def test_cv_integral_of_gaussian_product():
cv1 = ContinuousValue()
cv2 = ContinuousValue()
cv1.update(1)
cv2.update(1)
assert abs(cv1.integral_of_gaussian_product(cv2) - 1) <= 1e-6
def test_cv_scaled_unbiased_std():
cv = ContinuousValue()
for _ in range(1000):
cv.update(normalvariate(0, 2))
assert cv.scaled_unbiased_std(2) - 1 <= 0.1
assert cv.scaled_unbiased_std(1) == cv.scaled_unbiased_std(0)
assert cv.scaled_unbiased_std(1) == cv.scaled_unbiased_std(-1)
def test_cv_scaled_unbiased_mean():
nums = [random() for i in range(10)]
cv = ContinuousValue()
for n in nums:
cv.update(n)
assert cv.scaled_unbiased_mean(sum(nums)/len(nums), 1) <= 1e-6
assert (cv.scaled_unbiased_mean(sum(nums)/len(nums), 1) ==
cv.scaled_unbiased_mean(sum(nums)/len(nums), 0))
assert (cv.scaled_unbiased_mean(sum(nums)/len(nums), 1) ==
cv.scaled_unbiased_mean(sum(nums)/len(nums), -1))
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_copy():
cv = ContinuousValue()
for i in range(10):
cv.update(random())
cv2 = cv.copy()
assert cv.num == cv2.num
assert cv.mean == cv2.mean
assert cv.meanSq == cv2.meanSq
cv.update(1000)
assert cv.num != cv2.num
assert cv.mean != cv2.mean
assert cv.meanSq != cv2.meanSq
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_update():
cv = ContinuousValue()
cv.update(1)
assert cv.num == 1
assert cv.mean == 1
assert cv.meanSq == 0
cv.update(2)
assert cv.num == 2
assert cv.mean == 1.5
assert cv.meanSq == 0.5
cv = ContinuousValue()
samples = []
for i in range(1000):
s = normalvariate(0, 1)
cv.update(s)
samples.append(s)
assert cv.num == 1000
assert abs(cv.mean) <= 0.1
assert abs(cv.meanSq - sum([(v - cv.mean)**2 for v in samples])) <= 1e-5
def test_cv_biased_std():
cv = ContinuousValue()
for _ in range(1000):
cv.update(normalvariate(0, 1))
assert cv.biased_std() - 1 <= 0.1
def test_cv_unbiased_mean():
nums = [random() for i in range(10)]
cv = ContinuousValue()
for n in nums:
cv.update(n)
assert cv.unbiased_mean() - sum(nums)/len(nums) <= 1e-6