% NEWTC new time-course neural network model
%    [net,P,T] = newtc(p,t,D,S,TF)
%       p : input array
%       t : target array (time-course data)
%       D : layer delays
%       S : layer sizes
%      TF : transfer functions
%     net : feedforward network
%       P : sequential inputs for net
%       T : sequential outputs for net
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function [net,P,T] = newtc(p,t,D,S,TF)

l = size(p,1); % number of inputs
n = size(p,2); % number of observations
q = size(t,1); % number of time points
d = length(D); % number of delay lines

% Create extended input array
P = zeros(l+d,n*q);
for i = 1:n
   % duplicate inputs for all time points
   ps = p(:,i)*ones(1,q);
   P(1:l,(1:q)+((i-1)*q)) = ps;
end

% Create additional inputs that represent time-delays
P((l+1):(l+d),:) = repmat(reshape(t,1,n*q),d,1);
for i = 1:d
   P(i+l,:) = circshift(P(i+l,:),[0 D(i)]);
   for j = 0:(n-1)
      P(i+l,(j*q+1):(j*q+D(i))) = 0;
   end
end


% Create feedforward network
net = newff(minmax(P),S,TF);
net.biasConnect = zeros(size(net.biasConnect));

T = reshape(t,1,n*q);