function [wX, wY, xdim, ydim, n] = windowizeNARX(X,Y,x_delays,y_delays, steps)% Re-arrange the data points into a block Hankel matrix for (N)ARX time-series modeling% % >> [Xw,Yw] = windowizeNARX(X,Y,xdelays, ydelays, steps)% %  Rearrange the points of X and Y in a regressor matrix of the%  past inputs and outputs (Xw) and the future outputs (Yw). % % Full syntax%% >> [Xw, Yw, xdim, ydim, n] = windowizeNARX(X, Y, xdelays, ydelays, steps)% %       Outputs    %         Xw Matrix of the data used for input including the delays%         Yw Matrix of the data used for output including the next steps%         xdim(*) Number of dimensions in new input%         ydim(*) Number of dimensions in new output%         n(*) Number of new data points%       Inputs    %         X       : N x m vector with input data points%         Y       : N x d vector with output data points%         xdelays : Number of lags of X in new input%         ydelays : Number of lags of Y in new input%         steps(*): Number of future steps of Y in new output (by default 1)%% See also:%   windowize, predict, trainlssvm, simlssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabm=max(x_delays,y_delays);eval('steps;','steps = 1;');if steps == 0,   n = size(X,1)-m;else  n = size(X,1)-m -steps+1;endwX = zeros(n,size(X,2)*(x_delays+1)+size(Y,2)*(y_delays));wY = zeros(n,size(Y,2)*steps);xdim = size(wX,2);ydim = size(wY,2);hdx  = (x_delays+1)*size(X,2);for t=1:n,  for i=1:x_delays+1,    wX(t,1+((i-1)*size(X,2):i*size(X,2)-1)) = X(t+m-x_delays+i-1,:);  end    for i=1:y_delays,    wX(t,hdx + (i-1)*size(Y,2) + (1:size(Y,2))) = Y(t+m-y_delays+i-1,:);     end  for i=1:steps,    wY(t,i:i+size(Y,2)-1) = Y(t+m+i-1,:);     end  end