from __future__ import division, absolute_import, print_function import numpy as np from numpy.testing import ( TestCase, run_module_suite, assert_, assert_raises, assert_equal, assert_warns) from numpy import random from numpy.compat import asbytes import sys import warnings class TestSeed(TestCase): def test_scalar(self): s = np.random.RandomState(0) assert_equal(s.randint(1000), 684) s = np.random.RandomState(4294967295) assert_equal(s.randint(1000), 419) def test_array(self): s = np.random.RandomState(range(10)) assert_equal(s.randint(1000), 468) s = np.random.RandomState(np.arange(10)) assert_equal(s.randint(1000), 468) s = np.random.RandomState([0]) assert_equal(s.randint(1000), 973) s = np.random.RandomState([4294967295]) assert_equal(s.randint(1000), 265) def test_invalid_scalar(self): # seed must be an unsigned 32 bit integer assert_raises(TypeError, np.random.RandomState, -0.5) assert_raises(ValueError, np.random.RandomState, -1) def test_invalid_array(self): # seed must be an unsigned 32 bit integer assert_raises(TypeError, np.random.RandomState, [-0.5]) assert_raises(ValueError, np.random.RandomState, [-1]) assert_raises(ValueError, np.random.RandomState, [4294967296]) assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296]) assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296]) class TestBinomial(TestCase): 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) np.testing.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(TestCase): 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.randint(-5, -1) < -1) x = random.randint(-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(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2)) assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2)) assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape, (2, 2, 2)) assert_raises(TypeError, np.random.multinomial, 1, p, np.float(1)) class TestSetState(TestCase): def setUp(self): self.seed = 1234567890 self.prng = random.RandomState(self.seed) self.state = self.prng.get_state() def test_basic(self): old = self.prng.tomaxint(16) self.prng.set_state(self.state) new = self.prng.tomaxint(16) assert_(np.all(old == new)) def test_gaussian_reset(self): # Make sure the cached every-other-Gaussian is reset. old = self.prng.standard_normal(size=3) self.prng.set_state(self.state) new = self.prng.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.prng.standard_normal() state = self.prng.get_state() old = self.prng.standard_normal(size=3) self.prng.set_state(state) new = self.prng.standard_normal(size=3) assert_(np.all(old == new)) def test_backwards_compatibility(self): # Make sure we can accept old state tuples that do not have the # cached Gaussian value. old_state = self.state[:-2] x1 = self.prng.standard_normal(size=16) self.prng.set_state(old_state) x2 = self.prng.standard_normal(size=16) self.prng.set_state(self.state) x3 = self.prng.standard_normal(size=16) assert_(np.all(x1 == x2)) assert_(np.all(x1 == x3)) def test_negative_binomial(self): # Ensure that the negative binomial results take floating point # arguments without truncation. self.prng.negative_binomial(0.5, 0.5) class TestRandint(TestCase): rfunc = np.random.randint # valid integer/boolean types itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.int64, np.uint64] def test_unsupported_type(self): assert_raises(TypeError, self.rfunc, 1, dtype=np.float) def test_bounds_checking(self): for dt in self.itype: lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1 assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt) assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt) assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt) assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt) def test_rng_zero_and_extremes(self): for dt in self.itype: lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1 tgt = ubnd - 1 assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt) tgt = lbnd assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt) tgt = (lbnd + ubnd)//2 assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt) def test_in_bounds_fuzz(self): # Don't use fixed seed np.random.seed() for dt in self.itype[1:]: