def test_nontrivial_singular_matrix():
x_one = np.random.random(1000)
x_two = np.random.random(1000)*10
x_three = np.random.random(1000)
intercept = np.ones(1000)
y = np.random.random(1000)*5
X = np.column_stack((intercept, x_one, x_two, x_three, x_one))
assert np.linalg.matrix_rank(X) < X.shape[1]
res_singular = QuantReg(y, X).fit(0.5)
assert len(res_singular.params) == X.shape[1]
assert np.linalg.matrix_rank(res_singular.cov_params()) == X.shape[1] - 1
# prediction is correct even with singular exog
res_ns = QuantReg(y, X[:, :-1]).fit(0.5)
assert_allclose(res_singular.fittedvalues, res_ns.fittedvalues, rtol=0.01)
