m	Tue Feb 10 15:05:51 2015 +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" <[e-mail]> 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));
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