AlkantarClanX12
Your IP : 3.144.235.138
import sys
import hashlib
import pytest
import numpy as np
from numpy.linalg import LinAlgError
from numpy.testing import (
assert_, assert_raises, assert_equal, assert_allclose,
assert_warns, assert_no_warnings, assert_array_equal,
assert_array_almost_equal, suppress_warnings, IS_WASM)
from numpy.random import Generator, MT19937, SeedSequence, RandomState
random = Generator(MT19937())
JUMP_TEST_DATA = [
{
"seed": 0,
"steps": 10,
"initial": {"key_sha256": "bb1636883c2707b51c5b7fc26c6927af4430f2e0785a8c7bc886337f919f9edf", "pos": 9},
"jumped": {"key_sha256": "ff682ac12bb140f2d72fba8d3506cf4e46817a0db27aae1683867629031d8d55", "pos": 598},
},
{
"seed":384908324,
"steps":312,
"initial": {"key_sha256": "16b791a1e04886ccbbb4d448d6ff791267dc458ae599475d08d5cced29d11614", "pos": 311},
"jumped": {"key_sha256": "a0110a2cf23b56be0feaed8f787a7fc84bef0cb5623003d75b26bdfa1c18002c", "pos": 276},
},
{
"seed": [839438204, 980239840, 859048019, 821],
"steps": 511,
"initial": {"key_sha256": "d306cf01314d51bd37892d874308200951a35265ede54d200f1e065004c3e9ea", "pos": 510},
"jumped": {"key_sha256": "0e00ab449f01a5195a83b4aee0dfbc2ce8d46466a640b92e33977d2e42f777f8", "pos": 475},
},
]
@pytest.fixture(scope='module', params=[True, False])
def endpoint(request):
return request.param
class TestSeed:
def test_scalar(self):
s = Generator(MT19937(0))
assert_equal(s.integers(1000), 479)
s = Generator(MT19937(4294967295))
assert_equal(s.integers(1000), 324)
def test_array(self):
s = Generator(MT19937(range(10)))
assert_equal(s.integers(1000), 465)
s = Generator(MT19937(np.arange(10)))
assert_equal(s.integers(1000), 465)
s = Generator(MT19937([0]))
assert_equal(s.integers(1000), 479)
s = Generator(MT19937([4294967295]))
assert_equal(s.integers(1000), 324)
def test_seedsequence(self):
s = MT19937(SeedSequence(0))
assert_equal(s.random_raw(1), 2058676884)
def test_invalid_scalar(self):
# seed must be an unsigned 32 bit integer
assert_raises(TypeError, MT19937, -0.5)
assert_raises(ValueError, MT19937, -1)
def test_invalid_array(self):
# seed must be an unsigned integer
assert_raises(TypeError, MT19937, [-0.5])
assert_raises(ValueError, MT19937, [-1])
assert_raises(ValueError, MT19937, [1, -2, 4294967296])
def test_noninstantized_bitgen(self):
assert_raises(ValueError, Generator, 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.integers(-5, -1) < -1)
x = random.integers(-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])
assert_raises(ValueError, random.multinomial, [-1] * 10, [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 = Generator(MT19937(1432985819))
non_contig = random.multinomial(100, pvals=pvals)
random = Generator(MT19937(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()
random = Generator(MT19937(1432985819))
match = r"[\w\s]*pvals array is cast to 64-bit floating"
with pytest.raises(ValueError, match=match):
random.multinomial(1, pvals)
class TestMultivariateHypergeometric:
def setup_method(self):
self.seed = 8675309
def test_argument_validation(self):
# Error cases...
# `colors` must be a 1-d sequence
assert_raises(ValueError, random.multivariate_hypergeometric,
10, 4)
# Negative nsample
assert_raises(ValueError, random.multivariate_hypergeometric,
[2, 3, 4], -1)
# Negative color
assert_raises(ValueError, random.multivariate_hypergeometric,
[-1, 2, 3], 2)
# nsample exceeds sum(colors)
assert_raises(ValueError, random.multivariate_hypergeometric,
[2, 3, 4], 10)
# nsample exceeds sum(colors) (edge case of empty colors)
assert_raises(ValueError, random.multivariate_hypergeometric,
[], 1)
# Validation errors associated with very large values in colors.
assert_raises(ValueError, random.multivariate_hypergeometric,
[999999999, 101], 5, 1, 'marginals')
int64_info = np.iinfo(np.int64)
max_int64 = int64_info.max
max_int64_index = max_int64 // int64_info.dtype.itemsize
assert_raises(ValueError, random.multivariate_hypergeometric,
[max_int64_index - 100, 101], 5, 1, 'count')
@pytest.mark.parametrize('method', ['count', 'marginals'])
def test_edge_cases(self, method):
# Set the seed, but in fact, all the results in this test are
# deterministic, so we don't really need this.
random = Generator(MT19937(self.seed))
x = random.multivariate_hypergeometric([0, 0, 0], 0, method=method)
assert_array_equal(x, [0, 0, 0])
x = random.multivariate_hypergeometric([], 0, method=method)
assert_array_equal(x, [])
x = random.multivariate_hypergeometric([], 0, size=1, method=method)
assert_array_equal(x, np.empty((1, 0), dtype=np.int64))
x = random.multivariate_hypergeometric([1, 2, 3], 0, method=method)
assert_array_equal(x, [0, 0, 0])
x = random.multivariate_hypergeometric([9, 0, 0], 3, method=method)
assert_array_equal(x, [3, 0, 0])
colors = [1, 1, 0, 1, 1]
x = random.multivariate_hypergeometric(colors, sum(colors),
method=method)
assert_array_equal(x, colors)
x = random.multivariate_hypergeometric([3, 4, 5], 12, size=3,
method=method)
assert_array_equal(x, [[3, 4, 5]]*3)
# Cases for nsample:
# nsample < 10
# 10 <= nsample < colors.sum()/2
# colors.sum()/2 < nsample < colors.sum() - 10
# colors.sum() - 10 < nsample < colors.sum()
@pytest.mark.parametrize('nsample', [8, 25, 45, 55])
@pytest.mark.parametrize('method', ['count', 'marginals'])
@pytest.mark.parametrize('size', [5, (2, 3), 150000])
def test_typical_cases(self, nsample, method, size):
random = Generator(MT19937(self.seed))
colors = np.array([10, 5, 20, 25])
sample = random.multivariate_hypergeometric(colors, nsample, size,
method=method)
if isinstance(size, int):
expected_shape = (size,) + colors.shape
else:
expected_shape = size + colors.shape
assert_equal(sample.shape, expected_shape)
assert_((sample >= 0).all())
assert_((sample <= colors).all())
assert_array_equal(sample.sum(axis=-1),
np.full(size, fill_value=nsample, dtype=int))
if isinstance(size, int) and size >= 100000:
# This sample is large enough to compare its mean to
# the expected values.
assert_allclose(sample.mean(axis=0),
nsample * colors / colors.sum(),
rtol=1e-3, atol=0.005)
def test_repeatability1(self):
random = Generator(MT19937(self.seed))
sample = random.multivariate_hypergeometric([3, 4, 5], 5, size=5,
method='count')
expected = np.array([[2, 1, 2],
[2, 1, 2],
[1, 1, 3],
[2, 0, 3],
[2, 1, 2]])
assert_array_equal(sample, expected)
def test_repeatability2(self):
random = Generator(MT19937(self.seed))
sample = random.multivariate_hypergeometric([20, 30, 50], 50,
size=5,
method='marginals')
expected = np.array([[ 9, 17, 24],
[ 7, 13, 30],
[ 9, 15, 26],
[ 9, 17, 24],
[12, 14, 24]])
assert_array_equal(sample, expected)
def test_repeatability3(self):
random = Generator(MT19937(self.seed))
sample = random.multivariate_hypergeometric([20, 30, 50], 12,
size=5,
method='marginals')
expected = np.array([[2, 3, 7],
[5, 3, 4],
[2, 5, 5],
[5, 3, 4],
[1, 5, 6]])
assert_array_equal(sample, expected)
class TestSetState:
def setup_method(self):
self.seed = 1234567890
self.rg = Generator(MT19937(self.seed))
self.bit_generator = self.rg.bit_generator
self.state = self.bit_generator.state
self.legacy_state = (self.state['bit_generator'],
self.state['state']['key'],
self.state['state']['pos'])
def test_gaussian_reset(self):
# Make sure the cached every-other-Gaussian is reset.
old = self.rg.standard_normal(size=3)
self.bit_generator.state = self.state
new = self.rg.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.rg.standard_normal()
state = self.bit_generator.state
old = self.rg.standard_normal(size=3)
self.bit_generator.state = state
new = self.rg.standard_normal(size=3)
assert_(np.all(old == new))
def test_negative_binomial(self):
