return collections.deque(iterable, maxlen=n)
n = stat.window_size
X = [random.random() for _ in range(30)]
for i, x in enumerate(X):
stat.update(x)
if i >= 1:
assert math.isclose(stat.get(),
func(tail(X[:i + 1], n)),
abs_tol=1e-10)
@pytest.mark.parametrize(
'stat, func',
[(stats.Cov(), lambda x, y: np.cov(x, y)[0, 1]),
(stats.PearsonCorr(), lambda x, y: sp_stats.pearsonr(x, y)[0])])
def test_bivariate(stat, func):
# Shhh
np.warnings.filterwarnings('ignore')
X = [random.random() for _ in range(30)]
Y = [random.random() * x for x in X]
for i, (x, y) in enumerate(zip(X, Y)):
stat.update(x, y)
if i >= 1:
assert math.isclose(stat.get(),
func(X[:i + 1], Y[:i + 1]),
abs_tol=1e-10)
def __init__(self):
self.x_m = stats.Mean()
self.g_var = stats.Var()
self.h_var = stats.Var()
self.gh_cov = stats.Cov()
if i < len(X) - 2:
assert math.isclose((stat1 - stat2).get(), func(X[i + 1 :]), abs_tol=1e-10)
# Test inplace subtraction
X.extend(random.random() for _ in range(3))
for i in range(30, 33):
stat1.update(X[i])
stat1 -= stat2
assert math.isclose(stat1.get(), func(X[30:33]), abs_tol=1e-10)
@pytest.mark.parametrize(
"stat1, stat2, func",
[
(stats.Cov(ddof=0), stats.Cov(ddof=0), functools.partial(np.cov, ddof=0)),
(stats.Cov(ddof=1), stats.Cov(ddof=1), functools.partial(np.cov, ddof=1)),
],
)
def test_add_cov(stat1, stat2, func):
X = [random.random() for _ in range(30)]
Y = [random.random() for _ in range(30)]
W = [random.random() for _ in range(30)]
Z = [random.random() for _ in range(30)]
for i, (x, y, w, z) in enumerate(zip(X, Y, W, Z)):
stat1.update(x, y)
stat2.update(w, z)
if i >= 1:
assert math.isclose(