function CPT = CPD_to_CPT(CPD)% CPD_TO_CPT Convert the discrete CPD to tabular form (noisyor)% CPT = CPD_to_CPT(CPD)%% CPT(U1,...,Un, X) = Pr(X|U1,...,Un) where the Us are the parents (excluding leak).if ~isempty(CPD.CPT)  CPT = CPD.CPT; % remember to flush cache if params change (e.g., during learning)  return;endq = [CPD.leak_inhibit CPD.inhibit(:)'];% q(i) is the prob. that the i'th parent will be inhibited (flipped from 1 to 0).% q(1) is the leak inhibition probability, and length(q) = n + 1.if length(q)==1  CPT = [q  1-q];  return;endn = length(q);Bn = ind2subv(2*ones(1,n), 1:(2^n))-1;  % all n bit vectors, with the left most column toggling fastest (LSB)CPT = zeros(2^n, 2);% Pr(X=0 | U_1 .. U_n) = prod_{i: U_i = on} q_i =  prod_i q_i ^ U_i = exp(u' * log(q_i))% This method is problematic when q contains zerosQ = repmat(q(:)', 2^n, 1);Q(logical(~Bn)) = 1;CPT(:,1) = prod(Q,2);CPT(:,2) = 1-CPT(:,1);CPT = reshape(CPT(2:2:end), 2*ones(1,n)); % skip cases in which the leak is off       CPD.CPT = CPT;function CPT = CPD_to_CPT(CPD)% CPD_TO_CPT Convert the discrete CPD to tabular form (noisyor)% CPT = CPD_to_CPT(CPD)%% CPT(U1,...,Un, X) = Pr(X|U1,...,Un) where the Us are the parents (excluding leak).if ~isempty(CPD.CPT)  CPT = CPD.CPT; % remember to flush cache if params change (e.g., during learning)  return;endq = [CPD.leak_inhibit CPD.inhibit(:)'];% q(i) is the prob. that the i'th parent will be inhibited (flipped from 1 to 0).% q(1) is the leak inhibition probability, and length(q) = n + 1.if length(q)==1  CPT = [q  1-q];  return;endn = length(q);Bn = ind2subv(2*ones(1,n), 1:(2^n))-1;  % all n bit vectors, with the left most column toggling fastest (LSB)CPT = zeros(2^n, 2);% Pr(X=0 | U_1 .. U_n) = prod_{i: U_i = on} q_i =  prod_i q_i ^ U_i = exp(u' * log(q_i))% This method is problematic when q contains zerosQ = repmat(q(:)', 2^n, 1);Q(logical(~Bn)) = 1;CPT(:,1) = prod(Q,2);CPT(:,2) = 1-CPT(:,1);CPT = reshape(CPT(2:2:end), 2*ones(1,n)); % skip cases in which the leak is off       CPD.CPT = CPT;