def test_monocity():
digest = TDigest()
for i in range(10000):
digest.add(random.random())
for i in range(int(1e4) - 1):
q1 = i * 1e-4
q2 = (i + 1) * 1e-4
assert digest.quantile(q1) <= digest.quantile(q2)
assert digest.cdf(q1) <= digest.cdf(q2)
def test_monocity():
digest = TDigest()
for i in range(10000):
digest.add(random.random())
for i in range(int(1e4) - 1):
q1 = i * 1e-4
q2 = (i + 1) * 1e-4
assert digest.quantile(q1) <= digest.quantile(q2)
assert digest.cdf(q1) <= digest.cdf(q2)
def test_serialization():
digest = TDigest()
for i in range(100):
digest.add(random.random())
digest2 = pickle.loads(pickle.dumps(digest))
assert len(digest) == len(digest2)
assert len(digest.centroids) == len(digest2.centroids)
for c1, c2 in zip(digest.centroids, digest2.centroids):
assert c1.mean == c2.mean
assert c1.count == c2.count
for q in range(10000):
assert digest.quantile(q / 10000.) == digest2.quantile(q / 10000.)
assert digest.cdf(q / 10000.) == digest2.cdf(q / 10000.)
def test_serialization():
digest = TDigest()
for i in range(100):
digest.add(random.random())
digest2 = pickle.loads(pickle.dumps(digest))
assert len(digest) == len(digest2)
assert len(digest.centroids) == len(digest2.centroids)
for c1, c2 in zip(digest.centroids, digest2.centroids):
assert c1.mean == c2.mean
assert c1.count == c2.count
for q in range(10000):
assert digest.quantile(q / 10000.) == digest2.quantile(q / 10000.)
assert digest.cdf(q / 10000.) == digest2.cdf(q / 10000.)
def test_repeated_values():
digest = TDigest()
data = [rint(random.uniform(0, 1) * 10) / 10. for _ in range(10000)]
for d in data:
digest.add(d)
assert len(digest.centroids) < 10 * 1000.
for i in range(10):
z = i / 10.
for delta in [0.01, 0.02, 0.03, 0.07, 0.08, 0.09]:
q = z + delta
cdf = digest.cdf(q)
assert abs(z + 0.05 - cdf) < 0.02
estimate = digest.quantile(q)
assert abs(rint(q * 10) / 10. - estimate) < 0.001
def test_repeated_values():
digest = TDigest()
data = [rint(random.uniform(0, 1) * 10) / 10. for _ in range(10000)]
for d in data:
digest.add(d)
assert len(digest.centroids) < 10 * 1000.
for i in range(10):
z = i / 10.
for delta in [0.01, 0.02, 0.03, 0.07, 0.08, 0.09]:
q = z + delta
cdf = digest.cdf(q)
assert abs(z + 0.05 - cdf) < 0.02
estimate = digest.quantile(q)
assert abs(rint(q * 10) / 10. - estimate) < 0.001
