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
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
|
from numpy.testing import *
from numpy import random
import numpy as np
class TestRegression(TestCase):
def test_VonMises_range(self):
"""Make sure generated random variables are in [-pi, pi].
Regression test for ticket #986.
"""
for mu in np.linspace(-7., 7., 5):
r = random.mtrand.vonmises(mu,1,50)
assert np.all(r > -np.pi) and np.all(r <= np.pi)
def test_hypergeometric_range(self) :
"""Test for ticket #921"""
assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4))
assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0))
def test_logseries_convergence(self) :
"""Test for ticket #923"""
N = 1000
np.random.seed(0)
rvsn = np.random.logseries(0.8, size=N)
# these two frequency counts should be close to theoretical
# numbers with this large sample
# theoretical large N result is 0.49706795
freq = np.sum(rvsn == 1) / float(N)
msg = "Frequency was %f, should be > 0.45" % freq
assert_(freq > 0.45, msg)
# theoretical large N result is 0.19882718
freq = np.sum(rvsn == 2) / float(N)
msg = "Frequency was %f, should be < 0.23" % freq
assert_(freq < 0.23, msg)
def test_permutation_longs(self):
np.random.seed(1234)
a = np.random.permutation(12)
np.random.seed(1234)
b = np.random.permutation(12L)
assert_array_equal(a, b)
class TestMultinomial(TestCase):
def test_basic(self):
random.multinomial(100, [0.2, 0.8])
def test_zero_probability(self):
random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
def test_int_negative_interval(self):
assert -5 <= random.randint(-5,-1) < -1
x = random.randint(-5,-1,5)
assert np.all(-5 <= x)
assert np.all(x < -1)
class TestSetState(TestCase):
def setUp(self):
self.seed = 1234567890
self.prng = random.RandomState(self.seed)
self.state = self.prng.get_state()
def test_basic(self):
old = self.prng.tomaxint(16)
self.prng.set_state(self.state)
new = self.prng.tomaxint(16)
assert np.all(old == new)
def test_gaussian_reset(self):
""" Make sure the cached every-other-Gaussian is reset.
"""
old = self.prng.standard_normal(size=3)
self.prng.set_state(self.state)
new = self.prng.standard_normal(size=3)
assert np.all(old == new)
def test_gaussian_reset_in_media_res(self):
""" When the state is saved with a cached Gaussian, make sure the cached
Gaussian is restored.
"""
self.prng.standard_normal()
state = self.prng.get_state()
old = self.prng.standard_normal(size=3)
self.prng.set_state(state)
new = self.prng.standard_normal(size=3)
assert np.all(old == new)
def test_backwards_compatibility(self):
""" Make sure we can accept old state tuples that do not have the cached
Gaussian value.
"""
old_state = self.state[:-2]
x1 = self.prng.standard_normal(size=16)
self.prng.set_state(old_state)
x2 = self.prng.standard_normal(size=16)
self.prng.set_state(self.state)
x3 = self.prng.standard_normal(size=16)
assert np.all(x1 == x2)
assert np.all(x1 == x3)
def test_negative_binomial(self):
""" Ensure that the negative binomial results take floating point
arguments without truncation.
"""
self.prng.negative_binomial(0.5, 0.5)
if __name__ == "__main__":
run_module_suite()
|
