Mauch MIREX 2010

function pot = convert_to_pot(CPD, pot_type, domain, evidence)

% CONVERT_TO_POT Convert a discrete CPD to a potential

% pot = convert_to_pot(CPD, pot_type, domain, evidence)

%

% pots = CPD evaluated using evidence(domain)

ncases = size(domain,2);

assert(ncases==1); % not yet vectorized

sz = dom_sizes(CPD);

ns = zeros(1, max(domain));

ns(domain) = sz;

CPT1 = CPD_to_CPT(CPD);

spar = issparse(CPT1);

odom = domain(~isemptycell(evidence(domain)));

if spar

   T = convert_to_sparse_table(CPD, domain, evidence);

else 

   T = convert_to_table(CPD, domain, evidence);

end

switch pot_type

 case 'u',

  pot = upot(domain, sz, T, 0*myones(sz));  

 case 'd',

  ns(odom) = 1;

  pot = dpot(domain, ns(domain), T);          

 case {'c','g'},

  % Since we want the output to be a Gaussian, the whole family must be observed.

  % In other words, the potential is really just a constant.

  p = T;

  %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1)));

  ns(domain) = 0;

  pot = cpot(domain, ns(domain), log(p));       

 case 'cg',

  T = T(:);

  ns(odom) = 1;

  can = cell(1, length(T));

  for i=1:length(T)

    if T(i) == 0 

      can{i} = cpot([], [], -Inf); % bug fix by Bob Welch 20/2/04

    else

      can{i} = cpot([], [], log(T(i)));

    end;

  end

  pot = cgpot(domain, [], ns, can); 

 case 'scg'

  T = T(:);

  ns(odom) = 1;

  pot_array = cell(1, length(T));

  for i=1:length(T)

    pot_array{i} = scgcpot([], [], T(i));

  end

  pot = scgpot(domain, [], [], ns, pot_array);   

 otherwise,

  error(['unrecognized pot type ' pot_type])

end