for ubnd in [4, 8, 16]: vals = self.rfunc(2, ubnd, size=2**16, dtype=dt) assert_(vals.max() < ubnd) assert_(vals.min() >= 2) vals = self.rfunc(0, 2, size=2**16, dtype=np.bool) assert_(vals.max() < 2) assert_(vals.min() >= 0) def test_repeatability(self): import hashlib # We use a md5 hash of generated sequences of 1000 samples # in the range [0, 6) for all but np.bool, where the range # is [0, 2). Hashes are for little endian numbers. tgt = {'bool': '7dd3170d7aa461d201a65f8bcf3944b0', 'int16': '1b7741b80964bb190c50d541dca1cac1', 'int32': '4dc9fcc2b395577ebb51793e58ed1a05', 'int64': '17db902806f448331b5a758d7d2ee672', 'int8': '27dd30c4e08a797063dffac2490b0be6', 'uint16': '1b7741b80964bb190c50d541dca1cac1', 'uint32': '4dc9fcc2b395577ebb51793e58ed1a05', 'uint64': '17db902806f448331b5a758d7d2ee672', 'uint8': '27dd30c4e08a797063dffac2490b0be6'} for dt in self.itype[1:]: np.random.seed(1234) # view as little endian for hash if sys.byteorder == 'little': val = self.rfunc(0, 6, size=1000, dtype=dt) else: val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap() res = hashlib.md5(val.view(np.int8)).hexdigest() assert_(tgt[np.dtype(dt).name] == res) # bools do not depend on endianess np.random.seed(1234) val = self.rfunc(0, 2, size=1000, dtype=np.bool).view(np.int8) res = hashlib.md5(val).hexdigest() assert_(tgt[np.dtype(np.bool).name] == res) def test_respect_dtype_singleton(self): # See gh-7203 for dt in self.itype: lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1 sample = self.rfunc(lbnd, ubnd, dtype=dt) self.assertEqual(sample.dtype, np.dtype(dt)) for dt in (np.bool, np.int, np.long): lbnd = 0 if dt is np.bool else np.iinfo(dt).min ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1 # gh-7284: Ensure that we get Python data types sample = self.rfunc(lbnd, ubnd, dtype=dt) self.assertFalse(hasattr(sample, 'dtype')) self.assertEqual(type(sample), dt) class TestRandomDist(TestCase): # Make sure the random distribution returns the correct value for a # given seed def setUp(self): self.seed = 1234567890 def test_rand(self): np.random.seed(self.seed) actual = np.random.rand(3, 2) desired = np.array([[0.61879477158567997, 0.59162362775974664], [0.88868358904449662, 0.89165480011560816], [0.4575674820298663, 0.7781880808593471]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_randn(self): np.random.seed(self.seed) actual = np.random.randn(3, 2) desired = np.array([[1.34016345771863121, 1.73759122771936081], [1.498988344300628, -0.2286433324536169], [2.031033998682787, 2.17032494605655257]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_randint(self): np.random.seed(self.seed) actual = np.random.randint(-99, 99, size=(3, 2)) desired = np.array([[31, 3], [-52, 41], [-48, -66]]) np.testing.assert_array_equal(actual, desired) def test_random_integers(self): np.random.seed(self.seed) actual = np.random.random_integers(-99, 99, size=(3, 2)) desired = np.array([[31, 3], [-52, 41], [-48, -66]]) np.testing.assert_array_equal(actual, desired) def test_random_integers_max_int(self): # Tests whether random_integers 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 = np.random.random_integers(np.iinfo('l').max, np.iinfo('l').max) desired = np.iinfo('l').max np.testing.assert_equal(actual, desired) def test_random_integers_deprecated(self): with warnings.catch_warnings(): warnings.simplefilter("error", DeprecationWarning) # DeprecationWarning raised with high == None assert_raises(DeprecationWarning, np.random.random_integers, np.iinfo('l').max) # DeprecationWarning raised with high != None assert_raises(DeprecationWarning, np.random.random_integers, np.iinfo('l').max, np.iinfo('l').max) def test_random_sample(self): np.random.seed(self.seed) actual = np.random.random_sample((3, 2)) desired = np.array([[0.61879477158567997, 0.59162362775974664], [0.88868358904449662, 0.89165480011560816], [0.4575674820298663, 0.7781880808593471]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_choice_uniform_replace(self): np.random.seed(self.seed) actual = np.random.choice(4, 4) desired = np.array([2, 3, 2, 3]) np.testing.assert_array_equal(actual, desired) def