camir-aes2014: toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/convert_to

first hg version after svn

rev	line source
wolffd@0	1 function pot = convert_to_pot(CPD, pot_type, domain, evidence)
wolffd@0	2 % CONVERT_TO_POT Convert a tabular CPD to one or more potentials
wolffd@0	3 % pot = convert_to_pot(CPD, pot_type, domain, evidence)
wolffd@0	4
wolffd@0	5 % This is the same as discrete_CPD/convert_to_pot,
wolffd@0	6 % except we didn't want to the kernel to inherit methods like sample_node etc.
wolffd@0	7
wolffd@0	8 sz = CPD.sz;
wolffd@0	9 ns = zeros(1, max(domain));
wolffd@0	10 ns(domain) = sz;
wolffd@0	11
wolffd@0	12 odom = domain(~isemptycell(evidence(domain)));
wolffd@0	13 T = convert_to_table(CPD, domain, evidence);
wolffd@0	14
wolffd@0	15 switch pot_type
wolffd@0	16 case 'u',
wolffd@0	17 pot = upot(domain, sz, T, 0*myones(sz));
wolffd@0	18 case 'd',
wolffd@0	19 ns(odom) = 1;
wolffd@0	20 pot = dpot(domain, ns(domain), T);
wolffd@0	21 case 'c',
wolffd@0	22 % Since we want the output to be a Gaussian, the whole family must be observed.
wolffd@0	23 % In other words, the potential is really just a constant.
wolffd@0	24 p = T.p;
wolffd@0	25 %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1)));
wolffd@0	26 ns(domain) = 0;
wolffd@0	27 pot = cpot(domain, ns(domain), log(p));
wolffd@0	28 case 'cg',
wolffd@0	29 T = T(:);
wolffd@0	30 ns(odom) = 1;
wolffd@0	31 can = cell(1, length(T));
wolffd@0	32 for i=1:length(T)
wolffd@0	33 can{i} = cpot([], [], log(T(i)));
wolffd@0	34 end
wolffd@0	35 pot = cgpot(domain, [], ns, can);
wolffd@0	36 end
wolffd@0	37

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