# Ensure that the negative binomial results take floating point
# arguments without truncation.
self.rg.negative_binomial(0.5, 0.5)
class TestIntegers:
rfunc = random.integers
# valid integer/boolean types
itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
np.int32, np.uint32, np.int64, np.uint64]
def test_unsupported_type(self, endpoint):
assert_raises(TypeError, self.rfunc, 1, endpoint=endpoint, dtype=float)
def test_bounds_checking(self, endpoint):
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd,
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1,
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, ubnd, lbnd,
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, 1, 0, endpoint=endpoint,
dtype=dt)
assert_raises(ValueError, self.rfunc, [lbnd - 1], ubnd,
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, [lbnd], [ubnd + 1],
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, [ubnd], [lbnd],
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, 1, [0],
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, [ubnd+1], [ubnd],
endpoint=endpoint, dtype=dt)
def test_bounds_checking_array(self, endpoint):
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + (not endpoint)
assert_raises(ValueError, self.rfunc, [lbnd - 1] * 2, [ubnd] * 2,
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, [lbnd] * 2,
[ubnd + 1] * 2, endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, ubnd, [lbnd] * 2,
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, [1] * 2, 0,
endpoint=endpoint, dtype=dt)
def test_rng_zero_and_extremes(self, endpoint):
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
is_open = not endpoint
tgt = ubnd - 1
assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
endpoint=endpoint, dtype=dt), tgt)
assert_equal(self.rfunc([tgt], tgt + is_open, size=1000,
endpoint=endpoint, dtype=dt), tgt)
tgt = lbnd
assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
endpoint=endpoint, dtype=dt), tgt)
assert_equal(self.rfunc(tgt, [tgt + is_open], size=1000,
endpoint=endpoint, dtype=dt), tgt)
tgt = (lbnd + ubnd) // 2
assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
endpoint=endpoint, dtype=dt), tgt)
assert_equal(self.rfunc([tgt], [tgt + is_open],
size=1000, endpoint=endpoint, dtype=dt),
tgt)
def test_rng_zero_and_extremes_array(self, endpoint):
size = 1000
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
tgt = ubnd - 1
assert_equal(self.rfunc([tgt], [tgt + 1],
size=size, dtype=dt), tgt)
assert_equal(self.rfunc(
[tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
assert_equal(self.rfunc(
[tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
tgt = lbnd
assert_equal(self.rfunc([tgt], [tgt + 1],
size=size, dtype=dt), tgt)
assert_equal(self.rfunc(
[tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
assert_equal(self.rfunc(
[tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
tgt = (lbnd + ubnd) // 2
assert_equal(self.rfunc([tgt], [tgt + 1],
size=size, dtype=dt), tgt)
assert_equal(self.rfunc(
[tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
assert_equal(self.rfunc(
[tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
def test_full_range(self, endpoint):
# Test for ticket #1690
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
try:
self.rfunc(lbnd, ubnd, endpoint=endpoint, 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_full_range_array(self, endpoint):
# Test for ticket #1690
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
try:
self.rfunc([lbnd] * 2, [ubnd], endpoint=endpoint, 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, endpoint):
# Don't use fixed seed
random = Generator(MT19937())
for dt in self.itype[1:]:
for ubnd in [4, 8, 16]:
vals = self.rfunc(2, ubnd - endpoint, size=2 ** 16,
endpoint=endpoint, dtype=dt)
assert_(vals.max() < ubnd)
assert_(vals.min() >= 2)
vals = self.rfunc(0, 2 - endpoint, size=2 ** 16, endpoint=endpoint,
dtype=bool)
assert_(vals.max() < 2)
assert_(vals.min() >= 0)
def test_scalar_array_equiv(self, endpoint):
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
size = 1000
random = Generator(MT19937(1234))
scalar = random.integers(lbnd, ubnd, size=size, endpoint=endpoint,
dtype=dt)
random = Generator(MT19937(1234))
scalar_array = random.integers([lbnd], [ubnd], size=size,
endpoint=endpoint, dtype=dt)
random = Generator(MT19937(1234))
array = random.integers([lbnd] * size, [ubnd] *
size, size=size, endpoint=endpoint, dtype=dt)
assert_array_equal(scalar, scalar_array)
assert_array_equal(scalar, array)
def test_repeatability(self, endpoint):
# 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': '053594a9b82d656f967c54869bc6970aa0358cf94ad469c81478459c6a90eee3',
'int16': '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
'int32': 'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
'int64': '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
'int8': '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1',
'uint16': '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
'uint32': 'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
'uint64': '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
'uint8': '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1'}
for dt in self.itype[1:]:
random = Generator(MT19937(1234))
# view as little endian for hash
if sys.byteorder == 'little':
val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
dtype=dt)
else:
val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
dtype=dt).byteswap()
res = hashlib.sha256(val).hexdigest()
assert_(tgt[np.dtype(dt).name] == res)
# bools do not depend on endianness
random = Generator(MT19937(1234))
val = random.integers(0, 2 - endpoint, size=1000, endpoint=endpoint,
dtype=bool).view(np.int8)
res = hashlib.sha256(val).hexdigest()
assert_(tgt[np.dtype(bool).name] == res)
def test_repeatability_broadcasting(self, endpoint):
for dt in self.itype:
lbnd = 0 if dt in (bool, np.bool_) else np.iinfo(dt).min
ubnd = 2 if dt in (bool, np.bool_) else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
# view as little endian for hash
random = Generator(MT19937(1234))
val = random.integers(lbnd, ubnd, size=1000, endpoint=endpoint,
dtype=dt)
random = Generator(MT19937(1234))
val_bc = random.integers([lbnd] * 1000, ubnd, endpoint=endpoint,
dtype=dt)
assert_array_equal(val, val_bc)
random = Generator(MT19937(1234))
val_bc = random.integers([lbnd] * 1000, [ubnd] * 1000,
endpoint=endpoint, dtype=dt)
assert_array_equal(val, val_bc)
@pytest.mark.parametrize(
'bound, expected',
[(2**32 - 1, np.array([517043486, 1364798665, 1733884389, 1353720612,
3769704066, 1170797179, 4108474671])),
(2**32, np.array([517043487, 1364798666, 1733884390, 1353720613,
3769704067, 1170797180, 4108474672])),
(2**32 + 1, np.array([517043487, 1733884390, 3769704068, 4108474673,
1831631863, 1215661561, 3869512430]))]
)
def test_repeatability_32bit_boundary(self, bound, expected):
for size in [None, len(expected)]:
random = Generator(MT19937(1234))
x = random.integers(bound, size=size)
assert_equal(x, expected if size is not None else expected[0])
def test_repeatability_32bit_boundary_broadcasting(self):
desired = np.array([[[1622936284, 3620788691, 1659384060],
[1417365545, 760222891, 1909653332],
[3788118662, 660249498, 4092002593]],
[[3625610153, 2979601262, 3844162757],
[ 685800658, 120261497, 2694012896],
[1207779440, 1586594375, 3854335050]],
[[3004074748, 2310761796, 3012642217],
[2067714190, 2786677879, 1363865881],
[ 791663441, 1867303284, 2169727960]],
[[1939603804, 1250951100, 298950036],
[1040128489, 3791912209, 3317053765],
[3155528714, 61360675, 2305155588]],
[[ 817688762, 1335621943, 3288952434],
[1770890872, 1102951817, 1957607470],
[3099996017, 798043451, 48334215]]])
for size in [None, (5, 3, 3)]:
random = Generator(MT19937(12345))
x = random.integers([[-1], [0], [1]],
[2**32 - 1, 2**32, 2**32 + 1],
size=size)
assert_array_equal(x, desired if size is not None else desired[0])
def test_int64_uint64_broadcast_exceptions(self, endpoint):
configs = {np.uint64: ((0, 2**65), (-1, 2**62), (10, 9), (0, 0)),
np.int64: ((0, 2**64), (-(2**64), 2**62), (10, 9), (0, 0),
(-2**63-1, -2**63-1))}
for dtype in configs:
for config in configs[dtype]:
low, high = config
high = high - endpoint
low_a = np.array([[low]*10])
high_a = np.array([high] * 10)
assert_raises(ValueError, random.integers, low, high,
endpoint=endpoint, dtype=dtype)
assert_raises(ValueError, random.integers, low_a, high,
endpoint=endpoint, dtype=dtype)
assert_raises(ValueError, random.integers, low, high_a,
endpoint=endpoint, dtype=dtype)
assert_raises(ValueError, random.integers, low_a, high_a,
endpoint=endpoint, dtype=dtype)
low_o = np.array([[low]*10], dtype=object)
high_o = np.array([high] * 10, dtype=object)
assert_raises(ValueError, random.integers, low_o, high,
endpoint=endpoint, dtype=dtype)
assert_raises(ValueError, random.integers, low, high_o,
endpoint=endpoint, dtype=dtype)
assert_raises(ValueError, random.integers, low_o, high_o,
endpoint=endpoint, dtype=dtype)
def test_int64_uint64_corner_case(self, endpoint):