test_choice_nonuniform_replace(self): np.random.seed(self.seed) actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1]) desired = np.array([1, 1, 2, 2]) np.testing.assert_array_equal(actual, desired) def test_choice_uniform_noreplace(self): np.random.seed(self.seed) actual = np.random.choice(4, 3, replace=False) desired = np.array([0, 1, 3]) np.testing.assert_array_equal(actual, desired) def test_choice_nonuniform_noreplace(self): np.random.seed(self.seed) actual = np.random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1]) desired = np.array([2, 3, 1]) np.testing.assert_array_equal(actual, desired) def test_choice_noninteger(self): np.random.seed(self.seed) actual = np.random.choice(['a', 'b', 'c', 'd'], 4) desired = np.array(['c', 'd', 'c', 'd']) np.testing.assert_array_equal(actual, desired) def test_choice_exceptions(self): sample = np.random.choice assert_raises(ValueError, sample, -1, 3) assert_raises(ValueError, sample, 3., 3) assert_raises(ValueError, sample, [[1, 2], [3, 4]], 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) 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(np.random.choice(2, replace=True))) assert_(np.isscalar(np.random.choice(2, replace=False))) assert_(np.isscalar(np.random.choice(2, replace=True, p=p))) assert_(np.isscalar(np.random.choice(2, replace=False, p=p))) assert_(np.isscalar(np.random.choice([1, 2], replace=True))) assert_(np.random.choice([None], replace=True) is None) a = np.array([1, 2]) arr = np.empty(1, dtype=object) arr[0] = a assert_(np.random.choice(arr, replace=True) is a) # Check 0-d array s = tuple() assert_(not np.isscalar(np.random.choice(2, s, replace=True))) assert_(not np.isscalar(np.random.choice(2, s, replace=False))) assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p))) assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p))) assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True))) assert_(np.random.choice([None], s, replace=True).ndim == 0) a = np.array([1, 2]) arr = np.empty(1, dtype=object) arr[0] = a assert_(np.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_(np.random.choice(6, s, replace=True).shape, s) assert_(np.random.choice(6, s, replace=False).shape, s) assert_(np.random.choice(6, s, replace=True, p=p).shape, s) assert_(np.random.choice(6, s, replace=False, p=p).shape, s) assert_(np.random.choice(np.arange(6), s, replace=True).shape, s) def test_bytes(self): np.random.seed(self.seed) actual = np.random.bytes(10) desired = asbytes('\x82Ui\x9e\xff\x97+Wf\xa5') np.testing.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-4270 lambda x: np.asarray([(i, i) for i in x], [("a", object, 1), ("b", np.int32, 1)])]: np.random.seed(self.seed) alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]) np.random.shuffle(alist) actual = alist desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3]) np.testing.assert_array_equal(actual, desired) 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): np.random.shuffle(a) assert_equal( sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask])) np.random.shuffle(b) assert_equal( sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask])) def test_beta(self): np.random.seed(self.seed) actual = np.random.beta(.1, .9, size=(3, 2)) desired = np.array( [[1.45341850513746058e-02, 5.31297615662868145e-04], [1.85366619058432324e-06, 4.19214516800110563e-03], [1.58405155108498093e-04, 1.26252891949397652e-04]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_binomial(self): np.random.seed(self.seed) actual = np.random.binomial(100.123, .456, size=(3, 2)) desired = np.array([[37, 43], [42, 48], [46, 45]]) np.testing.assert_array_equal(actual, desired) def test_chisquare(self): np.random.seed(self.seed) actual = np.random.chisquare(50, size=(3, 2)) desired = np.array([[63.87858175501090585, 68.68407748911370447], [65.77116116901505904, 47.09686762438974483], [72.3828403199695174, 74.18408615260374006]]) np.testing.assert_array_almost_equal(actual, desired, decimal=13) def test_dirichlet(self): np.random.seed(self.seed) alpha = np.array([51.72840233779265162, 39.74494232180943953]) actual = np.random.mtrand.dirichlet(alpha, size=(3, 2)) desired = np.array([[[0.54539444573611562, 0.45460555426388438], [0.62345816822039413, 