OwlCyberSecurity - MANAGER

Edit File: test_generator_mt19937.py

import sys import hashlib import pytest import numpy as np from numpy.linalg import LinAlgError from numpy.testing import ( assert_, assert_raises, assert_equal, assert_allclose, assert_warns, assert_no_warnings, assert_array_equal, assert_array_almost_equal, suppress_warnings, IS_WASM) from numpy.random import Generator, MT19937, SeedSequence, RandomState random = Generator(MT19937()) JUMP_TEST_DATA = [ { "seed": 0, "steps": 10, "initial": {"key_sha256": "bb1636883c2707b51c5b7fc26c6927af4430f2e0785a8c7bc886337f919f9edf", "pos": 9}, "jumped": {"key_sha256": "ff682ac12bb140f2d72fba8d3506cf4e46817a0db27aae1683867629031d8d55", "pos": 598}, }, { "seed":384908324, "steps":312, "initial": {"key_sha256": "16b791a1e04886ccbbb4d448d6ff791267dc458ae599475d08d5cced29d11614", "pos": 311}, "jumped": {"key_sha256": "a0110a2cf23b56be0feaed8f787a7fc84bef0cb5623003d75b26bdfa1c18002c", "pos": 276}, }, { "seed": [839438204, 980239840, 859048019, 821], "steps": 511, "initial": {"key_sha256": "d306cf01314d51bd37892d874308200951a35265ede54d200f1e065004c3e9ea", "pos": 510}, "jumped": {"key_sha256": "0e00ab449f01a5195a83b4aee0dfbc2ce8d46466a640b92e33977d2e42f777f8", "pos": 475}, }, ] @pytest.fixture(scope='module', params=[True, False]) def endpoint(request): return request.param class TestSeed: def test_scalar(self): s = Generator(MT19937(0)) assert_equal(s.integers(1000), 479) s = Generator(MT19937(4294967295)) assert_equal(s.integers(1000), 324) def test_array(self): s = Generator(MT19937(range(10))) assert_equal(s.integers(1000), 465) s = Generator(MT19937(np.arange(10))) assert_equal(s.integers(1000), 465) s = Generator(MT19937([0])) assert_equal(s.integers(1000), 479) s = Generator(MT19937([4294967295])) assert_equal(s.integers(1000), 324) def test_seedsequence(self): s = MT19937(SeedSequence(0)) assert_equal(s.random_raw(1), 2058676884) def test_invalid_scalar(self): # seed must be an unsigned 32 bit integer assert_raises(TypeError, MT19937, -0.5) assert_raises(ValueError, MT19937, -1) def test_invalid_array(self): # seed must be an unsigned integer assert_raises(TypeError, MT19937, [-0.5]) assert_raises(ValueError, MT19937, [-1]) assert_raises(ValueError, MT19937, [1, -2, 4294967296]) def test_noninstantized_bitgen(self): assert_raises(ValueError, Generator, MT19937) class TestBinomial: def test_n_zero(self): # Tests the corner case of n == 0 for the binomial distribution. # binomial(0, p) should be zero for any p in [0, 1]. # This test addresses issue #3480. zeros = np.zeros(2, dtype='int') for p in [0, .5, 1]: assert_(random.binomial(0, p) == 0) assert_array_equal(random.binomial(zeros, p), zeros) def test_p_is_nan(self): # Issue #4571. assert_raises(ValueError, random.binomial, 1, np.nan) class TestMultinomial: def test_basic(self): random.multinomial(100, [0.2, 0.8]) def test_zero_probability(self): random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0]) def test_int_negative_interval(self): assert_(-5 <= random.integers(-5, -1) < -1) x = random.integers(-5, -1, 5) assert_(np.all(-5 <= x)) assert_(np.all(x < -1)) def test_size(self): # gh-3173 p = [0.5, 0.5] assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2)) assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2)) assert_equal(random.multinomial(1, p, np.array((2, 2))).shape, (2, 2, 2)) assert_raises(TypeError, random.multinomial, 1, p, float(1)) def test_invalid_prob(self): assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2]) assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9]) def test_invalid_n(self): assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2]) assert_raises(ValueError, random.multinomial, [-1] * 10, [0.8, 0.2]) def test_p_non_contiguous(self): p = np.arange(15.) p /= np.sum(p[1::3]) pvals = p[1::3] random = Generator(MT19937(1432985819)) non_contig = random.multinomial(100, pvals=pvals) random = Generator(MT19937(1432985819)) contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals)) assert_array_equal(non_contig, contig) def test_multinomial_pvals_float32(self): x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32) pvals = x / x.sum() random = Generator(MT19937(1432985819)) match = r"[\w\s]*pvals array is cast to 64-bit floating" with pytest.raises(ValueError, match=match): random.multinomial(1, pvals) class TestMultivariateHypergeometric: def setup_method(self): self.seed = 8675309 def test_argument_validation(self): # Error cases... # `colors` must be a 1-d sequence assert_raises(ValueError, random.multivariate_hypergeometric, 10, 4) # Negative nsample assert_raises(ValueError, random.multivariate_hypergeometric, [2, 3, 4], -1) # Negative color assert_raises(ValueError, random.multivariate_hypergeometric, [-1, 2, 3], 2) # nsample exceeds sum(colors) assert_raises(ValueError, random.multivariate_hypergeometric, [2, 3, 4], 10) # nsample exceeds sum(colors) (edge case of empty colors) assert_raises(ValueError, random.multivariate_hypergeometric, [], 1) # Validation errors associated with very large values in colors. assert_raises(ValueError, random.multivariate_hypergeometric, [999999999, 101], 5, 1, 'marginals') int64_info = np.iinfo(np.int64) max_int64 = int64_info.max max_int64_index = max_int64 // int64_info.dtype.itemsize assert_raises(ValueError, random.multivariate_hypergeometric, [max_int64_index - 100, 101], 5, 1, 'count') @pytest.mark.parametrize('method', ['count', 'marginals']) def test_edge_cases(self, method): # Set the seed, but in fact, all the results in this test are # deterministic, so we don't really need this. random = Generator(MT19937(self.seed)) x = random.multivariate_hypergeometric([0, 0, 0], 0, method=method) assert_array_equal(x, [0, 0, 0]) x = random.multivariate_hypergeometric([], 0, method=method) assert_array_equal(x, []) x = random.multivariate_hypergeometric([], 0, size=1, method=method) assert_array_equal(x, np.empty((1, 0), dtype=np.int64)) x = random.multivariate_hypergeometric([1, 2, 3], 0, method=method) assert_array_equal(x, [0, 0, 0]) x = random.multivariate_hypergeometric([9, 0, 0], 3, method=method) assert_array_equal(x, [3, 0, 0]) colors = [1, 1, 0, 1, 1] x = random.multivariate_hypergeometric(colors, sum(colors), method=method) assert_array_equal(x, colors) x = random.multivariate_hypergeometric([3, 4, 5], 12, size=3, method=method) assert_array_equal(x, [[3, 4, 5]]*3) # Cases for nsample: # nsample < 10 # 10 <= nsample < colors.sum()/2 # colors.sum()/2 < nsample < colors.sum() - 10 # colors.sum() - 10 < nsample < colors.sum() @pytest.mark.parametrize('nsample', [8, 25, 45, 55]) @pytest.mark.parametrize('method', ['count', 'marginals']) @pytest.mark.parametrize('size', [5, (2, 3), 150000]) def test_typical_cases(self, nsample, method, size): random = Generator(MT19937(self.seed)) colors = np.array([10, 5, 20, 25]) sample = random.multivariate_hypergeometric(colors, nsample, size, method=method) if isinstance(size, int): expected_shape = (size,) + colors.shape else: expected_shape = size + colors.shape assert_equal(sample.shape, expected_shape) assert_((sample >= 0).all()) assert_((sample <= colors).all()) assert_array_equal(sample.sum(axis=-1), np.full(size, fill_value=nsample, dtype=int)) if isinstance(size, int) and size >= 100000: # This sample is large enough to compare its mean to # the expected values. assert_allclose(sample.mean(axis=0), nsample * colors / colors.sum(), rtol=1e-3, atol=0.005) def test_repeatability1(self): random = Generator(MT19937(self.seed)) sample = random.multivariate_hypergeometric([3, 4, 5], 5, size=5, method='count') expected = np.array([[2, 1, 2], [2, 1, 2], [1, 1, 3], [2, 0, 3], [2, 1, 2]]) assert_array_equal(sample, expected) def test_repeatability2(self): random = Generator(MT19937(self.seed)) sample = random.multivariate_hypergeometric([20, 30, 50], 50, size=5, method='marginals') expected = np.array([[ 9, 17, 24], [ 7, 13, 30], [ 9, 15, 26], [ 9, 17, 24], [12, 14, 24]]) assert_array_equal(sample, expected) def test_repeatability3(self): random = Generator(MT19937(self.seed)) sample = random.multivariate_hypergeometric([20, 30, 50], 12, size=5, method='marginals') expected = np.array([[2, 3, 7], [5, 3, 4], [2, 5, 5], [5, 3, 4], [1, 5, 6]]) assert_array_equal(sample, expected) class TestSetState: def setup_method(self): self.seed = 1234567890 self.rg = Generator(MT19937(self.seed)) self.bit_generator = self.rg.bit_generator self.state = self.bit_generator.state self.legacy_state = (self.state['bit_generator'], self.state['state']['key'], self.state['state']['pos']) def test_gaussian_reset(self): # Make sure the cached every-other-Gaussian is reset. old = self.rg.standard_normal(size=3) self.bit_generator.state = self.state new = self.rg.standard_normal(size=3) assert_(np.all(old == new)) def test_gaussian_reset_in_media_res(self): # When the state is saved with a cached Gaussian, make sure the # cached Gaussian is restored. self.rg.standard_normal() state = self.bit_generator.state old = self.rg.standard_normal(size=3) self.bit_generator.state = state new = self.rg.standard_normal(size=3) assert_(np.all(old == new)) def test_negative_binomial(self): # Ensure that the negative binomial results take floating point # arguments without truncation. self.rg.negative_binomial(0.5, 0.5) class TestIntegers: rfunc = random.integers # valid integer/boolean types itype = [bool, np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.int64, np.uint64] def test_unsupported_type(self, endpoint): assert_raises(TypeError, self.rfunc, 1, endpoint=endpoint, dtype=float) def test_bounds_checking(self, endpoint): for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, ubnd, lbnd, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, 1, 0, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, [lbnd - 1], ubnd, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, [lbnd], [ubnd + 1], endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, [ubnd], [lbnd], endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, 1, [0], endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, [ubnd+1], [ubnd], endpoint=endpoint, dtype=dt) def test_bounds_checking_array(self, endpoint): for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + (not endpoint) assert_raises(ValueError, self.rfunc, [lbnd - 1] * 2, [ubnd] * 2, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, [lbnd] * 2, [ubnd + 1] * 2, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, ubnd, [lbnd] * 2, endpoint=endpoint, dtype=dt) assert_raises(ValueError, self.rfunc, [1] * 2, 0, endpoint=endpoint, dtype=dt) def test_rng_zero_and_extremes(self, endpoint): for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd is_open = not endpoint tgt = ubnd - 1 assert_equal(self.rfunc(tgt, tgt + is_open, size=1000, endpoint=endpoint, dtype=dt), tgt) assert_equal(self.rfunc([tgt], tgt + is_open, size=1000, endpoint=endpoint, dtype=dt), tgt) tgt = lbnd assert_equal(self.rfunc(tgt, tgt + is_open, size=1000, endpoint=endpoint, dtype=dt), tgt) assert_equal(self.rfunc(tgt, [tgt + is_open], size=1000, endpoint=endpoint, dtype=dt), tgt) tgt = (lbnd + ubnd) // 2 assert_equal(self.rfunc(tgt, tgt + is_open, size=1000, endpoint=endpoint, dtype=dt), tgt) assert_equal(self.rfunc([tgt], [tgt + is_open], size=1000, endpoint=endpoint, dtype=dt), tgt) def test_rng_zero_and_extremes_array(self, endpoint): size = 1000 for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd tgt = ubnd - 1 assert_equal(self.rfunc([tgt], [tgt + 1], size=size, dtype=dt), tgt) assert_equal(self.rfunc( [tgt] * size, [tgt + 1] * size, dtype=dt), tgt) assert_equal(self.rfunc( [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt) tgt = lbnd assert_equal(self.rfunc([tgt], [tgt + 1], size=size, dtype=dt), tgt) assert_equal(self.rfunc( [tgt] * size, [tgt + 1] * size, dtype=dt), tgt) assert_equal(self.rfunc( [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt) tgt = (lbnd + ubnd) // 2 assert_equal(self.rfunc([tgt], [tgt + 1], size=size, dtype=dt), tgt) assert_equal(self.rfunc( [tgt] * size, [tgt + 1] * size, dtype=dt), tgt) assert_equal(self.rfunc( [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt) def test_full_range(self, endpoint): # Test for ticket #1690 for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd try: self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt) except Exception as e: raise AssertionError("No error should have been raised, " "but one was with the following " "message:\n\n%s" % str(e)) def test_full_range_array(self, endpoint): # Test for ticket #1690 for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd try: self.rfunc([lbnd] * 2, [ubnd], endpoint=endpoint, dtype=dt) except Exception as e: raise AssertionError("No error should have been raised, " "but one was with the following " "message:\n\n%s" % str(e)) def test_in_bounds_fuzz(self, endpoint): # Don't use fixed seed random = Generator(MT19937()) for dt in self.itype[1:]: for ubnd in [4, 8, 16]: vals = self.rfunc(2, ubnd - endpoint, size=2 ** 16, endpoint=endpoint, dtype=dt) assert_(vals.max() < ubnd) assert_(vals.min() >= 2) vals = self.rfunc(0, 2 - endpoint, size=2 ** 16, endpoint=endpoint, dtype=bool) assert_(vals.max() < 2) assert_(vals.min() >= 0) def test_scalar_array_equiv(self, endpoint): for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd size = 1000 random = Generator(MT19937(1234)) scalar = random.integers(lbnd, ubnd, size=size, endpoint=endpoint, dtype=dt) random = Generator(MT19937(1234)) scalar_array = random.integers([lbnd], [ubnd], size=size, endpoint=endpoint, dtype=dt) random = Generator(MT19937(1234)) array = random.integers([lbnd] * size, [ubnd] * size, size=size, endpoint=endpoint, dtype=dt) assert_array_equal(scalar, scalar_array) assert_array_equal(scalar, array) def test_repeatability(self, endpoint): # We use a sha256 hash of generated sequences of 1000 samples # in the range [0, 6) for all but bool, where the range # is [0, 2). Hashes are for little endian numbers. tgt = {'bool': '053594a9b82d656f967c54869bc6970aa0358cf94ad469c81478459c6a90eee3', 'int16': '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4', 'int32': 'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b', 'int64': '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1', 'int8': '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1', 'uint16': '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4', 'uint32': 'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b', 'uint64': '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1', 'uint8': '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1'} for dt in self.itype[1:]: random = Generator(MT19937(1234)) # view as little endian for hash if sys.byteorder == 'little': val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint, dtype=dt) else: val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint, dtype=dt).byteswap() res = hashlib.sha256(val).hexdigest() assert_(tgt[np.dtype(dt).name] == res) # bools do not depend on endianness random = Generator(MT19937(1234)) val = random.integers(0, 2 - endpoint, size=1000, endpoint=endpoint, dtype=bool).view(np.int8) res = hashlib.sha256(val).hexdigest() assert_(tgt[np.dtype(bool).name] == res) def test_repeatability_broadcasting(self, endpoint): for dt in self.itype: lbnd = 0 if dt in (bool, np.bool_) else np.iinfo(dt).min ubnd = 2 if dt in (bool, np.bool_) else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd # view as little endian for hash random = Generator(MT19937(1234)) val = random.integers(lbnd, ubnd, size=1000, endpoint=endpoint, dtype=dt) random = Generator(MT19937(1234)) val_bc = random.integers([lbnd] * 1000, ubnd, endpoint=endpoint, dtype=dt) assert_array_equal(val, val_bc) random = Generator(MT19937(1234)) val_bc = random.integers([lbnd] * 1000, [ubnd] * 1000, endpoint=endpoint, dtype=dt) assert_array_equal(val, val_bc) @pytest.mark.parametrize( 'bound, expected', [(2**32 - 1, np.array([517043486, 1364798665, 1733884389, 1353720612, 3769704066, 1170797179, 4108474671])), (2**32, np.array([517043487, 1364798666, 1733884390, 1353720613, 3769704067, 1170797180, 4108474672])), (2**32 + 1, np.array([517043487, 1733884390, 3769704068, 4108474673, 1831631863, 1215661561, 3869512430]))] ) def test_repeatability_32bit_boundary(self, bound, expected): for size in [None, len(expected)]: random = Generator(MT19937(1234)) x = random.integers(bound, size=size) assert_equal(x, expected if size is not None else expected[0]) def test_repeatability_32bit_boundary_broadcasting(self): desired = np.array([[[1622936284, 3620788691, 1659384060], [1417365545, 760222891, 1909653332], [3788118662, 660249498, 4092002593]], [[3625610153, 2979601262, 3844162757], [ 685800658, 120261497, 2694012896], [1207779440, 1586594375, 3854335050]], [[3004074748, 2310761796, 3012642217], [2067714190, 2786677879, 1363865881], [ 791663441, 1867303284, 2169727960]], [[1939603804, 1250951100, 298950036], [1040128489, 3791912209, 3317053765], [3155528714, 61360675, 2305155588]], [[ 817688762, 1335621943, 3288952434], [1770890872, 1102951817, 1957607470], [3099996017, 798043451, 48334215]]]) for size in [None, (5, 3, 3)]: random = Generator(MT19937(12345)) x = random.integers([[-1], [0], [1]], [2**32 - 1, 2**32, 2**32 + 1], size=size) assert_array_equal(x, desired if size is not None else desired[0]) def test_int64_uint64_broadcast_exceptions(self, endpoint): configs = {np.uint64: ((0, 2**65), (-1, 2**62), (10, 9), (0, 0)), np.int64: ((0, 2**64), (-(2**64), 2**62), (10, 9), (0, 0), (-2**63-1, -2**63-1))} for dtype in configs: for config in configs[dtype]: low, high = config high = high - endpoint low_a = np.array([[low]*10]) high_a = np.array([high] * 10) assert_raises(ValueError, random.integers, low, high, endpoint=endpoint, dtype=dtype) assert_raises(ValueError, random.integers, low_a, high, endpoint=endpoint, dtype=dtype) assert_raises(ValueError, random.integers, low, high_a, endpoint=endpoint, dtype=dtype) assert_raises(ValueError, random.integers, low_a, high_a, endpoint=endpoint, dtype=dtype) low_o = np.array([[low]*10], dtype=object) high_o = np.array([high] * 10, dtype=object) assert_raises(ValueError, random.integers, low_o, high, endpoint=endpoint, dtype=dtype) assert_raises(ValueError, random.integers, low, high_o, endpoint=endpoint, dtype=dtype) assert_raises(ValueError, random.integers, low_o, high_o, endpoint=endpoint, dtype=dtype) def test_int64_uint64_corner_case(self, endpoint): # When stored in Numpy arrays, `lbnd` is casted # as np.int64, and `ubnd` is casted as np.uint64. # Checking whether `lbnd` >= `ubnd` used to be # done solely via direct comparison, which is incorrect # because when Numpy tries to compare both numbers, # it casts both to np.float64 because there is # no integer superset of np.int64 and np.uint64. However, # `ubnd` is too large to be represented in np.float64, # causing it be round down to np.iinfo(np.int64).max, # leading to a ValueError because `lbnd` now equals # the new `ubnd`. dt = np.int64 tgt = np.iinfo(np.int64).max lbnd = np.int64(np.iinfo(np.int64).max) ubnd = np.uint64(np.iinfo(np.int64).max + 1 - endpoint) # None of these function calls should # generate a ValueError now. actual = random.integers(lbnd, ubnd, endpoint=endpoint, dtype=dt) assert_equal(actual, tgt) def test_respect_dtype_singleton(self, endpoint): # See gh-7203 for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd dt = np.bool_ if dt is bool else dt sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt) assert_equal(sample.dtype, dt) for dt in (bool, int, np.compat.long): lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd # gh-7284: Ensure that we get Python data types sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt) assert not hasattr(sample, 'dtype') assert_equal(type(sample), dt) def test_respect_dtype_array(self, endpoint): # See gh-7203 for dt in self.itype: lbnd = 0 if dt is bool else np.iinfo(dt).min ubnd = 2 if dt is bool else np.iinfo(dt).max + 1 ubnd = ubnd - 1 if endpoint else ubnd dt = np.bool_ if dt is bool else dt sample = self.rfunc([lbnd], [ubnd], endpoint=endpoint, dtype=dt) assert_equal(sample.dtype, dt) sample = self.rfunc([lbnd] * 2, [ubnd] * 2, endpoint=endpoint, dtype=dt) assert_equal(sample.dtype, dt) def test_zero_size(self, endpoint): # See gh-7203 for dt in self.itype: sample = self.rfunc(0, 0, (3, 0, 4), endpoint=endpoint, dtype=dt) assert sample.shape == (3, 0, 4) assert sample.dtype == dt assert self.rfunc(0, -10, 0, endpoint=endpoint, dtype=dt).shape == (0,) assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape, (3, 0, 4)) assert_equal(random.integers(0, -10, size=0).shape, (0,)) assert_equal(random.integers(10, 10, size=0).shape, (0,)) def test_error_byteorder(self): other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4' with pytest.raises(ValueError): random.integers(0, 200, size=10, dtype=other_byteord_dt) # chi2max is the maximum acceptable chi-squared value. @pytest.mark.slow @pytest.mark.parametrize('sample_size,high,dtype,chi2max', [(5000000, 5, np.int8, 125.0), # p-value ~4.6e-25 (5000000, 7, np.uint8, 150.0), # p-value ~7.7e-30 (10000000, 2500, np.int16, 3300.0), # p-value ~3.0e-25 (50000000, 5000, np.uint16, 6500.0), # p-value ~3.5e-25 ]) def test_integers_small_dtype_chisquared(self, sample_size, high, dtype, chi2max): # Regression test for gh-14774. samples = random.integers(high, size=sample_size, dtype=dtype) values, counts = np.unique(samples, return_counts=True) expected = sample_size / high chi2 = ((counts - expected)**2 / expected).sum() assert chi2 < chi2max class TestRandomDist: # Make sure the random distribution returns the correct value for a # given seed def setup_method(self): self.seed = 1234567890 def test_integers(self): random = Generator(MT19937(self.seed)) actual = random.integers(-99, 99, size=(3, 2)) desired = np.array([[-80, -56], [41, 37], [-83, -16]]) assert_array_equal(actual, desired) def test_integers_masked(self): # Test masked rejection sampling algorithm to generate array of # uint32 in an interval. random = Generator(MT19937(self.seed)) actual = random.integers(0, 99, size=(3, 2), dtype=np.uint32) desired = np.array([[9, 21], [70, 68], [8, 41]], dtype=np.uint32) assert_array_equal(actual, desired) def test_integers_closed(self): random = Generator(MT19937(self.seed)) actual = random.integers(-99, 99, size=(3, 2), endpoint=True) desired = np.array([[-80, -56], [ 41, 38], [-83, -15]]) assert_array_equal(actual, desired) def test_integers_max_int(self): # Tests whether integers with closed=True can generate the # maximum allowed Python int that can be converted # into a C long. Previous implementations of this # method have thrown an OverflowError when attempting # to generate this integer. actual = random.integers(np.iinfo('l').max, np.iinfo('l').max, endpoint=True) desired = np.iinfo('l').max assert_equal(actual, desired) def test_random(self): random = Generator(MT19937(self.seed)) actual = random.random((3, 2)) desired = np.array([[0.096999199829214, 0.707517457682192], [0.084364834598269, 0.767731206553125], [0.665069021359413, 0.715487190596693]]) assert_array_almost_equal(actual, desired, decimal=15) random = Generator(MT19937(self.seed)) actual = random.random() assert_array_almost_equal(actual, desired[0, 0], decimal=15) def test_random_float(self): random = Generator(MT19937(self.seed)) actual = random.random((3, 2)) desired = np.array([[0.0969992 , 0.70751746], [0.08436483, 0.76773121], [0.66506902, 0.71548719]]) assert_array_almost_equal(actual, desired, decimal=7) def test_random_float_scalar(self): random = Generator(MT19937(self.seed)) actual = random.random(dtype=np.float32) desired = 0.0969992 assert_array_almost_equal(actual, desired, decimal=7) @pytest.mark.parametrize('dtype, uint_view_type', [(np.float32, np.uint32), (np.float64, np.uint64)]) def test_random_distribution_of_lsb(self, dtype, uint_view_type): random = Generator(MT19937(self.seed)) sample = random.random(100000, dtype=dtype) num_ones_in_lsb = np.count_nonzero(sample.view(uint_view_type) & 1) # The probability of a 1 in the least significant bit is 0.25. # With a sample size of 100000, the probability that num_ones_in_lsb # is outside the following range is less than 5e-11. assert 24100 < num_ones_in_lsb < 25900 def test_random_unsupported_type(self): assert_raises(TypeError, random.random, dtype='int32') def test_choice_uniform_replace(self): random = Generator(MT19937(self.seed)) actual = random.choice(4, 4) desired = np.array([0, 0, 2, 2], dtype=np.int64) assert_array_equal(actual, desired) def test_choice_nonuniform_replace(self): random = Generator(MT19937(self.seed)) actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1]) desired = np.array([0, 1, 0, 1], dtype=np.int64) assert_array_equal(actual, desired) def test_choice_uniform_noreplace(self): random = Generator(MT19937(self.seed)) actual = random.choice(4, 3, replace=False) desired = np.array([2, 0, 3], dtype=np.int64) assert_array_equal(actual, desired) actual = random.choice(4, 4, replace=False, shuffle=False) desired = np.arange(4, dtype=np.int64) assert_array_equal(actual, desired) def test_choice_nonuniform_noreplace(self): random = Generator(MT19937(self.seed)) actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1]) desired = np.array([0, 2, 3], dtype=np.int64) assert_array_equal(actual, desired) def test_choice_noninteger(self): random = Generator(MT19937(self.seed)) actual = random.choice(['a', 'b', 'c', 'd'], 4) desired = np.array(['a', 'a', 'c', 'c']) assert_array_equal(actual, desired) def test_choice_multidimensional_default_axis(self): random = Generator(MT19937(self.seed)) actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 3) desired = np.array([[0, 1], [0, 1], [4, 5]]) assert_array_equal(actual, desired) def test_choice_multidimensional_custom_axis(self): random = Generator(MT19937(self.seed)) actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 1, axis=1) desired = np.array([[0], [2], [4], [6]]) assert_array_equal(actual, desired) def test_choice_exceptions(self): sample = random.choice assert_raises(ValueError, sample, -1, 3) assert_raises(ValueError, sample, 3., 3) assert_raises(ValueError, sample, [], 3) assert_raises(ValueError, sample, [1, 2, 3, 4], 3, p=[[0.25, 0.25], [0.25, 0.25]]) assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2]) assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1]) assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4]) assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False) # gh-13087 assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False) assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False) assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False) assert_raises(ValueError, sample, [1, 2, 3], 2, replace=False, p=[1, 0, 0]) def test_choice_return_shape(self): p = [0.1, 0.9] # Check scalar assert_(np.isscalar(random.choice(2, replace=True))) assert_(np.isscalar(random.choice(2, replace=False))) assert_(np.isscalar(random.choice(2, replace=True, p=p))) assert_(np.isscalar(random.choice(2, replace=False, p=p))) assert_(np.isscalar(random.choice([1, 2], replace=True))) assert_(random.choice([None], replace=True) is None) a = np.array([1, 2]) arr = np.empty(1, dtype=object) arr[0] = a assert_(random.choice(arr, replace=True) is a) # Check 0-d array s = tuple() assert_(not np.isscalar(random.choice(2, s, replace=True))) assert_(not np.isscalar(random.choice(2, s, replace=False))) assert_(not np.isscalar(random.choice(2, s, replace=True, p=p))) assert_(not np.isscalar(random.choice(2, s, replace=False, p=p))) assert_(not np.isscalar(random.choice([1, 2], s, replace=True))) assert_(random.choice([None], s, replace=True).ndim == 0) a = np.array([1, 2]) arr = np.empty(1, dtype=object) arr[0] = a assert_(random.choice(arr, s, replace=True).item() is a) # Check multi dimensional array s = (2, 3) p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2] assert_equal(random.choice(6, s, replace=True).shape, s) assert_equal(random.choice(6, s, replace=False).shape, s) assert_equal(random.choice(6, s, replace=True, p=p).shape, s) assert_equal(random.choice(6, s, replace=False, p=p).shape, s) assert_equal(random.choice(np.arange(6), s, replace=True).shape, s) # Check zero-size assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape, (3, 0, 4)) assert_equal(random.integers(0, -10, size=0).shape, (0,)) assert_equal(random.integers(10, 10, size=0).shape, (0,)) assert_equal(random.choice(0, size=0).shape, (0,)) assert_equal(random.choice([], size=(0,)).shape, (0,)) assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape, (3, 0, 4)) assert_raises(ValueError, random.choice, [], 10) def test_choice_nan_probabilities(self): a = np.array([42, 1, 2]) p = [None, None, None] assert_raises(ValueError, random.choice, a, p=p) def test_choice_p_non_contiguous(self): p = np.ones(10) / 5 p[1::2] = 3.0 random = Generator(MT19937(self.seed)) non_contig = random.choice(5, 3, p=p[::2]) random = Generator(MT19937(self.seed)) contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2])) assert_array_equal(non_contig, contig) def test_choice_return_type(self): # gh 9867 p = np.ones(4) / 4. actual = random.choice(4, 2) assert actual.dtype == np.int64 actual = random.choice(4, 2, replace=False) assert actual.dtype == np.int64 actual = random.choice(4, 2, p=p) assert actual.dtype == np.int64 actual = random.choice(4, 2, p=p, replace=False) assert actual.dtype == np.int64 def test_choice_large_sample(self): choice_hash = '4266599d12bfcfb815213303432341c06b4349f5455890446578877bb322e222' random = Generator(MT19937(self.seed)) actual = random.choice(10000, 5000, replace=False) if sys.byteorder != 'little': actual = actual.byteswap() res = hashlib.sha256(actual.view(np.int8)).hexdigest() assert_(choice_hash == res) def test_bytes(self): random = Generator(MT19937(self.seed)) actual = random.bytes(10) desired = b'\x86\xf0\xd4\x18\xe1\x81\t8%\xdd' assert_equal(actual, desired) def test_shuffle(self): # Test lists, arrays (of various dtypes), and multidimensional versions # of both, c-contiguous or not: for conv in [lambda x: np.array([]), lambda x: x, lambda x: np.asarray(x).astype(np.int8), lambda x: np.asarray(x).astype(np.float32), lambda x: np.asarray(x).astype(np.complex64), lambda x: np.asarray(x).astype(object), lambda x: [(i, i) for i in x], lambda x: np.asarray([[i, i] for i in x]), lambda x: np.vstack([x, x]).T, # gh-11442 lambda x: (np.asarray([(i, i) for i in x], [("a", int), ("b", int)]) .view(np.recarray)), # gh-4270 lambda x: np.asarray([(i, i) for i in x], [("a", object, (1,)), ("b", np.int32, (1,))])]: random = Generator(MT19937(self.seed)) alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]) random.shuffle(alist) actual = alist desired = conv([4, 1, 9, 8, 0, 5, 3, 6, 2, 7]) assert_array_equal(actual, desired) def test_shuffle_custom_axis(self): random = Generator(MT19937(self.seed)) actual = np.arange(16).reshape((4, 4)) random.shuffle(actual, axis=1) desired = np.array([[ 0, 3, 1, 2], [ 4, 7, 5, 6], [ 8, 11, 9, 10], [12, 15, 13, 14]]) assert_array_equal(actual, desired) random = Generator(MT19937(self.seed)) actual = np.arange(16).reshape((4, 4)) random.shuffle(actual, axis=-1) assert_array_equal(actual, desired) def test_shuffle_custom_axis_empty(self): random = Generator(MT19937(self.seed)) desired = np.array([]).reshape((0, 6)) for axis in (0, 1): actual = np.array([]).reshape((0, 6)) random.shuffle(actual, axis=axis) assert_array_equal(actual, desired) def test_shuffle_axis_nonsquare(self): y1 = np.arange(20).reshape(2, 10) y2 = y1.copy() random = Generator(MT19937(self.seed)) random.shuffle(y1, axis=1) random = Generator(MT19937(self.seed)) random.shuffle(y2.T) assert_array_equal(y1, y2) def test_shuffle_masked(self): # gh-3263 a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1) b = np.ma.masked_values(np.arange(20) % 3 - 1, -1) a_orig = a.copy() b_orig = b.copy() for i in range(50): random.shuffle(a) assert_equal( sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask])) random.shuffle(b) assert_equal( sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask])) def test_shuffle_exceptions(self): random = Generator(MT19937(self.seed)) arr = np.arange(10) assert_raises(np.AxisError, random.shuffle, arr, 1) arr = np.arange(9).reshape((3, 3)) assert_raises(np.AxisError, random.shuffle, arr, 3) assert_raises(TypeError, random.shuffle, arr, slice(1, 2, None)) arr = [[1, 2, 3], [4, 5, 6]] assert_raises(NotImplementedError, random.shuffle, arr, 1) arr = np.array(3) assert_raises(TypeError, random.shuffle, arr) arr = np.ones((3, 2)) assert_raises(np.AxisError, random.shuffle, arr, 2) def