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Edit File: test_generator_mt19937_regressions.py
from numpy.testing import (assert_, assert_array_equal) import numpy as np import pytest from numpy.random import Generator, MT19937 mt19937 = Generator(MT19937()) 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 = mt19937.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(mt19937.hypergeometric(3, 18, 11, size=10) < 4)) assert_(np.all(mt19937.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 assert_(mt19937.hypergeometric(*args) > 0) def test_logseries_convergence(self): # Test for ticket #923 N = 1000 mt19937 = Generator(MT19937(0)) rvsn = mt19937.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]]: mt19937 = Generator(MT19937(12345)) shuffled = np.array(t, dtype=object) mt19937.shuffle(shuffled) expected = np.array([t[2], t[0], t[3], t[1]], dtype=object) assert_array_equal(np.array(shuffled, dtype=object), expected) def test_call_within_randomstate(self): # Check that custom BitGenerator does not call into global state res = np.array([1, 8, 0, 1, 5, 3, 3, 8, 1, 4]) for i in range(3): mt19937 = Generator(MT19937(i)) m = Generator(MT19937(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. mt19937.multivariate_normal([0], [[0]], size=1) mt19937.multivariate_normal([0], [[0]], size=np.int_(1)) mt19937.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 mt19937 = Generator(MT19937(1234567890)) x = mt19937.beta(0.0001, 0.0001, size=100) assert_(not np.any(np.isnan(x)), 'Nans in mt19937.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. mt19937 = Generator(MT19937(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 = mt19937.choice(a, p=probs) assert_(c in a) with pytest.raises(ValueError): mt19937.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 mt19937 = Generator(MT19937(1234)) a = np.array(['a', 'a' * 1000]) for _ in range(100): mt19937.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 mt19937 = Generator(MT19937(1234)) a = np.array([np.arange(1), np.arange(4)], dtype=object) for _ in range(1000): mt19937.shuffle(a) # Force Garbage Collection - should not segfault. import gc gc.collect() def test_permutation_subclass(self): class N(np.ndarray): pass mt19937 = Generator(MT19937(1)) orig = np.arange(3).view(N) perm = mt19937.permutation(orig) assert_array_equal(perm, np.array([2, 0, 1])) assert_array_equal(orig, np.arange(3).view(N)) class M: a = np.arange(5) def __array__(self): return self.a mt19937 = Generator(MT19937(1)) m = M() perm = mt19937.permutation(m) assert_array_equal(perm, np.array([4, 1, 3, 0, 2])) assert_array_equal(m.__array__(), np.arange(5)) def test_gamma_0(self): assert mt19937.standard_gamma(0.0) == 0.0 assert_array_equal(mt19937.standard_gamma([0.0]), 0.0) actual = mt19937.standard_gamma([0.0], dtype='float') expected = np.array([0.], dtype=np.float32) assert_array_equal(actual, expected)