Edit: test_randomstate_regression.py
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): 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 c = np.random.choice(10, p=[.1]*10, size=2) assert c.dtype == np.dtype(int) c = np.random.choice(10, p=[.1]*10, replace=False, size=2) assert c.dtype == np.dtype(int) c = np.random.choice(10, size=2) assert c.dtype == np.dtype(int) c = np.random.choice(10, replace=False, size=2) assert c.dtype == np.dtype(int) @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)
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