0.37654183177960598]], [[0.55206000085785778, 0.44793999914214233], [0.58964023305154301, 0.41035976694845688]], [[0.59266909280647828, 0.40733090719352177], [0.56974431743975207, 0.43025568256024799]]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_dirichlet_size(self): # gh-3173 p = np.array([51.72840233779265162, 39.74494232180943953]) assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2)) assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2)) assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2)) assert_raises(TypeError, np.random.dirichlet, p, np.float(1)) def test_exponential(self): np.random.seed(self.seed) actual = np.random.exponential(1.1234, size=(3, 2)) desired = np.array([[1.08342649775011624, 1.00607889924557314], [2.46628830085216721, 2.49668106809923884], [0.68717433461363442, 1.69175666993575979]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_f(self): np.random.seed(self.seed) actual = np.random.f(12, 77, size=(3, 2)) desired = np.array([[1.21975394418575878, 1.75135759791559775], [1.44803115017146489, 1.22108959480396262], [1.02176975757740629, 1.34431827623300415]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_gamma(self): np.random.seed(self.seed) actual = np.random.gamma(5, 3, size=(3, 2)) desired = np.array([[24.60509188649287182, 28.54993563207210627], [26.13476110204064184, 12.56988482927716078], [31.71863275789960568, 33.30143302795922011]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_geometric(self): np.random.seed(self.seed) actual = np.random.geometric(.123456789, size=(3, 2)) desired = np.array([[8, 7], [17, 17], [5, 12]]) np.testing.assert_array_equal(actual, desired) def test_gumbel(self): np.random.seed(self.seed) actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[0.19591898743416816, 0.34405539668096674], [-1.4492522252274278, -1.47374816298446865], [1.10651090478803416, -0.69535848626236174]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_hypergeometric(self): np.random.seed(self.seed) actual = np.random.hypergeometric(10.1, 5.5, 14, size=(3, 2)) desired = np.array([[10, 10], [10, 10], [9, 9]]) np.testing.assert_array_equal(actual, desired) # Test nbad = 0 actual = np.random.hypergeometric(5, 0, 3, size=4) desired = np.array([3, 3, 3, 3]) np.testing.assert_array_equal(actual, desired) actual = np.random.hypergeometric(15, 0, 12, size=4) desired = np.array([12, 12, 12, 12]) np.testing.assert_array_equal(actual, desired) # Test ngood = 0 actual = np.random.hypergeometric(0, 5, 3, size=4) desired = np.array([0, 0, 0, 0]) np.testing.assert_array_equal(actual, desired) actual = np.random.hypergeometric(0, 15, 12, size=4) desired = np.array([0, 0, 0, 0]) np.testing.assert_array_equal(actual, desired) def test_laplace(self): np.random.seed(self.seed) actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[0.66599721112760157, 0.52829452552221945], [3.12791959514407125, 3.18202813572992005], [-0.05391065675859356, 1.74901336242837324]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_logistic(self): np.random.seed(self.seed) actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[1.09232835305011444, 0.8648196662399954], [4.27818590694950185, 4.33897006346929714], [-0.21682183359214885, 2.63373365386060332]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_lognormal(self): np.random.seed(self.seed) actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2)) desired = np.array([[16.50698631688883822, 36.54846706092654784], [22.67886599981281748, 0.71617561058995771], [65.72798501792723869, 86.84341601437161273]]) np.testing.assert_array_almost_equal(actual, desired, decimal=13) def test_logseries(self): np.random.seed(self.seed) actual = np.random.logseries(p=.923456789, size=(3, 2)) desired = np.array([[2, 2], [6, 17], [3, 6]]) np.testing.assert_array_equal(actual, desired) def test_multinomial(self): np.random.seed(self.seed) actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2)) desired = np.array([[[4, 3, 5, 4, 2, 2], [5, 2, 8, 2, 2, 1]], [[3, 4, 3, 6, 0, 4], [2, 1, 4, 3, 6, 4]], [[4, 4, 2, 5, 2, 3], [4, 3, 4, 2, 3, 4]]]) np.testing.assert_array_equal(actual, desired) def test_multivariate_normal(self): np.random.seed(self.seed) mean = (.123456789, 10) # Hmm... not even symmetric. cov = [[1, 0], [1, 0]] size = (3, 2) actual = np.random.multivariate_normal(mean, cov, size) desired = np.array([[[-1.47027513018564449, 10.], [-1.65915081534845532, 10.]], [[-2.29186329304599745, 10.], [-1.77505606019580053, 10.]], [[-0.54970369430044119, 10.], [0.29768848031692957, 10.]]