Chris@87: from __future__ import division, absolute_import, print_function Chris@87: Chris@87: import numpy as np Chris@87: from numpy.testing import ( Chris@87: TestCase, run_module_suite, assert_, assert_raises, assert_equal, Chris@87: assert_warns) Chris@87: from numpy import random Chris@87: from numpy.compat import asbytes Chris@87: import sys Chris@87: Chris@87: class TestSeed(TestCase): Chris@87: def test_scalar(self): Chris@87: s = np.random.RandomState(0) Chris@87: assert_equal(s.randint(1000), 684) Chris@87: s = np.random.RandomState(4294967295) Chris@87: assert_equal(s.randint(1000), 419) Chris@87: Chris@87: def test_array(self): Chris@87: s = np.random.RandomState(range(10)) Chris@87: assert_equal(s.randint(1000), 468) Chris@87: s = np.random.RandomState(np.arange(10)) Chris@87: assert_equal(s.randint(1000), 468) Chris@87: s = np.random.RandomState([0]) Chris@87: assert_equal(s.randint(1000), 973) Chris@87: s = np.random.RandomState([4294967295]) Chris@87: assert_equal(s.randint(1000), 265) Chris@87: Chris@87: def test_invalid_scalar(self): Chris@87: # seed must be a unsigned 32 bit integers Chris@87: assert_raises(TypeError, np.random.RandomState, -0.5) Chris@87: assert_raises(ValueError, np.random.RandomState, -1) Chris@87: Chris@87: def test_invalid_array(self): Chris@87: # seed must be a unsigned 32 bit integers Chris@87: assert_raises(TypeError, np.random.RandomState, [-0.5]) Chris@87: assert_raises(ValueError, np.random.RandomState, [-1]) Chris@87: assert_raises(ValueError, np.random.RandomState, [4294967296]) Chris@87: assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296]) Chris@87: assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296]) Chris@87: Chris@87: class TestBinomial(TestCase): Chris@87: def test_n_zero(self): Chris@87: # Tests the corner case of n == 0 for the binomial distribution. Chris@87: # binomial(0, p) should be zero for any p in [0, 1]. Chris@87: # This test addresses issue #3480. Chris@87: zeros = np.zeros(2, dtype='int') Chris@87: for p in [0, .5, 1]: Chris@87: assert_(random.binomial(0, p) == 0) Chris@87: np.testing.assert_array_equal(random.binomial(zeros, p), zeros) Chris@87: Chris@87: def test_p_is_nan(self): Chris@87: # Issue #4571. Chris@87: assert_raises(ValueError, random.binomial, 1, np.nan) Chris@87: Chris@87: Chris@87: class TestMultinomial(TestCase): Chris@87: def test_basic(self): Chris@87: random.multinomial(100, [0.2, 0.8]) Chris@87: Chris@87: def test_zero_probability(self): Chris@87: random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0]) Chris@87: Chris@87: def test_int_negative_interval(self): Chris@87: assert_(-5 <= random.randint(-5, -1) < -1) Chris@87: x = random.randint(-5, -1, 5) Chris@87: assert_(np.all(-5 <= x)) Chris@87: assert_(np.all(x < -1)) Chris@87: Chris@87: def test_size(self): Chris@87: # gh-3173 Chris@87: p = [0.5, 0.5] Chris@87: assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) Chris@87: assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) Chris@87: assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) Chris@87: assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2)) Chris@87: assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2)) Chris@87: assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape, Chris@87: (2, 2, 2)) Chris@87: Chris@87: assert_raises(TypeError, np.random.multinomial, 1, p, Chris@87: np.float(1)) Chris@87: Chris@87: Chris@87: class TestSetState(TestCase): Chris@87: def setUp(self): Chris@87: self.seed = 1234567890 Chris@87: self.prng = random.RandomState(self.seed) Chris@87: self.state = self.prng.get_state() Chris@87: Chris@87: def test_basic(self): Chris@87: old = self.prng.tomaxint(16) Chris@87: self.prng.set_state(self.state) Chris@87: new = self.prng.tomaxint(16) Chris@87: assert_(np.all(old == new)) Chris@87: Chris@87: def test_gaussian_reset(self): Chris@87: # Make sure the cached every-other-Gaussian is reset. Chris@87: old = self.prng.standard_normal(size=3) Chris@87: self.prng.set_state(self.state) Chris@87: new = self.prng.standard_normal(size=3) Chris@87: assert_(np.all(old == new)) Chris@87: Chris@87: def test_gaussian_reset_in_media_res(self): Chris@87: # When the state is saved with a cached Gaussian, make sure the Chris@87: # cached Gaussian is restored. Chris@87: Chris@87: self.prng.standard_normal() Chris@87: state = self.prng.get_state() Chris@87: old = self.prng.standard_normal(size=3) Chris@87: self.prng.set_state(state) Chris@87: new = self.prng.standard_normal(size=3) Chris@87: assert_(np.all(old == new)) Chris@87: Chris@87: def test_backwards_compatibility(self): Chris@87: # Make sure we can accept old state tuples that do not have the Chris@87: # cached Gaussian value. Chris@87: old_state = self.state[:-2] Chris@87: x1 = self.prng.standard_normal(size=16) Chris@87: self.prng.set_state(old_state) Chris@87: x2 = self.prng.standard_normal(size=16) Chris@87: self.prng.set_state(self.state) Chris@87: x3 = self.prng.standard_normal(size=16) Chris@87: assert_(np.all(x1 == x2)) Chris@87: assert_(np.all(x1 == x3)) Chris@87: Chris@87: def test_negative_binomial(self): Chris@87: # Ensure that the negative binomial results take floating point Chris@87: # arguments without truncation. Chris@87: self.prng.negative_binomial(0.5, 0.5) Chris@87: Chris@87: class TestRandomDist(TestCase): Chris@87: # Make sure the random distrobution return the correct value for a Chris@87: # given seed Chris@87: Chris@87: def setUp(self): Chris@87: self.seed = 1234567890 Chris@87: Chris@87: def test_rand(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.rand(3, 2) Chris@87: desired = np.array([[0.61879477158567997, 0.59162362775974664], Chris@87: [0.88868358904449662, 0.89165480011560816], Chris@87: [0.4575674820298663, 0.7781880808593471]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_randn(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.randn(3, 2) Chris@87: desired = np.array([[1.34016345771863121, 1.73759122771936081], Chris@87: [1.498988344300628, -0.2286433324536169], Chris@87: [2.031033998682787, 2.17032494605655257]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_randint(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.randint(-99, 99, size=(3, 2)) Chris@87: desired = np.array([[31, 3], Chris@87: [-52, 41], Chris@87: [-48, -66]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_random_integers(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.random_integers(-99, 99, size=(3, 2)) Chris@87: desired = np.array([[31, 3], Chris@87: [-52, 41], Chris@87: [-48, -66]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_random_sample(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.random_sample((3, 2)) Chris@87: desired = np.array([[0.61879477158567997, 0.59162362775974664], Chris@87: [0.88868358904449662, 0.89165480011560816], Chris@87: [0.4575674820298663, 0.7781880808593471]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_choice_uniform_replace(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.choice(4, 4) Chris@87: desired = np.array([2, 3, 2, 3]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_choice_nonuniform_replace(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1]) Chris@87: desired = np.array([1, 1, 2, 2]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_choice_uniform_noreplace(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.choice(4, 3, replace=False) Chris@87: desired = np.array([0, 1, 3]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_choice_nonuniform_noreplace(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.choice(4, 3, replace=False, Chris@87: p=[0.1, 0.3, 0.5, 0.1]) Chris@87: desired = np.array([2, 3, 1]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_choice_noninteger(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.choice(['a', 'b', 'c', 'd'], 4) Chris@87: desired = np.array(['c', 'd', 'c', 'd']) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_choice_exceptions(self): Chris@87: sample = np.random.choice Chris@87: assert_raises(ValueError, sample, -1, 3) Chris@87: assert_raises(ValueError, sample, 3., 3) Chris@87: assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3) Chris@87: assert_raises(ValueError, sample, [], 3) Chris@87: assert_raises(ValueError, sample, [1, 2, 3, 4], 3, Chris@87: p=[[0.25, 0.25], [0.25, 0.25]]) Chris@87: assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2]) Chris@87: assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1]) Chris@87: assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4]) Chris@87: assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False) Chris@87: assert_raises(ValueError, sample, [1, 2, 3], 2, replace=False, Chris@87: p=[1, 0, 0]) Chris@87: Chris@87: def test_choice_return_shape(self): Chris@87: p = [0.1, 0.9] Chris@87: # Check scalar Chris@87: assert_(np.isscalar(np.random.choice(2, replace=True))) Chris@87: assert_(np.isscalar(np.random.choice(2, replace=False))) Chris@87: assert_(np.isscalar(np.random.choice(2, replace=True, p=p))) Chris@87: assert_(np.isscalar(np.random.choice(2, replace=False, p=p))) Chris@87: assert_(np.isscalar(np.random.choice([1, 2], replace=True))) Chris@87: assert_(np.random.choice([None], replace=True) is None) Chris@87: a = np.array([1, 2]) Chris@87: arr = np.empty(1, dtype=object) Chris@87: arr[0] = a Chris@87: assert_(np.random.choice(arr, replace=True) is a) Chris@87: Chris@87: # Check 0-d array Chris@87: s = tuple() Chris@87: assert_(not np.isscalar(np.random.choice(2, s, replace=True))) Chris@87: assert_(not np.isscalar(np.random.choice(2, s, replace=False))) Chris@87: assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p))) Chris@87: assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p))) Chris@87: assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True))) Chris@87: assert_(np.random.choice([None], s, replace=True).ndim == 0) Chris@87: a = np.array([1, 2]) Chris@87: arr = np.empty(1, dtype=object) Chris@87: arr[0] = a Chris@87: assert_(np.random.choice(arr, s, replace=True).item() is a) Chris@87: Chris@87: # Check multi dimensional array Chris@87: s = (2, 3) Chris@87: p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2] Chris@87: assert_(np.random.choice(6, s, replace=True).shape, s) Chris@87: assert_(np.random.choice(6, s, replace=False).shape, s) Chris@87: assert_(np.random.choice(6, s, replace=True, p=p).shape, s) Chris@87: assert_(np.random.choice(6, s, replace=False, p=p).shape, s) Chris@87: assert_(np.random.choice(np.arange(6), s, replace=True).shape, s) Chris@87: Chris@87: def test_bytes(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.bytes(10) Chris@87: desired = asbytes('\x82Ui\x9e\xff\x97+Wf\xa5') Chris@87: np.testing.assert_equal(actual, desired) Chris@87: Chris@87: def test_shuffle(self): Chris@87: # Test lists, arrays, and multidimensional versions of both: Chris@87: for conv in [lambda x: x, Chris@87: np.asarray, Chris@87: lambda x: [(i, i) for i in x], Chris@87: lambda x: np.asarray([(i, i) for i in x])]: Chris@87: np.random.seed(self.seed) Chris@87: alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]) Chris@87: np.random.shuffle(alist) Chris@87: actual = alist Chris@87: desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_shuffle_flexible(self): Chris@87: # gh-4270 Chris@87: arr = [(0, 1), (2, 3)] Chris@87: dt = np.dtype([('a', np.int32, 1), ('b', np.int32, 1)]) Chris@87: nparr = np.array(arr, dtype=dt) Chris@87: a, b = nparr[0].copy(), nparr[1].copy() Chris@87: for i in range(50): Chris@87: np.random.shuffle(nparr) Chris@87: assert_(a in nparr) Chris@87: assert_(b in nparr) Chris@87: Chris@87: def test_shuffle_masked(self): Chris@87: # gh-3263 Chris@87: a = np.ma.masked_values(np.reshape(range(20), (5,4)) % 3 - 1, -1) Chris@87: b = np.ma.masked_values(np.arange(20) % 3 - 1, -1) Chris@87: ma = np.ma.count_masked(a) Chris@87: mb = np.ma.count_masked(b) Chris@87: for i in range(50): Chris@87: np.random.shuffle(a) Chris@87: self.assertEqual(ma, np.ma.count_masked(a)) Chris@87: np.random.shuffle(b) Chris@87: self.assertEqual(mb, np.ma.count_masked(b)) Chris@87: Chris@87: def