1746 lines
68 KiB
Python
1746 lines
68 KiB
Python
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import warnings
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import pytest
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import numpy as np
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from numpy.testing import (
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assert_, assert_raises, assert_equal, assert_warns,
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assert_no_warnings, assert_array_equal, assert_array_almost_equal,
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suppress_warnings
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)
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from numpy import random
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import sys
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class TestSeed:
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def test_scalar(self):
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s = np.random.RandomState(0)
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assert_equal(s.randint(1000), 684)
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s = np.random.RandomState(4294967295)
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assert_equal(s.randint(1000), 419)
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def test_array(self):
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s = np.random.RandomState(range(10))
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assert_equal(s.randint(1000), 468)
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s = np.random.RandomState(np.arange(10))
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assert_equal(s.randint(1000), 468)
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s = np.random.RandomState([0])
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assert_equal(s.randint(1000), 973)
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s = np.random.RandomState([4294967295])
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assert_equal(s.randint(1000), 265)
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def test_invalid_scalar(self):
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# seed must be an unsigned 32 bit integer
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assert_raises(TypeError, np.random.RandomState, -0.5)
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assert_raises(ValueError, np.random.RandomState, -1)
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def test_invalid_array(self):
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# seed must be an unsigned 32 bit integer
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assert_raises(TypeError, np.random.RandomState, [-0.5])
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assert_raises(ValueError, np.random.RandomState, [-1])
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assert_raises(ValueError, np.random.RandomState, [4294967296])
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assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296])
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assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296])
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def test_invalid_array_shape(self):
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# gh-9832
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assert_raises(ValueError, np.random.RandomState,
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np.array([], dtype=np.int64))
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assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]])
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assert_raises(ValueError, np.random.RandomState, [[1, 2, 3],
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[4, 5, 6]])
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class TestBinomial:
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def test_n_zero(self):
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# Tests the corner case of n == 0 for the binomial distribution.
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# binomial(0, p) should be zero for any p in [0, 1].
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# This test addresses issue #3480.
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zeros = np.zeros(2, dtype='int')
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for p in [0, .5, 1]:
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assert_(random.binomial(0, p) == 0)
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assert_array_equal(random.binomial(zeros, p), zeros)
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def test_p_is_nan(self):
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# Issue #4571.
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assert_raises(ValueError, random.binomial, 1, np.nan)
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class TestMultinomial:
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def test_basic(self):
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random.multinomial(100, [0.2, 0.8])
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def test_zero_probability(self):
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random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
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def test_int_negative_interval(self):
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assert_(-5 <= random.randint(-5, -1) < -1)
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x = random.randint(-5, -1, 5)
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assert_(np.all(-5 <= x))
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assert_(np.all(x < -1))
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def test_size(self):
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# gh-3173
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p = [0.5, 0.5]
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assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
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assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
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assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
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assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
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assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
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assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape,
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(2, 2, 2))
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assert_raises(TypeError, np.random.multinomial, 1, p,
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float(1))
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def test_multidimensional_pvals(self):
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assert_raises(ValueError, np.random.multinomial, 10, [[0, 1]])
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assert_raises(ValueError, np.random.multinomial, 10, [[0], [1]])
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assert_raises(ValueError, np.random.multinomial, 10, [[[0], [1]], [[1], [0]]])
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assert_raises(ValueError, np.random.multinomial, 10, np.array([[0, 1], [1, 0]]))
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class TestSetState:
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def setup_method(self):
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self.seed = 1234567890
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self.prng = random.RandomState(self.seed)
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self.state = self.prng.get_state()
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def test_basic(self):
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old = self.prng.tomaxint(16)
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self.prng.set_state(self.state)
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new = self.prng.tomaxint(16)
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assert_(np.all(old == new))
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def test_gaussian_reset(self):
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# Make sure the cached every-other-Gaussian is reset.
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old = self.prng.standard_normal(size=3)
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self.prng.set_state(self.state)
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new = self.prng.standard_normal(size=3)
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assert_(np.all(old == new))
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def test_gaussian_reset_in_media_res(self):
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# When the state is saved with a cached Gaussian, make sure the
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# cached Gaussian is restored.
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self.prng.standard_normal()
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state = self.prng.get_state()
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old = self.prng.standard_normal(size=3)
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self.prng.set_state(state)
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new = self.prng.standard_normal(size=3)
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assert_(np.all(old == new))
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def test_backwards_compatibility(self):
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# Make sure we can accept old state tuples that do not have the
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# cached Gaussian value.
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old_state = self.state[:-2]
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x1 = self.prng.standard_normal(size=16)
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self.prng.set_state(old_state)
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x2 = self.prng.standard_normal(size=16)
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self.prng.set_state(self.state)
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x3 = self.prng.standard_normal(size=16)
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assert_(np.all(x1 == x2))
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assert_(np.all(x1 == x3))
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def test_negative_binomial(self):
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# Ensure that the negative binomial results take floating point
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# arguments without truncation.
