def test_cv_unbiased_std():
for i in range(10):
values = [random.normalvariate(0, 1) for i in range(10)]
cv = ContinuousValue()
cv.update_batch(values)
assert (cv.unbiased_std() -
((len(values) * utils.std(values) / (len(values) - 1)) /
utils.c4(len(values))) < 0.00000000001)
def test_cv_unbiased_std():
for i in range(10):
values = [random.normalvariate(0, 1) for i in range(10)]
cv = ContinuousValue()
cv.update_batch(values)
assert (cv.unbiased_std() -
((len(values) * utils.std(values) /
(len(values) - 1)) / utils.c4(len(values))) < 0.00000000001)
def test_cv_update():
for i in range(10):
values = []
cv = ContinuousValue()
for i in range(20):
x = random.normalvariate(0, 1)
values.append(x)
cv.update(x)
assert cv.biased_std() - utils.std(values) < 0.00000000001
def test_cv_update(self):
for i in range(10):
values = []
cv = ContinuousValue()
for i in range(20):
x = random.normalvariate(0, 1)
values.append(x)
cv.update(x)
assert cv.biased_std() - utils.std(values) < 0.00000000001
def test_cv_std():
for i in range(10):
values = [random.normalvariate(0, 1) for i in range(100)]
cv = ContinuousValue()
cv.update_batch(values)
assert cv.biased_std() - utils.std(values) < 0.00000000001
def test_cv_mean():
for i in range(10):
values = [random.normalvariate(0, 1) for i in range(100)]
cv = ContinuousValue()
cv.update_batch(values)
assert cv.mean - utils.mean(values) < 0.00000000001
def test_cv_combine():
for i in range(10):
values1 = [random.normalvariate(0, 1) for i in range(50)]
values2 = [random.normalvariate(0, 1) for i in range(50)]
values = values1 + values2
cv = ContinuousValue()
cv2 = ContinuousValue()
cv.update_batch(values1)
assert cv.biased_std() - utils.std(values1) < 0.00000000001
cv2.update_batch(values2)
assert cv2.biased_std() - utils.std(values2) < 0.00000000001
cv.combine(cv2)
assert cv.biased_std() - utils.std(values) < 0.00000000001
def test_cv_combine(self):
for i in range(10):
values1 = [random.normalvariate(0, 1) for i in range(50)]
values2 = [random.normalvariate(0, 1) for i in range(50)]
values = values1 + values2
cv = ContinuousValue()
cv2 = ContinuousValue()
cv.update_batch(values1)
assert cv.biased_std() - utils.std(values1) < 0.00000000001
cv2.update_batch(values2)
assert cv2.biased_std() - utils.std(values2) < 0.00000000001
cv.combine(cv2)
assert cv.biased_std() - utils.std(values) < 0.00000000001
def test_cv_std(self):
for i in range(10):
values = [random.normalvariate(0, 1) for i in range(100)]
cv = ContinuousValue()
cv.update_batch(values)
assert cv.biased_std() - utils.std(values) < 0.00000000001
def test_cv_mean(self):
for i in range(10):
values = [random.normalvariate(0, 1) for i in range(100)]
cv = ContinuousValue()
cv.update_batch(values)
assert cv.mean - utils.mean(values) < 0.00000000001
