1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
|
# ruff: noqa: D100 D103
import numpy as np
import pytest
from numpy.testing import assert_equal
from resample.empirical import cdf_gen, influence, quantile_function_gen
# high-quality platform-independent reproducible sequence of pseudo-random numbers
@pytest.fixture
def rng():
return np.random.Generator(np.random.PCG64(1))
def test_cdf_increasing(rng):
x = rng.normal(size=100)
cdf = cdf_gen(x)
result = [cdf(s) for s in np.linspace(x.min(), x.max(), 100)]
assert np.all(np.diff(result) >= 0)
def test_cdf_at_infinity():
cdf = cdf_gen(np.arange(10))
assert cdf(-np.inf) == 0.0
assert cdf(np.inf) == 1.0
def test_cdf_simple_cases():
cdf = cdf_gen([0, 1, 2, 3])
assert cdf(0) == 0.25
assert cdf(1) == 0.5
assert cdf(2) == 0.75
assert cdf(3) == 1.0
def test_cdf_on_array():
x = np.arange(4)
cdf = cdf_gen(x)
assert_equal(cdf(x), (x + 1) / len(x))
assert_equal(cdf(x + 1e-10), (x + 1) / len(x))
assert_equal(cdf(x - 1e-10), x / len(x))
def test_quantile_simple_cases():
q = quantile_function_gen([0, 1, 2, 3])
assert q(0.25) == 0
assert q(0.5) == 1
assert q(0.75) == 2
assert q(1.0) == 3
def test_quantile_on_array():
x = np.arange(4)
q = quantile_function_gen(x)
prob = (x + 1) / len(x)
assert_equal(q(prob), x)
def test_quantile_is_inverse_of_cdf(rng):
x = rng.normal(size=30)
y = cdf_gen(x)(x)
assert_equal(quantile_function_gen(x)(y), x)
@pytest.mark.parametrize("arg", [-1, 1.5])
def test_quantile_out_of_bounds_is_nan(arg):
q = quantile_function_gen(np.array([0, 1, 2, 3]))
assert np.isnan(q(arg))
def test_influence_shape():
n = 100
data = np.random.random(n)
emp = influence(np.mean, data)
assert len(emp) == n
|