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self.prng.negative_binomial(0.5, 0.5)
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class TestRandint:
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rfunc = np.random.randint
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# valid integer/boolean types
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itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16,
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np.int32, np.uint32, np.int64, np.uint64]
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def test_unsupported_type(self):
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assert_raises(TypeError, self.rfunc, 1, dtype=float)
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def test_bounds_checking(self):
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for dt in self.itype:
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lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
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ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
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assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
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assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
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assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
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assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
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def test_rng_zero_and_extremes(self):
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for dt in self.itype:
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lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
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ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
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tgt = ubnd - 1
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assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
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tgt = lbnd
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assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
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tgt = (lbnd + ubnd)//2
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assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
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def test_full_range(self):
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# Test for ticket #1690
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for dt in self.itype:
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lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
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ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
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try:
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self.rfunc(lbnd, ubnd, dtype=dt)
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except Exception as e:
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raise AssertionError("No error should have been raised, "
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"but one was with the following "
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"message:\n\n%s" % str(e))
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def test_in_bounds_fuzz(self):
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# Don't use fixed seed
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np.random.seed()
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for dt in self.itype[1:]:
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for ubnd in [4, 8, 16]:
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vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
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assert_(vals.max() < ubnd)
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assert_(vals.min() >= 2)
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vals = self.rfunc(0, 2, size=2**16, dtype=np.bool_)
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assert_(vals.max() < 2)
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assert_(vals.min() >= 0)
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def test_repeatability(self):
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import hashlib
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# We use a sha256 hash of generated sequences of 1000 samples
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# in the range [0, 6) for all but bool, where the range
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# is [0, 2). Hashes are for little endian numbers.
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tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
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'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
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'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
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'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
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'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
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'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
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'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
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'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
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'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}
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for dt in self.itype[1:]:
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np.random.seed(1234)
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# view as little endian for hash
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if sys.byteorder == 'little':
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val = self.rfunc(0, 6, size=1000, dtype=dt)
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else:
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val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
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res = hashlib.sha256(val.view(np.int8)).hexdigest()
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assert_(tgt[np.dtype(dt).name] == res)
