def test_cv_update_batch():
cv1 = ContinuousValue()
cv2 = ContinuousValue()
nums = [random() for i in range(10)]
for n in nums:
cv1.update(n)
cv2.update_batch(nums)
assert cv1.unbiased_mean() == cv2.unbiased_mean()
assert cv1.biased_std() == cv2.biased_std()
def test_cv_unbiased_std():
cv = ContinuousValue()
assert cv.unbiased_std() == 0
true_std = 10
error_biased = []
error_unbiased = []
for _ in range(100):
cv = ContinuousValue()
for _ in range(4):
cv.update(normalvariate(0, true_std))
error_biased.append(cv.biased_std() - true_std)
error_unbiased.append(cv.unbiased_std() - true_std)
assert abs(sum(error_unbiased)) < abs(sum(error_biased))
def test_cv_biased_std():
cv = ContinuousValue()
for _ in range(1000):
cv.update(normalvariate(0, 1))
assert cv.biased_std() - 1 <= 0.1