enter_evidence.m

贝叶斯网络的matlab实现。可以创建贝叶斯网络、训练模型

function [engine, loglik] = enter_evidence(engine, evidence, varargin)
% this is unchanged from bk_inf_engine.
% ENTER_EVIDENCE Add the specified evidence to the network (bk)
% [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 instead of 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

[ss T] = size(evidence);
engine.filter = filter;
engine.maximize = maximize;
engine.T = T;

if maximize
  error('BK does not yet support max propagation')
  % because it calls enter_soft_evidence, not enter_evidence
end

observed_bitv = ~isemptycell(evidence);
onodes = find(observed_bitv);
bnet = bnet_from_engine(engine);
pot_type = determine_pot_type(bnet, onodes); 
CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type);
[engine.clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed_bitv, pot_type, filter);