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# bools do not depend on endianness
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np.random.seed(1234)
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val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
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res = hashlib.sha256(val).hexdigest()
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assert_(tgt[np.dtype(bool).name] == res)
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def test_int64_uint64_corner_case(self):
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# When stored in Numpy arrays, `lbnd` is casted
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# as np.int64, and `ubnd` is casted as np.uint64.
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# Checking whether `lbnd` >= `ubnd` used to be
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# done solely via direct comparison, which is incorrect
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# because when Numpy tries to compare both numbers,
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# it casts both to np.float64 because there is
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# no integer superset of np.int64 and np.uint64. However,
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# `ubnd` is too large to be represented in np.float64,
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# causing it be round down to np.iinfo(np.int64).max,
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# leading to a ValueError because `lbnd` now equals
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# the new `ubnd`.
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dt = np.int64
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tgt = np.iinfo(np.int64).max
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lbnd = np.int64(np.iinfo(np.int64).max)
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ubnd = np.uint64(np.iinfo(np.int64).max + 1)
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# None of these function calls should
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# generate a ValueError now.
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actual = np.random.randint(lbnd, ubnd, dtype=dt)
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assert_equal(actual, tgt)
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def test_respect_dtype_singleton(self):
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# See gh-7203
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for dt in self.itype:
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lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
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ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
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sample = self.rfunc(lbnd, ubnd, dtype=dt)
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assert_equal(sample.dtype, np.dtype(dt))
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for dt in (bool, int, np.compat.long):
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lbnd = 0 if dt is bool else np.iinfo(dt).min
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ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
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# gh-7284: Ensure that we get Python data types
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sample = self.rfunc(lbnd, ubnd, dtype=dt)
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assert_(not hasattr(sample, 'dtype'))
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assert_equal(type(sample), dt)
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class TestRandomDist:
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# Make sure the random distribution returns the correct value for a
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# given seed
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def setup_method(self):
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self.seed = 1234567890
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def test_rand(self):
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np.random.seed(self.seed)
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actual = np.random.rand(3, 2)
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desired = np.array([[0.61879477158567997, 0.59162362775974664],
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[0.88868358904449662, 0.89165480011560816],
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[0.4575674820298663, 0.7781880808593471]])
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assert_array_almost_equal(actual, desired, decimal=15)
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def test_randn(self):
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np.random.seed(self.seed)
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actual = np.random.randn(3, 2)
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desired = np.array([[1.34016345771863121, 1.73759122771936081],
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[1.498988344300628, -0.2286433324536169],
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[2.031033998682787, 2.17032494605655257]])
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assert_array_almost_equal(actual, desired, decimal=15)
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def test_randint(self):
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np.random.seed(self.seed)
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actual = np.random.randint(-99, 99, size=(3, 2))
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desired = np.array([[31, 3],
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[-52, 41],
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[-48, -66]])
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assert_array_equal(actual, desired)
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def test_random_integers(self):
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|
|
np.random.seed(self.seed)
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
w = sup.record(DeprecationWarning)
|
||
|
|
actual = np.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)
|
||
|
|
|
||
|
|
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 = np.random.random_integers(np.iinfo('l').max,
|
||
|
|
np.iinfo('l').max)
|
||
|
|
assert_(len(w) == 1)
|
||
|
|
|
||
|
|
desired = np.iinfo('l').max
|
||
|
|
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,
|
||
|
|
