Sam Chaudry

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	import hashlib
	import pickle
	import sys
	import warnings

	import numpy as np
	import pytest
	from numpy.testing import (
	assert_, assert_raises, assert_equal, assert_warns,
	assert_no_warnings, assert_array_equal, assert_array_almost_equal,
	suppress_warnings, IS_WASM
	)

	from numpy.random import MT19937, PCG64
	from numpy import random

	INT_FUNCS = {'binomial': (100.0, 0.6),
	'geometric': (.5,),
	'hypergeometric': (20, 20, 10),
	'logseries': (.5,),
	'multinomial': (20, np.ones(6) / 6.0),
	'negative_binomial': (100, .5),
	'poisson': (10.0,),
	'zipf': (2,),
	}

	if np.iinfo(np.long).max < 2**32:
	# Windows and some 32-bit platforms, e.g., ARM
	INT_FUNC_HASHES = {'binomial': '2fbead005fc63942decb5326d36a1f32fe2c9d32c904ee61e46866b88447c263',
	'logseries': '23ead5dcde35d4cfd4ef2c105e4c3d43304b45dc1b1444b7823b9ee4fa144ebb',
	'geometric': '0d764db64f5c3bad48c8c33551c13b4d07a1e7b470f77629bef6c985cac76fcf',
	'hypergeometric': '7b59bf2f1691626c5815cdcd9a49e1dd68697251d4521575219e4d2a1b8b2c67',
	'multinomial': 'd754fa5b92943a38ec07630de92362dd2e02c43577fc147417dc5b9db94ccdd3',
	'negative_binomial': '8eb216f7cb2a63cf55605422845caaff002fddc64a7dc8b2d45acd477a49e824',
	'poisson': '70c891d76104013ebd6f6bcf30d403a9074b886ff62e4e6b8eb605bf1a4673b7',
	'zipf': '01f074f97517cd5d21747148ac6ca4074dde7fcb7acbaec0a936606fecacd93f',
	}
	else:
	INT_FUNC_HASHES = {'binomial': '8626dd9d052cb608e93d8868de0a7b347258b199493871a1dc56e2a26cacb112',
	'geometric': '8edd53d272e49c4fc8fbbe6c7d08d563d62e482921f3131d0a0e068af30f0db9',
	'hypergeometric': '83496cc4281c77b786c9b7ad88b74d42e01603a55c60577ebab81c3ba8d45657',
	'logseries': '65878a38747c176bc00e930ebafebb69d4e1e16cd3a704e264ea8f5e24f548db',
	'multinomial': '7a984ae6dca26fd25374479e118b22f55db0aedccd5a0f2584ceada33db98605',
	'negative_binomial': 'd636d968e6a24ae92ab52fe11c46ac45b0897e98714426764e820a7d77602a61',
	'poisson': '956552176f77e7c9cb20d0118fc9cf690be488d790ed4b4c4747b965e61b0bb4',
	'zipf': 'f84ba7feffda41e606e20b28dfc0f1ea9964a74574513d4a4cbc98433a8bfa45',
	}


	@pytest.fixture(scope='module', params=INT_FUNCS)
	def int_func(request):
	return (request.param, INT_FUNCS[request.param],
	INT_FUNC_HASHES[request.param])


	@pytest.fixture
	def restore_singleton_bitgen():
	"""Ensures that the singleton bitgen is restored after a test"""
	orig_bitgen = np.random.get_bit_generator()
	yield
	np.random.set_bit_generator(orig_bitgen)


	def assert_mt19937_state_equal(a, b):
	assert_equal(a['bit_generator'], b['bit_generator'])
	assert_array_equal(a['state']['key'], b['state']['key'])
	assert_array_equal(a['state']['pos'], b['state']['pos'])
	assert_equal(a['has_gauss'], b['has_gauss'])
	assert_equal(a['gauss'], b['gauss'])


	class TestSeed:
	def test_scalar(self):
	s = random.RandomState(0)
	assert_equal(s.randint(1000), 684)
	s = random.RandomState(4294967295)
	assert_equal(s.randint(1000), 419)

	def test_array(self):
	s = random.RandomState(range(10))
	assert_equal(s.randint(1000), 468)
	s = random.RandomState(np.arange(10))
	assert_equal(s.randint(1000), 468)
	s = random.RandomState([0])
	assert_equal(s.randint(1000), 973)
	s = random.RandomState([4294967295])
	assert_equal(s.randint(1000), 265)

	def test_invalid_scalar(self):
	# seed must be an unsigned 32 bit integer
	assert_raises(TypeError, random.RandomState, -0.5)
	assert_raises(ValueError, random.RandomState, -1)

	def test_invalid_array(self):
	# seed must be an unsigned 32 bit integer
	assert_raises(TypeError, random.RandomState, [-0.5])
	assert_raises(ValueError, random.RandomState, [-1])
	assert_raises(ValueError, random.RandomState, [4294967296])
	assert_raises(ValueError, random.RandomState, [1, 2, 4294967296])
	assert_raises(ValueError, random.RandomState, [1, -2, 4294967296])

	def test_invalid_array_shape(self):
	# gh-9832
	assert_raises(ValueError, random.RandomState, np.array([],
	dtype=np.int64))
	assert_raises(ValueError, random.RandomState, [[1, 2, 3]])
	assert_raises(ValueError, random.RandomState, [[1, 2, 3],
	[4, 5, 6]])

	def test_cannot_seed(self):
	rs = random.RandomState(PCG64(0))
	with assert_raises(TypeError):
	rs.seed(1234)

	def test_invalid_initialization(self):
	assert_raises(ValueError, random.RandomState, MT19937)


	class TestBinomial:
	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)
	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:
	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(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
	assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
	assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
	assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
	assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
	assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,
	(2, 2, 2))

	assert_raises(TypeError, random.multinomial, 1, p,
	float(1))

	def test_invalid_prob(self):
	assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])
	assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])

	def test_invalid_n(self):
	assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])

	def test_p_non_contiguous(self):
	p = np.arange(15.)
	p /= np.sum(p[1::3])
	pvals = p[1::3]
	random.seed(1432985819)
	non_contig = random.multinomial(100, pvals=pvals)
	random.seed(1432985819)
	contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
	assert_array_equal(non_contig, contig)

	def test_multinomial_pvals_float32(self):
	x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,
	1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)
	pvals = x / x.sum()
	match = r"[\w\s]*pvals array is cast to 64-bit floating"
	with pytest.raises(ValueError, match=match):
	random.multinomial(1, pvals)

	def test_multinomial_n_float(self):
	# Non-index integer types should gracefully truncate floats
	random.multinomial(100.5, [0.2, 0.8])

	class TestSetState:
	def setup_method(self):
	self.seed = 1234567890
	self.random_state = random.RandomState(self.seed)
	self.state = self.random_state.get_state()

	def test_basic(self):
	old = self.random_state.tomaxint(16)
	self.random_state.set_state(self.state)
	new = self.random_state.tomaxint(16)
	assert_(np.all(old == new))

	def test_gaussian_reset(self):
	# Make sure the cached every-other-Gaussian is reset.
	old = self.random_state.standard_normal(size=3)
	self.random_state.set_state(self.state)
	new = self.random_state.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.random_state.standard_normal()
	state = self.random_state.get_state()
	old = self.random_state.standard_normal(size=3)
	self.random_state.set_state(state)
	new = self.random_state.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.random_state.standard_normal(size=16)
	self.random_state.set_state(old_state)
	x2 = self.random_state.standard_normal(size=16)
	self.random_state.set_state(self.state)
	x3 = self.random_state.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.random_state.negative_binomial(0.5, 0.5)