test_shuffle_not_writeable(self): random = Generator(MT19937(self.seed)) a = np.zeros(5) a.flags.writeable = False with pytest.raises(ValueError, match='read-only'): random.shuffle(a) def test_permutation(self): random = Generator(MT19937(self.seed)) alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] actual = random.permutation(alist) desired = [4, 1, 9, 8, 0, 5, 3, 6, 2, 7] assert_array_equal(actual, desired) random = Generator(MT19937(self.seed)) arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T actual = random.permutation(arr_2d) assert_array_equal(actual, np.atleast_2d(desired).T) bad_x_str = "abcd" assert_raises(np.AxisError, random.permutation, bad_x_str) bad_x_float = 1.2 assert_raises(np.AxisError, random.permutation, bad_x_float) random = Generator(MT19937(self.seed)) integer_val = 10 desired = [3, 0, 8, 7, 9, 4, 2, 5, 1, 6] actual = random.permutation(integer_val) assert_array_equal(actual, desired) def test_permutation_custom_axis(self): a = np.arange(16).reshape((4, 4)) desired = np.array([[ 0, 3, 1, 2], [ 4, 7, 5, 6], [ 8, 11, 9, 10], [12, 15, 13, 14]]) random = Generator(MT19937(self.seed)) actual = random.permutation(a, axis=1) assert_array_equal(actual, desired) random = Generator(MT19937(self.seed)) actual = random.permutation(a, axis=-1) assert_array_equal(actual, desired) def test_permutation_exceptions(self): random = Generator(MT19937(self.seed)) arr = np.arange(10) assert_raises(np.AxisError, random.permutation, arr, 1) arr = np.arange(9).reshape((3, 3)) assert_raises(np.AxisError, random.permutation, arr, 3) assert_raises(TypeError, random.permutation, arr, slice(1, 2, None)) @pytest.mark.parametrize("dtype", [int, object]) @pytest.mark.parametrize("axis, expected", [(None, np.array([[3, 7, 0, 9, 10, 11], [8, 4, 2, 5, 1, 6]])), (0, np.array([[6, 1, 2, 9, 10, 11], [0, 7, 8, 3, 4, 5]])), (1, np.array([[ 5, 3, 4, 0, 2, 1], [11, 9, 10, 6, 8, 7]]))]) def test_permuted(self, dtype, axis, expected): random = Generator(MT19937(self.seed)) x = np.arange(12).reshape(2, 6).astype(dtype) random.permuted(x, axis=axis, out=x) assert_array_equal(x, expected) random = Generator(MT19937(self.seed)) x = np.arange(12).reshape(2, 6).astype(dtype) y = random.permuted(x, axis=axis) assert y.dtype == dtype assert_array_equal(y, expected) def test_permuted_with_strides(self): random = Generator(MT19937(self.seed)) x0 = np.arange(22).reshape(2, 11) x1 = x0.copy() x = x0[:, ::3] y = random.permuted(x, axis=1, out=x) expected = np.array([[0, 9, 3, 6], [14, 20, 11, 17]]) assert_array_equal(y, expected) x1[:, ::3] = expected # Verify that the original x0 was modified in-place as expected. assert_array_equal(x1, x0) def test_permuted_empty(self): y = random.permuted([]) assert_array_equal(y, []) @pytest.mark.parametrize('outshape', [(2, 3), 5]) def test_permuted_out_with_wrong_shape(self, outshape): a = np.array([1, 2, 3]) out = np.zeros(outshape, dtype=a.dtype) with pytest.raises(ValueError, match='same shape'): random.permuted(a, out=out) def test_permuted_out_with_wrong_type(self): out = np.zeros((3, 5), dtype=np.int32) x = np.ones((3, 5)) with pytest.raises(TypeError, match='Cannot cast'): random.permuted(x, axis=1, out=out) def test_permuted_not_writeable(self): x = np.zeros((2, 5)) x.flags.writeable = False with pytest.raises(ValueError, match='read-only'): random.permuted(x, axis=1, out=x) def test_beta(self): random = Generator(MT19937(self.seed)) actual = random.beta(.1, .9, size=(3, 2)) desired = np.array( [[1.083029353267698e-10, 2.449965303168024e-11], [2.397085162969853e-02, 3.590779671820755e-08], [2.830254190078299e-04, 1.744709918330393e-01]]) assert_array_almost_equal(actual, desired, decimal=15) def test_binomial(self): random = Generator(MT19937(self.seed)) actual = random.binomial(100.123, .456, size=(3, 2)) desired = np.array([[42, 41], [42, 48], [44, 50]]) assert_array_equal(actual, desired) random = Generator(MT19937(self.seed)) actual = random.binomial(100.123, .456) desired = 42 assert_array_equal(actual, desired) def test_chisquare(self): random = Generator(MT19937(self.seed)) actual = random.chisquare(50, size=(3, 2)) desired = np.array([[32.9850547060149, 39.0219480493301], [56.2006134779419, 57.3474165711485], [55.4243733880198, 55.4209797925213]]) assert_array_almost_equal(actual, desired, decimal=13) def test_dirichlet(self): random = Generator(MT19937(self.seed)) alpha = np.array([51.72840233779265162, 39.74494232180943953]) actual = random.dirichlet(alpha, size=(3, 2)) desired = np.array([[[0.5439892869558927, 0.45601071304410745], [0.5588917345860708, 0.4411082654139292 ]], [[0.5632074165063435, 0.43679258349365657], [0.54862581112627, 0.45137418887373015]], [[0.49961831357047226, 0.5003816864295278 ], [0.52374806183482, 0.47625193816517997]]]) assert_array_almost_equal(actual, desired, decimal=15) bad_alpha = np.array([5.4e-01, -1.0e-16]) assert_raises(ValueError, random.dirichlet, bad_alpha) random = Generator(MT19937(self.seed)) alpha = np.array([51.72840233779265162, 39.74494232180943953]) actual = random.dirichlet(alpha) assert_array_almost_equal(actual, desired[0, 0], decimal=15) def test_dirichlet_size(self): # gh-3173 p = np.array([51.72840233779265162, 39.74494232180943953]) assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2)) assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2)) assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2)) assert_raises(TypeError, random.dirichlet, p, float(1)) def test_dirichlet_bad_alpha(self): # gh-2089 alpha = np.array([5.4e-01, -1.0e-16]) assert_raises(ValueError, random.dirichlet, alpha) # gh-15876 assert_raises(ValueError, random.dirichlet, [[5, 1]]) assert_raises(ValueError, random.dirichlet, [[5], [1]]) assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]]) assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]])) def test_dirichlet_alpha_non_contiguous(self): a = np.array([51.72840233779265162, -1.0, 39.74494232180943953]) alpha = a[::2] random = Generator(MT19937(self.seed)) non_contig = random.dirichlet(alpha, size=(3, 2)) random = Generator(MT19937(self.seed)) contig = random.dirichlet(np.ascontiguousarray(alpha), size=(3, 2)) assert_array_almost_equal(non_contig, contig) def test_dirichlet_small_alpha(self): eps = 1.0e-9 # 1.0e-10 -> runtime x 10; 1e-11 -> runtime x 200, etc. alpha = eps * np.array([1., 1.0e-3]) random = Generator(MT19937(self.seed)) actual = random.dirichlet(alpha, size=(3, 2)) expected = np.array([ [[1., 0.], [1., 0.]], [[1., 0.], [1., 0.]], [[1., 0.], [1., 0.]] ]) assert_array_almost_equal(actual, expected, decimal=15) @pytest.mark.slow def test_dirichlet_moderately_small_alpha(self): # Use alpha.max() < 0.1 to trigger stick breaking code path alpha = np.array([0.02, 0.04, 0.03]) exact_mean = alpha / alpha.sum() random = Generator(MT19937(self.seed)) sample = random.dirichlet(alpha, size=20000000) sample_mean = sample.mean(axis=0) assert_allclose(sample_mean, exact_mean, rtol=1e-3) def test_exponential(self): random = Generator(MT19937(self.seed)) actual = random.exponential(1.1234, size=(3, 2)) desired = np.array([[0.098845481066258, 1.560752510746964], [0.075730916041636, 1.769098974710777], [1.488602544592235, 2.49684815275751 ]]) assert_array_almost_equal(actual, desired, decimal=15) def test_exponential_0(self): assert_equal(random.exponential(scale=0), 0) assert_raises(ValueError, random.exponential, scale=-0.) def test_f(self): random = Generator(MT19937(self.seed)) actual = random.f(12, 77, size=(3, 2)) desired = np.array([[0.461720027077085, 1.100441958872451], [1.100337455217484, 0.91421736740018 ], [0.500811891303113, 0.826802454552058]]) assert_array_almost_equal(actual, desired, decimal=15) def test_gamma(self): random = Generator(MT19937(self.seed)) actual = random.gamma(5, 3, size=(3, 2)) desired = np.array([[ 5.03850858902096, 7.9228656732049 ], [18.73983605132985, 19.57961681699238], [18.17897755150825, 18.17653912505234]]) assert_array_almost_equal(actual, desired, decimal=14) def test_gamma_0(self): assert_equal(random.gamma(shape=0, scale=0), 0) assert_raises(ValueError, random.gamma, shape=-0., scale=-0.) def test_geometric(self): random = Generator(MT19937(self.seed)) actual = random.geometric(.123456789, size=(3, 2)) desired = np.array([[1, 11], [1, 12], [11, 17]]) assert_array_equal(actual, desired) def test_geometric_exceptions(self): assert_raises(ValueError, random.geometric, 1.1) assert_raises(ValueError, random.geometric, [1.1] * 10) assert_raises(ValueError, random.geometric, -0.1) assert_raises(ValueError, random.geometric, [-0.1] * 10) with np.errstate(invalid='ignore'): assert_raises(ValueError, random.geometric, np.nan) assert_raises(ValueError, random.geometric, [np.nan] * 10) def test_gumbel(self): random = Generator(MT19937(self.seed)) actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[ 4.688397515056245, -0.289514845417841], [ 4.981176042584683, -0.633224272589149], [-0.055915275687488, -0.333962478257953]]) assert_array_almost_equal(actual, desired, decimal=15) def test_gumbel_0(self): assert_equal(random.gumbel(scale=0), 0) assert_raises(ValueError, random.gumbel, scale=-0.) def test_hypergeometric(self): random = Generator(MT19937(self.seed)) actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2)) desired = np.array([[ 9, 9], [ 9, 9], [10, 9]]) assert_array_equal(actual, desired) # Test nbad = 0 actual = random.hypergeometric(5, 0, 3, size=4) desired = np.array([3, 3, 3, 3]) assert_array_equal(actual, desired) actual = random.hypergeometric(15, 0, 12, size=4) desired = np.array([12, 12, 12, 12]) assert_array_equal(actual, desired) # Test ngood = 0 actual = random.hypergeometric(0, 5, 3, size=4) desired = np.array([0, 0, 0, 0]) assert_array_equal(actual, desired) actual = random.hypergeometric(0, 15, 12, size=4) desired = np.array([0, 0, 0, 0]) assert_array_equal(actual, desired) def test_laplace(self): random = Generator(MT19937(self.seed)) actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[-3.156353949272393, 1.195863024830054], [-3.435458081645966, 1.656882398925444], [ 0.924824032467446, 1.251116432209336]]) assert_array_almost_equal(actual, desired, decimal=15) def test_laplace_0(self): assert_equal(random.laplace(scale=0), 0) assert_raises(ValueError, random.laplace, scale=-0.) def test_logistic(self): random = Generator(MT19937(self.seed)) actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[-4.338584631510999, 1.890171436749954], [-4.64547787337966 , 2.514545562919217], [ 1.495389489198666, 1.967827627577474]]) assert_array_almost_equal(actual, desired, decimal=15) def test_lognormal(self): random = Generator(MT19937(self.seed)) actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2)) desired = np.array([[ 0.0268252166335, 13.9534486483053], [ 0.1204014788936, 2.2422077497792], [ 4.2484199496128, 12.0093343977523]]) assert_array_almost_equal(actual, desired, decimal=13) def