np.random.random_integers,
|
||
|
|
np.iinfo('l').max)
|
||
|
|
|
||
|
|
# DeprecationWarning raised with high != None
|
||
|
|
assert_raises(DeprecationWarning,
|
||
|
|
np.random.random_integers,
|
||
|
|
np.iinfo('l').max, np.iinfo('l').max)
|
||
|
|
|
||
|
|
def test_random(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.random((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_choice_uniform_replace(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.choice(4, 4)
|
||
|
|
desired = np.array([2, 3, 2, 3])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_choice_nonuniform_replace(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
|
||
|
|
desired = np.array([1, 1, 2, 2])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_choice_uniform_noreplace(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.choice(4, 3, replace=False)
|
||
|
|
desired = np.array([0, 1, 3])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_choice_nonuniform_noreplace(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.choice(4, 3, replace=False,
|
||
|
|
p=[0.1, 0.3, 0.5, 0.1])
|
||
|
|
desired = np.array([2, 3, 1])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_choice_noninteger(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.choice(['a', 'b', 'c', 'd'], 4)
|
||
|
|
desired = np.array(['c', 'd', 'c', 'd'])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_choice_exceptions(self):
|
||
|
|
sample = np.random.choice
|
||
|
|
assert_raises(ValueError, sample, -1, 3)
|
||
|
|
assert_raises(ValueError, sample, 3., 3)
|
||
|
|
assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
|
||
|
|
assert_raises(ValueError, sample, [], 3)
|
||
|
|
assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
|
||
|
|
p=[[0.25, 0.25], [0.25, 0.25]])
|
||
|
|
assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
|
||
|
|
assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
|
||
|
|
assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
|
||
|
|
assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
|
||
|
|
# 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(np.random.choice(2, replace=True)))
|
||
|
|
assert_(np.isscalar(np.random.choice(2, replace=False)))
|
||
|
|
assert_(np.isscalar(np.random.choice(2, replace=True, p=p)))
|
||
|
|
assert_(np.isscalar(np.random.choice(2, replace=False, p=p)))
|
||
|
|
assert_(np.isscalar(np.random.choice([1, 2], replace=True)))
|
||
|
|
assert_(np.random.choice([None], replace=True) is None)
|
||
|
|
a = np.array([1, 2])
|
||
|
|
arr = np.empty(1, dtype=object)
|
||
|
|
arr[0] = a
|
||
|
|
assert_(np.random.choice(arr, replace=True) is a)
|
||
|
|
|
||
|
|
# Check 0-d array
|
||
|
|
s = tuple()
|
||
|
|
assert_(not np.isscalar(np.random.choice(2, s, replace=True)))
|
||
|
|
assert_(not np.isscalar(np.random.choice(2, s, replace=False)))
|
||
|
|
assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p)))
|
||
|
|
assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p)))
|
||
|
|
assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True)))
|
||
|
|
assert_(np.random.choice([None], s, replace=True).ndim == 0)
|
||
|
|
a = np.array([1, 2])
|
||
|
|
arr = np.empty(1, dtype=object)
|
||
|
|
arr[0] = a
|
||
|
|
assert_(np.random.choice(arr, s, replace=True).item() is a)
|
||
|
|
|
||
|
|
# Check multi dimensional array
|
||
|
|
s = (2, 3)
|
||
|
|
p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
|
||
|
|
assert_equal(np.random.choice(6, s, replace=True).shape, s)
|
||
|
|
assert_equal(np.random.choice(6, s, replace=False).shape, s)
|
||
|
|
assert_equal(np.random.choice(6, s, replace=True, p=p).shape, s)
|
||
|
|
assert_equal(np.random.choice(6, s, replace=False, p=p).shape, s)
|
||
|
|
assert_equal(np.random.choice(np.arange(6), s, replace=True).shape, s)
|
||
|
|
|
||
|
|
# Check zero-size
|
||
|
|
assert_equal(np.random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
|
||
|
|
assert_equal(np.random.randint(0, -10, size=0).shape, (0,))
|
||
|
|
assert_equal(np.random.randint(10, 10, size=0).shape, (0,))
|
||
|
|
assert_equal(np.random.choice(0, size=0).shape, (0,))
|
||
|
|
assert_equal(np.random.choice([], size=(0,)).shape, (0,))
|
||
|
|
assert_equal(np.random.choice(['a', 'b'], size=(3, 0, 4)).shape,
|
||
|
|
(3, 0, 4))
|
||
|
|
assert_raises(ValueError, np.random.choice, [], 10)
|
||
|
|
|
||
|
|
def test_choice_nan_probabilities(self):
|
||
|
|
a = np.array([42, 1, 2])
|
||
|
|
p = [None, None, None]
|
||
|
|
assert_raises(ValueError, np.random.choice, a, p=p)
|
||
|
|
|
||
|
|
def test_bytes(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.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), ("b", np.int32)])]:
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
|
||
|
|
np.random.shuffle(alist)
|
||
|
|
actual = alist
|
||
|
|
desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
|
||
|
|
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):
|
||
|
|
np.random.shuffle(a)
|
||
|
|
assert_equal(
|
||
|
|
sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
|
||
|
|
np.random.shuffle(b)
|
||
|
|
assert_equal(
|
||
|
|
sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("random",
|
||
|
|
[np.random, np.random.RandomState(), np.random.default_rng()])
|
||
|
|
def test_shuffle_untyped_warning(self, random):
|
||
|
|
# Create a dict works like a sequence but isn't one
|
||
|
|
values = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}
|
||
|
|
with pytest.warns(UserWarning,
|
||
|
|
match="you are shuffling a 'dict' object") as rec:
|
||
|
|
random.shuffle(values)
|
||
|
|
assert "test_random" in rec[0].filename
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("random",
|
||
|
|
[np.random, np.random.RandomState(), np.random.default_rng()])
|
||
|
|
@pytest.mark.parametrize("use_array_like", [True, False])
|
||
|
|
def test_shuffle_no_object_unpacking(self, random, use_array_like):
|
||
|
|
class MyArr(np.ndarray):
|
||
|
|
pass
|
||
|
|
|
||
|
|
items = [
|
||
|
|
None, np.array([3]), np.float64(3), np.array(10), np.float64(7)
|
||
|
|
]
|
||
|
|
arr = np.array(items, dtype=object)
|
||
|
|
item_ids = {id(i) for i in items}
|
||
|
|
if use_array_like:
|
||
|
|
arr = arr.view(MyArr)
|
||
|
|
|
||
|
|
# The array was created fine, and did not modify any objects:
|
||
|
|
assert all(id(i) in item_ids for i in arr)
|
||
|
|
|
||
|
|
if use_array_like and not isinstance(random, np.random.Generator):
|
||
|
|
# The old API gives incorrect results, but warns about it.
|
||
|
|
with pytest.warns(UserWarning,
|
||
|
|
match="Shuffling a one dimensional array.*"):
|
