https://github.com/python/cpython/commit/fd0ea63f82bf9b8f766ea40cfa5befa653461e8a
commit: fd0ea63f82bf9b8f766ea40cfa5befa653461e8a
branch: main
author: Raymond Hettinger <[email protected]>
committer: rhettinger <[email protected]>
date: 2024-05-05T01:35:06-05:00
summary:
Minor edit: Simplify and tighten the distribution test (gh-118585)
Simplify and tighten the distribution test
files:
M Lib/test/test_statistics.py
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index fe6c59c30dae28..a60791e9b6e1f5 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -2482,29 +2482,30 @@ def test_kde_random(self):
# 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):
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