Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_asia_bnet.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 bnet = mk_asia_bnet(CPD_type, p, arity) % MK_ASIA_BNET Make the 'Asia' bayes net. % % BNET = MK_ASIA_BNET uses the parameters specified on p21 of Cowell et al, % "Probabilistic networks and expert systems", Springer Verlag 1999. % % BNET = MK_ASIA_BNET('cpt', p) uses random parameters drawn from a Dirichlet(p,p,...) % distribution. If p << 1, this is nearly deterministic; if p >> 1, this is nearly uniform. % % BNET = MK_ASIA_BNET('bool') makes each CPT a random boolean function. % % BNET = MK_ASIA_BNET('gauss') makes each CPT a random linear Gaussian distribution. % % BNET = MK_ASIA_BNET('orig') is the same as MK_ASIA_BNET. % % BNET = MK_ASIA_BNET('cpt', p, arity) can specify non-binary nodes. if nargin == 0, CPD_type = 'orig'; end if nargin < 3, arity = 2; end Smoking = 1; Bronchitis = 2; LungCancer = 3; VisitToAsia = 4; TB = 5; TBorCancer = 6; Dys = 7; Xray = 8; n = 8; dag = zeros(n); dag(Smoking, [Bronchitis LungCancer]) = 1; dag(Bronchitis, Dys) = 1; dag(LungCancer, TBorCancer) = 1; dag(VisitToAsia, TB) = 1; dag(TB, TBorCancer) = 1; dag(TBorCancer, [Dys Xray]) = 1; ns = arity*ones(1,n); if strcmp(CPD_type, 'gauss') dnodes = []; else dnodes = 1:n; end bnet = mk_bnet(dag, ns, 'discrete', dnodes); switch CPD_type case 'orig', % true is 2, false is 1 bnet.CPD{VisitToAsia} = tabular_CPD(bnet, VisitToAsia, [0.99 0.01]); bnet.CPD{Bronchitis} = tabular_CPD(bnet, Bronchitis, [0.7 0.4 0.3 0.6]); % minka: bug fix bnet.CPD{Dys} = tabular_CPD(bnet, Dys, [0.9 0.2 0.3 0.1 0.1 0.8 0.7 0.9]); bnet.CPD{TBorCancer} = tabular_CPD(bnet, TBorCancer, [1 0 0 0 0 1 1 1]); % minka: bug fix bnet.CPD{LungCancer} = tabular_CPD(bnet, LungCancer, [0.99 0.9 0.01 0.1]); bnet.CPD{Smoking} = tabular_CPD(bnet, Smoking, [0.5 0.5]); bnet.CPD{TB} = tabular_CPD(bnet, TB, [0.99 0.95 0.01 0.05]); bnet.CPD{Xray} = tabular_CPD(bnet, Xray, [0.95 0.02 0.05 0.98]); case 'bool', for i=1:n bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd'); end case 'gauss', for i=1:n bnet.CPD{i} = gaussian_CPD(bnet, i, 'cov', 1*eye(ns(i))); end case 'cpt', for i=1:n bnet.CPD{i} = tabular_CPD(bnet, i, p); end end