	def test_get_state_warning(self):
	rs = random.RandomState(PCG64())
	with suppress_warnings() as sup:
	w = sup.record(RuntimeWarning)
	state = rs.get_state()
	assert_(len(w) == 1)
	assert isinstance(state, dict)
	assert state['bit_generator'] == 'PCG64'

	def test_invalid_legacy_state_setting(self):
	state = self.random_state.get_state()
	new_state = ('Unknown', ) + state[1:]
	assert_raises(ValueError, self.random_state.set_state, new_state)
	assert_raises(TypeError, self.random_state.set_state,
	np.array(new_state, dtype=object))
	state = self.random_state.get_state(legacy=False)
	del state['bit_generator']
	assert_raises(ValueError, self.random_state.set_state, state)

	def test_pickle(self):
	self.random_state.seed(0)
	self.random_state.random_sample(100)
	self.random_state.standard_normal()
	pickled = self.random_state.get_state(legacy=False)
	assert_equal(pickled['has_gauss'], 1)
	rs_unpick = pickle.loads(pickle.dumps(self.random_state))
	unpickled = rs_unpick.get_state(legacy=False)
	assert_mt19937_state_equal(pickled, unpickled)

	def test_state_setting(self):
	attr_state = self.random_state.__getstate__()
	self.random_state.standard_normal()
	self.random_state.__setstate__(attr_state)
	state = self.random_state.get_state(legacy=False)
	assert_mt19937_state_equal(attr_state, state)

	def test_repr(self):
	assert repr(self.random_state).startswith('RandomState(MT19937)')


	class TestRandint:

	rfunc = random.randint

	# valid integer/boolean types
	itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
	np.int32, np.uint32, np.int64, np.uint64]

	def test_unsupported_type(self):
	assert_raises(TypeError, self.rfunc, 1, dtype=float)

	def test_bounds_checking(self):
	for dt in self.itype:
	lbnd = 0 if dt is np.bool else np.iinfo(dt).min
	ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
	assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
	assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
	assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
	assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)

	def test_rng_zero_and_extremes(self):
	for dt in self.itype:
	lbnd = 0 if dt is np.bool else np.iinfo(dt).min
	ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1

	tgt = ubnd - 1
	assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)

	tgt = lbnd
	assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)

	tgt = (lbnd + ubnd)//2
	assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)

	def test_full_range(self):
	# Test for ticket #1690

	for dt in self.itype:
	lbnd = 0 if dt is np.bool else np.iinfo(dt).min
	ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1

	try:
	self.rfunc(lbnd, ubnd, dtype=dt)
	except Exception as e:
	raise AssertionError("No error should have been raised, "
	"but one was with the following "
	"message:\n\n%s" % str(e))

	def test_in_bounds_fuzz(self):
	# Don't use fixed seed
	random.seed()

	for dt in self.itype[1:]:
	for ubnd in [4, 8, 16]:
	vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
	assert_(vals.max() < ubnd)
	assert_(vals.min() >= 2)

	vals = self.rfunc(0, 2, size=2**16, dtype=np.bool)

	assert_(vals.max() < 2)
	assert_(vals.min() >= 0)

	def test_repeatability(self):
	# We use a sha256 hash of generated sequences of 1000 samples
	# in the range [0, 6) for all but bool, where the range
	# is [0, 2). Hashes are for little endian numbers.
	tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
	'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
	'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
	'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
	'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
	'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
	'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
	'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
	'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}

	for dt in self.itype[1:]:
	random.seed(1234)

	# view as little endian for hash
	if sys.byteorder == 'little':
	val = self.rfunc(0, 6, size=1000, dtype=dt)
	else:
	val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()

	res = hashlib.sha256(val.view(np.int8)).hexdigest()
	assert_(tgt[np.dtype(dt).name] == res)

	# bools do not depend on endianness
	random.seed(1234)
	val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
	res = hashlib.sha256(val).hexdigest()
	assert_(tgt[np.dtype(bool).name] == res)

	@pytest.mark.skipif(np.iinfo('l').max < 2**32,
	reason='Cannot test with 32-bit C long')
	def test_repeatability_32bit_boundary_broadcasting(self):
	desired = np.array([[[3992670689, 2438360420, 2557845020],
	[4107320065, 4142558326, 3216529513],
	[1605979228, 2807061240, 665605495]],
	[[3211410639, 4128781000, 457175120],
	[1712592594, 1282922662, 3081439808],
	[3997822960, 2008322436, 1563495165]],
	[[1398375547, 4269260146, 115316740],
	[3414372578, 3437564012, 2112038651],
	[3572980305, 2260248732, 3908238631]],
	[[2561372503, 223155946, 3127879445],
	[ 441282060, 3514786552, 2148440361],
	[1629275283, 3479737011, 3003195987]],
	[[ 412181688, 940383289, 3047321305],
	[2978368172, 764731833, 2282559898],
	[ 105711276, 720447391, 3596512484]]])
	for size in [None, (5, 3, 3)]:
	random.seed(12345)
	x = self.rfunc([[-1], [0], [1]], [232 - 1, 232, 2**32 + 1],
	size=size)
	assert_array_equal(x, desired if size is not None else desired[0])

	def test_int64_uint64_corner_case(self):
	# When stored in Numpy arrays, `lbnd` is casted
	# as np.int64, and `ubnd` is casted as np.uint64.
	# Checking whether `lbnd` >= `ubnd` used to be
	# done solely via direct comparison, which is incorrect
	# because when Numpy tries to compare both numbers,
	# it casts both to np.float64 because there is
	# no integer superset of np.int64 and np.uint64. However,
	# `ubnd` is too large to be represented in np.float64,
	# causing it be round down to np.iinfo(np.int64).max,
	# leading to a ValueError because `lbnd` now equals
	# the new `ubnd`.

	dt = np.int64
	tgt = np.iinfo(np.int64).max
	lbnd = np.int64(np.iinfo(np.int64).max)
	ubnd = np.uint64(np.iinfo(np.int64).max + 1)

	# None of these function calls should
	# generate a ValueError now.
	actual = random.randint(lbnd, ubnd, dtype=dt)
	assert_equal(actual, tgt)

	def test_respect_dtype_singleton(self):
	# See gh-7203
	for dt in self.itype:
	lbnd = 0 if dt is np.bool else np.iinfo(dt).min
	ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1

	sample = self.rfunc(lbnd, ubnd, dtype=dt)
	assert_equal(sample.dtype, np.dtype(dt))

	for dt in (bool, int):
	# The legacy random generation forces the use of "long" on this
	# branch even when the input is `int` and the default dtype
	# for int changed (dtype=int is also the functions default)
	op_dtype = "long" if dt is int else "bool"
	lbnd = 0 if dt is bool else np.iinfo(op_dtype).min
	ubnd = 2 if dt is bool else np.iinfo(op_dtype).max + 1

	sample = self.rfunc(lbnd, ubnd, dtype=dt)
	assert_(not hasattr(sample, 'dtype'))
	assert_equal(type(sample), dt)


	class TestRandomDist:
	# Make sure the random distribution returns the correct value for a
	# given seed

	def setup_method(self):
	self.seed = 1234567890

	def test_rand(self):
	random.seed(self.seed)
	actual = random.rand(3, 2)
	desired = np.array([[0.61879477158567997, 0.59162362775974664],
	[0.88868358904449662, 0.89165480011560816],
	[0.4575674820298663, 0.7781880808593471]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_rand_singleton(self):
	random.seed(self.seed)
	actual = random.rand()
	desired = 0.61879477158567997
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_randn(self):
	random.seed(self.seed)
	actual = random.randn(3, 2)
	desired = np.array([[1.34016345771863121, 1.73759122771936081],
	[1.498988344300628, -0.2286433324536169],
	[2.031033998682787, 2.17032494605655257]])
	assert_array_almost_equal(actual, desired, decimal=15)