test_beta(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.beta(.1, .9, size=(3, 2)) Chris@87: desired = np.array( Chris@87: [[1.45341850513746058e-02, 5.31297615662868145e-04], Chris@87: [1.85366619058432324e-06, 4.19214516800110563e-03], Chris@87: [1.58405155108498093e-04, 1.26252891949397652e-04]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_binomial(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.binomial(100.123, .456, size=(3, 2)) Chris@87: desired = np.array([[37, 43], Chris@87: [42, 48], Chris@87: [46, 45]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_chisquare(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.chisquare(50, size=(3, 2)) Chris@87: desired = np.array([[63.87858175501090585, 68.68407748911370447], Chris@87: [65.77116116901505904, 47.09686762438974483], Chris@87: [72.3828403199695174, 74.18408615260374006]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=13) Chris@87: Chris@87: def test_dirichlet(self): Chris@87: np.random.seed(self.seed) Chris@87: alpha = np.array([51.72840233779265162, 39.74494232180943953]) Chris@87: actual = np.random.mtrand.dirichlet(alpha, size=(3, 2)) Chris@87: desired = np.array([[[0.54539444573611562, 0.45460555426388438], Chris@87: [0.62345816822039413, 0.37654183177960598]], Chris@87: [[0.55206000085785778, 0.44793999914214233], Chris@87: [0.58964023305154301, 0.41035976694845688]], Chris@87: [[0.59266909280647828, 0.40733090719352177], Chris@87: [0.56974431743975207, 0.43025568256024799]]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_dirichlet_size(self): Chris@87: # gh-3173 Chris@87: p = np.array([51.72840233779265162, 39.74494232180943953]) Chris@87: assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) Chris@87: assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) Chris@87: assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) Chris@87: assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2)) Chris@87: assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2)) Chris@87: assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2)) Chris@87: Chris@87: assert_raises(TypeError, np.random.dirichlet, p, np.float(1)) Chris@87: Chris@87: def test_exponential(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.exponential(1.1234, size=(3, 2)) Chris@87: desired = np.array([[1.08342649775011624, 1.00607889924557314], Chris@87: [2.46628830085216721, 2.49668106809923884], Chris@87: [0.68717433461363442, 1.69175666993575979]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_f(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.f(12, 77, size=(3, 2)) Chris@87: desired = np.array([[1.21975394418575878, 1.75135759791559775], Chris@87: [1.44803115017146489, 1.22108959480396262], Chris@87: [1.02176975757740629, 1.34431827623300415]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_gamma(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.gamma(5, 3, size=(3, 2)) Chris@87: desired = np.array([[24.60509188649287182, 28.54993563207210627], Chris@87: [26.13476110204064184, 12.56988482927716078], Chris@87: [31.71863275789960568, 33.30143302795922011]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=14) Chris@87: Chris@87: def test_geometric(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.geometric(.123456789, size=(3, 2)) Chris@87: desired = np.array([[8, 7], Chris@87: [17, 17], Chris@87: [5, 12]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_gumbel(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2)) Chris@87: desired = np.array([[0.19591898743416816, 0.34405539668096674], Chris@87: [-1.4492522252274278, -1.47374816298446865], Chris@87: [1.10651090478803416, -0.69535848626236174]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_hypergeometric(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.hypergeometric(10.1, 5.5, 14, size=(3, 2)) Chris@87: desired = np.array([[10, 10], Chris@87: [10, 10], Chris@87: [9, 9]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: # Test nbad = 0 Chris@87: actual = np.random.hypergeometric(5, 0, 3, size=4) Chris@87: