def test_calculate(mus, flags, expected):
# type: (List[float], Dict[str, bool], Dict[str, List[float]]) -> None
s_weights, s_mus, s_sigmas = \
_ParzenEstimator._calculate(mus, -1.0, 1.0, prior_weight=1.0,
consider_prior=flags['prior'],
consider_magic_clip=flags['magic_clip'],
consider_endpoints=flags['endpoints'],
weights_func=default_weights)
# Result contains an additional value for a prior distribution if consider_prior is True.
np.testing.assert_almost_equal(s_weights, expected['weights'])
np.testing.assert_almost_equal(s_mus, expected['mus'])
np.testing.assert_almost_equal(s_sigmas, expected['sigmas'])
def test_calculate_shape_check(mus, prior, magic_clip, endpoints):
# type: (List[float], bool, bool, bool) -> None
s_weights, s_mus, s_sigmas = \
_ParzenEstimator._calculate(mus, -1.0, 1.0, prior_weight=1.0,
consider_prior=prior,
consider_magic_clip=magic_clip,
consider_endpoints=endpoints,
weights_func=default_weights)
# Result contains an additional value for a prior distribution if prior is True.
assert len(s_weights) == len(mus) + int(prior)
assert len(s_mus) == len(mus) + int(prior)
assert len(s_sigmas) == len(mus) + int(prior)