diff options
Diffstat (limited to 'numpy/random/tests/test_random.py')
| -rw-r--r-- | numpy/random/tests/test_random.py | 344 |
1 files changed, 179 insertions, 165 deletions
diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py index b64c9d6cd..897a8e6f0 100644 --- a/numpy/random/tests/test_random.py +++ b/numpy/random/tests/test_random.py @@ -6,6 +6,7 @@ from numpy.testing import ( assert_warns) from numpy import random from numpy.compat import asbytes +import sys class TestSeed(TestCase): def test_scalar(self): @@ -60,7 +61,7 @@ class TestMultinomial(TestCase): 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) + assert_(-5 <= random.randint(-5, -1) < -1) x = random.randint(-5, -1, 5) assert_(np.all(-5 <= x)) assert_(np.all(x < -1)) @@ -68,15 +69,15 @@ class TestMultinomial(TestCase): 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, + 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, + assert_raises(TypeError, np.random.multinomial, 1, p, np.float(1)) @@ -93,17 +94,16 @@ class TestSetState(TestCase): assert_(np.all(old == new)) def test_gaussian_reset(self): - """ Make sure the cached every-other-Gaussian is reset. - """ + # 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. - """ + # 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) @@ -112,9 +112,8 @@ class TestSetState(TestCase): 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. - """ + # 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) @@ -125,56 +124,55 @@ class TestSetState(TestCase): assert_(np.all(x1 == x3)) def test_negative_binomial(self): - """ Ensure that the negative binomial results take floating point - arguments without truncation. - """ + # 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 - """ + # 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 ]]) + 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]]) + 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]]) + 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]]) + 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 ]]) + 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): @@ -304,9 +302,10 @@ class TestRandomDist(TestCase): 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]]) + 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): @@ -320,26 +319,26 @@ class TestRandomDist(TestCase): 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]]) + 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]) + 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]]]) + 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]) + 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)) @@ -352,49 +351,49 @@ class TestRandomDist(TestCase): 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]]) + 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]]) + 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]]) + 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]]) + 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]]) + 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]]) + [10, 10], + [9, 9]]) np.testing.assert_array_equal(actual, desired) # Test nbad = 0 @@ -418,49 +417,49 @@ class TestRandomDist(TestCase): 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]]) + 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]]) + 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]]) + 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]]) + 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]]]) + [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) + mean = (.123456789, 10) # Hmm... not even symmetric. cov = [[1, 0], [1, 0]] size = (3, 2) @@ -470,7 +469,7 @@ class TestRandomDist(TestCase): [[-2.29186329304599745, 10.], [-1.77505606019580053, 10.]], [[-0.54970369430044119, 10.], - [ 0.29768848031692957, 10.]]]) + [0.29768848031692957, 10.]]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) # Check for default size, was raising deprecation warning @@ -479,50 +478,50 @@ class TestRandomDist(TestCase): np.testing.assert_array_almost_equal(actual, desired, decimal=15) # Check that non positive-semidefinite covariance raises warning - mean= [0, 0] + mean = [0, 0] cov = [[1, 1 + 1e-10], [1 + 1e-10, 1]] - rng = np.random.multivariate_normal 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]]) + [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]]) + 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 ]]) + 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]]) + 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]]) + 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 @@ -533,7 +532,7 @@ class TestRandomDist(TestCase): def test_poisson(self): np.random.seed(self.seed) - actual = np.random.poisson(lam = .123456789, size=(3, 2)) + actual = np.random.poisson(lam=.123456789, size=(3, 2)) desired = np.array([[0, 0], [1, 0], [0, 0]]) @@ -549,84 +548,95 @@ class TestRandomDist(TestCase): 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]]) + 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]]) + 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]]) + 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]]) + 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 ]]) + 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]]) + 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]]) + 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 ]]) + 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]]) + 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]]) + 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): @@ -637,31 +647,31 @@ class TestRandomDist(TestCase): 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]]) + 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]]) + 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)) + actual = np.random.zipf(a=1.23, size=(3, 2)) desired = np.array([[66, 29], - [ 1, 1], - [ 3, 13]]) + [1, 1], + [3, 13]]) np.testing.assert_array_equal(actual, desired) -class TestThread: - """ make sure each state produces the same sequence even in threads """ +class TestThread(object): + # make sure each state produces the same sequence even in threads def setUp(self): self.seeds = range(4) @@ -681,7 +691,11 @@ class TestThread: for s, o in zip(self.seeds, out2): function(np.random.RandomState(s), o) - np.testing.assert_array_equal(out1, out2) + # 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): |