# 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 - endpoint)
# None of these function calls should
# generate a ValueError now.
actual = random.integers(lbnd, ubnd, endpoint=endpoint, dtype=dt)
assert_equal(actual, tgt)
def test_respect_dtype_singleton(self, endpoint):
# See gh-7203
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
dt = np.bool_ if dt is bool else dt
sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
assert_equal(sample.dtype, dt)
for dt in (bool, int, np.compat.long):
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
# gh-7284: Ensure that we get Python data types
sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
assert not hasattr(sample, 'dtype')
assert_equal(type(sample), dt)
def test_respect_dtype_array(self, endpoint):
# See gh-7203
for dt in self.itype:
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
ubnd = ubnd - 1 if endpoint else ubnd
dt = np.bool_ if dt is bool else dt
sample = self.rfunc([lbnd], [ubnd], endpoint=endpoint, dtype=dt)
assert_equal(sample.dtype, dt)
sample = self.rfunc([lbnd] * 2, [ubnd] * 2, endpoint=endpoint,
dtype=dt)
assert_equal(sample.dtype, dt)
def test_zero_size(self, endpoint):
# See gh-7203
for dt in self.itype:
sample = self.rfunc(0, 0, (3, 0, 4), endpoint=endpoint, dtype=dt)
assert sample.shape == (3, 0, 4)
assert sample.dtype == dt
assert self.rfunc(0, -10, 0, endpoint=endpoint,
dtype=dt).shape == (0,)
assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape,
(3, 0, 4))
assert_equal(random.integers(0, -10, size=0).shape, (0,))
assert_equal(random.integers(10, 10, size=0).shape, (0,))
def test_error_byteorder(self):
other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'
with pytest.raises(ValueError):
random.integers(0, 200, size=10, dtype=other_byteord_dt)
# chi2max is the maximum acceptable chi-squared value.
@pytest.mark.slow
@pytest.mark.parametrize('sample_size,high,dtype,chi2max',
[(5000000, 5, np.int8, 125.0), # p-value ~4.6e-25
(5000000, 7, np.uint8, 150.0), # p-value ~7.7e-30
(10000000, 2500, np.int16, 3300.0), # p-value ~3.0e-25
(50000000, 5000, np.uint16, 6500.0), # p-value ~3.5e-25
])
def test_integers_small_dtype_chisquared(self, sample_size, high,
dtype, chi2max):
# Regression test for gh-14774.
samples = random.integers(high, size=sample_size, dtype=dtype)
values, counts = np.unique(samples, return_counts=True)
expected = sample_size / high
chi2 = ((counts - expected)**2 / expected).sum()
assert chi2 < chi2max
class TestRandomDist:
# Make sure the random distribution returns the correct value for a
# given seed
def setup_method(self):
self.seed = 1234567890
def test_integers(self):
random = Generator(MT19937(self.seed))
actual = random.integers(-99, 99, size=(3, 2))
desired = np.array([[-80, -56], [41, 37], [-83, -16]])
assert_array_equal(actual, desired)
def test_integers_masked(self):
# Test masked rejection sampling algorithm to generate array of
# uint32 in an interval.
random = Generator(MT19937(self.seed))
actual = random.integers(0, 99, size=(3, 2), dtype=np.uint32)
desired = np.array([[9, 21], [70, 68], [8, 41]], dtype=np.uint32)
assert_array_equal(actual, desired)
def test_integers_closed(self):
random = Generator(MT19937(self.seed))
actual = random.integers(-99, 99, size=(3, 2), endpoint=True)
desired = np.array([[-80, -56], [ 41, 38], [-83, -15]])
assert_array_equal(actual, desired)
def test_integers_max_int(self):
# Tests whether integers with closed=True 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.
actual = random.integers(np.iinfo('l').max, np.iinfo('l').max,
endpoint=True)
desired = np.iinfo('l').max
assert_equal(actual, desired)
def test_random(self):
random = Generator(MT19937(self.seed))
actual = random.random((3, 2))
desired = np.array([[0.096999199829214, 0.707517457682192],
[0.084364834598269, 0.767731206553125],
[0.665069021359413, 0.715487190596693]])
assert_array_almost_equal(actual, desired, decimal=15)
random = Generator(MT19937(self.seed))
actual = random.random()
assert_array_almost_equal(actual, desired[0, 0], decimal=15)
def test_random_float(self):
random = Generator(MT19937(self.seed))
actual = random.random((3, 2))
desired = np.array([[0.0969992 , 0.70751746],
[0.08436483, 0.76773121],
[0.66506902, 0.71548719]])
assert_array_almost_equal(actual, desired, decimal=7)
def test_random_float_scalar(self):
random = Generator(MT19937(self.seed))
actual = random.random(dtype=np.float32)
desired = 0.0969992
assert_array_almost_equal(actual, desired, decimal=7)
@pytest.mark.parametrize('dtype, uint_view_type',
[(np.float32, np.uint32),
(np.float64, np.uint64)])
def test_random_distribution_of_lsb(self, dtype, uint_view_type):
random = Generator(MT19937(self.seed))
sample = random.random(100000, dtype=dtype)
num_ones_in_lsb = np.count_nonzero(sample.view(uint_view_type) & 1)
# The probability of a 1 in the least significant bit is 0.25.
# With a sample size of 100000, the probability that num_ones_in_lsb
# is outside the following range is less than 5e-11.
assert 24100 < num_ones_in_lsb < 25900
def test_random_unsupported_type(self):
assert_raises(TypeError, random.random, dtype='int32')
def test_choice_uniform_replace(self):
random = Generator(MT19937(self.seed))
actual = random.choice(4, 4)
desired = np.array([0, 0, 2, 2], dtype=np.int64)
assert_array_equal(actual, desired)
def test_choice_nonuniform_replace(self):
random = Generator(MT19937(self.seed))
actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
desired = np.array([0, 1, 0, 1], dtype=np.int64)
assert_array_equal(actual, desired)
def test_choice_uniform_noreplace(self):
random = Generator(MT19937(self.seed))
actual = random.choice(4, 3, replace=False)
desired = np.array([2, 0, 3], dtype=np.int64)
assert_array_equal(actual, desired)
actual = random.choice(4, 4, replace=False, shuffle=False)
desired = np.arange(4, dtype=np.int64)
assert_array_equal(actual, desired)
def test_choice_nonuniform_noreplace(self):
random = Generator(MT19937(self.seed))
actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
desired = np.array([0, 2, 3], dtype=np.int64)
assert_array_equal(actual, desired)
def test_choice_noninteger(self):
random = Generator(MT19937(self.seed))
actual = random.choice(['a', 'b', 'c', 'd'], 4)
desired = np.array(['a', 'a', 'c', 'c'])
assert_array_equal(actual, desired)
def test_choice_multidimensional_default_axis(self):
random = Generator(MT19937(self.seed))
actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 3)
desired = np.array([[0, 1], [0, 1], [4, 5]])
assert_array_equal(actual, desired)
def test_choice_multidimensional_custom_axis(self):
random = Generator(MT19937(self.seed))
actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 1, axis=1)
desired = np.array([[0], [2], [4], [6]])
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, [], 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.integers(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
assert_equal(random.integers(0, -10, size=0).shape, (0,))
assert_equal(random.integers(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 = Generator(MT19937(self.seed))
non_contig = random.choice(5, 3, p=p[::2])
random = Generator(MT19937(self.seed))
contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
assert_array_equal(non_contig, contig)
def test_choice_return_type(self):
# gh 9867
p = np.ones(4) / 4.
