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Edit File: test_regression.py
import sys from numpy.testing import ( assert_, assert_array_equal, assert_raises, ) from numpy import random import numpy as np 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.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': # Check for 64-bit systems args.append((2**40 - 2, 2**40 - 2, 2**40 - 2)) 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) / 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]]: np.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 = 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)], dtype=object) for _ in range(1000): np.random.shuffle(a) # Force Garbage Collection - should not segfault. import gc gc.collect() def test_permutation_subclass(self): class N(np.ndarray): pass np.random.seed(1) orig = np.arange(3).view(N) perm = np.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): return self.a np.random.seed(1) m = M() perm = np.random.permutation(m) assert_array_equal(perm, np.array([2, 1, 4, 0, 3])) assert_array_equal(m.__array__(), np.arange(5))