| 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) |