	random.seed(self.seed)
	actual = random.randn()
	assert_array_almost_equal(actual, desired[0, 0], decimal=15)

	def test_randint(self):
	random.seed(self.seed)
	actual = random.randint(-99, 99, size=(3, 2))
	desired = np.array([[31, 3],
	[-52, 41],
	[-48, -66]])
	assert_array_equal(actual, desired)

	def test_random_integers(self):
	random.seed(self.seed)
	with suppress_warnings() as sup:
	w = sup.record(DeprecationWarning)
	actual = random.random_integers(-99, 99, size=(3, 2))
	assert_(len(w) == 1)
	desired = np.array([[31, 3],
	[-52, 41],
	[-48, -66]])
	assert_array_equal(actual, desired)

	random.seed(self.seed)
	with suppress_warnings() as sup:
	w = sup.record(DeprecationWarning)
	actual = random.random_integers(198, size=(3, 2))
	assert_(len(w) == 1)
	assert_array_equal(actual, desired + 100)

	def test_tomaxint(self):
	random.seed(self.seed)
	rs = random.RandomState(self.seed)
	actual = rs.tomaxint(size=(3, 2))
	if np.iinfo(np.long).max == 2147483647:
	desired = np.array([[1328851649, 731237375],
	[1270502067, 320041495],
	[1908433478, 499156889]], dtype=np.int64)
	else:
	desired = np.array([[5707374374421908479, 5456764827585442327],
	[8196659375100692377, 8224063923314595285],
	[4220315081820346526, 7177518203184491332]],
	dtype=np.int64)

	assert_equal(actual, desired)

	rs.seed(self.seed)
	actual = rs.tomaxint()
	assert_equal(actual, desired[0, 0])

	def test_random_integers_max_int(self):
	# Tests whether random_integers can generate the
	# maximum allowed Python int that can be converted
	# into a C long. Previous implementations of this
	# method have thrown an OverflowError when attempting
	# to generate this integer.
	with suppress_warnings() as sup:
	w = sup.record(DeprecationWarning)
	actual = random.random_integers(np.iinfo('l').max,
	np.iinfo('l').max)
	assert_(len(w) == 1)

	desired = np.iinfo('l').max
	assert_equal(actual, desired)
	with suppress_warnings() as sup:
	w = sup.record(DeprecationWarning)
	typer = np.dtype('l').type
	actual = random.random_integers(typer(np.iinfo('l').max),
	typer(np.iinfo('l').max))
	assert_(len(w) == 1)
	assert_equal(actual, desired)

	def test_random_integers_deprecated(self):
	with warnings.catch_warnings():
	warnings.simplefilter("error", DeprecationWarning)

	# DeprecationWarning raised with high == None
	assert_raises(DeprecationWarning,
	random.random_integers,
	np.iinfo('l').max)

	# DeprecationWarning raised with high != None
	assert_raises(DeprecationWarning,
	random.random_integers,
	np.iinfo('l').max, np.iinfo('l').max)

	def test_random_sample(self):
	random.seed(self.seed)
	actual = random.random_sample((3, 2))
	desired = np.array([[0.61879477158567997, 0.59162362775974664],
	[0.88868358904449662, 0.89165480011560816],
	[0.4575674820298663, 0.7781880808593471]])
	assert_array_almost_equal(actual, desired, decimal=15)

	random.seed(self.seed)
	actual = random.random_sample()
	assert_array_almost_equal(actual, desired[0, 0], decimal=15)

	def test_choice_uniform_replace(self):
	random.seed(self.seed)
	actual = random.choice(4, 4)
	desired = np.array([2, 3, 2, 3])
	assert_array_equal(actual, desired)

	def test_choice_nonuniform_replace(self):
	random.seed(self.seed)
	actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
	desired = np.array([1, 1, 2, 2])
	assert_array_equal(actual, desired)

	def test_choice_uniform_noreplace(self):
	random.seed(self.seed)
	actual = random.choice(4, 3, replace=False)
	desired = np.array([0, 1, 3])
	assert_array_equal(actual, desired)

	def test_choice_nonuniform_noreplace(self):
	random.seed(self.seed)
	actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
	desired = np.array([2, 3, 1])
	assert_array_equal(actual, desired)

	def test_choice_noninteger(self):
	random.seed(self.seed)
	actual = random.choice(['a', 'b', 'c', 'd'], 4)
	desired = np.array(['c', 'd', 'c', 'd'])
	assert_array_equal(actual, desired)

	def test_choice_exceptions(self):
	sample = 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)
	# gh-13087
	assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
	assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
	assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), 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(random.choice(2, replace=True)))
	assert_(np.isscalar(random.choice(2, replace=False)))
	assert_(np.isscalar(random.choice(2, replace=True, p=p)))
	assert_(np.isscalar(random.choice(2, replace=False, p=p)))
	assert_(np.isscalar(random.choice([1, 2], replace=True)))
	assert_(random.choice([None], replace=True) is None)
	a = np.array([1, 2])
	arr = np.empty(1, dtype=object)
	arr[0] = a
	assert_(random.choice(arr, replace=True) is a)

	# Check 0-d array
	s = tuple()
	assert_(not np.isscalar(random.choice(2, s, replace=True)))
	assert_(not np.isscalar(random.choice(2, s, replace=False)))
	assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))
	assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))
	assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))
	assert_(random.choice([None], s, replace=True).ndim == 0)
	a = np.array([1, 2])
	arr = np.empty(1, dtype=object)
	arr[0] = a
	assert_(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_equal(random.choice(6, s, replace=True).shape, s)
	assert_equal(random.choice(6, s, replace=False).shape, s)
	assert_equal(random.choice(6, s, replace=True, p=p).shape, s)
	assert_equal(random.choice(6, s, replace=False, p=p).shape, s)
	assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)

	# Check zero-size
	assert_equal(random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
	assert_equal(random.randint(0, -10, size=0).shape, (0,))
	assert_equal(random.randint(10, 10, size=0).shape, (0,))
	assert_equal(random.choice(0, size=0).shape, (0,))
	assert_equal(random.choice([], size=(0,)).shape, (0,))
	assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,
	(3, 0, 4))
	assert_raises(ValueError, random.choice, [], 10)

	def test_choice_nan_probabilities(self):
	a = np.array([42, 1, 2])
	p = [None, None, None]
	assert_raises(ValueError, random.choice, a, p=p)

	def test_choice_p_non_contiguous(self):
	p = np.ones(10) / 5
	p[1::2] = 3.0
	random.seed(self.seed)
	non_contig = random.choice(5, 3, p=p[::2])
	random.seed(self.seed)
	contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
	assert_array_equal(non_contig, contig)

	def test_bytes(self):
	random.seed(self.seed)
	actual = random.bytes(10)
	desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
	assert_equal(actual, desired)

	def test_shuffle(self):
	# Test lists, arrays (of various dtypes), and multidimensional versions
	# of both, c-contiguous or not:
	for conv in [lambda x: np.array([]),
	lambda x: x,
	lambda x: np.asarray(x).astype(np.int8),
	lambda x: np.asarray(x).astype(np.float32),
	lambda x: np.asarray(x).astype(np.complex64),
	lambda x: np.asarray(x).astype(object),
	lambda x: [(i, i) for i in x],
	lambda x: np.asarray([[i, i] for i in x]),
	lambda x: np.vstack([x, x]).T,
	# gh-11442
	lambda x: (np.asarray([(i, i) for i in x],
	[("a", int), ("b", int)])
	.view(np.recarray)),
	# gh-4270
	lambda x: np.asarray([(i, i) for i in x],
	[("a", object, (1,)),
	("b", np.int32, (1,))])]:
	random.seed(self.seed)
	alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
	random.shuffle(alist)
	actual = alist
	desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
	assert_array_equal(actual, desired)