desired = np.array([3, 3, 3, 3]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: actual = np.random.hypergeometric(15, 0, 12, size=4) Chris@87: desired = np.array([12, 12, 12, 12]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: # Test ngood = 0 Chris@87: actual = np.random.hypergeometric(0, 5, 3, size=4) Chris@87: desired = np.array([0, 0, 0, 0]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: actual = np.random.hypergeometric(0, 15, 12, size=4) Chris@87: desired = np.array([0, 0, 0, 0]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_laplace(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2)) Chris@87: desired = np.array([[0.66599721112760157, 0.52829452552221945], Chris@87: [3.12791959514407125, 3.18202813572992005], Chris@87: [-0.05391065675859356, 1.74901336242837324]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_logistic(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2)) Chris@87: desired = np.array([[1.09232835305011444, 0.8648196662399954], Chris@87: [4.27818590694950185, 4.33897006346929714], Chris@87: [-0.21682183359214885, 2.63373365386060332]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_lognormal(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2)) Chris@87: desired = np.array([[16.50698631688883822, 36.54846706092654784], Chris@87: [22.67886599981281748, 0.71617561058995771], Chris@87: [65.72798501792723869, 86.84341601437161273]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=13) Chris@87: Chris@87: def test_logseries(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.logseries(p=.923456789, size=(3, 2)) Chris@87: desired = np.array([[2, 2], Chris@87: [6, 17], Chris@87: [3, 6]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_multinomial(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2)) Chris@87: desired = np.array([[[4, 3, 5, 4, 2, 2], Chris@87: [5, 2, 8, 2, 2, 1]], Chris@87: [[3, 4, 3, 6, 0, 4], Chris@87: [2, 1, 4, 3, 6, 4]], Chris@87: [[4, 4, 2, 5, 2, 3], Chris@87: [4, 3, 4, 2, 3, 4]]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_multivariate_normal(self): Chris@87: np.random.seed(self.seed) Chris@87: mean = (.123456789, 10) Chris@87: # Hmm... not even symmetric. Chris@87: cov = [[1, 0], [1, 0]] Chris@87: size = (3, 2) Chris@87: actual = np.random.multivariate_normal(mean, cov, size) Chris@87: desired = np.array([[[-1.47027513018564449, 10.], Chris@87: [-1.65915081534845532, 10.]], Chris@87: [[-2.29186329304599745, 10.], Chris@87: [-1.77505606019580053, 10.]], Chris@87: [[-0.54970369430044119, 10.], Chris@87: [0.29768848031692957, 10.]]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: # Check for default size, was raising deprecation warning Chris@87: actual = np.random.multivariate_normal(mean, cov) Chris@87: desired = np.array([-0.79441224511977482, 10.]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: # Check that non positive-semidefinite covariance raises warning Chris@87: mean = [0, 0] Chris@87: cov = [[1, 1 + 1e-10], [1 + 1e-10, 1]] Chris@87: assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov) Chris@87: Chris@87: def test_negative_binomial(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2)) Chris@87: desired = np.array([[848, 841], Chris@87: [892, 611], Chris@87: [779, 647]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_noncentral_chisquare(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2)) Chris@87: desired = np.array([[23.91905354498517511, 13.35324692733826346], Chris@87: [31.22452661329736401, 16.60047399466177254], Chris@87: [5.03461598262724586, 17.94973089023519464]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=14) Chris@87: Chris@87: def test_noncentral_f(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1, Chris@87: size=(3, 2)) Chris@87: desired = np.array([[1.40598099674926669, 0.34207973179285761], Chris@87: [3.57715069265772545, 7.92632662577829805], Chris@87: [0.43741599463544162, 1.1774208752428319]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=14) Chris@87: Chris@87: def