from numpy.testing import TestCase, run_module_suite, assert_,\ assert_raises from numpy import random from numpy.compat import asbytes import numpy as np 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)) 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 TestRandomDist(TestCase): """ Make sure the random distrobution return 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_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, [[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))) # 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))) # 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, and multidimensional versions of both: for conv in [lambda x: x, np.asarray, lambda x: [(i, i) for i in x], lambda x: np.asarray([(i, i) for i in x])]: 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_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_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) 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) 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) 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) 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_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_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) if __name__ == "__main__": run_module_suite()