	def test_shuffle_masked(self):
	# gh-3263
	a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
	b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
	a_orig = a.copy()
	b_orig = b.copy()
	for i in range(50):
	random.shuffle(a)
	assert_equal(
	sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
	random.shuffle(b)
	assert_equal(
	sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))

	def test_shuffle_invalid_objects(self):
	x = np.array(3)
	assert_raises(TypeError, random.shuffle, x)

	def test_permutation(self):
	random.seed(self.seed)
	alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
	actual = random.permutation(alist)
	desired = [0, 1, 9, 6, 2, 4, 5, 8, 7, 3]
	assert_array_equal(actual, desired)

	random.seed(self.seed)
	arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T
	actual = random.permutation(arr_2d)
	assert_array_equal(actual, np.atleast_2d(desired).T)

	random.seed(self.seed)
	bad_x_str = "abcd"
	assert_raises(IndexError, random.permutation, bad_x_str)

	random.seed(self.seed)
	bad_x_float = 1.2
	assert_raises(IndexError, random.permutation, bad_x_float)

	integer_val = 10
	desired = [9, 0, 8, 5, 1, 3, 4, 7, 6, 2]

	random.seed(self.seed)
	actual = random.permutation(integer_val)
	assert_array_equal(actual, desired)

	def test_beta(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_binomial(self):
	random.seed(self.seed)
	actual = random.binomial(100.123, .456, size=(3, 2))
	desired = np.array([[37, 43],
	[42, 48],
	[46, 45]])
	assert_array_equal(actual, desired)

	random.seed(self.seed)
	actual = random.binomial(100.123, .456)
	desired = 37
	assert_array_equal(actual, desired)

	def test_chisquare(self):
	random.seed(self.seed)
	actual = random.chisquare(50, size=(3, 2))
	desired = np.array([[63.87858175501090585, 68.68407748911370447],
	[65.77116116901505904, 47.09686762438974483],
	[72.3828403199695174, 74.18408615260374006]])
	assert_array_almost_equal(actual, desired, decimal=13)

	def test_dirichlet(self):
	random.seed(self.seed)
	alpha = np.array([51.72840233779265162, 39.74494232180943953])
	actual = random.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]]])
	assert_array_almost_equal(actual, desired, decimal=15)
	bad_alpha = np.array([5.4e-01, -1.0e-16])
	assert_raises(ValueError, random.dirichlet, bad_alpha)

	random.seed(self.seed)
	alpha = np.array([51.72840233779265162, 39.74494232180943953])
	actual = random.dirichlet(alpha)
	assert_array_almost_equal(actual, desired[0, 0], decimal=15)

	def test_dirichlet_size(self):
	# gh-3173
	p = np.array([51.72840233779265162, 39.74494232180943953])
	assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
	assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
	assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
	assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
	assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
	assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))

	assert_raises(TypeError, random.dirichlet, p, float(1))

	def test_dirichlet_bad_alpha(self):
	# gh-2089
	alpha = np.array([5.4e-01, -1.0e-16])
	assert_raises(ValueError, random.dirichlet, alpha)

	def test_dirichlet_alpha_non_contiguous(self):
	a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
	alpha = a[::2]
	random.seed(self.seed)
	non_contig = random.dirichlet(alpha, size=(3, 2))
	random.seed(self.seed)
	contig = random.dirichlet(np.ascontiguousarray(alpha),
	size=(3, 2))
	assert_array_almost_equal(non_contig, contig)

	def test_exponential(self):
	random.seed(self.seed)
	actual = random.exponential(1.1234, size=(3, 2))
	desired = np.array([[1.08342649775011624, 1.00607889924557314],
	[2.46628830085216721, 2.49668106809923884],
	[0.68717433461363442, 1.69175666993575979]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_exponential_0(self):
	assert_equal(random.exponential(scale=0), 0)
	assert_raises(ValueError, random.exponential, scale=-0.)

	def test_f(self):
	random.seed(self.seed)
	actual = random.f(12, 77, size=(3, 2))
	desired = np.array([[1.21975394418575878, 1.75135759791559775],
	[1.44803115017146489, 1.22108959480396262],
	[1.02176975757740629, 1.34431827623300415]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_gamma(self):
	random.seed(self.seed)
	actual = random.gamma(5, 3, size=(3, 2))
	desired = np.array([[24.60509188649287182, 28.54993563207210627],
	[26.13476110204064184, 12.56988482927716078],
	[31.71863275789960568, 33.30143302795922011]])
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_gamma_0(self):
	assert_equal(random.gamma(shape=0, scale=0), 0)
	assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)

	def test_geometric(self):
	random.seed(self.seed)
	actual = random.geometric(.123456789, size=(3, 2))
	desired = np.array([[8, 7],
	[17, 17],
	[5, 12]])
	assert_array_equal(actual, desired)

	def test_geometric_exceptions(self):
	assert_raises(ValueError, random.geometric, 1.1)
	assert_raises(ValueError, random.geometric, [1.1] * 10)
	assert_raises(ValueError, random.geometric, -0.1)
	assert_raises(ValueError, random.geometric, [-0.1] * 10)
	with suppress_warnings() as sup:
	sup.record(RuntimeWarning)
	assert_raises(ValueError, random.geometric, np.nan)
	assert_raises(ValueError, random.geometric, [np.nan] * 10)

	def test_gumbel(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_gumbel_0(self):
	assert_equal(random.gumbel(scale=0), 0)
	assert_raises(ValueError, random.gumbel, scale=-0.)

	def test_hypergeometric(self):
	random.seed(self.seed)
	actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
	desired = np.array([[10, 10],
	[10, 10],
	[9, 9]])
	assert_array_equal(actual, desired)

	# Test nbad = 0
	actual = random.hypergeometric(5, 0, 3, size=4)
	desired = np.array([3, 3, 3, 3])
	assert_array_equal(actual, desired)

	actual = random.hypergeometric(15, 0, 12, size=4)
	desired = np.array([12, 12, 12, 12])
	assert_array_equal(actual, desired)

	# Test ngood = 0
	actual = random.hypergeometric(0, 5, 3, size=4)
	desired = np.array([0, 0, 0, 0])
	assert_array_equal(actual, desired)

	actual = random.hypergeometric(0, 15, 12, size=4)
	desired = np.array([0, 0, 0, 0])
	assert_array_equal(actual, desired)

	def test_laplace(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_laplace_0(self):
	assert_equal(random.laplace(scale=0), 0)
	assert_raises(ValueError, random.laplace, scale=-0.)

	def test_logistic(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_lognormal(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=13)

	def test_lognormal_0(self):
	assert_equal(random.lognormal(sigma=0), 1)
	assert_raises(ValueError, random.lognormal, sigma=-0.)

	def test_logseries(self):
	random.seed(self.seed)
	actual = random.logseries(p=.923456789, size=(3, 2))
	desired = np.array([[2, 2],
	[6, 17],
	[3, 6]])
	assert_array_equal(actual, desired)

	def test_logseries_zero(self):
	assert random.logseries(0) == 1

	@pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
	def test_logseries_exceptions(self, value):
	with np.errstate(invalid="ignore"):
	with pytest.raises(ValueError):
	random.logseries(value)
	with pytest.raises(ValueError):
	# contiguous path:
	random.logseries(np.array([value] * 10))
	with pytest.raises(ValueError):
	# non-contiguous path:
	random.logseries(np.array([value] * 10)[::2])

	def test_multinomial(self):
	random.seed(self.seed)
	actual = 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]]])
	assert_array_equal(actual, desired)

	def test_multivariate_normal(self):
	random.seed(self.seed)
	mean = (.123456789, 10)
	cov = [[1, 0], [0, 1]]
	size = (3, 2)
	actual = random.multivariate_normal(mean, cov, size)
	desired = np.array([[[1.463620246718631, 11.73759122771936],
	[1.622445133300628, 9.771356667546383]],
	[[2.154490787682787, 12.170324946056553],
	[1.719909438201865, 9.230548443648306]],
	[[0.689515026297799, 9.880729819607714],
	[-0.023054015651998, 9.201096623542879]]])

	assert_array_almost_equal(actual, desired, decimal=15)