test_normal(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2)) Chris@87: desired = np.array([[2.80378370443726244, 3.59863924443872163], Chris@87: [3.121433477601256, -0.33382987590723379], Chris@87: [4.18552478636557357, 4.46410668111310471]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_pareto(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.pareto(a=.123456789, size=(3, 2)) Chris@87: desired = np.array( Chris@87: [[2.46852460439034849e+03, 1.41286880810518346e+03], Chris@87: [5.28287797029485181e+07, 6.57720981047328785e+07], Chris@87: [1.40840323350391515e+02, 1.98390255135251704e+05]]) Chris@87: # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this Chris@87: # matrix differs by 24 nulps. Discussion: Chris@87: # http://mail.scipy.org/pipermail/numpy-discussion/2012-September/063801.html Chris@87: # Consensus is that this is probably some gcc quirk that affects Chris@87: # rounding but not in any important way, so we just use a looser Chris@87: # tolerance on this test: Chris@87: np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30) Chris@87: Chris@87: def test_poisson(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.poisson(lam=.123456789, size=(3, 2)) Chris@87: desired = np.array([[0, 0], Chris@87: [1, 0], Chris@87: [0, 0]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: def test_poisson_exceptions(self): Chris@87: lambig = np.iinfo('l').max Chris@87: lamneg = -1 Chris@87: assert_raises(ValueError, np.random.poisson, lamneg) Chris@87: assert_raises(ValueError, np.random.poisson, [lamneg]*10) Chris@87: assert_raises(ValueError, np.random.poisson, lambig) Chris@87: assert_raises(ValueError, np.random.poisson, [lambig]*10) Chris@87: Chris@87: def test_power(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.power(a=.123456789, size=(3, 2)) Chris@87: desired = np.array([[0.02048932883240791, 0.01424192241128213], Chris@87: [0.38446073748535298, 0.39499689943484395], Chris@87: [0.00177699707563439, 0.13115505880863756]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_rayleigh(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.rayleigh(scale=10, size=(3, 2)) Chris@87: desired = np.array([[13.8882496494248393, 13.383318339044731], Chris@87: [20.95413364294492098, 21.08285015800712614], Chris@87: [11.06066537006854311, 17.35468505778271009]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=14) Chris@87: Chris@87: def test_standard_cauchy(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.standard_cauchy(size=(3, 2)) Chris@87: desired = np.array([[0.77127660196445336, -6.55601161955910605], Chris@87: [0.93582023391158309, -2.07479293013759447], Chris@87: [-4.74601644297011926, 0.18338989290760804]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_standard_exponential(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.standard_exponential(size=(3, 2)) Chris@87: desired = np.array([[0.96441739162374596, 0.89556604882105506], Chris@87: [2.1953785836319808, 2.22243285392490542], Chris@87: [0.6116915921431676, 1.50592546727413201]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_standard_gamma(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.standard_gamma(shape=3, size=(3, 2)) Chris@87: desired = np.array([[5.50841531318455058, 6.62953470301903103], Chris@87: [5.93988484943779227, 2.31044849402133989], Chris@87: [7.54838614231317084, 8.012756093271868]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=14) Chris@87: Chris@87: def test_standard_normal(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.standard_normal(size=(3, 2)) Chris@87: desired = np.array([[1.34016345771863121, 1.73759122771936081], Chris@87: [1.498988344300628, -0.2286433324536169], Chris@87: [2.031033998682787, 2.17032494605655257]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_standard_t(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.standard_t(df=10, size=(3, 2)) Chris@87: desired = np.array([[0.97140611862659965, -0.08830486548450577], Chris@87: [1.36311143689505321, -0.55317463909867071], Chris@87: [-0.18473749069684214, 