Mauch MIREX 2010

function CPD = deterministic_CPD(bnet, self, fname, pfail)

% DETERMINISTIC_CPD Make a tabular CPD representing a (noisy) deterministic function

%

% CPD = deterministic_CPD(bnet, self, fname)

% This calls feval(fname, pvals) for each possible vector of parent values.

% e.g., suppose there are 2 ternary parents, then pvals = 

%  [1 1], [2 1], [3 1],   [1 2], [2 2], [3 2],   [1 3], [2 3], [3 3]

% If v = feval(fname, pvals(i)), then

%  CPD(x | parents=pvals(i)) = 1 if x==v, and = 0 if x<>v

% e.g., suppose X4 = X2 AND (NOT X3). Then

%    bnet.CPD{4} = deterministic_CPD(bnet, 4, inline('((x(1)-1) & ~(x(2)-1)) + 1'));  

% Note that x(1) refers pvals(1) = X2, and x(2) refers to pvals(2)=X3

% See also boolean_CPD.

%

% CPD = deterministic_CPD(bnet, self, fname, pfail)

% will put probability mass 1-pfail on f(parents), and distribute pfail over the other values.

% This is useful for simulating noisy deterministic functions.

% If pfail is omitted, it is set to 0.

%

if nargin==0

  % This occurs if we are trying to load an object from a file.

  CPD = tabular_CPD(bnet, self);

  return;

elseif isa(bnet, 'deterministic_CPD')

  % This might occur if we are copying an object.

  CPD = bnet;

  return;

end

if nargin < 4, pfail = 0; end

ps = parents(bnet.dag, self);

ns = bnet.node_sizes;

psizes = ns(ps);

self_size = ns(self);

psucc = 1-pfail;

CPT = zeros(prod(psizes), self_size);

pvals = zeros(1, length(ps));

for i=1:prod(psizes)

  pvals = ind2subv(psizes, i);

  x = feval(fname, pvals);

  %fprintf('%d ', [pvals x]); fprintf('\n');

  if psucc == 1

    CPT(i, x) = 1;

  else

    CPT(i, x) = psucc;

    rest = mysetdiff(1:self_size, x);

    CPT(i, rest) = pfail/length(rest);

  end

end

CPT = reshape(CPT, [psizes self_size]);  

CPD = tabular_CPD(bnet, self, 'CPT',CPT, 'clamped',1);