211 lines
7.5 KiB
Python
211 lines
7.5 KiB
Python
import sys
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import pytest
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from numpy.testing import (
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assert_, assert_array_equal, assert_raises,
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)
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from numpy.compat import long
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import numpy as np
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from numpy.random import mtrand as random
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class TestRegression(object):
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def test_VonMises_range(self):
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# Make sure generated random variables are in [-pi, pi].
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# Regression test for ticket #986.
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for mu in np.linspace(-7., 7., 5):
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r = random.vonmises(mu, 1, 50)
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assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
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def test_hypergeometric_range(self):
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# Test for ticket #921
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assert_(np.all(random.hypergeometric(3, 18, 11, size=10) < 4))
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assert_(np.all(random.hypergeometric(18, 3, 11, size=10) > 0))
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# Test for ticket #5623
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args = [
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(2**20 - 2, 2**20 - 2, 2**20 - 2), # Check for 32-bit systems
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]
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is_64bits = sys.maxsize > 2**32
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if is_64bits and sys.platform != 'win32':
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# Check for 64-bit systems
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args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
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for arg in args:
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assert_(random.hypergeometric(*arg) > 0)
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def test_logseries_convergence(self):
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# Test for ticket #923
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N = 1000
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random.seed(0)
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rvsn = random.logseries(0.8, size=N)
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# these two frequency counts should be close to theoretical
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# numbers with this large sample
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# theoretical large N result is 0.49706795
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freq = np.sum(rvsn == 1) / float(N)
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msg = "Frequency was %f, should be > 0.45" % freq
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assert_(freq > 0.45, msg)
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# theoretical large N result is 0.19882718
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freq = np.sum(rvsn == 2) / float(N)
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msg = "Frequency was %f, should be < 0.23" % freq
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assert_(freq < 0.23, msg)
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def test_permutation_longs(self):
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random.seed(1234)
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a = random.permutation(12)
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random.seed(1234)
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b = random.permutation(long(12))
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assert_array_equal(a, b)
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def test_shuffle_mixed_dimension(self):
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# Test for trac ticket #2074
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for t in [[1, 2, 3, None],
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[(1, 1), (2, 2), (3, 3), None],
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[1, (2, 2), (3, 3), None],
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[(1, 1), 2, 3, None]]:
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random.seed(12345)
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shuffled = list(t)
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random.shuffle(shuffled)
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assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]])
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def test_call_within_randomstate(self):
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# Check that custom RandomState does not call into global state
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m = random.RandomState()
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res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
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for i in range(3):
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random.seed(i)
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m.seed(4321)
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# If m.state is not honored, the result will change
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assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
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def test_multivariate_normal_size_types(self):
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# Test for multivariate_normal issue with 'size' argument.
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# Check that the multivariate_normal size argument can be a
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# numpy integer.
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random.multivariate_normal([0], [[0]], size=1)
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random.multivariate_normal([0], [[0]], size=np.int_(1))
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random.multivariate_normal([0], [[0]], size=np.int64(1))
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def test_beta_small_parameters(self):
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# Test that beta with small a and b parameters does not produce
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# NaNs due to roundoff errors causing 0 / 0, gh-5851
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random.seed(1234567890)
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x = random.beta(0.0001, 0.0001, size=100)
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assert_(not np.any(np.isnan(x)), 'Nans in random.beta')
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def test_choice_sum_of_probs_tolerance(self):
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# The sum of probs should be 1.0 with some tolerance.
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# For low precision dtypes the tolerance was too tight.
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# See numpy github issue 6123.
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random.seed(1234)
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a = [1, 2, 3]
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counts = [4, 4, 2]
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for dt in np.float16, np.float32, np.float64:
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probs = np.array(counts, dtype=dt) / sum(counts)
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c = random.choice(a, p=probs)
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assert_(c in a)
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assert_raises(ValueError, random.choice, a, p=probs*0.9)
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def test_shuffle_of_array_of_different_length_strings(self):
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# Test that permuting an array of different length strings
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# will not cause a segfault on garbage collection
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# Tests gh-7710
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random.seed(1234)
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a = np.array(['a', 'a' * 1000])
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for _ in range(100):
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random.shuffle(a)
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# Force Garbage Collection - should not segfault.
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import gc
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gc.collect()
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def test_shuffle_of_array_of_objects(self):
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# Test that permuting an array of objects will not cause
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# a segfault on garbage collection.
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# See gh-7719
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random.seed(1234)
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a = np.array([np.arange(1), np.arange(4)])
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for _ in range(1000):
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random.shuffle(a)
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# Force Garbage Collection - should not segfault.
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import gc
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gc.collect()
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def test_permutation_subclass(self):
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class N(np.ndarray):
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pass
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random.seed(1)
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orig = np.arange(3).view(N)
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perm = random.permutation(orig)
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assert_array_equal(perm, np.array([0, 2, 1]))
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assert_array_equal(orig, np.arange(3).view(N))
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class M(object):
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a = np.arange(5)
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def __array__(self):
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return self.a
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random.seed(1)
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m = M()
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perm = random.permutation(m)
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assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
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assert_array_equal(m.__array__(), np.arange(5))
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def test_warns_byteorder(self):
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# GH 13159
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other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'
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with pytest.deprecated_call(match='non-native byteorder is not'):
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random.randint(0, 200, size=10, dtype=other_byteord_dt)
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def test_named_argument_initialization(self):
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# GH 13669
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rs1 = np.random.RandomState(123456789)
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rs2 = np.random.RandomState(seed=123456789)
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assert rs1.randint(0, 100) == rs2.randint(0, 100)
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def test_choice_retun_dtype(self):
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# GH 9867
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c = np.random.choice(10, p=[.1]*10, size=2)
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assert c.dtype == np.dtype(int)
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c = np.random.choice(10, p=[.1]*10, replace=False, size=2)
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assert c.dtype == np.dtype(int)
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c = np.random.choice(10, size=2)
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assert c.dtype == np.dtype(int)
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c = np.random.choice(10, replace=False, size=2)
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assert c.dtype == np.dtype(int)
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@pytest.mark.skipif(np.iinfo('l').max < 2**32,
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reason='Cannot test with 32-bit C long')
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def test_randint_117(self):
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# GH 14189
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random.seed(0)
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expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,
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2588848963, 3684848379, 2340255427, 3638918503,
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1819583497, 2678185683], dtype='int64')
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actual = random.randint(2**32, size=10)
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assert_array_equal(actual, expected)
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def test_p_zero_stream(self):
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# Regression test for gh-14522. Ensure that future versions
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# generate the same variates as version 1.16.
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np.random.seed(12345)
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assert_array_equal(random.binomial(1, [0, 0.25, 0.5, 0.75, 1]),
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[0, 0, 0, 1, 1])
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def test_n_zero_stream(self):
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# Regression test for gh-14522. Ensure that future versions
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# generate the same variates as version 1.16.
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np.random.seed(8675309)
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expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[3, 4, 2, 3, 3, 1, 5, 3, 1, 3]])
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assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)),
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expected)
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