test_lognormal_0(self): assert_equal(random.lognormal(sigma=0), 1) assert_raises(ValueError, random.lognormal, sigma=-0.) def test_logseries(self): random = Generator(MT19937(self.seed)) actual = random.logseries(p=.923456789, size=(3, 2)) desired = np.array([[14, 17], [3, 18], [5, 1]]) assert_array_equal(actual, desired) def test_logseries_zero(self): random = Generator(MT19937(self.seed)) assert random.logseries(0) == 1 @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.]) def test_logseries_exceptions(self, value): random = Generator(MT19937(self.seed)) with np.errstate(invalid="ignore"): with pytest.raises(ValueError): random.logseries(value) with pytest.raises(ValueError): # contiguous path: random.logseries(np.array([value] * 10)) with pytest.raises(ValueError): # non-contiguous path: random.logseries(np.array([value] * 10)[::2]) def test_multinomial(self): random = Generator(MT19937(self.seed)) actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2)) desired = np.array([[[1, 5, 1, 6, 4, 3], [4, 2, 6, 2, 4, 2]], [[5, 3, 2, 6, 3, 1], [4, 4, 0, 2, 3, 7]], [[6, 3, 1, 5, 3, 2], [5, 5, 3, 1, 2, 4]]]) assert_array_equal(actual, desired) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm") @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"]) def test_multivariate_normal(self, method): random = Generator(MT19937(self.seed)) mean = (.123456789, 10) cov = [[1, 0], [0, 1]] size = (3, 2) actual = random.multivariate_normal(mean, cov, size, method=method) desired = np.array([[[-1.747478062846581, 11.25613495182354 ], [-0.9967333370066214, 10.342002097029821 ]], [[ 0.7850019631242964, 11.181113712443013 ], [ 0.8901349653255224, 8.873825399642492 ]], [[ 0.7130260107430003, 9.551628690083056 ], [ 0.7127098726541128, 11.991709234143173 ]]]) assert_array_almost_equal(actual, desired, decimal=15) # Check for default size, was raising deprecation warning actual = random.multivariate_normal(mean, cov, method=method) desired = np.array([0.233278563284287, 9.424140804347195]) assert_array_almost_equal(actual, desired, decimal=15) # Check that non symmetric covariance input raises exception when # check_valid='raises' if using default svd method. mean = [0, 0] cov = [[1, 2], [1, 2]] assert_raises(ValueError, random.multivariate_normal, mean, cov, check_valid='raise') # Check that non positive-semidefinite covariance warns with # RuntimeWarning cov = [[1, 2], [2, 1]] assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov) assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov, method='eigh') assert_raises(LinAlgError, random.multivariate_normal, mean, cov, method='cholesky') # and that it doesn't warn with RuntimeWarning check_valid='ignore' assert_no_warnings(random.multivariate_normal, mean, cov, check_valid='ignore') # and that it raises with RuntimeWarning check_valid='raises' assert_raises(ValueError, random.multivariate_normal, mean, cov, check_valid='raise') assert_raises(ValueError, random.multivariate_normal, mean, cov, check_valid='raise', method='eigh') # check degenerate samples from singular covariance matrix cov = [[1, 1], [1, 1]] if method in ('svd', 'eigh'): samples = random.multivariate_normal(mean, cov, size=(3, 2), method=method) assert_array_almost_equal(samples[..., 0], samples[..., 1], decimal=6) else: assert_raises(LinAlgError, random.multivariate_normal, mean, cov, method='cholesky') cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32) with suppress_warnings() as sup: random.multivariate_normal(mean, cov, method=method) w = sup.record(RuntimeWarning) assert len(w) == 0 mu = np.zeros(2) cov = np.eye(2) assert_raises(ValueError, random.multivariate_normal, mean, cov, check_valid='other') assert_raises(ValueError, random.multivariate_normal, np.zeros((2, 1, 1)), cov) assert_raises(ValueError, random.multivariate_normal, mu, np.empty((3, 2))) assert_raises(ValueError, random.multivariate_normal, mu, np.eye(3)) @pytest.mark.parametrize('mean, cov', [([0], [[1+1j]]), ([0j], [[1]])]) def test_multivariate_normal_disallow_complex(self, mean, cov): random = Generator(MT19937(self.seed)) with pytest.raises(TypeError, match="must not be complex"): random.multivariate_normal(mean, cov) @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"]) def test_multivariate_normal_basic_stats(self, method): random = Generator(MT19937(self.seed)) n_s = 1000 mean = np.array([1, 2]) cov = np.array([[2, 1], [1, 2]]) s = random.multivariate_normal(mean, cov, size=(n_s,), method=method) s_center = s - mean cov_emp = (s_center.T @ s_center) / (n_s - 1) # these are pretty loose and are only designed to detect major errors assert np.all(np.abs(s_center.mean(-2)) < 0.1) assert np.all(np.abs(cov_emp - cov) < 0.2) def test_negative_binomial(self): random = Generator(MT19937(self.seed)) actual = random.negative_binomial(n=100, p=.12345, size=(3, 2)) desired = np.array([[543, 727], [775, 760], [600, 674]]) assert_array_equal(actual, desired) def test_negative_binomial_exceptions(self): with np.errstate(invalid='ignore'): assert_raises(ValueError, random.negative_binomial, 100, np.nan) assert_raises(ValueError, random.negative_binomial, 100, [np.nan] * 10) def test_negative_binomial_p0_exception(self): # Verify that p=0 raises an exception. with assert_raises(ValueError): x = random.negative_binomial(1, 0) def test_negative_binomial_invalid_p_n_combination(self): # Verify that values of p and n that would result in an overflow # or infinite loop raise an exception. with np.errstate(invalid='ignore'): assert_raises(ValueError, random.negative_binomial, 2**62, 0.1) assert_raises(ValueError, random.negative_binomial, [2**62], [0.1]) def test_noncentral_chisquare(self): random = Generator(MT19937(self.seed)) actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2)) desired = np.array([[ 1.70561552362133, 15.97378184942111], [13.71483425173724, 20.17859633310629], [11.3615477156643 , 3.67891108738029]]) assert_array_almost_equal(actual, desired, decimal=14) actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2)) desired = np.array([[9.41427665607629e-04, 1.70473157518850e-04], [1.14554372041263e+00, 1.38187755933435e-03], [1.90659181905387e+00, 1.21772577941822e+00]]) assert_array_almost_equal(actual, desired, decimal=14) random = Generator(MT19937(self.seed)) actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2)) desired = np.array([[0.82947954590419, 1.80139670767078], [6.58720057417794, 7.00491463609814], [6.31101879073157, 6.30982307753005]]) assert_array_almost_equal(actual, desired, decimal=14) def test_noncentral_f(self): random = Generator(MT19937(self.seed)) actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1, size=(3, 2)) desired = np.array([[0.060310671139 , 0.23866058175939], [0.86860246709073, 0.2668510459738 ], [0.23375780078364, 1.88922102885943]]) assert_array_almost_equal(actual, desired, decimal=14) def test_noncentral_f_nan(self): random = Generator(MT19937(self.seed)) actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan) assert np.isnan(actual) def test_normal(self): random = Generator(MT19937(self.seed)) actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[-3.618412914693162, 2.635726692647081], [-2.116923463013243, 0.807460983059643], [ 1.446547137248593, 2.485684213886024]]) assert_array_almost_equal(actual, desired, decimal=15) def test_normal_0(self): assert_equal(random.normal(scale=0), 0) assert_raises(ValueError, random.normal, scale=-0.) def test_pareto(self): random = Generator(MT19937(self.seed)) actual = random.pareto(a=.123456789, size=(3, 2)) desired = np.array([[1.0394926776069018e+00, 7.7142534343505773e+04], [7.2640150889064703e-01, 3.4650454783825594e+05], [4.5852344481994740e+04, 6.5851383009539105e+07]]) # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this # matrix differs by 24 nulps. Discussion: # https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html # Consensus is that this is probably some gcc quirk that affects # rounding but not in any important way, so we just use a looser # tolerance on this test: np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30) def test_poisson(self): random = Generator(MT19937(self.seed)) actual = random.poisson(lam=.123456789, size=(3, 2)) desired = np.array([[0, 0], [0, 0], [0, 0]]) assert_array_equal(actual, desired) def test_poisson_exceptions(self): lambig = np.iinfo('int64').max lamneg = -1 assert_raises(ValueError, random.poisson, lamneg) assert_raises(ValueError, random.poisson, [lamneg] * 10) assert_raises(ValueError, random.poisson, lambig) assert_raises(ValueError, random.poisson, [lambig] * 10) with np.errstate(invalid='ignore'): assert_raises(ValueError, random.poisson, np.nan) assert_raises(ValueError, random.poisson, [np.nan] * 10) def test_power(self): random = Generator(MT19937(self.seed)) actual = random.power(a=.123456789, size=(3, 2)) desired = np.array([[1.977857368842754e-09, 9.806792196620341e-02], [2.482442984543471e-10, 1.527108843266079e-01], [8.188283434244285e-02, 3.950547209346948e-01]]) assert_array_almost_equal(actual, desired, decimal=15) def test_rayleigh(self): random = Generator(MT19937(self.seed)) actual = random.rayleigh(scale=10, size=(3, 2)) desired = np.array([[4.19494429102666, 16.66920198906598], [3.67184544902662, 17.74695521962917], [16.27935397855501, 21.08355560691792]]) assert_array_almost_equal(actual, desired, decimal=14) def test_rayleigh_0(self): assert_equal(random.rayleigh(scale=0), 0) assert_raises(ValueError, random.rayleigh, scale=-0.) def test_standard_cauchy(self): random = Generator(MT19937(self.seed)) actual = random.standard_cauchy(size=(3, 2)) desired = np.array([[-1.489437778266206, -3.275389641569784], [ 0.560102864910406, -0.680780916282552], [-1.314912905226277, 0.295852965660225]]) assert_array_almost_equal(actual, desired, decimal=15) def test_standard_exponential(self): random = Generator(MT19937(self.seed)) actual = random.standard_exponential(size=(3, 2), method='inv') desired = np.array([[0.102031839440643, 1.229350298474972], [0.088137284693098, 1.459859985522667], [1.093830802293668, 1.256977002164613]]) assert_array_almost_equal(actual, desired, decimal=15) def test_standard_expoential_type_error(self): assert_raises(TypeError, random.standard_exponential, dtype=np.int32) def test_standard_gamma(self): random = Generator(MT19937(self.seed)) actual = random.standard_gamma(shape=3, size=(3, 2)) desired = np.array([[0.62970724056362, 1.22379851271008], [3.899412530884 , 4.12479964250139], [3.74994102464584, 3.74929307690815]]) assert_array_almost_equal(actual, desired, decimal=14) def test_standard_gammma_scalar_float(self): random = Generator(MT19937(self.seed)) actual = random.standard_gamma(3, dtype=np.float32) desired = 2.9242148399353027 assert_array_almost_equal(actual, desired, decimal=6) def test_standard_gamma_float(self): random = Generator(MT19937(self.seed)) actual = random.standard_gamma(shape=3, size=(3, 