	# Check for default size, was raising deprecation warning
	actual = random.multivariate_normal(mean, cov)
	desired = np.array([0.895289569463708, 9.17180864067987])
	assert_array_almost_equal(actual, desired, decimal=15)

	# Check that non positive-semidefinite covariance warns with
	# RuntimeWarning
	mean = [0, 0]
	cov = [[1, 2], [2, 1]]
	assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)

	# and that it doesn't warn with RuntimeWarning check_valid='ignore'
	assert_no_warnings(random.multivariate_normal, mean, cov,
	check_valid='ignore')

	# and that it raises with RuntimeWarning check_valid='raises'
	assert_raises(ValueError, random.multivariate_normal, mean, cov,
	check_valid='raise')

	cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
	with suppress_warnings() as sup:
	random.multivariate_normal(mean, cov)
	w = sup.record(RuntimeWarning)
	assert len(w) == 0

	mu = np.zeros(2)
	cov = np.eye(2)
	assert_raises(ValueError, random.multivariate_normal, mean, cov,
	check_valid='other')
	assert_raises(ValueError, random.multivariate_normal,
	np.zeros((2, 1, 1)), cov)
	assert_raises(ValueError, random.multivariate_normal,
	mu, np.empty((3, 2)))
	assert_raises(ValueError, random.multivariate_normal,
	mu, np.eye(3))

	def test_negative_binomial(self):
	random.seed(self.seed)
	actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
	desired = np.array([[848, 841],
	[892, 611],
	[779, 647]])
	assert_array_equal(actual, desired)

	def test_negative_binomial_exceptions(self):
	with suppress_warnings() as sup:
	sup.record(RuntimeWarning)
	assert_raises(ValueError, random.negative_binomial, 100, np.nan)
	assert_raises(ValueError, random.negative_binomial, 100,
	[np.nan] * 10)

	def test_noncentral_chisquare(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=14)

	actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
	desired = np.array([[1.47145377828516666, 0.15052899268012659],
	[0.00943803056963588, 1.02647251615666169],
	[0.332334982684171, 0.15451287602753125]])
	assert_array_almost_equal(actual, desired, decimal=14)

	random.seed(self.seed)
	actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
	desired = np.array([[9.597154162763948, 11.725484450296079],
	[10.413711048138335, 3.694475922923986],
	[13.484222138963087, 14.377255424602957]])
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_noncentral_f(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_noncentral_f_nan(self):
	random.seed(self.seed)
	actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
	assert np.isnan(actual)

	def test_normal(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_normal_0(self):
	assert_equal(random.normal(scale=0), 0)
	assert_raises(ValueError, random.normal, scale=-0.)

	def test_pareto(self):
	random.seed(self.seed)
	actual = 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:
	# https://mail.python.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):
	random.seed(self.seed)
	actual = random.poisson(lam=.123456789, size=(3, 2))
	desired = np.array([[0, 0],
	[1, 0],
	[0, 0]])
	assert_array_equal(actual, desired)

	def test_poisson_exceptions(self):
	lambig = np.iinfo('l').max
	lamneg = -1
	assert_raises(ValueError, random.poisson, lamneg)
	assert_raises(ValueError, random.poisson, [lamneg] * 10)
	assert_raises(ValueError, random.poisson, lambig)
	assert_raises(ValueError, random.poisson, [lambig] * 10)
	with suppress_warnings() as sup:
	sup.record(RuntimeWarning)
	assert_raises(ValueError, random.poisson, np.nan)
	assert_raises(ValueError, random.poisson, [np.nan] * 10)

	def test_power(self):
	random.seed(self.seed)
	actual = random.power(a=.123456789, size=(3, 2))
	desired = np.array([[0.02048932883240791, 0.01424192241128213],
	[0.38446073748535298, 0.39499689943484395],
	[0.00177699707563439, 0.13115505880863756]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_rayleigh(self):
	random.seed(self.seed)
	actual = random.rayleigh(scale=10, size=(3, 2))
	desired = np.array([[13.8882496494248393, 13.383318339044731],
	[20.95413364294492098, 21.08285015800712614],
	[11.06066537006854311, 17.35468505778271009]])
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_rayleigh_0(self):
	assert_equal(random.rayleigh(scale=0), 0)
	assert_raises(ValueError, random.rayleigh, scale=-0.)

	def test_standard_cauchy(self):
	random.seed(self.seed)
	actual = random.standard_cauchy(size=(3, 2))
	desired = np.array([[0.77127660196445336, -6.55601161955910605],
	[0.93582023391158309, -2.07479293013759447],
	[-4.74601644297011926, 0.18338989290760804]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_standard_exponential(self):
	random.seed(self.seed)
	actual = random.standard_exponential(size=(3, 2))
	desired = np.array([[0.96441739162374596, 0.89556604882105506],
	[2.1953785836319808, 2.22243285392490542],
	[0.6116915921431676, 1.50592546727413201]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_standard_gamma(self):
	random.seed(self.seed)
	actual = random.standard_gamma(shape=3, size=(3, 2))
	desired = np.array([[5.50841531318455058, 6.62953470301903103],
	[5.93988484943779227, 2.31044849402133989],
	[7.54838614231317084, 8.012756093271868]])
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_standard_gamma_0(self):
	assert_equal(random.standard_gamma(shape=0), 0)
	assert_raises(ValueError, random.standard_gamma, shape=-0.)

	def test_standard_normal(self):
	random.seed(self.seed)
	actual = random.standard_normal(size=(3, 2))
	desired = np.array([[1.34016345771863121, 1.73759122771936081],
	[1.498988344300628, -0.2286433324536169],
	[2.031033998682787, 2.17032494605655257]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_randn_singleton(self):
	random.seed(self.seed)
	actual = random.randn()
	desired = np.array(1.34016345771863121)
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_standard_t(self):
	random.seed(self.seed)
	actual = random.standard_t(df=10, size=(3, 2))
	desired = np.array([[0.97140611862659965, -0.08830486548450577],
	[1.36311143689505321, -0.55317463909867071],
	[-0.18473749069684214, 0.61181537341755321]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_triangular(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_uniform(self):
	random.seed(self.seed)
	actual = 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]])
	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 = random.uniform
	assert_raises(OverflowError, func, -np.inf, 0)
	assert_raises(OverflowError, func, 0, np.inf)
	assert_raises(OverflowError, func, fmin, fmax)
	assert_raises(OverflowError, func, [-np.inf], [0])
	assert_raises(OverflowError, func, [0], [np.inf])

