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Add more tests for pdf() and cdf() (GH-12190)

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Raymond Hettinger 2019-03-06 02:31:14 -08:00 committed by Miss Islington (bot)
parent 4fffd380a4
commit 18ee50d5da
1 changed files with 29 additions and 3 deletions
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32
Lib/test/test_statistics.py
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@ -2101,14 +2101,28 @@ class TestNormalDist(unittest.TestCase):
self.assertLess(X.pdf(99), X.pdf(100))
self.assertLess(X.pdf(101), X.pdf(100))
# Test symmetry
self.assertAlmostEqual(X.pdf(99), X.pdf(101))
self.assertAlmostEqual(X.pdf(98), X.pdf(102))
self.assertAlmostEqual(X.pdf(97), X.pdf(103))
for i in range(50):
self.assertAlmostEqual(X.pdf(100 - i), X.pdf(100 + i))
# Test vs CDF
dx = 2.0 ** -10
for x in range(90, 111):
est_pdf = (X.cdf(x + dx) - X.cdf(x)) / dx
self.assertAlmostEqual(X.pdf(x), est_pdf, places=4)
# Test vs table of known values -- CRC 26th Edition
Z = NormalDist()
for x, px in enumerate([
0.3989, 0.3989, 0.3989, 0.3988, 0.3986,
0.3984, 0.3982, 0.3980, 0.3977, 0.3973,
0.3970, 0.3965, 0.3961, 0.3956, 0.3951,
0.3945, 0.3939, 0.3932, 0.3925, 0.3918,
0.3910, 0.3902, 0.3894, 0.3885, 0.3876,
0.3867, 0.3857, 0.3847, 0.3836, 0.3825,
0.3814, 0.3802, 0.3790, 0.3778, 0.3765,
0.3752, 0.3739, 0.3725, 0.3712, 0.3697,
0.3683, 0.3668, 0.3653, 0.3637, 0.3621,
0.3605, 0.3589, 0.3572, 0.3555, 0.3538,
]):
self.assertAlmostEqual(Z.pdf(x / 100.0), px, places=4)
# Error case: variance is zero
Y = NormalDist(100, 0)
with self.assertRaises(statistics.StatisticsError):
@ -2127,6 +2141,18 @@ class TestNormalDist(unittest.TestCase):
self.assertEqual(cdfs, sorted(cdfs))
# Verify center
self.assertAlmostEqual(X.cdf(100), 0.50)
# Check against a table of known values
# https://en.wikipedia.org/wiki/Standard_normal_table#Cumulative
Z = NormalDist()
for z, cum_prob in [
(0.00, 0.50000), (0.01, 0.50399), (0.02, 0.50798),
(0.14, 0.55567), (0.29, 0.61409), (0.33, 0.62930),
(0.54, 0.70540), (0.60, 0.72575), (1.17, 0.87900),
(1.60, 0.94520), (2.05, 0.97982), (2.89, 0.99807),
(3.52, 0.99978), (3.98, 0.99997), (4.07, 0.99998),
]:
self.assertAlmostEqual(Z.cdf(z), cum_prob, places=5)
self.assertAlmostEqual(Z.cdf(-z), 1.0 - cum_prob, places=5)
# Error case: variance is zero
Y = NormalDist(100, 0)
with self.assertRaises(statistics.StatisticsError):