actual = random.choice(4, 2)
assert actual.dtype == np.int64
actual = random.choice(4, 2, replace=False)
assert actual.dtype == np.int64
actual = random.choice(4, 2, p=p)
assert actual.dtype == np.int64
actual = random.choice(4, 2, p=p, replace=False)
assert actual.dtype == np.int64
def test_choice_large_sample(self):
choice_hash = '4266599d12bfcfb815213303432341c06b4349f5455890446578877bb322e222'
random = Generator(MT19937(self.seed))
actual = random.choice(10000, 5000, replace=False)
if sys.byteorder != 'little':
actual = actual.byteswap()
res = hashlib.sha256(actual.view(np.int8)).hexdigest()
assert_(choice_hash == res)
def test_bytes(self):
random = Generator(MT19937(self.seed))
actual = random.bytes(10)
desired = b'\x86\xf0\xd4\x18\xe1\x81\t8%\xdd'
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 = Generator(MT19937(self.seed))
alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
random.shuffle(alist)
actual = alist
desired = conv([4, 1, 9, 8, 0, 5, 3, 6, 2, 7])
assert_array_equal(actual, desired)
def test_shuffle_custom_axis(self):
random = Generator(MT19937(self.seed))
actual = np.arange(16).reshape((4, 4))
random.shuffle(actual, axis=1)
desired = np.array([[ 0, 3, 1, 2],
[ 4, 7, 5, 6],
[ 8, 11, 9, 10],
[12, 15, 13, 14]])
assert_array_equal(actual, desired)
random = Generator(MT19937(self.seed))
actual = np.arange(16).reshape((4, 4))
random.shuffle(actual, axis=-1)
assert_array_equal(actual, desired)
def test_shuffle_custom_axis_empty(self):
random = Generator(MT19937(self.seed))
desired = np.array([]).reshape((0, 6))
for axis in (0, 1):
actual = np.array([]).reshape((0, 6))
random.shuffle(actual, axis=axis)
assert_array_equal(actual, desired)
def test_shuffle_axis_nonsquare(self):
y1 = np.arange(20).reshape(2, 10)
y2 = y1.copy()
random = Generator(MT19937(self.seed))
random.shuffle(y1, axis=1)
random = Generator(MT19937(self.seed))
random.shuffle(y2.T)
assert_array_equal(y1, y2)
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_exceptions(self):
random = Generator(MT19937(self.seed))
arr = np.arange(10)
assert_raises(np.AxisError, random.shuffle, arr, 1)
arr = np.arange(9).reshape((3, 3))
assert_raises(np.AxisError, random.shuffle, arr, 3)
assert_raises(TypeError, random.shuffle, arr, slice(1, 2, None))
arr = [[1, 2, 3], [4, 5, 6]]
assert_raises(NotImplementedError, random.shuffle, arr, 1)
arr = np.array(3)
assert_raises(TypeError, random.shuffle, arr)
arr = np.ones((3, 2))
assert_raises(np.AxisError, random.shuffle, arr, 2)
def test_shuffle_not_writeable(self):
random = Generator(MT19937(self.seed))
a = np.zeros(5)
a.flags.writeable = False
with pytest.raises(ValueError, match='read-only'):
random.shuffle(a)
def test_permutation(self):
random = Generator(MT19937(self.seed))
alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
actual = random.permutation(alist)
desired = [4, 1, 9, 8, 0, 5, 3, 6, 2, 7]
assert_array_equal(actual, desired)
random = Generator(MT19937(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)
bad_x_str = "abcd"
assert_raises(np.AxisError, random.permutation, bad_x_str)
bad_x_float = 1.2
assert_raises(np.AxisError, random.permutation, bad_x_float)
random = Generator(MT19937(self.seed))
integer_val = 10
desired = [3, 0, 8, 7, 9, 4, 2, 5, 1, 6]
actual = random.permutation(integer_val)
assert_array_equal(actual, desired)
def test_permutation_custom_axis(self):
a = np.arange(16).reshape((4, 4))
desired = np.array([[ 0, 3, 1, 2],
[ 4, 7, 5, 6],
[ 8, 11, 9, 10],
[12, 15, 13, 14]])
random = Generator(MT19937(self.seed))
actual = random.permutation(a, axis=1)
assert_array_equal(actual, desired)
random = Generator(MT19937(self.seed))
actual = random.permutation(a, axis=-1)
assert_array_equal(actual, desired)
def test_permutation_exceptions(self):
random = Generator(MT19937(self.seed))
arr = np.arange(10)
assert_raises(np.AxisError, random.permutation, arr, 1)
arr = np.arange(9).reshape((3, 3))
assert_raises(np.AxisError, random.permutation, arr, 3)
assert_raises(TypeError, random.permutation, arr, slice(1, 2, None))
@pytest.mark.parametrize("dtype", [int, object])
@pytest.mark.parametrize("axis, expected",
[(None, np.array([[3, 7, 0, 9, 10, 11],
[8, 4, 2, 5, 1, 6]])),
(0, np.array([[6, 1, 2, 9, 10, 11],
[0, 7, 8, 3, 4, 5]])),
(1, np.array([[ 5, 3, 4, 0, 2, 1],
[11, 9, 10, 6, 8, 7]]))])
def test_permuted(self, dtype, axis, expected):
random = Generator(MT19937(self.seed))
x = np.arange(12).reshape(2, 6).astype(dtype)
random.permuted(x, axis=axis, out=x)
assert_array_equal(x, expected)
random = Generator(MT19937(self.seed))
x = np.arange(12).reshape(2, 6).astype(dtype)
y = random.permuted(x, axis=axis)
assert y.dtype == dtype
assert_array_equal(y, expected)
def test_permuted_with_strides(self):
random = Generator(MT19937(self.seed))
x0 = np.arange(22).reshape(2, 11)
x1 = x0.copy()
x = x0[:, ::3]
y = random.permuted(x, axis=1, out=x)
expected = np.array([[0, 9, 3, 6],
[14, 20, 11, 17]])
assert_array_equal(y, expected)
x1[:, ::3] = expected
# Verify that the original x0 was modified in-place as expected.
assert_array_equal(x1, x0)
def test_permuted_empty(self):
y = random.permuted([])
assert_array_equal(y, [])
@pytest.mark.parametrize('outshape', [(2, 3), 5])
def test_permuted_out_with_wrong_shape(self, outshape):
a = np.array([1, 2, 3])
out = np.zeros(outshape, dtype=a.dtype)
with pytest.raises(ValueError, match='same shape'):
random.permuted(a, out=out)
def test_permuted_out_with_wrong_type(self):
out = np.zeros((3, 5), dtype=np.int32)
x = np.ones((3, 5))
with pytest.raises(TypeError, match='Cannot cast'):
random.permuted(x, axis=1, out=out)
def test_permuted_not_writeable(self):
x = np.zeros((2, 5))
x.flags.writeable = False
with pytest.raises(ValueError, match='read-only'):
random.permuted(x, axis=1, out=x)
def test_beta(self):
random = Generator(MT19937(self.seed))
actual = random.beta(.1, .9, size=(3, 2))
desired = np.array(
[[1.083029353267698e-10, 2.449965303168024e-11],
[2.397085162969853e-02, 3.590779671820755e-08],
[2.830254190078299e-04, 1.744709918330393e-01]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_binomial(self):
random = Generator(MT19937(self.seed))
actual = random.binomial(100.123, .456, size=(3, 2))
desired = np.array([[42, 41],
[42, 48],
[44, 50]])
assert_array_equal(actual, desired)
random = Generator(MT19937(self.seed))
actual = random.binomial(100.123, .456)
desired = 42
assert_array_equal(actual, desired)
def test_chisquare(self):
random = Generator(MT19937(self.seed))
actual = random.chisquare(50, size=(3, 2))
desired = np.array([[32.9850547060149, 39.0219480493301],
[56.2006134779419, 57.3474165711485],
[55.4243733880198, 55.4209797925213]])
assert_array_almost_equal(actual, desired, decimal=13)
def test_dirichlet(self):
random = Generator(MT19937(self.seed))
alpha = np.array([51.72840233779265162, 39.74494232180943953])
actual = random.dirichlet(alpha, size=(3, 2))
desired = np.array([[[0.5439892869558927, 0.45601071304410745],
[0.5588917345860708, 0.4411082654139292 ]],
[[0.5632074165063435, 0.43679258349365657],
[0.54862581112627, 0.45137418887373015]],
[[0.49961831357047226, 0.5003816864295278 ],
[0.52374806183482, 0.47625193816517997]]])
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 = Generator(MT19937(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)
# gh-15876
assert_raises(ValueError, random.dirichlet, [[5, 1]])
assert_raises(ValueError, random.dirichlet, [[5], [1]])
assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
def test_dirichlet_alpha_non_contiguous(self):
a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
alpha = a[::2]
random = Generator(MT19937(self.seed))
non_contig = random.dirichlet(alpha, size=(3, 2))
random = Generator(MT19937(self.seed))
contig = random.dirichlet(np.ascontiguousarray(alpha),
size=(3, 2))
assert_array_almost_equal(non_contig, contig)
def test_dirichlet_small_alpha(self):
eps = 1.0e-9 # 1.0e-10 -> runtime x 10; 1e-11 -> runtime x 200, etc.
alpha = eps * np.array([1., 1.0e-3])
random = Generator(MT19937(self.seed))
actual = random.dirichlet(alpha, size=(3, 2))
expected = np.array([
[[1., 0.],
[1., 0.]],
[[1., 0.],
[1., 0.]],
[[1., 0.],
[1., 0.]]