0.61181537341755321]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_triangular(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.triangular(left=5.12, mode=10.23, right=20.34, Chris@87: size=(3, 2)) Chris@87: desired = np.array([[12.68117178949215784, 12.4129206149193152], Chris@87: [16.20131377335158263, 16.25692138747600524], Chris@87: [11.20400690911820263, 14.4978144835829923]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=14) Chris@87: Chris@87: def test_uniform(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2)) Chris@87: desired = np.array([[6.99097932346268003, 6.73801597444323974], Chris@87: [9.50364421400426274, 9.53130618907631089], Chris@87: [5.48995325769805476, 8.47493103280052118]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_vonmises(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2)) Chris@87: desired = np.array([[2.28567572673902042, 2.89163838442285037], Chris@87: [0.38198375564286025, 2.57638023113890746], Chris@87: [1.19153771588353052, 1.83509849681825354]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_vonmises_small(self): Chris@87: # check infinite loop, gh-4720 Chris@87: np.random.seed(self.seed) Chris@87: r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6) Chris@87: np.testing.assert_(np.isfinite(r).all()) Chris@87: Chris@87: def test_wald(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2)) Chris@87: desired = np.array([[3.82935265715889983, 5.13125249184285526], Chris@87: [0.35045403618358717, 1.50832396872003538], Chris@87: [0.24124319895843183, 0.22031101461955038]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=14) Chris@87: Chris@87: def test_weibull(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.weibull(a=1.23, size=(3, 2)) Chris@87: desired = np.array([[0.97097342648766727, 0.91422896443565516], Chris@87: [1.89517770034962929, 1.91414357960479564], Chris@87: [0.67057783752390987, 1.39494046635066793]]) Chris@87: np.testing.assert_array_almost_equal(actual, desired, decimal=15) Chris@87: Chris@87: def test_zipf(self): Chris@87: np.random.seed(self.seed) Chris@87: actual = np.random.zipf(a=1.23, size=(3, 2)) Chris@87: desired = np.array([[66, 29], Chris@87: [1, 1], Chris@87: [3, 13]]) Chris@87: np.testing.assert_array_equal(actual, desired) Chris@87: Chris@87: Chris@87: class TestThread(object): Chris@87: # make sure each state produces the same sequence even in threads Chris@87: def setUp(self): Chris@87: self.seeds = range(4) Chris@87: Chris@87: def check_function(self, function, sz): Chris@87: from threading import Thread Chris@87: Chris@87: out1 = np.empty((len(self.seeds),) + sz) Chris@87: out2 = np.empty((len(self.seeds),) + sz) Chris@87: Chris@87: # threaded generation Chris@87: t = [Thread(target=function, args=(np.random.RandomState(s), o)) Chris@87: for s, o in zip(self.seeds, out1)] Chris@87: [x.start() for x in t] Chris@87: [x.join() for x in t] Chris@87: Chris@87: # the same serial Chris@87: for s, o in zip(self.seeds, out2): Chris@87: function(np.random.RandomState(s), o) Chris@87: Chris@87: # these platforms change x87 fpu precision mode in threads Chris@87: if (np.intp().dtype.itemsize == 4 and Chris@87: (sys.platform == "win32" or Chris@87: sys.platform.startswith("gnukfreebsd"))): Chris@87: np.testing.assert_array_almost_equal(out1, out2) Chris@87: else: Chris@87: np.testing.assert_array_equal(out1, out2) Chris@87: Chris@87: def test_normal(self): Chris@87: def gen_random(state, out): Chris@87: out[...] = state.normal(size=10000) Chris@87: self.check_function(gen_random, sz=(10000,)) Chris@87: Chris@87: def test_exp(self): Chris@87: def gen_random(state, out): Chris@87: out[...] = state.exponential(scale=np.ones((100, 1000))) Chris@87: self.check_function(gen_random, sz=(100, 1000)) Chris@87: Chris@87: def test_multinomial(self): Chris@87: def gen_random(state, out): Chris@87: out[...] = state.multinomial(10, [1/6.]*6, size=10000) Chris@87: self.check_function(gen_random, sz=(10000,6)) Chris@87: Chris@87: Chris@87: if __name__ == "__main__": Chris@87: run_module_suite()