	# (fmax / 1e17) - fmin is within range, so this should not throw
	# account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
	# DBL_MAX by increasing fmin a bit
	random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)

	def test_scalar_exception_propagation(self):
	# Tests that exceptions are correctly propagated in distributions
	# when called with objects that throw exceptions when converted to
	# scalars.
	#
	# Regression test for gh: 8865

	class ThrowingFloat(np.ndarray):
	def __float__(self):
	raise TypeError

	throwing_float = np.array(1.0).view(ThrowingFloat)
	assert_raises(TypeError, random.uniform, throwing_float,
	throwing_float)

	class ThrowingInteger(np.ndarray):
	def __int__(self):
	raise TypeError

	throwing_int = np.array(1).view(ThrowingInteger)
	assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)

	def test_vonmises(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_vonmises_small(self):
	# check infinite loop, gh-4720
	random.seed(self.seed)
	r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
	assert_(np.isfinite(r).all())

	def test_vonmises_large(self):
	# guard against changes in RandomState when Generator is fixed
	random.seed(self.seed)
	actual = random.vonmises(mu=0., kappa=1e7, size=3)
	desired = np.array([4.634253748521111e-04,
	3.558873596114509e-04,
	-2.337119622577433e-04])
	assert_array_almost_equal(actual, desired, decimal=8)

	def test_vonmises_nan(self):
	random.seed(self.seed)
	r = random.vonmises(mu=0., kappa=np.nan)
	assert_(np.isnan(r))

	def test_wald(self):
	random.seed(self.seed)
	actual = 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]])
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_weibull(self):
	random.seed(self.seed)
	actual = random.weibull(a=1.23, size=(3, 2))
	desired = np.array([[0.97097342648766727, 0.91422896443565516],
	[1.89517770034962929, 1.91414357960479564],
	[0.67057783752390987, 1.39494046635066793]])
	assert_array_almost_equal(actual, desired, decimal=15)

	def test_weibull_0(self):
	random.seed(self.seed)
	assert_equal(random.weibull(a=0, size=12), np.zeros(12))
	assert_raises(ValueError, random.weibull, a=-0.)

	def test_zipf(self):
	random.seed(self.seed)
	actual = random.zipf(a=1.23, size=(3, 2))
	desired = np.array([[66, 29],
	[1, 1],
	[3, 13]])
	assert_array_equal(actual, desired)


	class TestBroadcast:
	# tests that functions that broadcast behave
	# correctly when presented with non-scalar arguments
	def setup_method(self):
	self.seed = 123456789

	def set_seed(self):
	random.seed(self.seed)

	def test_uniform(self):
	low = [0]
	high = [1]
	uniform = random.uniform
	desired = np.array([0.53283302478975902,
	0.53413660089041659,
	0.50955303552646702])

	self.set_seed()
	actual = uniform(low * 3, high)
	assert_array_almost_equal(actual, desired, decimal=14)

	self.set_seed()
	actual = uniform(low, high * 3)
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_normal(self):
	loc = [0]
	scale = [1]
	bad_scale = [-1]
	normal = random.normal
	desired = np.array([2.2129019979039612,
	2.1283977976520019,
	1.8417114045748335])

	self.set_seed()
	actual = normal(loc * 3, scale)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, normal, loc * 3, bad_scale)

	self.set_seed()
	actual = normal(loc, scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, normal, loc, bad_scale * 3)

	def test_beta(self):
	a = [1]
	b = [2]
	bad_a = [-1]
	bad_b = [-2]
	beta = random.beta
	desired = np.array([0.19843558305989056,
	0.075230336409423643,
	0.24976865978980844])

	self.set_seed()
	actual = beta(a * 3, b)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, beta, bad_a * 3, b)
	assert_raises(ValueError, beta, a * 3, bad_b)

	self.set_seed()
	actual = beta(a, b * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, beta, bad_a, b * 3)
	assert_raises(ValueError, beta, a, bad_b * 3)

	def test_exponential(self):
	scale = [1]
	bad_scale = [-1]
	exponential = random.exponential
	desired = np.array([0.76106853658845242,
	0.76386282278691653,
	0.71243813125891797])

	self.set_seed()
	actual = exponential(scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, exponential, bad_scale * 3)

	def test_standard_gamma(self):
	shape = [1]
	bad_shape = [-1]
	std_gamma = random.standard_gamma
	desired = np.array([0.76106853658845242,
	0.76386282278691653,
	0.71243813125891797])

	self.set_seed()
	actual = std_gamma(shape * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, std_gamma, bad_shape * 3)

	def test_gamma(self):
	shape = [1]
	scale = [2]
	bad_shape = [-1]
	bad_scale = [-2]
	gamma = random.gamma
	desired = np.array([1.5221370731769048,
	1.5277256455738331,
	1.4248762625178359])

	self.set_seed()
	actual = gamma(shape * 3, scale)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, gamma, bad_shape * 3, scale)
	assert_raises(ValueError, gamma, shape * 3, bad_scale)

	self.set_seed()
	actual = gamma(shape, scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, gamma, bad_shape, scale * 3)
	assert_raises(ValueError, gamma, shape, bad_scale * 3)

	def test_f(self):
	dfnum = [1]
	dfden = [2]
	bad_dfnum = [-1]
	bad_dfden = [-2]
	f = random.f
	desired = np.array([0.80038951638264799,
	0.86768719635363512,
	2.7251095168386801])

	self.set_seed()
	actual = f(dfnum * 3, dfden)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, f, bad_dfnum * 3, dfden)
	assert_raises(ValueError, f, dfnum * 3, bad_dfden)

	self.set_seed()
	actual = f(dfnum, dfden * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, f, bad_dfnum, dfden * 3)
	assert_raises(ValueError, f, dfnum, bad_dfden * 3)

	def test_noncentral_f(self):
	dfnum = [2]
	dfden = [3]
	nonc = [4]
	bad_dfnum = [0]
	bad_dfden = [-1]
	bad_nonc = [-2]
	nonc_f = random.noncentral_f
	desired = np.array([9.1393943263705211,
	13.025456344595602,
	8.8018098359100545])

	self.set_seed()
	actual = nonc_f(dfnum * 3, dfden, nonc)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))

	assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
	assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
	assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)

	self.set_seed()
	actual = nonc_f(dfnum, dfden * 3, nonc)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
	assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
	assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)

	self.set_seed()
	actual = nonc_f(dfnum, dfden, nonc * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
	assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
	assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)

	def test_noncentral_f_small_df(self):
	self.set_seed()
	desired = np.array([6.869638627492048, 0.785880199263955])
	actual = random.noncentral_f(0.9, 0.9, 2, size=2)
	assert_array_almost_equal(actual, desired, decimal=14)

	def test_chisquare(self):
	df = [1]
	bad_df = [-1]
	chisquare = random.chisquare
	desired = np.array([0.57022801133088286,
	0.51947702108840776,
	0.1320969254923558])

	self.set_seed()
	actual = chisquare(df * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, chisquare, bad_df * 3)

	def test_noncentral_chisquare(self):
	df = [1]
	nonc = [2]
	bad_df = [-1]
	bad_nonc = [-2]
	nonc_chi = random.noncentral_chisquare
	desired = np.array([9.0015599467913763,
	4.5804135049718742,
	6.0872302432834564])

	self.set_seed()
	actual = nonc_chi(df * 3, nonc)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
	assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)

	self.set_seed()
	actual = nonc_chi(df, nonc * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
	assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)

	def test_standard_t(self):
	df = [1]
	bad_df = [-1]
	t = random.standard_t
	desired = np.array([3.0702872575217643,
	5.8560725167361607,
	1.0274791436474273])

	self.set_seed()
	actual = t(df * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, t, bad_df * 3)
	assert_raises(ValueError, random.standard_t, bad_df * 3)

	def test_vonmises(self):
	mu = [2]
	kappa = [1]
	bad_kappa = [-1]
	vonmises = random.vonmises
	desired = np.array([2.9883443664201312,
	-2.7064099483995943,
	-1.8672476700665914])

	self.set_seed()
	actual = vonmises(mu * 3, kappa)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, vonmises, mu * 3, bad_kappa)

