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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 = rand(rows=300, cols=20, sparsity=1.0, seed=1);
y = rand(rows=300, cols=1, sparsity=1.0, seed=1);

N = 200;
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"); #numValues = 3, 5, 7
paramRanges = list(seq(0,2), 10^seq(0,-4), 10^seq(-6,-12)); #3*5*7 = 105
[B1, opt] = gridSearch(X=Xtrain, y=ytrain, train="lm", predict="l2norm",
  numB=ncol(X)+1, params=params, paramValues=paramRanges, verbose=FALSE);
l1 = l2norm(Xtest, ytest, B1);

write(l1, $1, format="text");

