diff toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/enter_evidence.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/dynamic/@kalman_inf_engine/enter_evidence.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,83 @@
+function [engine, loglik] = enter_evidence(engine, evidence, varargin)
+% ENTER_EVIDENCE Add the specified evidence to the network (kalman)
+% [engine, loglik] = enter_evidence(engine, evidence, ...)
+%
+% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector)
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default value in brackets]
+%
+% maximize - if 1, does max-product (same as sum-product for Gaussians!), else sum-product [0]
+% filter -   if 1, do filtering, else smoothing [0]
+%
+% e.g., engine = enter_evidence(engine, ev, 'maximize', 1)
+
+maximize = 0;
+filter = 0;
+
+% parse optional params
+args = varargin;
+nargs = length(args);
+if nargs > 0
+  for i=1:2:nargs
+    switch args{i},
+     case 'maximize', maximize = args{i+1}; 
+     case 'filter', filter = args{i+1}; 
+     otherwise,  
+      error(['invalid argument name ' args{i}]);       
+    end
+  end
+end
+
+assert(~maximize);
+
+bnet = bnet_from_engine(engine);
+n = length(bnet.intra);
+onodes = bnet.observed;
+hnodes = mysetdiff(1:n, onodes);
+T = size(evidence, 2);
+ns = bnet.node_sizes;
+O = sum(ns(onodes));
+data = reshape(cat(1, evidence{onodes,:}), [O T]);
+
+A = engine.trans_mat;
+C = engine.obs_mat;
+Q = engine.trans_cov;
+R = engine.obs_cov;
+init_x = engine.init_state;
+init_V = engine.init_cov;
+
+if filter
+  [x, V, VV, loglik] = kalman_filter(data, A, C, Q, R, init_x, init_V);
+else
+  [x, V, VV, loglik] = kalman_smoother(data, A, C, Q, R, init_x, init_V);
+end
+
+  
+% Wrap the posterior inside a potential, so it can be marginalized easily
+engine.one_slice_marginal = cell(1,T);
+engine.two_slice_marginal = cell(1,T);
+ns(onodes) = 0;
+ns(onodes+n) = 0;
+ss = length(bnet.intra);
+for t=1:T
+  dom = (1:n);
+  engine.one_slice_marginal{t} = mpot(dom+(t-1)*ss, ns(dom), 1, x(:,t), V(:,:,t));
+end
+% for t=1:T-1
+%   dom = (1:(2*n));
+%   mu = [x(:,t); x(:,t)];
+%   Sigma = [V(:,:,t) VV(:,:,t+1)';
+% 	   VV(:,:,t+1) V(:,:,t+1)];
+%   engine.two_slice_marginal{t} = mpot(dom+(t-1)*ss, ns(dom), 1, mu, Sigma);
+% end
+for t=2:T
+  %dom = (1:(2*n));
+  current_slice = hnodes;
+  next_slice = hnodes + ss;
+  dom = [current_slice next_slice];   
+  mu = [x(:,t-1); x(:,t)];
+  Sigma = [V(:,:,t-1) VV(:,:,t)';
+	   VV(:,:,t) V(:,:,t)];
+  engine.two_slice_marginal{t-1} = mpot(dom+(t-2)*ss, ns(dom), 1, mu, Sigma);
+end