2)) desired = np.array([[0.62971, 1.2238 ], [3.89941, 4.1248 ], [3.74994, 3.74929]]) assert_array_almost_equal(actual, desired, decimal=5) def test_standard_gammma_float_out(self): actual = np.zeros((3, 2), dtype=np.float32) random = Generator(MT19937(self.seed)) random.standard_gamma(10.0, out=actual, dtype=np.float32) desired = np.array([[10.14987, 7.87012], [ 9.46284, 12.56832], [13.82495, 7.81533]], dtype=np.float32) assert_array_almost_equal(actual, desired, decimal=5) random = Generator(MT19937(self.seed)) random.standard_gamma(10.0, out=actual, size=(3, 2), dtype=np.float32) assert_array_almost_equal(actual, desired, decimal=5) def test_standard_gamma_unknown_type(self): assert_raises(TypeError, random.standard_gamma, 1., dtype='int32') def test_out_size_mismatch(self): out = np.zeros(10) assert_raises(ValueError, random.standard_gamma, 10.0, size=20, out=out) assert_raises(ValueError, random.standard_gamma, 10.0, size=(10, 1), out=out) def test_standard_gamma_0(self): assert_equal(random.standard_gamma(shape=0), 0) assert_raises(ValueError, random.standard_gamma, shape=-0.) def test_standard_normal(self): random = Generator(MT19937(self.seed)) actual = random.standard_normal(size=(3, 2)) desired = np.array([[-1.870934851846581, 1.25613495182354 ], [-1.120190126006621, 0.342002097029821], [ 0.661545174124296, 1.181113712443012]]) assert_array_almost_equal(actual, desired, decimal=15) def test_standard_normal_unsupported_type(self): assert_raises(TypeError, random.standard_normal, dtype=np.int32) def test_standard_t(self): random = Generator(MT19937(self.seed)) actual = random.standard_t(df=10, size=(3, 2)) desired = np.array([[-1.484666193042647, 0.30597891831161 ], [ 1.056684299648085, -0.407312602088507], [ 0.130704414281157, -2.038053410490321]]) assert_array_almost_equal(actual, desired, decimal=15) def test_triangular(self): random = Generator(MT19937(self.seed)) actual = random.triangular(left=5.12, mode=10.23, right=20.34, size=(3, 2)) desired = np.array([[ 7.86664070590917, 13.6313848513185 ], [ 7.68152445215983, 14.36169131136546], [13.16105603911429, 13.72341621856971]]) assert_array_almost_equal(actual, desired, decimal=14) def test_uniform(self): random = Generator(MT19937(self.seed)) actual = random.uniform(low=1.23, high=10.54, size=(3, 2)) desired = np.array([[2.13306255040998 , 7.816987531021207], [2.015436610109887, 8.377577533009589], [7.421792588856135, 7.891185744455209]]) assert_array_almost_equal(actual, desired, decimal=15) def test_uniform_range_bounds(self): fmin = np.finfo('float').min fmax = np.finfo('float').max func = random.uniform assert_raises(OverflowError, func, -np.inf, 0) assert_raises(OverflowError, func, 0, np.inf) assert_raises(OverflowError, func, fmin, fmax) assert_raises(OverflowError, func, [-np.inf], [0]) assert_raises(OverflowError, func, [0], [np.inf]) # (fmax / 1e17) - fmin is within range, so this should not throw # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX > # DBL_MAX by increasing fmin a bit random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17) def test_uniform_zero_range(self): func = random.uniform result = func(1.5, 1.5) assert_allclose(result, 1.5) result = func([0.0, np.pi], [0.0, np.pi]) assert_allclose(result, [0.0, np.pi]) result = func([[2145.12], [2145.12]], [2145.12, 2145.12]) assert_allclose(result, 2145.12 + np.zeros((2, 2))) def test_uniform_neg_range(self): func = random.uniform assert_raises(ValueError, func, 2, 1) assert_raises(ValueError, func, [1, 2], [1, 1]) assert_raises(ValueError, func, [[0, 1],[2, 3]], 2) def test_scalar_exception_propagation(self): # Tests that exceptions are correctly propagated in distributions # when called with objects that throw exceptions when converted to # scalars. # # Regression test for gh: 8865 class ThrowingFloat(np.ndarray): def __float__(self): raise TypeError throwing_float = np.array(1.0).view(ThrowingFloat) assert_raises(TypeError, random.uniform, throwing_float, throwing_float) class ThrowingInteger(np.ndarray): def __int__(self): raise TypeError throwing_int = np.array(1).view(ThrowingInteger) assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1) def test_vonmises(self): random = Generator(MT19937(self.seed)) actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2)) desired = np.array([[ 1.107972248690106, 2.841536476232361], [ 1.832602376042457, 1.945511926976032], [-0.260147475776542, 2.058047492231698]]) assert_array_almost_equal(actual, desired, decimal=15) def test_vonmises_small(self): # check infinite loop, gh-4720 random = Generator(MT19937(self.seed)) r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6) assert_(np.isfinite(r).all()) def test_vonmises_nan(self): random = Generator(MT19937(self.seed)) r = random.vonmises(mu=0., kappa=np.nan) assert_(np.isnan(r)) @pytest.mark.parametrize("kappa", [1e4, 1e15]) def test_vonmises_large_kappa(self, kappa): random = Generator(MT19937(self.seed)) rs = RandomState(random.bit_generator) state = random.bit_generator.state random_state_vals = rs.vonmises(0, kappa, size=10) random.bit_generator.state = state gen_vals = random.vonmises(0, kappa, size=10) if kappa < 1e6: assert_allclose(random_state_vals, gen_vals) else: assert np.all(random_state_vals != gen_vals) @pytest.mark.parametrize("mu", [-7., -np.pi, -3.1, np.pi, 3.2]) @pytest.mark.parametrize("kappa", [1e-9, 1e-6, 1, 1e3, 1e15]) def test_vonmises_large_kappa_range(self, mu, kappa): random = Generator(MT19937(self.seed)) r = random.vonmises(mu, kappa, 50) assert_(np.all(r > -np.pi) and np.all(r <= np.pi)) def test_wald(self): random = Generator(MT19937(self.seed)) actual = random.wald(mean=1.23, scale=1.54, size=(3, 2)) desired = np.array([[0.26871721804551, 3.2233942732115 ], [2.20328374987066, 2.40958405189353], [2.07093587449261, 0.73073890064369]]) assert_array_almost_equal(actual, desired, decimal=14) def test_weibull(self): random = Generator(MT19937(self.seed)) actual = random.weibull(a=1.23, size=(3, 2)) desired = np.array([[0.138613914769468, 1.306463419753191], [0.111623365934763, 1.446570494646721], [1.257145775276011, 1.914247725027957]]) assert_array_almost_equal(actual, desired, decimal=15) def test_weibull_0(self): random = Generator(MT19937(self.seed)) assert_equal(random.weibull(a=0, size=12), np.zeros(12)) assert_raises(ValueError, random.weibull, a=-0.) def test_zipf(self): random = Generator(MT19937(self.seed)) actual = random.zipf(a=1.23, size=(3, 2)) desired = np.array([[ 1, 1], [ 10, 867], [354, 2]]) assert_array_equal(actual, desired) class TestBroadcast: # tests that functions that broadcast behave # correctly when presented with non-scalar arguments def setup_method(self): self.seed = 123456789 def test_uniform(self): random = Generator(MT19937(self.seed)) low = [0] high = [1] uniform = random.uniform desired = np.array([0.16693771389729, 0.19635129550675, 0.75563050964095]) random = Generator(MT19937(self.seed)) actual = random.uniform(low * 3, high) assert_array_almost_equal(actual, desired, decimal=14) random = Generator(MT19937(self.seed)) actual = random.uniform(low, high * 3) assert_array_almost_equal(actual, desired, decimal=14) def test_normal(self): loc = [0] scale = [1] bad_scale = [-1] random = Generator(MT19937(self.seed)) desired = np.array([-0.38736406738527, 0.79594375042255, 0.0197076236097]) random = Generator(MT19937(self.seed)) actual = random.normal(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.normal, loc * 3, bad_scale) random = Generator(MT19937(self.seed)) normal = random.normal actual = normal(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, normal, loc, bad_scale * 3) def test_beta(self): a = [1] b = [2] bad_a = [-1] bad_b = [-2] desired = np.array([0.18719338682602, 0.73234824491364, 0.17928615186455]) random = Generator(MT19937(self.seed)) beta = random.beta actual = beta(a * 3, b) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, beta, bad_a * 3, b) assert_raises(ValueError, beta, a * 3, bad_b) random = Generator(MT19937(self.seed)) actual = random.beta(a, b * 3) assert_array_almost_equal(actual, desired, decimal=14) def test_exponential(self): scale = [1] bad_scale = [-1] desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629]) random = Generator(MT19937(self.seed)) actual = random.exponential(scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.exponential, bad_scale * 3) def test_standard_gamma(self): shape = [1] bad_shape = [-1] desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629]) random = Generator(MT19937(self.seed)) std_gamma = random.standard_gamma actual = std_gamma(shape * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, std_gamma, bad_shape * 3) def test_gamma(self): shape = [1] scale = [2] bad_shape = [-1] bad_scale = [-2] desired = np.array([1.34491986425611, 0.42760990636187, 1.4355697857258]) random = Generator(MT19937(self.seed)) gamma = random.gamma actual = gamma(shape * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gamma, bad_shape * 3, scale) assert_raises(ValueError, gamma, shape * 3, bad_scale) random = Generator(MT19937(self.seed)) gamma = random.gamma actual = gamma(shape, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gamma, bad_shape, scale * 3) assert_raises(ValueError, gamma, shape, bad_scale * 3) def test_f(self): dfnum = [1] dfden = [2] bad_dfnum = [-1] bad_dfden = [-2] desired = np.array([0.07765056244107, 7.72951397913186, 0.05786093891763]) random = Generator(MT19937(self.seed)) f = random.f actual = f(dfnum * 3, dfden) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, f, bad_dfnum * 3, dfden) assert_raises(ValueError, f, dfnum * 3, bad_dfden) random = Generator(MT19937(self.seed)) f = random.f actual = f(dfnum, dfden * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, f, bad_dfnum, dfden * 3) assert_raises(ValueError, f, dfnum, bad_dfden * 3) def test_noncentral_f(self): dfnum = [2] dfden = [3] nonc = [4] bad_dfnum = [0] bad_dfden = [-1] bad_nonc = [-2] desired = np.array([2.02434240411421, 12.91838601070124, 1.24395160354629]) random = Generator(MT19937(self.seed)) nonc_f = random.noncentral_f actual = nonc_f(dfnum * 3, dfden, nonc) assert_array_almost_equal(actual, desired, decimal=14) assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3))) assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc) assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc) assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc) random = Generator(MT19937(self.seed)) nonc_f = random.noncentral_f actual = nonc_f(dfnum, dfden * 3, nonc) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc) assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc) assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc) random = Generator(MT19937(self.seed)) nonc_f = random.noncentral_f actual = nonc_f(dfnum, dfden, nonc * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3) assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3) assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3) def