function idx=HW13(X,Y)
% Input:  X is n x p and Y is n x m.  Output the indices found by the OLS
% algorithm.

[n,p]=size(X);  [n,m]=size(Y);

%Normalize X and Y

for i=1:p-1  
    
  %Compute cos^2(theta) between the p columns of X and m columns of Y
  %(Should be a p x m matrix for the first time through).
  C=

  %Get the best row norm of C.  You might find Matlab's max function useful
  %  (You should look up the help file for it)
  
  %Put the winning column as xwin and delete the corresponding column from
  % the matrix X.
  xwin=
  
  %Deflate the remaining columns of X.  You may use a loop if you are more
  %comfortable with that:
  
  [nn,pp]=size(X);
  for j=1:pp
    X(:,j)=X(:,j)- %Subtract the appropriate projection.
  end
  
  %Re-normalize the columns of X:
  
  
end