/usr/lib/python2.7/dist-packages/numpy/random/tests/test_regression.py is in python-numpy 1:1.12.1-3.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | from __future__ import division, absolute_import, print_function
import sys
from numpy.testing import (TestCase, run_module_suite, assert_,
assert_array_equal, assert_raises)
from numpy import random
from numpy.compat import long
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))
# Test for ticket #5623
args = [
(2**20 - 2, 2**20 - 2, 2**20 - 2), # Check for 32-bit systems
]
is_64bits = sys.maxsize > 2**32
if is_64bits and sys.platform != 'win32':
args.append((2**40 - 2, 2**40 - 2, 2**40 - 2)) # Check for 64-bit systems
for arg in args:
assert_(np.random.hypergeometric(*arg) > 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(long(12))
assert_array_equal(a, b)
def test_randint_range(self):
# Test for ticket #1690
lmax = np.iinfo('l').max
lmin = np.iinfo('l').min
try:
random.randint(lmin, lmax)
except:
raise AssertionError
def test_shuffle_mixed_dimension(self):
# Test for trac ticket #2074
for t in [[1, 2, 3, None],
[(1, 1), (2, 2), (3, 3), None],
[1, (2, 2), (3, 3), None],
[(1, 1), 2, 3, None]]:
np.random.seed(12345)
shuffled = list(t)
random.shuffle(shuffled)
assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]])
def test_call_within_randomstate(self):
# Check that custom RandomState does not call into global state
m = np.random.RandomState()
res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
for i in range(3):
np.random.seed(i)
m.seed(4321)
# If m.state is not honored, the result will change
assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
def test_multivariate_normal_size_types(self):
# Test for multivariate_normal issue with 'size' argument.
# Check that the multivariate_normal size argument can be a
# numpy integer.
np.random.multivariate_normal([0], [[0]], size=1)
np.random.multivariate_normal([0], [[0]], size=np.int_(1))
np.random.multivariate_normal([0], [[0]], size=np.int64(1))
def test_beta_small_parameters(self):
# Test that beta with small a and b parameters does not produce
# NaNs due to roundoff errors causing 0 / 0, gh-5851
np.random.seed(1234567890)
x = np.random.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
def test_choice_sum_of_probs_tolerance(self):
# The sum of probs should be 1.0 with some tolerance.
# For low precision dtypes the tolerance was too tight.
# See numpy github issue 6123.
np.random.seed(1234)
a = [1, 2, 3]
counts = [4, 4, 2]
for dt in np.float16, np.float32, np.float64:
probs = np.array(counts, dtype=dt) / sum(counts)
c = np.random.choice(a, p=probs)
assert_(c in a)
assert_raises(ValueError, np.random.choice, a, p=probs*0.9)
def test_shuffle_of_array_of_different_length_strings(self):
# Test that permuting an array of different length strings
# will not cause a segfault on garbage collection
# Tests gh-7710
np.random.seed(1234)
a = np.array(['a', 'a' * 1000])
for _ in range(100):
np.random.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
def test_shuffle_of_array_of_objects(self):
# Test that permuting an array of objects will not cause
# a segfault on garbage collection.
# See gh-7719
np.random.seed(1234)
a = np.array([np.arange(1), np.arange(4)])
for _ in range(1000):
np.random.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
if __name__ == "__main__":
run_module_suite()
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