||
|
|
random.shuffle(arr)
|
||
|
|
else:
|
||
|
|
random.shuffle(arr)
|
||
|
|
assert all(id(i) in item_ids for i in arr)
|
||
|
|
|
||
|
|
def test_shuffle_memoryview(self):
|
||
|
|
# gh-18273
|
||
|
|
# allow graceful handling of memoryviews
|
||
|
|
# (treat the same as arrays)
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
a = np.arange(5).data
|
||
|
|
np.random.shuffle(a)
|
||
|
|
assert_equal(np.asarray(a), [0, 1, 4, 3, 2])
|
||
|
|
rng = np.random.RandomState(self.seed)
|
||
|
|
rng.shuffle(a)
|
||
|
|
assert_equal(np.asarray(a), [0, 1, 2, 3, 4])
|
||
|
|
rng = np.random.default_rng(self.seed)
|
||
|
|
rng.shuffle(a)
|
||
|
|
assert_equal(np.asarray(a), [4, 1, 0, 3, 2])
|
||
|
|
|
||
|
|
def test_shuffle_not_writeable(self):
|
||
|
|
a = np.zeros(3)
|
||
|
|
a.flags.writeable = False
|
||
|
|
with pytest.raises(ValueError, match='read-only'):
|
||
|
|
np.random.shuffle(a)
|
||
|
|
|
||
|
|
def test_beta(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.beta(.1, .9, size=(3, 2))
|
||
|
|
desired = np.array(
|
||
|
|
[[1.45341850513746058e-02, 5.31297615662868145e-04],
|
||
|
|
[1.85366619058432324e-06, 4.19214516800110563e-03],
|
||
|
|
[1.58405155108498093e-04, 1.26252891949397652e-04]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_binomial(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.binomial(100, .456, size=(3, 2))
|
||
|
|
desired = np.array([[37, 43],
|
||
|
|
[42, 48],
|
||
|
|
[46, 45]])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_chisquare(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.chisquare(50, size=(3, 2))
|
||
|
|
desired = np.array([[63.87858175501090585, 68.68407748911370447],
|
||
|
|
[65.77116116901505904, 47.09686762438974483],
|
||
|
|
[72.3828403199695174, 74.18408615260374006]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=13)
|
||
|
|
|
||
|
|
def test_dirichlet(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
alpha = np.array([51.72840233779265162, 39.74494232180943953])
|
||
|
|
actual = np.random.mtrand.dirichlet(alpha, size=(3, 2))
|
||
|
|
desired = np.array([[[0.54539444573611562, 0.45460555426388438],
|
||
|
|
[0.62345816822039413, 0.37654183177960598]],
|
||
|
|
[[0.55206000085785778, 0.44793999914214233],
|
||
|
|
[0.58964023305154301, 0.41035976694845688]],
|
||
|
|
[[0.59266909280647828, 0.40733090719352177],
|
||
|
|
[0.56974431743975207, 0.43025568256024799]]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_dirichlet_size(self):
|
||
|
|
# gh-3173
|
||
|
|
p = np.array([51.72840233779265162, 39.74494232180943953])
|
||
|
|
assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
|
||
|
|
assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
|
||
|
|
assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
|
||
|
|
assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
|
||
|
|
assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
|
||
|
|
assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
|
||
|
|
|
||
|
|
assert_raises(TypeError, np.random.dirichlet, p, float(1))
|
||
|
|
|
||
|
|
def test_dirichlet_bad_alpha(self):
|
||
|
|
# gh-2089
|
||
|
|
alpha = np.array([5.4e-01, -1.0e-16])
|
||
|
|
assert_raises(ValueError, np.random.mtrand.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_exponential(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.exponential(1.1234, size=(3, 2))
|
||
|
|
desired = np.array([[1.08342649775011624, 1.00607889924557314],
|
||
|
|
[2.46628830085216721, 2.49668106809923884],
|
||
|
|
[0.68717433461363442, 1.69175666993575979]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_exponential_0(self):
|
||
|
|
assert_equal(np.random.exponential(scale=0), 0)
|
||
|
|
assert_raises(ValueError, np.random.exponential, scale=-0.)
|
||
|
|
|
||
|
|
def test_f(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.f(12, 77, size=(3, 2))
|
||
|
|
desired = np.array([[1.21975394418575878, 1.75135759791559775],
|
||
|
|
[1.44803115017146489, 1.22108959480396262],
|
||
|
|
[1.02176975757740629, 1.34431827623300415]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_gamma(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.gamma(5, 3, size=(3, 2))
|
||
|
|
desired = np.array([[24.60509188649287182, 28.54993563207210627],
|
||
|
|
[26.13476110204064184, 12.56988482927716078],
|
||
|
|
[31.71863275789960568, 33.30143302795922011]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
def test_gamma_0(self):
|
||
|
|
assert_equal(np.random.gamma(shape=0, scale=0), 0)
|
||
|
|
assert_raises(ValueError, np.random.gamma, shape=-0., scale=-0.)
|
||
|
|
|
||
|
|
def test_geometric(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.geometric(.123456789, size=(3, 2))
|
||
|
|
desired = np.array([[8, 7],
|
||
|
|
[17, 17],
|
||
|
|
[5, 12]])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_gumbel(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
|
||
|
|
desired = np.array([[0.19591898743416816, 0.34405539668096674],
|
||
|
|
[-1.4492522252274278, -1.47374816298446865],
|
||
|
|
[1.10651090478803416, -0.69535848626236174]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_gumbel_0(self):
|
||
|
|
assert_equal(np.random.gumbel(scale=0), 0)
|
||
|
|
assert_raises(ValueError, np.random.gumbel, scale=-0.)
|
||
|
|
|
||
|
|
def test_hypergeometric(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.hypergeometric(10, 5, 14, size=(3, 2))
|
||
|
|
desired = np.array([[10, 10],
|
||
|
|
[10, 10],
|
||
|
|
[9, 9]])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
# Test nbad = 0
|
||
|
|
actual = np.random.hypergeometric(5, 0, 3, size=4)
|
||
|
|
desired = np.array([3, 3, 3, 3])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
actual = np.random.hypergeometric(15, 0, 12, size=4)
|
||
|
|
desired = np.array([12, 12, 12, 12])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
# Test ngood = 0
|
||
|
|
actual = np.random.hypergeometric(0, 5, 3, size=4)
|
||
|
|
desired = np.array([0, 0, 0, 0])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
actual = np.random.hypergeometric(0, 15, 12, size=4)
|
||
|
|
desired = np.array([0, 0, 0, 0])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_laplace(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
|
||
|
|
desired = np.array([[0.66599721112760157, 0.52829452552221945],
|
||
|
|
[3.12791959514407125, 3.18202813572992005],
|
||
|
|
[-0.05391065675859356, 1.74901336242837324]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_laplace_0(self):
|
||
|
|
assert_equal(np.random.laplace(scale=0), 0)
|
||
|
|
assert_raises(ValueError, np.random.laplace, scale=-0.)