])
assert_array_almost_equal(actual, expected, decimal=15)
@pytest.mark.slow
def test_dirichlet_moderately_small_alpha(self):
# Use alpha.max() < 0.1 to trigger stick breaking code path
alpha = np.array([0.02, 0.04, 0.03])
exact_mean = alpha / alpha.sum()
random = Generator(MT19937(self.seed))
sample = random.dirichlet(alpha, size=20000000)
sample_mean = sample.mean(axis=0)
assert_allclose(sample_mean, exact_mean, rtol=1e-3)
def test_exponential(self):
random = Generator(MT19937(self.seed))
actual = random.exponential(1.1234, size=(3, 2))
desired = np.array([[0.098845481066258, 1.560752510746964],
[0.075730916041636, 1.769098974710777],
[1.488602544592235, 2.49684815275751 ]])
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 = Generator(MT19937(self.seed))
actual = random.f(12, 77, size=(3, 2))
desired = np.array([[0.461720027077085, 1.100441958872451],
[1.100337455217484, 0.91421736740018 ],
[0.500811891303113, 0.826802454552058]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_gamma(self):
random = Generator(MT19937(self.seed))
actual = random.gamma(5, 3, size=(3, 2))
desired = np.array([[ 5.03850858902096, 7.9228656732049 ],
[18.73983605132985, 19.57961681699238],
[18.17897755150825, 18.17653912505234]])
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 = Generator(MT19937(self.seed))
actual = random.geometric(.123456789, size=(3, 2))
desired = np.array([[1, 11],
[1, 12],
[11, 17]])
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 np.errstate(invalid='ignore'):
assert_raises(ValueError, random.geometric, np.nan)
assert_raises(ValueError, random.geometric, [np.nan] * 10)
def test_gumbel(self):
random = Generator(MT19937(self.seed))
actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[ 4.688397515056245, -0.289514845417841],
[ 4.981176042584683, -0.633224272589149],
[-0.055915275687488, -0.333962478257953]])
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 = Generator(MT19937(self.seed))
actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
desired = np.array([[ 9, 9],
[ 9, 9],
[10, 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 = Generator(MT19937(self.seed))
actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[-3.156353949272393, 1.195863024830054],
[-3.435458081645966, 1.656882398925444],
[ 0.924824032467446, 1.251116432209336]])
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 = Generator(MT19937(self.seed))
actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[-4.338584631510999, 1.890171436749954],
[-4.64547787337966 , 2.514545562919217],
[ 1.495389489198666, 1.967827627577474]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_lognormal(self):
random = Generator(MT19937(self.seed))
actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
desired = np.array([[ 0.0268252166335, 13.9534486483053],
[ 0.1204014788936, 2.2422077497792],
[ 4.2484199496128, 12.0093343977523]])
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 = Generator(MT19937(self.seed))
actual = random.logseries(p=.923456789, size=(3, 2))
desired = np.array([[14, 17],
[3, 18],
[5, 1]])
assert_array_equal(actual, desired)
def test_logseries_zero(self):
random = Generator(MT19937(self.seed))
assert random.logseries(0) == 1
@pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
def test_logseries_exceptions(self, value):
random = Generator(MT19937(self.seed))
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 = Generator(MT19937(self.seed))
actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))
desired = np.array([[[1, 5, 1, 6, 4, 3],
[4, 2, 6, 2, 4, 2]],
[[5, 3, 2, 6, 3, 1],
[4, 4, 0, 2, 3, 7]],
[[6, 3, 1, 5, 3, 2],
[5, 5, 3, 1, 2, 4]]])
assert_array_equal(actual, desired)
@pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
@pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
def test_multivariate_normal(self, method):
random = Generator(MT19937(self.seed))
mean = (.123456789, 10)
cov = [[1, 0], [0, 1]]
size = (3, 2)
actual = random.multivariate_normal(mean, cov, size, method=method)
desired = np.array([[[-1.747478062846581, 11.25613495182354 ],
[-0.9967333370066214, 10.342002097029821 ]],
[[ 0.7850019631242964, 11.181113712443013 ],
[ 0.8901349653255224, 8.873825399642492 ]],
[[ 0.7130260107430003, 9.551628690083056 ],
[ 0.7127098726541128, 11.991709234143173 ]]])
assert_array_almost_equal(actual, desired, decimal=15)
# Check for default size, was raising deprecation warning
actual = random.multivariate_normal(mean, cov, method=method)
desired = np.array([0.233278563284287, 9.424140804347195])
assert_array_almost_equal(actual, desired, decimal=15)
# Check that non symmetric covariance input raises exception when
# check_valid='raises' if using default svd method.
mean = [0, 0]
cov = [[1, 2], [1, 2]]
assert_raises(ValueError, random.multivariate_normal, mean, cov,
check_valid='raise')
# Check that non positive-semidefinite covariance warns with
# RuntimeWarning
cov = [[1, 2], [2, 1]]
assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov,
method='eigh')
assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
method='cholesky')
# 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')
assert_raises(ValueError, random.multivariate_normal, mean, cov,
check_valid='raise', method='eigh')
# check degenerate samples from singular covariance matrix
cov = [[1, 1], [1, 1]]
if method in ('svd', 'eigh'):
samples = random.multivariate_normal(mean, cov, size=(3, 2),
method=method)
assert_array_almost_equal(samples[..., 0], samples[..., 1],
decimal=6)
else:
assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
method='cholesky')
cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
with suppress_warnings() as sup:
random.multivariate_normal(mean, cov, method=method)
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))
@pytest.mark.parametrize('mean, cov', [([0], [[1+1j]]), ([0j], [[1]])])
def test_multivariate_normal_disallow_complex(self, mean, cov):
random = Generator(MT19937(self.seed))
with pytest.raises(TypeError, match="must not be complex"):
random.multivariate_normal(mean, cov)
@pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
def test_multivariate_normal_basic_stats(self, method):
random = Generator(MT19937(self.seed))
n_s = 1000
mean = np.array([1, 2])
cov = np.array([[2, 1], [1, 2]])
s = random.multivariate_normal(mean, cov, size=(n_s,), method=method)
s_center = s - mean
cov_emp = (s_center.T @ s_center) / (n_s - 1)
# these are pretty loose and are only designed to detect major errors
assert np.all(np.abs(s_center.mean(-2)) < 0.1)
assert np.all(np.abs(cov_emp - cov) < 0.2)
def test_negative_binomial(self):
random = Generator(MT19937(self.seed))
actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
desired = np.array([[543, 727],
[775, 760],
[600, 674]])
assert_array_equal(actual, desired)
def test_negative_binomial_exceptions(self):
with np.errstate(invalid='ignore'):
assert_raises(ValueError, random.negative_binomial, 100, np.nan)
assert_raises(ValueError, random.negative_binomial, 100,
[np.nan] * 10)
def test_negative_binomial_p0_exception(self):
# Verify that p=0 raises an exception.
with assert_raises(ValueError):
x = random.negative_binomial(1, 0)
def test_negative_binomial_invalid_p_n_combination(self):
