def test_nopscaler(target, observed):
s = scaler.NOPScaler()
target_scaled, scale = s(target, observed)
assert mx.nd.norm(target - target_scaled) == 0
assert mx.nd.norm(mx.nd.ones_like(target).mean(axis=s.axis) - scale) == 0
1e-6 * mx.nd.ones(shape=(5, 3)),
),
(
scaler.MeanScaler(minimum_scale=1e-6),
mx.nd.random.normal(shape=(5, 30, 1)),
mx.nd.zeros(shape=(5, 30, 1)),
1e-6 * mx.nd.ones(shape=(5, 1)),
),
(
scaler.MeanScaler(minimum_scale=1e-12),
mx.nd.random.normal(shape=(5, 30, 3)),
mx.nd.zeros(shape=(5, 30, 3)),
1e-12 * mx.nd.ones(shape=(5, 3)),
),
(
scaler.NOPScaler(),
mx.nd.random.normal(shape=(10, 20, 30)),
mx.nd.random.normal(shape=(10, 20, 30)) > 0,
mx.nd.ones(shape=(10, 30)),
),
(
scaler.NOPScaler(),
mx.nd.random.normal(shape=(10, 20, 30)),
mx.nd.ones(shape=(10, 20, 30)),
mx.nd.ones(shape=(10, 30)),
),
(
scaler.NOPScaler(),
mx.nd.random.normal(shape=(10, 20, 30)),
mx.nd.zeros(shape=(10, 20, 30)),
mx.nd.ones(shape=(10, 30)),