import sys
import pytest
from numpy.testing import (
assert_, assert_array_equal, assert_raises,
)
import numpy as np
from numpy import random
class TestRegression:
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.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(random.hypergeometric(3, 18, 11, size=10) < 4))
assert_(np.all(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':
# Check for 64-bit systems
args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
for arg in args:
assert_(random.hypergeometric(*arg) > 0)
def test_logseries_convergence(self):
# Test for ticket #923
N = 1000
random.seed(0)
rvsn = 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) / N
msg = f'Frequency was {freq:f}, should be > 0.45'
assert_(freq > 0.45, msg)
# theoretical large N result is 0.19882718
freq = np.sum(rvsn == 2) / N
msg = f'Frequency was {freq:f}, should be < 0.23'
assert_(freq < 0.23, msg)
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]]:
random.seed(12345)
shuffled = list(t)
random.shuffle(shuffled)
expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)
assert_array_equal(np.array(shuffled, dtype=object), expected)
def test_call_within_randomstate(self):
# Check that custom RandomState does not call into global state
m = random.RandomState()
res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
for i in range(3):
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.
random.multivariate_normal([0], [[0]], size=1)
random.multivariate_normal([0], [[0]], size=np.int_(1))
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
random.seed(1234567890)
x = random.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in 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.
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 = random.choice(a, p=probs)
assert_(c in a)
assert_raises(ValueError, 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
random.seed(1234)
a = np.array(['a', 'a' * 1000])
for _ in range(100):
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
random.seed(1234)
a = np.array([np.arange(1), np.arange(4)], dtype=object)
for _ in range(1000):
random.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
def test_permutation_subclass(self):
class N(np.ndarray):
pass
random.seed(1)
orig = np.arange(3).view(N)
perm = random.permutation(orig)
assert_array_equal(perm, np.array([0, 2, 1]))
assert_array_equal(orig, np.arange(3).view(N))
class M:
a = np.arange(5)
def __array__(self, dtype=None, copy=None):
return self.a
random.seed(1)
m = M()
perm = random.permutation(m)
assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
assert_array_equal(m.__array__(), np.arange(5))
def test_warns_byteorder(self):
# GH 13159
other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'
with pytest.deprecated_call(match='non-native byteorder is not'):
random.randint(0, 200, size=10, dtype=other_byteord_dt)
def test_named_argument_initialization(self):
# GH 13669
rs1 = np.random.RandomState(123456789)
rs2 = np.random.RandomState(seed=123456789)
assert rs1.randint(0, 100) == rs2.randint(0, 100)
def test_choice_retun_dtype(self):
# GH 9867, now long since the NumPy default changed.
c = np.random.choice(10, p=[.1]*10, size=2)
assert c.dtype == np.dtype(np.long)
c = np.random.choice(10, p=[.1]*10, replace=False, size=2)
assert c.dtype == np.dtype(np.long)
c = np.random.choice(10, size=2)
assert c.dtype == np.dtype(np.long)
c = np.random.choice(10, replace=False, size=2)
assert c.dtype == np.dtype(np.long)
@pytest.mark.skipif(np.iinfo('l').max < 2**32,
reason='Cannot test with 32-bit C long')
def test_randint_117(self):
# GH 14189
random.seed(0)
expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,
2588848963, 3684848379, 2340255427, 3638918503,
1819583497, 2678185683], dtype='int64')
actual = random.randint(2**32, size=10)
assert_array_equal(actual, expected)
def test_p_zero_stream(self):
# Regression test for gh-14522. Ensure that future versions
# generate the same variates as version 1.16.
np.random.seed(12345)
assert_array_equal(random.binomial(1, [0, 0.25, 0.5, 0.75, 1]),
[0, 0, 0, 1, 1])
def test_n_zero_stream(self):
# Regression test for gh-14522. Ensure that future versions
# generate the same variates as version 1.16.
np.random.seed(8675309)
expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[3, 4, 2, 3, 3, 1, 5, 3, 1, 3]])
assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)),
expected)
def test_multinomial_empty():
# gh-20483
# Ensure that empty p-vals are correctly handled
assert random.multinomial(10, []).shape == (0,)
assert random.multinomial(3, [], size=(7, 5, 3)).shape == (7, 5, 3, 0)
def test_multinomial_1d_pval():
# gh-20483
with pytest.raises(TypeError, match="pvals must be a 1-d"):
random.multinomial(10, 0.3)