enter_evidence.m

来自「Bayes网络工具箱」· M 代码 · 共 42 行

function [engine, loglik] = enter_evidence(engine, evidence, filter)
% ENTER_EVIDENCE Add the specified evidence to the network (hmm)
% [engine, loglik] = enter_evidence(engine, evidence, filter)
%
% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector)
% If filter = 1, we do filtering, otherwise smoothing (default).

if nargin < 3, filter = 0; end

bnet = bnet_from_engine(engine);
n = length(bnet.intra);
onodes = engine.onodes;
T = size(evidence, 2);
assert(~any(isemptycell(evidence(onodes,:))));

obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence);

if engine.oneslice
  [alpha, beta, gamma, loglik] = forwards_backwards_oneslice(engine.prior, engine.transmat, obslik, filter);
else
  [gamma, xi, loglik] = forwards_backwards(engine.prior, engine.transmat, obslik,  filter);
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 = bnet.node_sizes;
ns(onodes) = 1;
ns(onodes+n) = 1;
ss = length(bnet.intra);
for t=1:T
  dom = (1:n);
  engine.one_slice_marginal{t} = dpot(dom+(t-1)*ss, ns(dom), myreshape(gamma(:,t), ns(dom)));
end
if ~engine.oneslice
  for t=1:T-1
    dom = (1:(2*n));
    engine.two_slice_marginal{t} = dpot(dom+(t-1)*ss, ns(dom), myreshape(xi(:,:,t), ns(dom)));
  end
end