	self.set_seed()
	actual = vonmises(mu, kappa * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, vonmises, mu, bad_kappa * 3)

	def test_pareto(self):
	a = [1]
	bad_a = [-1]
	pareto = random.pareto
	desired = np.array([1.1405622680198362,
	1.1465519762044529,
	1.0389564467453547])

	self.set_seed()
	actual = pareto(a * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, pareto, bad_a * 3)
	assert_raises(ValueError, random.pareto, bad_a * 3)

	def test_weibull(self):
	a = [1]
	bad_a = [-1]
	weibull = random.weibull
	desired = np.array([0.76106853658845242,
	0.76386282278691653,
	0.71243813125891797])

	self.set_seed()
	actual = weibull(a * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, weibull, bad_a * 3)
	assert_raises(ValueError, random.weibull, bad_a * 3)

	def test_power(self):
	a = [1]
	bad_a = [-1]
	power = random.power
	desired = np.array([0.53283302478975902,
	0.53413660089041659,
	0.50955303552646702])

	self.set_seed()
	actual = power(a * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, power, bad_a * 3)
	assert_raises(ValueError, random.power, bad_a * 3)

	def test_laplace(self):
	loc = [0]
	scale = [1]
	bad_scale = [-1]
	laplace = random.laplace
	desired = np.array([0.067921356028507157,
	0.070715642226971326,
	0.019290950698972624])

	self.set_seed()
	actual = laplace(loc * 3, scale)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, laplace, loc * 3, bad_scale)

	self.set_seed()
	actual = laplace(loc, scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, laplace, loc, bad_scale * 3)

	def test_gumbel(self):
	loc = [0]
	scale = [1]
	bad_scale = [-1]
	gumbel = random.gumbel
	desired = np.array([0.2730318639556768,
	0.26936705726291116,
	0.33906220393037939])

	self.set_seed()
	actual = gumbel(loc * 3, scale)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, gumbel, loc * 3, bad_scale)

	self.set_seed()
	actual = gumbel(loc, scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, gumbel, loc, bad_scale * 3)

	def test_logistic(self):
	loc = [0]
	scale = [1]
	bad_scale = [-1]
	logistic = random.logistic
	desired = np.array([0.13152135837586171,
	0.13675915696285773,
	0.038216792802833396])

	self.set_seed()
	actual = logistic(loc * 3, scale)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, logistic, loc * 3, bad_scale)

	self.set_seed()
	actual = logistic(loc, scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, logistic, loc, bad_scale * 3)
	assert_equal(random.logistic(1.0, 0.0), 1.0)

	def test_lognormal(self):
	mean = [0]
	sigma = [1]
	bad_sigma = [-1]
	lognormal = random.lognormal
	desired = np.array([9.1422086044848427,
	8.4013952870126261,
	6.3073234116578671])

	self.set_seed()
	actual = lognormal(mean * 3, sigma)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
	assert_raises(ValueError, random.lognormal, mean * 3, bad_sigma)

	self.set_seed()
	actual = lognormal(mean, sigma * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
	assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)

	def test_rayleigh(self):
	scale = [1]
	bad_scale = [-1]
	rayleigh = random.rayleigh
	desired = np.array([1.2337491937897689,
	1.2360119924878694,
	1.1936818095781789])

	self.set_seed()
	actual = rayleigh(scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, rayleigh, bad_scale * 3)

	def test_wald(self):
	mean = [0.5]
	scale = [1]
	bad_mean = [0]
	bad_scale = [-2]
	wald = random.wald
	desired = np.array([0.11873681120271318,
	0.12450084820795027,
	0.9096122728408238])

	self.set_seed()
	actual = wald(mean * 3, scale)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, wald, bad_mean * 3, scale)
	assert_raises(ValueError, wald, mean * 3, bad_scale)
	assert_raises(ValueError, random.wald, bad_mean * 3, scale)
	assert_raises(ValueError, random.wald, mean * 3, bad_scale)

	self.set_seed()
	actual = wald(mean, scale * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, wald, bad_mean, scale * 3)
	assert_raises(ValueError, wald, mean, bad_scale * 3)
	assert_raises(ValueError, wald, 0.0, 1)
	assert_raises(ValueError, wald, 0.5, 0.0)

	def test_triangular(self):
	left = [1]
	right = [3]
	mode = [2]
	bad_left_one = [3]
	bad_mode_one = [4]
	bad_left_two, bad_mode_two = right * 2
	triangular = random.triangular
	desired = np.array([2.03339048710429,
	2.0347400359389356,
	2.0095991069536208])

	self.set_seed()
	actual = triangular(left * 3, mode, right)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
	assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
	assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
	right)

	self.set_seed()
	actual = triangular(left, mode * 3, right)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
	assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
	assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
	right)

	self.set_seed()
	actual = triangular(left, mode, right * 3)
	assert_array_almost_equal(actual, desired, decimal=14)
	assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
	assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
	assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
	right * 3)

	assert_raises(ValueError, triangular, 10., 0., 20.)
	assert_raises(ValueError, triangular, 10., 25., 20.)
	assert_raises(ValueError, triangular, 10., 10., 10.)

	def test_binomial(self):
	n = [1]
	p = [0.5]
	bad_n = [-1]
	bad_p_one = [-1]
	bad_p_two = [1.5]
	binom = random.binomial
	desired = np.array([1, 1, 1])

	self.set_seed()
	actual = binom(n * 3, p)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, binom, bad_n * 3, p)
	assert_raises(ValueError, binom, n * 3, bad_p_one)
	assert_raises(ValueError, binom, n * 3, bad_p_two)

	self.set_seed()
	actual = binom(n, p * 3)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, binom, bad_n, p * 3)
	assert_raises(ValueError, binom, n, bad_p_one * 3)
	assert_raises(ValueError, binom, n, bad_p_two * 3)

	def test_negative_binomial(self):
	n = [1]
	p = [0.5]
	bad_n = [-1]
	bad_p_one = [-1]
	bad_p_two = [1.5]
	neg_binom = random.negative_binomial
	desired = np.array([1, 0, 1])

	self.set_seed()
	actual = neg_binom(n * 3, p)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, neg_binom, bad_n * 3, p)
	assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
	assert_raises(ValueError, neg_binom, n * 3, bad_p_two)

	self.set_seed()
	actual = neg_binom(n, p * 3)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, neg_binom, bad_n, p * 3)
	assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
	assert_raises(ValueError, neg_binom, n, bad_p_two * 3)

	def test_poisson(self):
	max_lam = random.RandomState()._poisson_lam_max

	lam = [1]
	bad_lam_one = [-1]
	bad_lam_two = [max_lam * 2]
	poisson = random.poisson
	desired = np.array([1, 1, 0])

	self.set_seed()
	actual = poisson(lam * 3)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, poisson, bad_lam_one * 3)
	assert_raises(ValueError, poisson, bad_lam_two * 3)

	def test_zipf(self):
	a = [2]
	bad_a = [0]
	zipf = random.zipf
	desired = np.array([2, 2, 1])

	self.set_seed()
	actual = zipf(a * 3)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, zipf, bad_a * 3)
	with np.errstate(invalid='ignore'):
	assert_raises(ValueError, zipf, np.nan)
	assert_raises(ValueError, zipf, [0, 0, np.nan])

	def test_geometric(self):
	p = [0.5]
	bad_p_one = [-1]
	bad_p_two = [1.5]
	geom = random.geometric
	desired = np.array([2, 2, 2])

	self.set_seed()
	actual = geom(p * 3)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, geom, bad_p_one * 3)
	assert_raises(ValueError, geom, bad_p_two * 3)