test_noncentral_f_small_df(self): random = Generator(MT19937(self.seed)) desired = np.array([0.04714867120827, 0.1239390327694]) actual = random.noncentral_f(0.9, 0.9, 2, size=2) assert_array_almost_equal(actual, desired, decimal=14) def test_chisquare(self): df = [1] bad_df = [-1] desired = np.array([0.05573640064251, 1.47220224353539, 2.9469379318589]) random = Generator(MT19937(self.seed)) actual = random.chisquare(df * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.chisquare, bad_df * 3) def test_noncentral_chisquare(self): df = [1] nonc = [2] bad_df = [-1] bad_nonc = [-2] desired = np.array([0.07710766249436, 5.27829115110304, 0.630732147399]) random = Generator(MT19937(self.seed)) nonc_chi = random.noncentral_chisquare actual = nonc_chi(df * 3, nonc) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_chi, bad_df * 3, nonc) assert_raises(ValueError, nonc_chi, df * 3, bad_nonc) random = Generator(MT19937(self.seed)) nonc_chi = random.noncentral_chisquare actual = nonc_chi(df, nonc * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, nonc_chi, bad_df, nonc * 3) assert_raises(ValueError, nonc_chi, df, bad_nonc * 3) def test_standard_t(self): df = [1] bad_df = [-1] desired = np.array([-1.39498829447098, -1.23058658835223, 0.17207021065983]) random = Generator(MT19937(self.seed)) actual = random.standard_t(df * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.standard_t, bad_df * 3) def test_vonmises(self): mu = [2] kappa = [1] bad_kappa = [-1] desired = np.array([2.25935584988528, 2.23326261461399, -2.84152146503326]) random = Generator(MT19937(self.seed)) actual = random.vonmises(mu * 3, kappa) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.vonmises, mu * 3, bad_kappa) random = Generator(MT19937(self.seed)) actual = random.vonmises(mu, kappa * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.vonmises, mu, bad_kappa * 3) def test_pareto(self): a = [1] bad_a = [-1] desired = np.array([0.95905052946317, 0.2383810889437 , 1.04988745750013]) random = Generator(MT19937(self.seed)) actual = random.pareto(a * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.pareto, bad_a * 3) def test_weibull(self): a = [1] bad_a = [-1] desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629]) random = Generator(MT19937(self.seed)) actual = random.weibull(a * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.weibull, bad_a * 3) def test_power(self): a = [1] bad_a = [-1] desired = np.array([0.48954864361052, 0.19249412888486, 0.51216834058807]) random = Generator(MT19937(self.seed)) actual = random.power(a * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.power, bad_a * 3) def test_laplace(self): loc = [0] scale = [1] bad_scale = [-1] desired = np.array([-1.09698732625119, -0.93470271947368, 0.71592671378202]) random = Generator(MT19937(self.seed)) laplace = random.laplace actual = laplace(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, laplace, loc * 3, bad_scale) random = Generator(MT19937(self.seed)) laplace = random.laplace actual = laplace(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, laplace, loc, bad_scale * 3) def test_gumbel(self): loc = [0] scale = [1] bad_scale = [-1] desired = np.array([1.70020068231762, 1.52054354273631, -0.34293267607081]) random = Generator(MT19937(self.seed)) gumbel = random.gumbel actual = gumbel(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gumbel, loc * 3, bad_scale) random = Generator(MT19937(self.seed)) gumbel = random.gumbel actual = gumbel(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, gumbel, loc, bad_scale * 3) def test_logistic(self): loc = [0] scale = [1] bad_scale = [-1] desired = np.array([-1.607487640433, -1.40925686003678, 1.12887112820397]) random = Generator(MT19937(self.seed)) actual = random.logistic(loc * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.logistic, loc * 3, bad_scale) random = Generator(MT19937(self.seed)) actual = random.logistic(loc, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.logistic, loc, bad_scale * 3) assert_equal(random.logistic(1.0, 0.0), 1.0) def test_lognormal(self): mean = [0] sigma = [1] bad_sigma = [-1] desired = np.array([0.67884390500697, 2.21653186290321, 1.01990310084276]) random = Generator(MT19937(self.seed)) lognormal = random.lognormal actual = lognormal(mean * 3, sigma) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, lognormal, mean * 3, bad_sigma) random = Generator(MT19937(self.seed)) actual = random.lognormal(mean, sigma * 3) assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3) def test_rayleigh(self): scale = [1] bad_scale = [-1] desired = np.array( [1.1597068009872629, 0.6539188836253857, 1.1981526554349398] ) random = Generator(MT19937(self.seed)) actual = random.rayleigh(scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.rayleigh, bad_scale * 3) def test_wald(self): mean = [0.5] scale = [1] bad_mean = [0] bad_scale = [-2] desired = np.array([0.38052407392905, 0.50701641508592, 0.484935249864]) random = Generator(MT19937(self.seed)) actual = random.wald(mean * 3, scale) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.wald, bad_mean * 3, scale) assert_raises(ValueError, random.wald, mean * 3, bad_scale) random = Generator(MT19937(self.seed)) actual = random.wald(mean, scale * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, random.wald, bad_mean, scale * 3) assert_raises(ValueError, random.wald, mean, bad_scale * 3) def test_triangular(self): left = [1] right = [3] mode = [2] bad_left_one = [3] bad_mode_one = [4] bad_left_two, bad_mode_two = right * 2 desired = np.array([1.57781954604754, 1.62665986867957, 2.30090130831326]) random = Generator(MT19937(self.seed)) triangular = random.triangular actual = triangular(left * 3, mode, right) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, triangular, bad_left_one * 3, mode, right) assert_raises(ValueError, triangular, left * 3, bad_mode_one, right) assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two, right) random = Generator(MT19937(self.seed)) triangular = random.triangular actual = triangular(left, mode * 3, right) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, triangular, bad_left_one, mode * 3, right) assert_raises(ValueError, triangular, left, bad_mode_one * 3, right) assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3, right) random = Generator(MT19937(self.seed)) triangular = random.triangular actual = triangular(left, mode, right * 3) assert_array_almost_equal(actual, desired, decimal=14) assert_raises(ValueError, triangular, bad_left_one, mode, right * 3) assert_raises(ValueError, triangular, left, bad_mode_one, right * 3) assert_raises(ValueError, triangular, bad_left_two, bad_mode_two, right * 3) assert_raises(ValueError, triangular, 10., 0., 20.) assert_raises(ValueError, triangular, 10., 25., 20.) assert_raises(ValueError, triangular, 10., 10., 10.) def test_binomial(self): n = [1] p = [0.5] bad_n = [-1] bad_p_one = [-1] bad_p_two = [1.5] desired = np.array([0, 0, 1]) random = Generator(MT19937(self.seed)) binom = random.binomial actual = binom(n * 3, p) assert_array_equal(actual, desired) assert_raises(ValueError, binom, bad_n * 3, p) assert_raises(ValueError, binom, n * 3, bad_p_one) assert_raises(ValueError, binom, n * 3, bad_p_two) random = Generator(MT19937(self.seed)) actual = random.binomial(n, p * 3) assert_array_equal(actual, desired) assert_raises(ValueError, binom, bad_n, p * 3) assert_raises(ValueError, binom, n, bad_p_one * 3) assert_raises(ValueError, binom, n, bad_p_two * 3) def test_negative_binomial(self): n = [1] p = [0.5] bad_n = [-1] bad_p_one = [-1] bad_p_two = [1.5] desired = np.array([0, 2, 1], dtype=np.int64) random = Generator(MT19937(self.seed)) neg_binom = random.negative_binomial actual = neg_binom(n * 3, p) assert_array_equal(actual, desired) assert_raises(ValueError, neg_binom, bad_n * 3, p) assert_raises(ValueError, neg_binom, n * 3, bad_p_one) assert_raises(ValueError, neg_binom, n * 3, bad_p_two) random = Generator(MT19937(self.seed)) neg_binom = random.negative_binomial actual = neg_binom(n, p * 3) assert_array_equal(actual, desired) assert_raises(ValueError, neg_binom, bad_n, p * 3) assert_raises(ValueError, neg_binom, n, bad_p_one * 3) assert_raises(ValueError, neg_binom, n, bad_p_two * 3) def test_poisson(self): lam = [1] bad_lam_one = [-1] desired = np.array([0, 0, 3]) random = Generator(MT19937(self.seed)) max_lam = random._poisson_lam_max bad_lam_two = [max_lam * 2] poisson = random.poisson actual = poisson(lam * 3) assert_array_equal(actual, desired) assert_raises(ValueError, poisson, bad_lam_one * 3) assert_raises(ValueError, poisson, bad_lam_two * 3) def test_zipf(self): a = [2] bad_a = [0] desired = np.array([1, 8, 1]) random = Generator(MT19937(self.seed)) zipf = random.zipf actual = zipf(a * 3) assert_array_equal(actual, desired) assert_raises(ValueError, zipf, bad_a * 3) with np.errstate(invalid='ignore'): assert_raises(ValueError, zipf, np.nan) assert_raises(ValueError, zipf, [0, 0, np.nan]) def test_geometric(self): p = [0.5] bad_p_one = [-1] bad_p_two = [1.5] desired = np.array([1, 1, 3]) random = Generator(MT19937(self.seed)) geometric = random.geometric actual = geometric(p * 3) assert_array_equal(actual, desired) assert_raises(ValueError, geometric, bad_p_one * 3) assert_raises(ValueError, geometric, bad_p_two * 3) def test_hypergeometric(self): ngood = [1] nbad = [2] nsample = [2] bad_ngood = [-1] bad_nbad = [-2] bad_nsample_one = [-1] bad_nsample_two = [4] desired = np.array([0, 0, 1]) random = Generator(MT19937(self.seed)) actual = random.hypergeometric(ngood * 3, nbad, nsample) assert_array_equal(actual, desired) assert_raises(ValueError, random.hypergeometric, bad_ngood * 3, nbad, nsample) assert_raises(ValueError, random.hypergeometric, ngood * 3, bad_nbad, nsample) assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_one) assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_two) random = Generator(MT19937(self.seed)) actual = random.hypergeometric(ngood, nbad * 3, nsample) assert_array_equal(actual, desired) assert_raises(ValueError, random.hypergeometric, bad_ngood, nbad * 3, nsample) assert_raises(ValueError, random.hypergeometric, ngood, bad_nbad * 3, nsample) assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_one) assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_two) random = Generator(MT19937(self.seed)) hypergeom = random.hypergeometric actual = hypergeom(ngood, nbad, nsample * 3) assert_array_equal(actual, desired) assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3) assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3) assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3) assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3) assert_raises(ValueError, hypergeom, -1, 10, 20) assert_raises(ValueError, hypergeom, 10, -1, 