numpy.random.seed(1)
X = numpy.random.random((npoints, ndims))
w = numpy.random.random(ndims)
y = X.dot(w) + numpy.random.random(npoints) * 0.1
clf = sklearn.linear_model.LassoLarsCV(fit_intercept=False, normalize=False,
    max_n_alphas=1000000.0)
clf.fit(X, y)
print(max(abs(clf.coef_ - w)))
print(clf.alphas_.shape)
ValueError: A value in x_new is below the interpolation range