# Check the statistic has a working __str__ and name method
assert isinstance(str(stat), str)
if isinstance(stat, stats.Univariate):
assert isinstance(stat.name, str)
@pytest.mark.parametrize(
'stat, func',
[(stats.Kurtosis(bias=True), sp_stats.kurtosis),
(stats.Kurtosis(bias=False),
functools.partial(sp_stats.kurtosis, bias=False)),
(stats.Mean(), statistics.mean), (stats.Skew(bias=True), sp_stats.skew),
(stats.Skew(bias=False), functools.partial(sp_stats.skew, bias=False)),
(stats.Var(ddof=0), np.var),
(stats.Var(), functools.partial(np.var, ddof=1))])
def test_univariate(stat, func):
# Shut up
np.warnings.filterwarnings('ignore')
X = [random.random() for _ in range(30)]
for i, x in enumerate(X):
stat.update(x)
try:
assert math.isclose(stat.get(), func(X[:i + 1]), abs_tol=1e-10)
except AssertionError:
# Errors for the first value are acceptable
if i == 0:
def __init__(self, seed=None):
super().__init__()
self.variance = stats.Var()
self.mean = stats.Mean()
self.seed = seed
self._rng = random.Random(seed)