|
||
|
|
|
||
|
|
def test_logistic(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
|
||
|
|
desired = np.array([[1.09232835305011444, 0.8648196662399954],
|
||
|
|
[4.27818590694950185, 4.33897006346929714],
|
||
|
|
[-0.21682183359214885, 2.63373365386060332]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_lognormal(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
|
||
|
|
desired = np.array([[16.50698631688883822, 36.54846706092654784],
|
||
|
|
[22.67886599981281748, 0.71617561058995771],
|
||
|
|
[65.72798501792723869, 86.84341601437161273]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=13)
|
||
|
|
|
||
|
|
def test_lognormal_0(self):
|
||
|
|
assert_equal(np.random.lognormal(sigma=0), 1)
|
||
|
|
assert_raises(ValueError, np.random.lognormal, sigma=-0.)
|
||
|
|
|
||
|
|
def test_logseries(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.logseries(p=.923456789, size=(3, 2))
|
||
|
|
desired = np.array([[2, 2],
|
||
|
|
[6, 17],
|
||
|
|
[3, 6]])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_multinomial(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2))
|
||
|
|
desired = np.array([[[4, 3, 5, 4, 2, 2],
|
||
|
|
[5, 2, 8, 2, 2, 1]],
|
||
|
|
[[3, 4, 3, 6, 0, 4],
|
||
|
|
[2, 1, 4, 3, 6, 4]],
|
||
|
|
[[4, 4, 2, 5, 2, 3],
|
||
|
|
[4, 3, 4, 2, 3, 4]]])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_multivariate_normal(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
mean = (.123456789, 10)
|
||
|
|
cov = [[1, 0], [0, 1]]
|
||
|
|
size = (3, 2)
|
||
|
|
actual = np.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 = np.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, np.random.multivariate_normal, mean, cov)
|
||
|
|
|
||
|
|
# and that it doesn't warn with RuntimeWarning check_valid='ignore'
|
||
|
|
assert_no_warnings(np.random.multivariate_normal, mean, cov,
|
||
|
|
check_valid='ignore')
|
||
|
|
|
||
|
|
# and that it raises with RuntimeWarning check_valid='raises'
|
||
|
|
assert_raises(ValueError, np.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:
|
||
|
|
np.random.multivariate_normal(mean, cov)
|
||
|
|
w = sup.record(RuntimeWarning)
|
||
|
|
assert len(w) == 0
|
||
|
|
|
||
|
|
def test_negative_binomial(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2))
|
||
|
|
desired = np.array([[848, 841],
|
||
|
|
[892, 611],
|
||
|
|
[779, 647]])
|
||
|
|
assert_array_equal(actual, desired)
|
||
|
|
|
||
|
|
def test_noncentral_chisquare(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
|
||
|
|
desired = np.array([[23.91905354498517511, 13.35324692733826346],
|
||
|
|
[31.22452661329736401, 16.60047399466177254],
|
||
|
|
[5.03461598262724586, 17.94973089023519464]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
actual = np.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)
|
||
|
|
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.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):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1,
|
||
|
|
size=(3, 2))
|
||
|
|
desired = np.array([[1.40598099674926669, 0.34207973179285761],
|
||
|
|
[3.57715069265772545, 7.92632662577829805],
|
||
|
|
[0.43741599463544162, 1.1774208752428319]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
def test_normal(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2))
|
||
|
|
desired = np.array([[2.80378370443726244, 3.59863924443872163],
|
||
|
|
[3.121433477601256, -0.33382987590723379],
|
||
|
|
[4.18552478636557357, 4.46410668111310471]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_normal_0(self):
|
||
|
|
assert_equal(np.random.normal(scale=0), 0)
|
||
|
|
assert_raises(ValueError, np.random.normal, scale=-0.)
|
||
|
|
|
||
|
|
def test_pareto(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.pareto(a=.123456789, size=(3, 2))
|
||
|
|
desired = np.array(
|
||
|
|
[[2.46852460439034849e+03, 1.41286880810518346e+03],
|
||
|
|
[5.28287797029485181e+07, 6.57720981047328785e+07],
|
||
|
|
[1.40840323350391515e+02, 1.98390255135251704e+05]])
|
||
|
|
# For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
|
||
|
|
# matrix differs by 24 nulps. Discussion:
|
||
|
|
# 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):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.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, np.random.poisson, lamneg)
|
||
|
|
assert_raises(ValueError, np.random.poisson, [lamneg]*10)
|
||
|
|
assert_raises(ValueError, np.random.poisson, lambig)
|
||
|
|
assert_raises(ValueError, np.random.poisson, [lambig]*10)
|
||
|
|
|
||
|
|
def test_power(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.power(a=.123456789, size=(3, 2))
|
||
|
|
desired = np.array([[0.02048932883240791, 0.01424192241128213],
|
||
|
|
[0.38446073748535298, 0.39499689943484395],
|
||
|
|
[0.00177699707563439, 0.13115505880863756]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_rayleigh(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.rayleigh(scale=10, size=(3, 2))
|
||
|
|
desired = np.array([[13.8882496494248393, 13.383318339044731],
|
||
|
|
[20.95413364294492098, 21.08285015800712614],
|
||
|
|
[11.06066537006854311, 17.35468505778271009]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
def test_rayleigh_0(self):
|
||
|
|
assert_equal(np.random.rayleigh(scale=0), 0)
|
||
|
|
assert_raises(ValueError, np.random.rayleigh, scale=-0.)
