function E=calce(net,Ac,T,TS)
%CALCE Calculate layer errors.
%
%	Synopsis
%
%	  El = calce(net,Ac,Tl,TS)
%
%	Description
%
%	  This function calculates the errors of each layer in
%	  response to layer outputs and targets.
%
%	  El = CALCE(NET,Ac,Tl,TS) takes,
%	    NET - Neural network.
%	    Ac  - Combined layer outputs.
%	    Tl  - Layer targets.
%	    Q   - Concurrent size.
%	  and returns,
%	    El  - Layer errors.
%
%	Examples
%
%	  Here we create a linear network with a single input element
%	  ranging from 0 to 1, two neurons, and a tap delay on the
%	  input with taps at 0, 2, and 4 timesteps.  The network is
%	  also given a recurrent connection from layer 1 to itself with
%	  tap delays of [1 2].
%
%	    net = newlin([0 1],2);
%	    net.layerConnect(1,1) = 1;
%	    net.layerWeights{1,1}.delays = [1 2];
%
%	  Here is a single (Q = 1) input sequence P with 5 timesteps (TS = 5),
%	  and the 4 initial input delay conditions Pi, combined inputs Pc,
%	  and delayed inputs Pd.
%
%	    P = {0 0.1 0.3 0.6 0.4};
%	    Pi = {0.2 0.3 0.4 0.1};
%	    Pc = [Pi P];
%	    Pd = calcpd(net,5,1,Pc);
%
%	  Here the two initial layer delay conditions for each of the
%	  two neurons are defined, and the networks combined outputs Ac
%	  and other signals are calculated.
%
%	    Ai = {[0.5; 0.1] [0.6; 0.5]};
%	    [Ac,N,LWZ,IWZ,BZ] = calca(net,Pd,Ai,1,5);
%
%	  Here we define the layer targets for the two neurons for
%	  each of the five time steps, and calculate the layer errors.
%	  
%	    Tl = {[0.1;0.2] [0.3;0.1], [0.5;0.6] [0.8;0.9], [0.5;0.1]};
%	    El = calce(net,Ac,Tl,5)
%
%	  Here we view the network's error for layer 1 at timestep 2.
%
%	    El{1,2}

% Mark Beale, 11-31-97
% Mark Beale, Updated help, 5-25-98
% Copyright 1992-2000 The MathWorks, Inc.
% $Revision: 1.5 $ $Date: 2000/06/15 04:22:53 $

E = cell(net.numLayers,TS);
delays = net.numLayerDelays;

for ts = 1:TS
  for i=net.hint.targetInd;
    E{i,ts} = T{i,ts} - Ac{i,ts+delays};
  end
end
