function w = windowize(A,window_array)% Re-arrange the data points into a Hankel matrix for (N)AR time-series modeling% % >> w = windowize(A, window)%% Use windowize function to make a nonlinear AR predictor with a% nonlinear regressor. The last elements of the resulting matrix% will contain the future values of the time-series, the others% will contain the past inputs. window is the relative index of% data points in matrix A, that are selected to make a window. Each% window is put in a row of matrix W. The matrix W contains as many% rows as there are different windows selected in A. % % Schematically, this becomes% % >> A = [a1 a2 a3;%          b1 b2 b3;%          c1 c2 c3;%          d1 d2 d3;  %          e1 e2 e3;%          f1 f2 f3;%          g1 g2 g3];% % >> W = windowize(A, [1 2 3])% %    W = %      a1 a2 a3  b1 b2 b3  c1 c2 c3%      b1 b2 b3  c1 c2 c3  d1 d2 d3  %      c1 c2 c3  d1 d2 d3  e1 e2 e3  %      d1 d2 d3  e1 e2 e3  f1 f2 f3 %      e1 e2 e3  f1 f2 f3  g1 g2 g3% % The function windowizeNARX converts the time-series and his% exogeneous variables into a block hankel format useful for% training a nonlinear function approximation as a nonlinear ARX% model.  % % Full syntax%     (The length of window is denoted by w.)% % >> Xw = windowize(X, window)% %       Outputs    %         Xw : (N-w+1) x w matrix of the sequences of windows over X%       Inputs    %         X  : N x 1 vector with data points%         w  : w x 1 vector with the relative indices of one window% %    % see also:%   windowizeNARX, predict, trainlssvm, simlssvm% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabl = max(window_array);w = zeros(size(A,1)-l+1,length(window_array)*size(A,2));for i=1:size(A,1)-l+1,  for j = 1:length(window_array),    w(i,(j-1)*size(A,2)+1:j*size(A,2)) = A(i-1+window_array(j),:);  endend