# Verify that values of p and n that would result in an overflow
# or infinite loop raise an exception.
with np.errstate(invalid='ignore'):
assert_raises(ValueError, random.negative_binomial, 2**62, 0.1)
assert_raises(ValueError, random.negative_binomial, [2**62], [0.1])
def test_noncentral_chisquare(self):
random = Generator(MT19937(self.seed))
actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
desired = np.array([[ 1.70561552362133, 15.97378184942111],
[13.71483425173724, 20.17859633310629],
[11.3615477156643 , 3.67891108738029]])
assert_array_almost_equal(actual, desired, decimal=14)
actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
desired = np.array([[9.41427665607629e-04, 1.70473157518850e-04],
[1.14554372041263e+00, 1.38187755933435e-03],
[1.90659181905387e+00, 1.21772577941822e+00]])
assert_array_almost_equal(actual, desired, decimal=14)
random = Generator(MT19937(self.seed))
actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
desired = np.array([[0.82947954590419, 1.80139670767078],
[6.58720057417794, 7.00491463609814],
[6.31101879073157, 6.30982307753005]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_noncentral_f(self):
random = Generator(MT19937(self.seed))
actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,
size=(3, 2))
desired = np.array([[0.060310671139 , 0.23866058175939],
[0.86860246709073, 0.2668510459738 ],
[0.23375780078364, 1.88922102885943]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_noncentral_f_nan(self):
random = Generator(MT19937(self.seed))
actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
assert np.isnan(actual)
def test_normal(self):
random = Generator(MT19937(self.seed))
actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[-3.618412914693162, 2.635726692647081],
[-2.116923463013243, 0.807460983059643],
[ 1.446547137248593, 2.485684213886024]])
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 = Generator(MT19937(self.seed))
actual = random.pareto(a=.123456789, size=(3, 2))
desired = np.array([[1.0394926776069018e+00, 7.7142534343505773e+04],
[7.2640150889064703e-01, 3.4650454783825594e+05],
[4.5852344481994740e+04, 6.5851383009539105e+07]])
# 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 = Generator(MT19937(self.seed))
actual = random.poisson(lam=.123456789, size=(3, 2))
desired = np.array([[0, 0],
[0, 0],
[0, 0]])
assert_array_equal(actual, desired)
def test_poisson_exceptions(self):
lambig = np.iinfo('int64').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 np.errstate(invalid='ignore'):
assert_raises(ValueError, random.poisson, np.nan)
assert_raises(ValueError, random.poisson, [np.nan] * 10)
def test_power(self):
random = Generator(MT19937(self.seed))
actual = random.power(a=.123456789, size=(3, 2))
desired = np.array([[1.977857368842754e-09, 9.806792196620341e-02],
[2.482442984543471e-10, 1.527108843266079e-01],
[8.188283434244285e-02, 3.950547209346948e-01]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_rayleigh(self):
random = Generator(MT19937(self.seed))
actual = random.rayleigh(scale=10, size=(3, 2))
desired = np.array([[4.19494429102666, 16.66920198906598],
[3.67184544902662, 17.74695521962917],
[16.27935397855501, 21.08355560691792]])
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 = Generator(MT19937(self.seed))
actual = random.standard_cauchy(size=(3, 2))
desired = np.array([[-1.489437778266206, -3.275389641569784],
[ 0.560102864910406, -0.680780916282552],
[-1.314912905226277, 0.295852965660225]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_exponential(self):
random = Generator(MT19937(self.seed))
actual = random.standard_exponential(size=(3, 2), method='inv')
desired = np.array([[0.102031839440643, 1.229350298474972],
[0.088137284693098, 1.459859985522667],
[1.093830802293668, 1.256977002164613]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_expoential_type_error(self):
assert_raises(TypeError, random.standard_exponential, dtype=np.int32)
def test_standard_gamma(self):
random = Generator(MT19937(self.seed))
actual = random.standard_gamma(shape=3, size=(3, 2))
desired = np.array([[0.62970724056362, 1.22379851271008],
[3.899412530884 , 4.12479964250139],
[3.74994102464584, 3.74929307690815]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_standard_gammma_scalar_float(self):
random = Generator(MT19937(self.seed))
actual = random.standard_gamma(3, dtype=np.float32)
desired = 2.9242148399353027
assert_array_almost_equal(actual, desired, decimal=6)
def test_standard_gamma_float(self):
random = Generator(MT19937(self.seed))
actual = random.standard_gamma(shape=3, size=(3, 2))
desired = np.array([[0.62971, 1.2238 ],
[3.89941, 4.1248 ],
[3.74994, 3.74929]])
assert_array_almost_equal(actual, desired, decimal=5)
def test_standard_gammma_float_out(self):
actual = np.zeros((3, 2), dtype=np.float32)
random = Generator(MT19937(self.seed))
random.standard_gamma(10.0, out=actual, dtype=np.float32)
desired = np.array([[10.14987, 7.87012],
[ 9.46284, 12.56832],
[13.82495, 7.81533]], dtype=np.float32)
assert_array_almost_equal(actual, desired, decimal=5)
random = Generator(MT19937(self.seed))
random.standard_gamma(10.0, out=actual, size=(3, 2), dtype=np.float32)
assert_array_almost_equal(actual, desired, decimal=5)
def test_standard_gamma_unknown_type(self):
assert_raises(TypeError, random.standard_gamma, 1.,
dtype='int32')
def test_out_size_mismatch(self):
out = np.zeros(10)
assert_raises(ValueError, random.standard_gamma, 10.0, size=20,
out=out)
assert_raises(ValueError, random.standard_gamma, 10.0, size=(10, 1),
out=out)
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 = Generator(MT19937(self.seed))
actual = random.standard_normal(size=(3, 2))
desired = np.array([[-1.870934851846581, 1.25613495182354 ],
[-1.120190126006621, 0.342002097029821],
[ 0.661545174124296, 1.181113712443012]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_normal_unsupported_type(self):
assert_raises(TypeError, random.standard_normal, dtype=np.int32)
def test_standard_t(self):
random = Generator(MT19937(self.seed))
actual = random.standard_t(df=10, size=(3, 2))
desired = np.array([[-1.484666193042647, 0.30597891831161 ],
[ 1.056684299648085, -0.407312602088507],
[ 0.130704414281157, -2.038053410490321]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_triangular(self):
random = Generator(MT19937(self.seed))
actual = random.triangular(left=5.12, mode=10.23, right=20.34,
size=(3, 2))
desired = np.array([[ 7.86664070590917, 13.6313848513185 ],
[ 7.68152445215983, 14.36169131136546],
[13.16105603911429, 13.72341621856971]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_uniform(self):
random = Generator(MT19937(self.seed))
actual = random.uniform(low=1.23, high=10.54, size=(3, 2))
desired = np.array([[2.13306255040998 , 7.816987531021207],
[2.015436610109887, 8.377577533009589],
[7.421792588856135, 7.891185744455209]])
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_uniform_zero_range(self):
func = random.uniform
result = func(1.5, 1.5)
assert_allclose(result, 1.5)
result = func([0.0, np.pi], [0.0, np.pi])
assert_allclose(result, [0.0, np.pi])
result = func([[2145.12], [2145.12]], [2145.12, 2145.12])
assert_allclose(result, 2145.12 + np.zeros((2, 2)))
def test_uniform_neg_range(self):
func = random.uniform
assert_raises(ValueError, func, 2, 1)
assert_raises(ValueError, func, [1, 2], [1, 1])
assert_raises(ValueError, func, [[0, 1],[2, 3]], 2)
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 = Generator(MT19937(self.seed))
actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
desired = np.array([[ 1.107972248690106, 2.841536476232361],
[ 1.832602376042457, 1.945511926976032],
[-0.260147475776542, 2.058047492231698]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_vonmises_small(self):
# check infinite loop, gh-4720
random = Generator(MT19937(self.seed))
r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
assert_(np.isfinite(r).all())
def test_vonmises_nan(self):
random = Generator(MT19937(self.seed))
r = random.vonmises(mu=0., kappa=np.nan)
assert_(np.isnan(r))
@pytest.mark.parametrize("kappa", [1e4, 1e15])
def test_vonmises_large_kappa(self, kappa):
random = Generator(MT19937(self.seed))
rs = RandomState(random.bit_generator)
state = random.bit_generator.state
random_state_vals = rs.vonmises(0, kappa, size=10)
random.bit_generator.state = state
gen_vals = random.vonmises(0, kappa, size=10)
if kappa < 1e6:
assert_allclose(random_state_vals, gen_vals)
else:
assert np.all(random_state_vals != gen_vals)
@pytest.mark.parametrize("mu", [-7., -np.pi, -3.1, np.pi, 3.2])
@pytest.mark.parametrize("kappa", [1e-9, 1e-6, 1, 1e3, 1e15])
def test_vonmises_large_kappa_range(self, mu, kappa):
random = Generator(MT19937(self.seed))
r = random.vonmises(mu, kappa, 50)
assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
def test_wald(self):
random = Generator(MT19937(self.seed))
actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))
desired = np.array([[0.26871721804551, 3.2233942732115 ],
[2.20328374987066, 2.40958405189353],
[2.07093587449261, 0.73073890064369]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_weibull(self):
random = Generator(MT19937(self.seed))
actual = random.weibull(a=1.23, size=(3, 2))
desired = np.array([[0.138613914769468, 1.306463419753191],
[0.111623365934763, 1.446570494646721],
[1.257145775276011, 1.914247725027957]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_weibull_0(self):
random = Generator(MT19937(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 = Generator(MT19937(self.seed))
actual = random.zipf(a=1.23, size=(3, 2))
desired = np.array([[ 1, 1],
[ 10, 867],
[354, 2]])
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 test_uniform(self):
random = Generator(MT19937(self.seed))
low = [0]
high = [1]
uniform = random.uniform
desired = np.array([0.16693771389729, 0.19635129550675, 0.75563050964095])
random = Generator(MT19937(self.seed))
actual = random.uniform(low * 3, high)
assert_array_almost_equal(actual, desired, decimal=14)