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) # Check for default size, was raising deprecation warning actual = np.random.multivariate_normal(mean, cov) desired = np.array([-0.79441224511977482, 10.]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) # Check that non positive-semidefinite covariance raises warning mean = [0, 0] cov = [[1, 1 + 1e-10], [1 + 1e-10, 1]] assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov) def test_negative_binomial(self): np.random.seed(self.seed) actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2)) desired = np.array([[848, 841], [892, 611], [779, 647]]) np.testing.assert_array_equal(actual, desired) def test_noncentral_chisquare(self): np.random.seed(self.seed) actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2)) desired = np.array([[23.91905354498517511, 13.35324692733826346], [31.22452661329736401, 16.60047399466177254], [5.03461598262724586, 17.94973089023519464]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) actual = np.random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2)) desired = np.array([[ 1.47145377828516666, 0.15052899268012659], [ 0.00943803056963588, 1.02647251615666169], [ 0.332334982684171 , 0.15451287602753125]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) np.random.seed(self.seed) actual = np.random.noncentral_chisquare(df=5, nonc=0, size=(3, 2)) desired = np.array([[9.597154162763948, 11.725484450296079], [10.413711048138335, 3.694475922923986], [13.484222138963087, 14.377255424602957]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_noncentral_f(self): np.random.seed(self.seed) actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1, size=(3, 2)) desired = np.array([[1.40598099674926669, 0.34207973179285761], [3.57715069265772545, 7.92632662577829805], [0.43741599463544162, 1.1774208752428319]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_normal(self): np.random.seed(self.seed) actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2)) desired = np.array([[2.80378370443726244, 3.59863924443872163], [3.121433477601256, -0.33382987590723379], [4.18552478636557357, 4.46410668111310471]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_pareto(self): np.random.seed(self.seed) actual = np.random.pareto(a=.123456789, size=(3, 2)) desired = np.array( [[2.46852460439034849e+03, 1.41286880810518346e+03], [5.28287797029485181e+07, 6.57720981047328785e+07], [1.40840323350391515e+02, 1.98390255135251704e+05]]) # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this # matrix differs by 24 nulps. Discussion: # http://mail.scipy.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): np.random.seed(self.seed) actual = np.random.poisson(lam=.123456789, size=(3, 2)) desired = np.array([[0, 0], [1, 0], [0, 0]]) np.testing.assert_array_equal(actual, desired) def test_poisson_exceptions(self): lambig = np.iinfo('l').max lamneg = -1 assert_raises(ValueError, np.random.poisson, lamneg) assert_raises(ValueError, np.random.poisson, [lamneg]*10) assert_raises(ValueError, np.random.poisson, lambig) assert_raises(ValueError, np.random.poisson, [lambig]*10) def test_power(self): np.random.seed(self.seed) actual = np.random.power(a=.123456789, size=(3, 2)) desired = np.array([[0.02048932883240791, 0.01424192241128213], [0.38446073748535298, 0.39499689943484395], [0.00177699707563439, 0.13115505880863756]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_rayleigh(self): np.random.seed(self.seed) actual = np.random.rayleigh(scale=10, size=(3, 2)) desired = np.array([[13.8882496494248393, 13.383318339044731], [20.95413364294492098, 21.08285015800712614], [11.06066537006854311, 17.35468505778271009]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_standard_cauchy(self): np.random.seed(self.seed) actual = np.random.standard_cauchy(size=(3, 2)) desired = np.array([[0.77127660196445336, -6.55601161955910605], [0.93582023391158309, -2.07479293013759447], [-4.74601644297011926, 0.18338989290760804]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_standard_exponential(self): np.random.seed(self.seed) actual = np.random.standard_exponential(size=(3, 2)) desired = np.array([[0.96441739162374596, 0.89556604882105506], [2.1953785836319808, 2.22243285392490542], [0.6116915921431676, 1.50592546727413201]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_standard_gamma(self): np.random.seed(self.seed) actual = np.random.standard_gamma(shape=3, size=(3, 2)) desired = np.array([[5.50841531318455058, 6.62953470301903103], [5.93988484943779227, 2.31044849402133989], [7.54838614231317084, 8.012756093271868]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_standard_normal(self): np.random.seed(self.seed) actual = np.random.standard_normal(size=(3, 2)) desired = np.array([[1.34016345771863121, 1.73759122771936081], [1.498988344300628, -0.2286433324536169], [2.031033998682787, 2.17032494605655257]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_standard_t(self): np.random.seed(self.seed) actual = np.random.standard_t(df=10, size=(3, 2)) desired = np.array([[0.97140611862659965, -0.08830486548450577], [1.36311143689505321, -0.55317463909867071], [-0.18473749069684214, 0.61181537341755321]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_triangular(self): np.random.seed(self.seed) actual = np.random.triangular(left=5.12, mode=10.23, right=20.34, size=(3, 2)) desired = np.array([[12.68117178949215784, 12.4129206149193152], [16.20131377335158263, 16.25692138747600524], [11.20400690911820263, 14.4978144835829923]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_uniform(self): np.random.seed(self.seed) actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2)) desired = np.array([[6.99097932346268003, 6.73801597444323974], [9.50364421400426274, 9.53130618907631089], [5.48995325769805476, 8.47493103280052118]]) np.testing.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 = np.random.uniform np.testing.assert_raises(OverflowError, func, -np.inf, 0) np.testing.assert_raises(OverflowError, func, 0, np.inf) np.testing.assert_raises(OverflowError, func, fmin, fmax) # (fmax / 1e17) - fmin is within range, so this should not throw np.random.uniform(low=fmin, high=fmax / 1e17) def test_vonmises(self): np.random.seed(self.seed) actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2)) desired = np.array([[2.28567572673902042, 2.89163838442285037], [0.38198375564286025, 2.57638023113890746], [1.19153771588353052, 1.83509849681825354]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_vonmises_small(self): # check infinite loop, gh-4720 np.random.seed(self.seed) r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6) np.testing.assert_(np.isfinite(r).all()) def test_wald(self): np.random.seed(self.seed) actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2)) desired = np.array([[3.82935265715889983, 5.13125249184285526], [0.35045403618358717, 1.50832396872003538], [0.24124319895843183, 0.22031101461955038]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_weibull(self): np.random.seed(self.seed) actual = np.random.weibull(a=1.23, size=(3, 2)) desired = np.array([[0.97097342648766727, 0.91422896443565516], [1.89517770034962929, 1.91414357960479564], [0.67057783752390987, 1.39494046635066793]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_zipf(self): np.random.seed(self.seed) actual = np.random.zipf(a=1.23, size=(3, 2)) desired = np.array([[66, 29], [1, 1], [3, 13]]) np.testing.assert_array_equal(actual, desired) class TestThread(object): # make sure each state produces the same sequence even in threads def setUp(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=(np.random.RandomState(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(np.random.RandomState(s), o) # these platforms change x87 fpu precision mode in threads if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"): np.testing.assert_array_almost_equal(out1, out2) else: np.testing.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)) if __name__ == "__main__": run_module_suite()