20) assert_raises(ValueError, hypergeom, 10, 10, -1) assert_raises(ValueError, hypergeom, 10, 10, 25) # ValueError for arguments that are too big. assert_raises(ValueError, hypergeom, 2**30, 10, 20) assert_raises(ValueError, hypergeom, 999, 2**31, 50) assert_raises(ValueError, hypergeom, 999, [2**29, 2**30], 1000) def test_logseries(self): p = [0.5] bad_p_one = [2] bad_p_two = [-1] desired = np.array([1, 1, 1]) random = Generator(MT19937(self.seed)) logseries = random.logseries actual = logseries(p * 3) assert_array_equal(actual, desired) assert_raises(ValueError, logseries, bad_p_one * 3) assert_raises(ValueError, logseries, bad_p_two * 3) def test_multinomial(self): random = Generator(MT19937(self.seed)) actual = random.multinomial([5, 20], [1 / 6.] * 6, size=(3, 2)) desired = np.array([[[0, 0, 2, 1, 2, 0], [2, 3, 6, 4, 2, 3]], [[1, 0, 1, 0, 2, 1], [7, 2, 2, 1, 4, 4]], [[0, 2, 0, 1, 2, 0], [3, 2, 3, 3, 4, 5]]], dtype=np.int64) assert_array_equal(actual, desired) random = Generator(MT19937(self.seed)) actual = random.multinomial([5, 20], [1 / 6.] * 6) desired = np.array([[0, 0, 2, 1, 2, 0], [2, 3, 6, 4, 2, 3]], dtype=np.int64) assert_array_equal(actual, desired) random = Generator(MT19937(self.seed)) actual = random.multinomial([5, 20], [[1 / 6.] * 6] * 2) desired = np.array([[0, 0, 2, 1, 2, 0], [2, 3, 6, 4, 2, 3]], dtype=np.int64) assert_array_equal(actual, desired) random = Generator(MT19937(self.seed)) actual = random.multinomial([[5], [20]], [[1 / 6.] * 6] * 2) desired = np.array([[[0, 0, 2, 1, 2, 0], [0, 0, 2, 1, 1, 1]], [[4, 2, 3, 3, 5, 3], [7, 2, 2, 1, 4, 4]]], dtype=np.int64) assert_array_equal(actual, desired) @pytest.mark.parametrize("n", [10, np.array([10, 10]), np.array([[[10]], [[10]]]) ] ) def test_multinomial_pval_broadcast(self, n): random = Generator(MT19937(self.seed)) pvals = np.array([1 / 4] * 4) actual = random.multinomial(n, pvals) n_shape = tuple() if isinstance(n, int) else n.shape expected_shape = n_shape + (4,) assert actual.shape == expected_shape pvals = np.vstack([pvals, pvals]) actual = random.multinomial(n, pvals) expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + (4,) assert actual.shape == expected_shape pvals = np.vstack([[pvals], [pvals]]) actual = random.multinomial(n, pvals) expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) assert actual.shape == expected_shape + (4,) actual = random.multinomial(n, pvals, size=(3, 2) + expected_shape) assert actual.shape == (3, 2) + expected_shape + (4,) with pytest.raises(ValueError): # Ensure that size is not broadcast actual = random.multinomial(n, pvals, size=(1,) * 6) def test_invalid_pvals_broadcast(self): random = Generator(MT19937(self.seed)) pvals = [[1 / 6] * 6, [1 / 4] * 6] assert_raises(ValueError, random.multinomial, 1, pvals) assert_raises(ValueError, random.multinomial, 6, 0.5) def test_empty_outputs(self): random = Generator(MT19937(self.seed)) actual = random.multinomial(np.empty((10, 0, 6), "i8"), [1 / 6] * 6) assert actual.shape == (10, 0, 6, 6) actual = random.multinomial(12, np.empty((10, 0, 10))) assert actual.shape == (10, 0, 10) actual = random.multinomial(np.empty((3, 0, 7), "i8"), np.empty((3, 0, 7, 4))) assert actual.shape == (3, 0, 7, 4) @pytest.mark.skipif(IS_WASM, reason="can't start thread") class TestThread: # make sure each state produces the same sequence even in threads def setup_method(self): self.seeds = range(4) def check_function(self, function, sz): from threading import Thread out1 = np.empty((len(self.seeds),) + sz) out2 = np.empty((len(self.seeds),) + sz) # threaded generation t = [Thread(target=function, args=(Generator(MT19937(s)), o)) for s, o in zip(self.seeds, out1)] [x.start() for x in t] [x.join() for x in t] # the same serial for s, o in zip(self.seeds, out2): function(Generator(MT19937(s)), o) # these platforms change x87 fpu precision mode in threads if np.intp().dtype.itemsize == 4 and sys.platform == "win32": assert_array_almost_equal(out1, out2) else: assert_array_equal(out1, out2) def test_normal(self): def gen_random(state, out): out[...] = state.normal(size=10000) self.check_function(gen_random, sz=(10000,)) def test_exp(self): def gen_random(state, out): out[...] = state.exponential(scale=np.ones((100, 1000))) self.check_function(gen_random, sz=(100, 1000)) def test_multinomial(self): def gen_random(state, out): out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000) self.check_function(gen_random, sz=(10000, 6)) # See Issue #4263 class TestSingleEltArrayInput: def setup_method(self): self.argOne = np.array([2]) self.argTwo = np.array([3]) self.argThree = np.array([4]) self.tgtShape = (1,) def test_one_arg_funcs(self): funcs = (random.exponential, random.standard_gamma, random.chisquare, random.standard_t, random.pareto, random.weibull, random.power, random.rayleigh, random.poisson, random.zipf, random.geometric, random.logseries) probfuncs = (random.geometric, random.logseries) for func in funcs: if func in probfuncs: # p < 1.0 out = func(np.array([0.5])) else: out = func(self.argOne) assert_equal(out.shape, self.tgtShape) def test_two_arg_funcs(self): funcs = (random.uniform, random.normal, random.beta, random.gamma, random.f, random.noncentral_chisquare, random.vonmises, random.laplace, random.gumbel, random.logistic, random.lognormal, random.wald, random.binomial, random.negative_binomial) probfuncs = (random.binomial, random.negative_binomial) for func in funcs: if func in probfuncs: # p <= 1 argTwo = np.array([0.5]) else: argTwo = self.argTwo out = func(self.argOne, argTwo) assert_equal(out.shape, self.tgtShape) out = func(self.argOne[0], argTwo) assert_equal(out.shape, self.tgtShape) out = func(self.argOne, argTwo[0]) assert_equal(out.shape, self.tgtShape) def test_integers(self, endpoint): itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.int64, np.uint64] func = random.integers high = np.array([1]) low = np.array([0]) for dt in itype: out = func(low, high, endpoint=endpoint, dtype=dt) assert_equal(out.shape, self.tgtShape) out = func(low[0], high, endpoint=endpoint, dtype=dt) assert_equal(out.shape, self.tgtShape) out = func(low, high[0], endpoint=endpoint, dtype=dt) assert_equal(out.shape, self.tgtShape) def test_three_arg_funcs(self): funcs = [random.noncentral_f, random.triangular, random.hypergeometric] for func in funcs: out = func(self.argOne, self.argTwo, self.argThree) assert_equal(out.shape, self.tgtShape) out = func(self.argOne[0], self.argTwo, self.argThree) assert_equal(out.shape, self.tgtShape) out = func(self.argOne, self.argTwo[0], self.argThree) assert_equal(out.shape, self.tgtShape) @pytest.mark.parametrize("config", JUMP_TEST_DATA) def test_jumped(config): # Each config contains the initial seed, a number of raw steps # the sha256 hashes of the initial and the final states' keys and # the position of the initial and the final state. # These were produced using the original C implementation. seed = config["seed"] steps = config["steps"] mt19937 = MT19937(seed) # Burn step mt19937.random_raw(steps) key = mt19937.state["state"]["key"] if sys.byteorder == 'big': key = key.byteswap() sha256 = hashlib.sha256(key) assert mt19937.state["state"]["pos"] == config["initial"]["pos"] assert sha256.hexdigest() == config["initial"]["key_sha256"] jumped = mt19937.jumped() key = jumped.state["state"]["key"] if sys.byteorder == 'big': key = key.byteswap() sha256 = hashlib.sha256(key) assert jumped.state["state"]["pos"] == config["jumped"]["pos"] assert sha256.hexdigest() == config["jumped"]["key_sha256"] def test_broadcast_size_error(): mu = np.ones(3) sigma = np.ones((4, 3)) size = (10, 4, 2) assert random.normal(mu, sigma, size=(5, 4, 3)).shape == (5, 4, 3) with pytest.raises(ValueError): random.normal(mu, sigma, size=size) with pytest.raises(ValueError): random.normal(mu, sigma, size=(1, 3)) with pytest.raises(ValueError): random.normal(mu, sigma, size=(4, 1, 1)) # 1 arg shape = np.ones((4, 3)) with pytest.raises(ValueError): random.standard_gamma(shape, size=size) with pytest.raises(ValueError): random.standard_gamma(shape, size=(3,)) with pytest.raises(ValueError): random.standard_gamma(shape, size=3) # Check out out = np.empty(size) with pytest.raises(ValueError): random.standard_gamma(shape, out=out) # 2 arg with pytest.raises(ValueError): random.binomial(1, [0.3, 0.7], size=(2, 1)) with pytest.raises(ValueError): random.binomial([1, 2], 0.3, size=(2, 1)) with pytest.raises(ValueError): random.binomial([1, 2], [0.3, 0.7], size=(2, 1)) with pytest.raises(ValueError): random.multinomial([2, 2], [.3, .7], size=(2, 1)) # 3 arg a = random.chisquare(5, size=3) b = random.chisquare(5, size=(4, 3)) c = random.chisquare(5, size=(5, 4, 3)) assert random.noncentral_f(a, b, c).shape == (5, 4, 3) with pytest.raises(ValueError, match=r"Output size \(6, 5, 1, 1\) is"): random.noncentral_f(a, b, c, size=(6, 5, 1, 1)) def test_broadcast_size_scalar(): mu = np.ones(3) sigma = np.ones(3) random.normal(mu, sigma, size=3) with pytest.raises(ValueError): random.normal(mu, sigma, size=2) def test_ragged_shuffle(): # GH 18142 seq = [[], [], 1] gen = Generator(MT19937(0)) assert_no_warnings(gen.shuffle, seq) assert seq == [1, [], []] @pytest.mark.parametrize("high", [-2, [-2]]) @pytest.mark.parametrize("endpoint", [True, False]) def test_single_arg_integer_exception(high, endpoint): # GH 14333 gen = Generator(MT19937(0)) msg = 'high < 0' if endpoint else 'high <= 0' with pytest.raises(ValueError, match=msg): gen.integers(high, endpoint=endpoint) msg = 'low > high' if endpoint else 'low >= high' with pytest.raises(ValueError, match=msg): gen.integers(-1, high, endpoint=endpoint) with pytest.raises(ValueError, match=msg): gen.integers([-1], high, endpoint=endpoint) @pytest.mark.parametrize("dtype", ["f4", "f8"]) def test_c_contig_req_out(dtype): # GH 18704 out = np.empty((2, 3), order="F", dtype=dtype) shape = [1, 2, 3] with pytest.raises(ValueError, match="Supplied output array"): random.standard_gamma(shape, out=out, dtype=dtype) with pytest.raises(ValueError, match="Supplied output array"): random.standard_gamma(shape, out=out, size=out.shape, dtype=dtype) @pytest.mark.parametrize("dtype", ["f4", "f8"]) @pytest.mark.parametrize("order", ["F", "C"]) @pytest.mark.parametrize("dist", [random.standard_normal, random.random]) def test_contig_req_out(dist, order, dtype): # GH 18704 out = np.empty((2, 3), dtype=dtype, order=order) variates = dist(out=out, dtype=dtype) assert variates is out variates = dist(out=out, dtype=dtype, size=out.shape) assert variates is out def test_generator_ctor_old_style_pickle(): rg = np.random.Generator(np.random.PCG64DXSM(0)) rg.standard_normal(1) # Directly call reduce which is used in pickling ctor, args, state_a = rg.__reduce__() # Simulate unpickling an old pickle that only has the name assert args[:1] == ("PCG64DXSM",) b = ctor(*args[:1]) b.bit_generator.state = state_a state_b = b.bit_generator.state assert state_a == state_b