random = Generator(MT19937(self.seed))
actual = random.uniform(low, high * 3)
assert_array_almost_equal(actual, desired, decimal=14)
def test_normal(self):
loc = [0]
scale = [1]
bad_scale = [-1]
random = Generator(MT19937(self.seed))
desired = np.array([-0.38736406738527, 0.79594375042255, 0.0197076236097])
random = Generator(MT19937(self.seed))
actual = random.normal(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.normal, loc * 3, bad_scale)
random = Generator(MT19937(self.seed))
normal = random.normal
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]
desired = np.array([0.18719338682602, 0.73234824491364, 0.17928615186455])
random = Generator(MT19937(self.seed))
beta = random.beta
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)
random = Generator(MT19937(self.seed))
actual = random.beta(a, b * 3)
assert_array_almost_equal(actual, desired, decimal=14)
def test_exponential(self):
scale = [1]
bad_scale = [-1]
desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
random = Generator(MT19937(self.seed))
actual = random.exponential(scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.exponential, bad_scale * 3)
def test_standard_gamma(self):
shape = [1]
bad_shape = [-1]
desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
random = Generator(MT19937(self.seed))
std_gamma = random.standard_gamma
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]
desired = np.array([1.34491986425611, 0.42760990636187, 1.4355697857258])
random = Generator(MT19937(self.seed))
gamma = random.gamma
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)
random = Generator(MT19937(self.seed))
gamma = random.gamma
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]
desired = np.array([0.07765056244107, 7.72951397913186, 0.05786093891763])
random = Generator(MT19937(self.seed))
f = random.f
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)
random = Generator(MT19937(self.seed))
f = random.f
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]
desired = np.array([2.02434240411421, 12.91838601070124, 1.24395160354629])
random = Generator(MT19937(self.seed))
nonc_f = random.noncentral_f
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)
random = Generator(MT19937(self.seed))
nonc_f = random.noncentral_f
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)
random = Generator(MT19937(self.seed))
nonc_f = random.noncentral_f
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):
random = Generator(MT19937(self.seed))
desired = np.array([0.04714867120827, 0.1239390327694])
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]
desired = np.array([0.05573640064251, 1.47220224353539, 2.9469379318589])
random = Generator(MT19937(self.seed))
actual = random.chisquare(df * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.chisquare, bad_df * 3)
def test_noncentral_chisquare(self):
df = [1]
nonc = [2]
bad_df = [-1]
bad_nonc = [-2]
desired = np.array([0.07710766249436, 5.27829115110304, 0.630732147399])
random = Generator(MT19937(self.seed))
nonc_chi = random.noncentral_chisquare
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)
random = Generator(MT19937(self.seed))
nonc_chi = random.noncentral_chisquare
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]
desired = np.array([-1.39498829447098, -1.23058658835223, 0.17207021065983])
random = Generator(MT19937(self.seed))
actual = random.standard_t(df * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.standard_t, bad_df * 3)
def test_vonmises(self):
mu = [2]
kappa = [1]
bad_kappa = [-1]
desired = np.array([2.25935584988528, 2.23326261461399, -2.84152146503326])
random = Generator(MT19937(self.seed))
actual = random.vonmises(mu * 3, kappa)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.vonmises, mu * 3, bad_kappa)
random = Generator(MT19937(self.seed))
actual = random.vonmises(mu, kappa * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.vonmises, mu, bad_kappa * 3)
def test_pareto(self):
a = [1]
bad_a = [-1]
desired = np.array([0.95905052946317, 0.2383810889437 , 1.04988745750013])
random = Generator(MT19937(self.seed))
actual = random.pareto(a * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.pareto, bad_a * 3)
def test_weibull(self):
a = [1]
bad_a = [-1]
desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
random = Generator(MT19937(self.seed))
actual = random.weibull(a * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.weibull, bad_a * 3)
def test_power(self):
a = [1]
bad_a = [-1]
desired = np.array([0.48954864361052, 0.19249412888486, 0.51216834058807])
random = Generator(MT19937(self.seed))
actual = random.power(a * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.power, bad_a * 3)
def test_laplace(self):
loc = [0]
scale = [1]
bad_scale = [-1]
desired = np.array([-1.09698732625119, -0.93470271947368, 0.71592671378202])
random = Generator(MT19937(self.seed))
laplace = random.laplace
actual = laplace(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, laplace, loc * 3, bad_scale)
random = Generator(MT19937(self.seed))
laplace = random.laplace
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]
desired = np.array([1.70020068231762, 1.52054354273631, -0.34293267607081])
random = Generator(MT19937(self.seed))
gumbel = random.gumbel
actual = gumbel(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, gumbel, loc * 3, bad_scale)
random = Generator(MT19937(self.seed))
gumbel = random.gumbel
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]
desired = np.array([-1.607487640433, -1.40925686003678, 1.12887112820397])
random = Generator(MT19937(self.seed))
actual = random.logistic(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.logistic, loc * 3, bad_scale)
random = Generator(MT19937(self.seed))
actual = random.logistic(loc, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.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]
desired = np.array([0.67884390500697, 2.21653186290321, 1.01990310084276])
random = Generator(MT19937(self.seed))
lognormal = random.lognormal
actual = lognormal(mean * 3, sigma)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
random = Generator(MT19937(self.seed))
actual = random.lognormal(mean, sigma * 3)
assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)
def test_rayleigh(self):
scale = [1]
bad_scale = [-1]
desired = np.array(
[1.1597068009872629,
0.6539188836253857,
1.1981526554349398]
)
random = Generator(MT19937(self.seed))
actual = random.rayleigh(scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.rayleigh, bad_scale * 3)
def test_wald(self):
mean = [0.5]
scale = [1]
bad_mean = [0]
bad_scale = [-2]
desired = np.array([0.38052407392905, 0.50701641508592, 0.484935249864])
random = Generator(MT19937(self.seed))
actual = random.wald(mean * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.wald, bad_mean * 3, scale)
assert_raises(ValueError, random.wald, mean * 3, bad_scale)
random = Generator(MT19937(self.seed))
actual = random.wald(mean, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, random.wald, bad_mean, scale * 3)
assert_raises(ValueError, random.wald, mean, bad_scale * 3)
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
desired = np.array([1.57781954604754, 1.62665986867957, 2.30090130831326])
random = Generator(MT19937(self.seed))
triangular = random.triangular
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)
random = Generator(MT19937(self.seed))
triangular = random.triangular
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)
random = Generator(MT19937(self.seed))
triangular = random.triangular
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]
desired = np.array([0, 0, 1])
random = Generator(MT19937(self.seed))
binom = random.binomial
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)
random = Generator(MT19937(self.seed))
actual = random.binomial(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]
desired = np.array([0, 2, 1], dtype=np.int64)
random = Generator(MT19937(self.seed))
neg_binom = random.negative_binomial
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)
random = Generator(MT19937(self.seed))
neg_binom = random.negative_binomial
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):
lam = [1]
bad_lam_one = [-1]
desired = np.array([0, 0, 3])
random = Generator(MT19937(self.seed))
max_lam = random._poisson_lam_max
bad_lam_two = [max_lam * 2]
poisson = random.poisson
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]
desired = np.array([1, 8, 1])
random = Generator(MT19937(self.seed))
zipf = random.zipf
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]
desired = np.array([1, 1, 3])
random = Generator(MT19937(self.seed))
geometric = random.geometric
actual = geometric(p * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, geometric, bad_p_one * 3)
assert_raises(ValueError, geometric, bad_p_two * 3)
def test_hypergeometric(self):
ngood = [1]
nbad = [2]
nsample = [2]
bad_ngood = [-1]
bad_nbad = [-2]
bad_nsample_one = [-1]
bad_nsample_two = [4]
desired = np.array([0, 0, 1])
random = Generator(MT19937(self.seed))
actual = random.hypergeometric(ngood * 3, nbad, nsample)
assert_array_equal(actual, desired)
assert_raises(ValueError, random.hypergeometric, bad_ngood * 3, nbad, nsample)
assert_raises(ValueError, random.hypergeometric, ngood * 3, bad_nbad, nsample)
assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_one)
assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_two)
random = Generator(MT19937(self.seed))
actual = random.hypergeometric(ngood, nbad * 3, nsample)
assert_array_equal(actual, desired)
assert_raises(ValueError, random.hypergeometric, bad_ngood, nbad * 3, nsample)
assert_raises(ValueError, random.hypergeometric, ngood, bad_nbad * 3, nsample)
assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_one)
assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_two)
random = Generator(MT19937(self.seed))
hypergeom = random.hypergeometric
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, -1)
assert_raises(ValueError, hypergeom, 10, 10, 25)
