text: 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
  
  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 a unsigned 32 bit integers
          assert_raises(TypeError, np.random.RandomState, -0.5)
          assert_raises(ValueError, np.random.RandomState, -1)
  
      def test_invalid_array(self):
          # seed must be a unsigned 32 bit integers
          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 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, 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, 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_shuffle_flexible(self):
          # gh-4270
          arr = [(0, 1), (2, 3)]
          dt = np.dtype([('a', np.int32, 1), ('b', np.int32, 1)])
          nparr = np.array(arr, dtype=dt)
          a, b = nparr[0].copy(), nparr[1].copy()
          for i in range(50):
              np.random.shuffle(nparr)
              assert_(a in nparr)
              assert_(b in nparr)
  
      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)
          ma = np.ma.count_masked(a)
          mb = np.ma.count_masked(b)
          for i in range(50):
              np.random.shuffle(a)
              self.assertEqual(ma, np.ma.count_masked(a))
              np.random.shuffle(b)
              self.assertEqual(mb, np.ma.count_masked(b))
  
      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)
  
      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_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" or
                   sys.platform.startswith("gnukfreebsd"))):
              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()
  