	def test_hypergeometric(self):
	ngood = [1]
	nbad = [2]
	nsample = [2]
	bad_ngood = [-1]
	bad_nbad = [-2]
	bad_nsample_one = [0]
	bad_nsample_two = [4]
	hypergeom = random.hypergeometric
	desired = np.array([1, 1, 1])

	self.set_seed()
	actual = hypergeom(ngood * 3, nbad, nsample)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
	assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
	assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
	assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)

	self.set_seed()
	actual = hypergeom(ngood, nbad * 3, nsample)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
	assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
	assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
	assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)

	self.set_seed()
	actual = hypergeom(ngood, nbad, nsample * 3)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
	assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
	assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
	assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)

	assert_raises(ValueError, hypergeom, -1, 10, 20)
	assert_raises(ValueError, hypergeom, 10, -1, 20)
	assert_raises(ValueError, hypergeom, 10, 10, 0)
	assert_raises(ValueError, hypergeom, 10, 10, 25)

	def test_logseries(self):
	p = [0.5]
	bad_p_one = [2]
	bad_p_two = [-1]
	logseries = random.logseries
	desired = np.array([1, 1, 1])

	self.set_seed()
	actual = logseries(p * 3)
	assert_array_equal(actual, desired)
	assert_raises(ValueError, logseries, bad_p_one * 3)
	assert_raises(ValueError, logseries, bad_p_two * 3)


	@pytest.mark.skipif(IS_WASM, reason="can't start thread")
	class TestThread:
	# make sure each state produces the same sequence even in threads
	def setup_method(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=(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(random.RandomState(s), o)

	# these platforms change x87 fpu precision mode in threads
	if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
	assert_array_almost_equal(out1, out2)
	else:
	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))


	# See Issue #4263
	class TestSingleEltArrayInput:
	def setup_method(self):
	self.argOne = np.array([2])
	self.argTwo = np.array([3])
	self.argThree = np.array([4])
	self.tgtShape = (1,)

	def test_one_arg_funcs(self):
	funcs = (random.exponential, random.standard_gamma,
	random.chisquare, random.standard_t,
	random.pareto, random.weibull,
	random.power, random.rayleigh,
	random.poisson, random.zipf,
	random.geometric, random.logseries)

	probfuncs = (random.geometric, random.logseries)

	for func in funcs:
	if func in probfuncs: # p < 1.0
	out = func(np.array([0.5]))

	else:
	out = func(self.argOne)

	assert_equal(out.shape, self.tgtShape)

	def test_two_arg_funcs(self):
	funcs = (random.uniform, random.normal,
	random.beta, random.gamma,
	random.f, random.noncentral_chisquare,
	random.vonmises, random.laplace,
	random.gumbel, random.logistic,
	random.lognormal, random.wald,
	random.binomial, random.negative_binomial)

	probfuncs = (random.binomial, random.negative_binomial)

	for func in funcs:
	if func in probfuncs: # p <= 1
	argTwo = np.array([0.5])

	else:
	argTwo = self.argTwo

	out = func(self.argOne, argTwo)
	assert_equal(out.shape, self.tgtShape)

	out = func(self.argOne[0], argTwo)
	assert_equal(out.shape, self.tgtShape)

	out = func(self.argOne, argTwo[0])
	assert_equal(out.shape, self.tgtShape)

	def test_three_arg_funcs(self):
	funcs = [random.noncentral_f, random.triangular,
	random.hypergeometric]

	for func in funcs:
	out = func(self.argOne, self.argTwo, self.argThree)
	assert_equal(out.shape, self.tgtShape)

	out = func(self.argOne[0], self.argTwo, self.argThree)
	assert_equal(out.shape, self.tgtShape)

	out = func(self.argOne, self.argTwo[0], self.argThree)
	assert_equal(out.shape, self.tgtShape)


	# Ensure returned array dtype is correct for platform
	def test_integer_dtype(int_func):
	random.seed(123456789)
	fname, args, sha256 = int_func
	f = getattr(random, fname)
	actual = f(*args, size=2)
	assert_(actual.dtype == np.dtype('l'))


	def test_integer_repeat(int_func):
	random.seed(123456789)
	fname, args, sha256 = int_func
	f = getattr(random, fname)
	val = f(*args, size=1000000)
	if sys.byteorder != 'little':
	val = val.byteswap()
	res = hashlib.sha256(val.view(np.int8)).hexdigest()
	assert_(res == sha256)


	def test_broadcast_size_error():
	# GH-16833
	with pytest.raises(ValueError):
	random.binomial(1, [0.3, 0.7], size=(2, 1))
	with pytest.raises(ValueError):
	random.binomial([1, 2], 0.3, size=(2, 1))
	with pytest.raises(ValueError):
	random.binomial([1, 2], [0.3, 0.7], size=(2, 1))


	def test_randomstate_ctor_old_style_pickle():
	rs = np.random.RandomState(MT19937(0))
	rs.standard_normal(1)
	# Directly call reduce which is used in pickling
	ctor, args, state_a = rs.__reduce__()
	# Simulate unpickling an old pickle that only has the name
	assert args[0].__class__.__name__ == "MT19937"
	b = ctor(*("MT19937",))
	b.set_state(state_a)
	state_b = b.get_state(legacy=False)

	assert_equal(state_a['bit_generator'], state_b['bit_generator'])
	assert_array_equal(state_a['state']['key'], state_b['state']['key'])
	assert_array_equal(state_a['state']['pos'], state_b['state']['pos'])
	assert_equal(state_a['has_gauss'], state_b['has_gauss'])
	assert_equal(state_a['gauss'], state_b['gauss'])


	def test_hot_swap(restore_singleton_bitgen):
	# GH 21808
	def_bg = np.random.default_rng(0)
	bg = def_bg.bit_generator
	np.random.set_bit_generator(bg)
	assert isinstance(np.random.mtrand._rand._bit_generator, type(bg))

	second_bg = np.random.get_bit_generator()
	assert bg is second_bg


	def test_seed_alt_bit_gen(restore_singleton_bitgen):
	# GH 21808
	bg = PCG64(0)
	np.random.set_bit_generator(bg)
	state = np.random.get_state(legacy=False)
	np.random.seed(1)
	new_state = np.random.get_state(legacy=False)
	print(state)
	print(new_state)
	assert state["bit_generator"] == "PCG64"
	assert state["state"]["state"] != new_state["state"]["state"]
	assert state["state"]["inc"] != new_state["state"]["inc"]


	def test_state_error_alt_bit_gen(restore_singleton_bitgen):
	# GH 21808
	state = np.random.get_state()
	bg = PCG64(0)
	np.random.set_bit_generator(bg)
	with pytest.raises(ValueError, match="state must be for a PCG64"):
	np.random.set_state(state)


	def test_swap_worked(restore_singleton_bitgen):
	# GH 21808
	np.random.seed(98765)
	vals = np.random.randint(0, 2 ** 30, 10)
	bg = PCG64(0)
	state = bg.state
	np.random.set_bit_generator(bg)
	state_direct = np.random.get_state(legacy=False)
	for field in state:
	assert state[field] == state_direct[field]
	np.random.seed(98765)
	pcg_vals = np.random.randint(0, 2 ** 30, 10)
	assert not np.all(vals == pcg_vals)
	new_state = bg.state
	assert new_state["state"]["state"] != state["state"]["state"]
	assert new_state["state"]["inc"] == new_state["state"]["inc"]


	def test_swapped_singleton_against_direct(restore_singleton_bitgen):
	np.random.set_bit_generator(PCG64(98765))
	singleton_vals = np.random.randint(0, 2 ** 30, 10)
	rg = np.random.RandomState(PCG64(98765))
	non_singleton_vals = rg.randint(0, 2 ** 30, 10)
	assert_equal(non_singleton_vals, singleton_vals)