# ValueError for arguments that are too big.
assert_raises(ValueError, hypergeom, 2**30, 10, 20)
assert_raises(ValueError, hypergeom, 999, 2**31, 50)
assert_raises(ValueError, hypergeom, 999, [2**29, 2**30], 1000)
def test_logseries(self):
p = [0.5]
bad_p_one = [2]
bad_p_two = [-1]
desired = np.array([1, 1, 1])
random = Generator(MT19937(self.seed))
logseries = random.logseries
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)
def test_multinomial(self):
random = Generator(MT19937(self.seed))
actual = random.multinomial([5, 20], [1 / 6.] * 6, size=(3, 2))
desired = np.array([[[0, 0, 2, 1, 2, 0],
[2, 3, 6, 4, 2, 3]],
[[1, 0, 1, 0, 2, 1],
[7, 2, 2, 1, 4, 4]],
[[0, 2, 0, 1, 2, 0],
[3, 2, 3, 3, 4, 5]]], dtype=np.int64)
assert_array_equal(actual, desired)
random = Generator(MT19937(self.seed))
actual = random.multinomial([5, 20], [1 / 6.] * 6)
desired = np.array([[0, 0, 2, 1, 2, 0],
[2, 3, 6, 4, 2, 3]], dtype=np.int64)
assert_array_equal(actual, desired)
random = Generator(MT19937(self.seed))
actual = random.multinomial([5, 20], [[1 / 6.] * 6] * 2)
desired = np.array([[0, 0, 2, 1, 2, 0],
[2, 3, 6, 4, 2, 3]], dtype=np.int64)
assert_array_equal(actual, desired)
random = Generator(MT19937(self.seed))
actual = random.multinomial([[5], [20]], [[1 / 6.] * 6] * 2)
desired = np.array([[[0, 0, 2, 1, 2, 0],
[0, 0, 2, 1, 1, 1]],
[[4, 2, 3, 3, 5, 3],
[7, 2, 2, 1, 4, 4]]], dtype=np.int64)
assert_array_equal(actual, desired)
@pytest.mark.parametrize("n", [10,
np.array([10, 10]),
np.array([[[10]], [[10]]])
]
)
def test_multinomial_pval_broadcast(self, n):
random = Generator(MT19937(self.seed))
pvals = np.array([1 / 4] * 4)
actual = random.multinomial(n, pvals)
n_shape = tuple() if isinstance(n, int) else n.shape
expected_shape = n_shape + (4,)
assert actual.shape == expected_shape
pvals = np.vstack([pvals, pvals])
actual = random.multinomial(n, pvals)
expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + (4,)
assert actual.shape == expected_shape
pvals = np.vstack([[pvals], [pvals]])
actual = random.multinomial(n, pvals)
expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1])
assert actual.shape == expected_shape + (4,)
actual = random.multinomial(n, pvals, size=(3, 2) + expected_shape)
assert actual.shape == (3, 2) + expected_shape + (4,)
with pytest.raises(ValueError):
# Ensure that size is not broadcast
actual = random.multinomial(n, pvals, size=(1,) * 6)
def test_invalid_pvals_broadcast(self):
random = Generator(MT19937(self.seed))
pvals = [[1 / 6] * 6, [1 / 4] * 6]
assert_raises(ValueError, random.multinomial, 1, pvals)
assert_raises(ValueError, random.multinomial, 6, 0.5)
def test_empty_outputs(self):
random = Generator(MT19937(self.seed))
actual = random.multinomial(np.empty((10, 0, 6), "i8"), [1 / 6] * 6)
assert actual.shape == (10, 0, 6, 6)
actual = random.multinomial(12, np.empty((10, 0, 10)))
assert actual.shape == (10, 0, 10)
actual = random.multinomial(np.empty((3, 0, 7), "i8"),
np.empty((3, 0, 7, 4)))
assert actual.shape == (3, 0, 7, 4)
@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=(Generator(MT19937(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(Generator(MT19937(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_integers(self, endpoint):
itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16,
np.int32, np.uint32, np.int64, np.uint64]
func = random.integers
high = np.array([1])
low = np.array([0])
for dt in itype:
out = func(low, high, endpoint=endpoint, dtype=dt)
assert_equal(out.shape, self.tgtShape)
out = func(low[0], high, endpoint=endpoint, dtype=dt)
assert_equal(out.shape, self.tgtShape)
out = func(low, high[0], endpoint=endpoint, dtype=dt)
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)
@pytest.mark.parametrize("config", JUMP_TEST_DATA)
def test_jumped(config):
# Each config contains the initial seed, a number of raw steps
# the sha256 hashes of the initial and the final states' keys and
# the position of the initial and the final state.
# These were produced using the original C implementation.
seed = config["seed"]
steps = config["steps"]
mt19937 = MT19937(seed)
# Burn step
mt19937.random_raw(steps)
key = mt19937.state["state"]["key"]
if sys.byteorder == 'big':
key = key.byteswap()
sha256 = hashlib.sha256(key)
assert mt19937.state["state"]["pos"] == config["initial"]["pos"]
assert sha256.hexdigest() == config["initial"]["key_sha256"]
jumped = mt19937.jumped()
key = jumped.state["state"]["key"]
if sys.byteorder == 'big':
key = key.byteswap()
sha256 = hashlib.sha256(key)
assert jumped.state["state"]["pos"] == config["jumped"]["pos"]
assert sha256.hexdigest() == config["jumped"]["key_sha256"]
def test_broadcast_size_error():
mu = np.ones(3)
sigma = np.ones((4, 3))
size = (10, 4, 2)
assert random.normal(mu, sigma, size=(5, 4, 3)).shape == (5, 4, 3)
with pytest.raises(ValueError):
random.normal(mu, sigma, size=size)
with pytest.raises(ValueError):
random.normal(mu, sigma, size=(1, 3))
with pytest.raises(ValueError):
random.normal(mu, sigma, size=(4, 1, 1))
# 1 arg
shape = np.ones((4, 3))
with pytest.raises(ValueError):
random.standard_gamma(shape, size=size)
with pytest.raises(ValueError):
random.standard_gamma(shape, size=(3,))
with pytest.raises(ValueError):
random.standard_gamma(shape, size=3)
# Check out
out = np.empty(size)
with pytest.raises(ValueError):
random.standard_gamma(shape, out=out)
# 2 arg
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))
with pytest.raises(ValueError):
random.multinomial([2, 2], [.3, .7], size=(2, 1))
# 3 arg
a = random.chisquare(5, size=3)
b = random.chisquare(5, size=(4, 3))
c = random.chisquare(5, size=(5, 4, 3))
assert random.noncentral_f(a, b, c).shape == (5, 4, 3)
with pytest.raises(ValueError, match=r"Output size \(6, 5, 1, 1\) is"):
random.noncentral_f(a, b, c, size=(6, 5, 1, 1))
def test_broadcast_size_scalar():
mu = np.ones(3)
sigma = np.ones(3)
random.normal(mu, sigma, size=3)
with pytest.raises(ValueError):
random.normal(mu, sigma, size=2)
def test_ragged_shuffle():
# GH 18142
seq = [[], [], 1]
gen = Generator(MT19937(0))
assert_no_warnings(gen.shuffle, seq)
assert seq == [1, [], []]
@pytest.mark.parametrize("high", [-2, [-2]])
@pytest.mark.parametrize("endpoint", [True, False])
def test_single_arg_integer_exception(high, endpoint):
# GH 14333
gen = Generator(MT19937(0))
msg = 'high < 0' if endpoint else 'high <= 0'
with pytest.raises(ValueError, match=msg):
gen.integers(high, endpoint=endpoint)
msg = 'low > high' if endpoint else 'low >= high'
with pytest.raises(ValueError, match=msg):
gen.integers(-1, high, endpoint=endpoint)
with pytest.raises(ValueError, match=msg):
gen.integers([-1], high, endpoint=endpoint)
@pytest.mark.parametrize("dtype", ["f4", "f8"])
def test_c_contig_req_out(dtype):
# GH 18704
out = np.empty((2, 3), order="F", dtype=dtype)
shape = [1, 2, 3]
with pytest.raises(ValueError, match="Supplied output array"):
random.standard_gamma(shape, out=out, dtype=dtype)
with pytest.raises(ValueError, match="Supplied output array"):
random.standard_gamma(shape, out=out, size=out.shape, dtype=dtype)
@pytest.mark.parametrize("dtype", ["f4", "f8"])
@pytest.mark.parametrize("order", ["F", "C"])
@pytest.mark.parametrize("dist", [random.standard_normal, random.random])
def test_contig_req_out(dist, order, dtype):
# GH 18704
out = np.empty((2, 3), dtype=dtype, order=order)
variates = dist(out=out, dtype=dtype)
assert variates is out
variates = dist(out=out, dtype=dtype, size=out.shape)
assert variates is out
def test_generator_ctor_old_style_pickle():
rg = np.random.Generator(np.random.PCG64DXSM(0))
rg.standard_normal(1)
# Directly call reduce which is used in pickling
ctor, args, state_a = rg.__reduce__()
# Simulate unpickling an old pickle that only has the name
assert args[:1] == ("PCG64DXSM",)
b = ctor(*args[:1])
b.bit_generator.state = state_a
state_b = b.bit_generator.state
assert state_a == state_b