def test_euclidean_distances_upcast_sym(batch_size, x_array_constr):
# check batches handling when X is Y (#13910)
rng = np.random.RandomState(0)
X = rng.random_sample((100, 10)).astype(np.float32)
X[X < 0.8] = 0
expected = squareform(pdist(X))
X = x_array_constr(X)
distances = _euclidean_distances_upcast(X, Y=X, batch_size=batch_size)
distances = np.sqrt(np.maximum(distances, 0))
# the default rtol=1e-7 is too close to the float32 precision
# and fails due too rounding errors.
assert_allclose(distances, expected, rtol=1e-6)
def test_euclidean_distances_upcast_sym(batch_size, x_array_constr):
# check batches handling when X is Y (#13910)
rng = np.random.RandomState(0)
X = rng.random_sample((100, 10)).astype(np.float32)
X[X < 0.8] = 0
expected = squareform(pdist(X))
X = x_array_constr(X)
distances = _euclidean_distances_upcast(X, Y=X, batch_size=batch_size)
distances = np.sqrt(np.maximum(distances, 0))
# the default rtol=1e-7 is too close to the float32 precision
# and fails due too rounding errors.
assert_allclose(distances, expected, rtol=1e-6)
