enter_evidence.m

来自「Bayes网络工具箱」· M 代码 · 共 50 行

function [engine, loglik] = enter_evidence(engine, evidence, filter)
% ENTER_EVIDENCE Add the specified evidence to the network (ff)
% [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, else smoothing. (Default: filter = 0.)

if nargin < 3, filter = 0; end

[ss T] = size(evidence);
observed = ~isemptycell(evidence);
bnet = bnet_from_engine(engine);
pot_type = determine_pot_type(find(observed(:,1)), bnet.cnodes_slice, bnet.intra);
% we assume we can use the same pot_type in all slices

% Convert CPDs of instantiated nodes to potential form
CPDpot = cell(ss,T);      
t = 1;
for n=1:ss
  fam = family(bnet.dag, n);
  e = bnet.equiv_class(n, 1);
  CPDpot{n,t} = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1));
end
for t=2:T
  for n=1:ss
    fam = family(bnet.dag, n, 2);
    e = bnet.equiv_class(n, 2);
    CPDpot{n,t} = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t));
  end
end       

% Now convert CPDs on observed nodes to be potentials just on their parents
assert(pot_type == 'd');
onodes = engine.onodes(:)';
ns = bnet.node_sizes_slice;
ns(onodes) = 1;
for t=1:T
  for i=onodes
    p = parents(bnet.dag, i);
    %CPDpot{i,t} = set_domain_pot(CPDpot{i,t}, p); % leaves size too long
    temp = pot_to_marginal(CPDpot{i,t});
    CPDpot{i,t} = dpot(p, ns(p), temp.T); % assumes pot_type = d
  end
end

[engine.marginals, engine.fwd, engine.back, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter);

engine.CPDpot = CPDpot;
engine.filter = filter;