|
||
|
|
|
||
|
|
def test_standard_cauchy(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.standard_cauchy(size=(3, 2))
|
||
|
|
desired = np.array([[0.77127660196445336, -6.55601161955910605],
|
||
|
|
[0.93582023391158309, -2.07479293013759447],
|
||
|
|
[-4.74601644297011926, 0.18338989290760804]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_standard_exponential(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.standard_exponential(size=(3, 2))
|
||
|
|
desired = np.array([[0.96441739162374596, 0.89556604882105506],
|
||
|
|
[2.1953785836319808, 2.22243285392490542],
|
||
|
|
[0.6116915921431676, 1.50592546727413201]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_standard_gamma(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.standard_gamma(shape=3, size=(3, 2))
|
||
|
|
desired = np.array([[5.50841531318455058, 6.62953470301903103],
|
||
|
|
[5.93988484943779227, 2.31044849402133989],
|
||
|
|
[7.54838614231317084, 8.012756093271868]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
def test_standard_gamma_0(self):
|
||
|
|
assert_equal(np.random.standard_gamma(shape=0), 0)
|
||
|
|
assert_raises(ValueError, np.random.standard_gamma, shape=-0.)
|
||
|
|
|
||
|
|
def test_standard_normal(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.standard_normal(size=(3, 2))
|
||
|
|
desired = np.array([[1.34016345771863121, 1.73759122771936081],
|
||
|
|
[1.498988344300628, -0.2286433324536169],
|
||
|
|
[2.031033998682787, 2.17032494605655257]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_standard_t(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.standard_t(df=10, size=(3, 2))
|
||
|
|
desired = np.array([[0.97140611862659965, -0.08830486548450577],
|
||
|
|
[1.36311143689505321, -0.55317463909867071],
|
||
|
|
[-0.18473749069684214, 0.61181537341755321]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_triangular(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.triangular(left=5.12, mode=10.23, right=20.34,
|
||
|
|
size=(3, 2))
|
||
|
|
desired = np.array([[12.68117178949215784, 12.4129206149193152],
|
||
|
|
[16.20131377335158263, 16.25692138747600524],
|
||
|
|
[11.20400690911820263, 14.4978144835829923]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
def test_uniform(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2))
|
||
|
|
desired = np.array([[6.99097932346268003, 6.73801597444323974],
|
||
|
|
[9.50364421400426274, 9.53130618907631089],
|
||
|
|
[5.48995325769805476, 8.47493103280052118]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_uniform_range_bounds(self):
|
||
|
|
fmin = np.finfo('float').min
|
||
|
|
fmax = np.finfo('float').max
|
||
|
|
|
||
|
|
func = np.random.uniform
|
||
|
|
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
|
||
|
|
np.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, np.random.uniform, throwing_float,
|
||
|
|
throwing_float)
|
||
|
|
|
||
|
|
class ThrowingInteger(np.ndarray):
|
||
|
|
def __int__(self):
|
||
|
|
raise TypeError
|
||
|
|
|
||
|
|
__index__ = __int__
|
||
|
|
|
||
|
|
throwing_int = np.array(1).view(ThrowingInteger)
|
||
|
|
assert_raises(TypeError, np.random.hypergeometric, throwing_int, 1, 1)
|
||
|
|
|
||
|
|
def test_vonmises(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
|
||
|
|
desired = np.array([[2.28567572673902042, 2.89163838442285037],
|
||
|
|
[0.38198375564286025, 2.57638023113890746],
|
||
|
|
[1.19153771588353052, 1.83509849681825354]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_vonmises_small(self):
|
||
|
|
# check infinite loop, gh-4720
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
|
||
|
|
np.testing.assert_(np.isfinite(r).all())
|
||
|
|
|
||
|
|
def test_wald(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2))
|
||
|
|
desired = np.array([[3.82935265715889983, 5.13125249184285526],
|
||
|
|
[0.35045403618358717, 1.50832396872003538],
|
||
|
|
[0.24124319895843183, 0.22031101461955038]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
def test_weibull(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.weibull(a=1.23, size=(3, 2))
|
||
|
|
desired = np.array([[0.97097342648766727, 0.91422896443565516],
|
||
|
|
[1.89517770034962929, 1.91414357960479564],
|
||
|
|
[0.67057783752390987, 1.39494046635066793]])
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=15)
|
||
|
|
|
||
|
|
def test_weibull_0(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
assert_equal(np.random.weibull(a=0, size=12), np.zeros(12))
|
||
|
|
assert_raises(ValueError, np.random.weibull, a=-0.)
