Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/enter_evidence.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, 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