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Minor edit: Simplify and tighten the distribution test (gh-118585)

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Simplify and tighten the distribution test
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Raymond Hettinger 2024-05-05 01:35:06 -05:00 • committed by GitHub
parent 3b32575ed6
commit fd0ea63f82
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1 changed files with 11 additions and 10 deletions
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21
Lib/test/test_statistics.py
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@ -2482,29 +2482,30 @@ class TestKDE(unittest.TestCase):
# Approximate distribution test: Compare a random sample to the expected distribution
data = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2, 7.8, 14.3, 15.1, 15.3, 15.8, 17.0]
xarr = [x / 10 for x in range(-100, 250)]
n = 1_000_000
h = 1.75
dx = 0.1
def p_expected(x):
return F_hat(x + dx) - F_hat(x - dx)
def p_observed(x):
# P(x-dx <= X < x+dx) / (2*dx)
i = bisect.bisect_left(big_sample, x - dx)
j = bisect.bisect_right(big_sample, x + dx)
# P(x <= X < x+dx)
i = bisect.bisect_left(big_sample, x)
j = bisect.bisect_left(big_sample, x + dx)
return (j - i) / len(big_sample)
def p_expected(x):
# P(x <= X < x+dx)
return F_hat(x + dx) - F_hat(x)
for kernel in kernels:
with self.subTest(kernel=kernel):
F_hat = statistics.kde(data, h, kernel, cumulative=True)
rand = kde_random(data, h, kernel, seed=8675309**2)
big_sample = sorted([rand() for i in range(n)])
F_hat = statistics.kde(data, h, kernel, cumulative=True)
for x in range(-40, 190):
x /= 10
self.assertTrue(math.isclose(p_observed(x), p_expected(x), abs_tol=0.001))
for x in xarr:
self.assertTrue(math.isclose(p_observed(x), p_expected(x), abs_tol=0.0005))
class TestQuantiles(unittest.TestCase):