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l2norm = function(Matrix[Double] X, Matrix[Double] y, Matrix[Double] B) 
  return (Matrix[Double] loss)
{
  yhat = lmPredict(X=X, B=B, ytest=y)
  loss = as.matrix(sum((y - yhat)^2));
}

X = read($1);
y = read($2);

N = 300;
Xtrain = X[1:N,];
ytrain = y[1:N,];
Xtest = X[(N+1):nrow(X),];
ytest = y[(N+1):nrow(X),];

params = list("icpt","reg", "tol", "maxi");
# paramRanges = list(seq(0,1,2),10^seq(0,-4), 10^seq(-6,-12), 10^seq(1,3));
paramRanges = list(seq(0,1),10^seq(0,0), 10^seq(-6,-6), 10^seq(1,2));
[B1, opt] = gridSearch(X=Xtrain, y=ytrain, train="lm", predict="l2norm",
  numB=ncol(X)+1, params=params, paramValues=paramRanges, cv=TRUE, cvk=3);
B2 = lm(X=Xtrain, y=ytrain, verbose=FALSE);

l1 = l2norm(Xtest, ytest, B1);
l2 = l2norm(Xtest, ytest, B2);
R = as.scalar(l1 < l2);

write(R, $3)
