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
76
77
78
79
80
81
82
83
84
85
86
87
88
89
|
import numpy as np
from numpy.testing import assert_allclose
from astroML.resample import bootstrap, jackknife
from astroML.stats import mean_sigma
def test_jackknife_results():
np.random.seed(0)
x = np.random.normal(0, 1, 100)
mu1, sig1 = jackknife(x, np.mean, kwargs=dict(axis=1))
mu2, sig2 = jackknife(x, np.std, kwargs=dict(axis=1))
assert_allclose([mu1, sig1, mu2, sig2],
[0.0598080155345, 0.100288031685,
1.01510470168, 0.0649020337599])
def test_jackknife_multiple():
np.random.seed(0)
x = np.random.normal(0, 1, 100)
mu1, sig1 = jackknife(x, np.mean, kwargs=dict(axis=1))
mu2, sig2 = jackknife(x, np.std, kwargs=dict(axis=1))
res = jackknife(x, mean_sigma, kwargs=dict(axis=1))
assert_allclose(res[0], (mu1, sig1))
assert_allclose(res[1], (mu2, sig2))
def test_bootstrap_results():
np.random.seed(0)
x = np.random.normal(0, 1, 100)
distribution = bootstrap(x, 100, np.mean, kwargs=dict(axis=1),
random_state=0)
mu, sigma = mean_sigma(distribution)
assert_allclose([mu, sigma], [0.08139846, 0.10465327])
def test_bootstrap_multiple():
np.random.seed(0)
x = np.random.normal(0, 1, 100)
dist_mean = bootstrap(x, 100, np.mean, kwargs=dict(axis=1),
random_state=0)
dist_std = bootstrap(x, 100, np.std, kwargs=dict(axis=1),
random_state=0)
res = bootstrap(x, 100, mean_sigma, kwargs=dict(axis=1),
random_state=0)
assert_allclose(res[0], dist_mean)
assert_allclose(res[1], dist_std)
def test_bootstrap_covar():
np.random.seed(0)
mean = [0., 0.]
covar = [[10., 3.], [3., 20.]]
x = np.random.multivariate_normal(mean, covar, 1000)
dist_cov = bootstrap(x, 10000, np.cov, kwargs=dict(rowvar=0), random_state=0)
assert_allclose(covar[0][0], dist_cov[0][0], atol=2.*0.4)
def test_bootstrap_pass_indices():
np.random.seed(0)
x = np.random.normal(0, 1, 100)
dist1 = bootstrap(x, 100, np.mean,
kwargs=dict(axis=1), random_state=0)
dist2 = bootstrap(x, 100, lambda i: np.mean(x[i], axis=1),
pass_indices=True, random_state=0)
assert_allclose(dist1, dist2)
def test_jackknife_pass_indices():
np.random.seed(0)
x = np.random.normal(0, 1, 100)
res1 = jackknife(x, np.mean,
kwargs=dict(axis=1))
res2 = jackknife(x, lambda i: np.mean(x[i], axis=1),
pass_indices=True)
assert_allclose(res1, res2)
|