|
||
|
|
|
||
|
|
def test_zipf(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
actual = np.random.zipf(a=1.23, size=(3, 2))
|
||
|
|
desired = np.array([[66, 29],
|
||
|
|
[1, 1],
|
||
|
|
[3, 13]])
|
||
|
|
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 setSeed(self):
|
||
|
|
np.random.seed(self.seed)
|
||
|
|
|
||
|
|
# TODO: Include test for randint once it can broadcast
|
||
|
|
# Can steal the test written in PR #6938
|
||
|
|
|
||
|
|
def test_uniform(self):
|
||
|
|
low = [0]
|
||
|
|
high = [1]
|
||
|
|
uniform = np.random.uniform
|
||
|
|
desired = np.array([0.53283302478975902,
|
||
|
|
0.53413660089041659,
|
||
|
|
0.50955303552646702])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = uniform(low * 3, high)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.normal
|
||
|
|
desired = np.array([2.2129019979039612,
|
||
|
|
2.1283977976520019,
|
||
|
|
1.8417114045748335])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = normal(loc * 3, scale)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, normal, loc * 3, bad_scale)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.beta
|
||
|
|
desired = np.array([0.19843558305989056,
|
||
|
|
0.075230336409423643,
|
||
|
|
0.24976865978980844])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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 = np.random.exponential
|
||
|
|
desired = np.array([0.76106853658845242,
|
||
|
|
0.76386282278691653,
|
||
|
|
0.71243813125891797])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.standard_gamma
|
||
|
|
desired = np.array([0.76106853658845242,
|
||
|
|
0.76386282278691653,
|
||
|
|
0.71243813125891797])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.gamma
|
||
|
|
desired = np.array([1.5221370731769048,
|
||
|
|
1.5277256455738331,
|
||
|
|
1.4248762625178359])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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 = np.random.f
|
||
|
|
desired = np.array([0.80038951638264799,
|
||
|
|
0.86768719635363512,
|
||
|
|
2.7251095168386801])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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 = np.random.noncentral_f
|
||
|
|
desired = np.array([9.1393943263705211,
|
||
|
|
13.025456344595602,
|
||
|
|
8.8018098359100545])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = nonc_f(dfnum * 3, dfden, nonc)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
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.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
desired = np.array([6.869638627492048, 0.785880199263955])
|
||
|
|
actual = np.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 = np.random.chisquare
|
||
|
|
desired = np.array([0.57022801133088286,
|
||
|
|
0.51947702108840776,
|
||
|
|
0.1320969254923558])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.noncentral_chisquare
|
||
|
|
desired = np.array([9.0015599467913763,
|
||
|
|
4.5804135049718742,
|
||
|
|
6.0872302432834564])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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 = np.random.standard_t
|
||
|
|
desired = np.array([3.0702872575217643,
|
||
|
|
5.8560725167361607,
|
||
|
|
1.0274791436474273])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = t(df * 3)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, t, bad_df * 3)
|
||
|
|
|
||
|
|
def test_vonmises(self):
|
||
|
|
mu = [2]
|
||
|
|
kappa = [1]
|
||
|
|
bad_kappa = [-1]
|
||
|
|
vonmises = np.random.vonmises
|
||
|
|
desired = np.array([2.9883443664201312,
|
||
|
|
-2.7064099483995943,
|
||
|
|
-1.8672476700665914])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = vonmises(mu * 3, kappa)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.pareto
|
||
|
|
desired = np.array([1.1405622680198362,
|
||
|
|
1.1465519762044529,
|
||
|
|
1.0389564467453547])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = pareto(a * 3)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, pareto, bad_a * 3)
|
||
|
|
|
||
|
|
def test_weibull(self):
|
||
|
|
a = [1]
|
||
|
|
bad_a = [-1]
|
||
|
|
weibull = np.random.weibull
|
||
|
|
desired = np.array([0.76106853658845242,
|
||
|
|
0.76386282278691653,
|
||
|
|
0.71243813125891797])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = weibull(a * 3)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, weibull, bad_a * 3)
|
||
|
|
|
||
|
|
def test_power(self):
|
||
|
|
a = [1]
|
||
|
|
bad_a = [-1]
|
||
|
|
power = np.random.power
|
||
|
|
desired = np.array([0.53283302478975902,
|
||
|
|
0.53413660089041659,
|
||
|
|
0.50955303552646702])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = power(a * 3)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, power, bad_a * 3)
|
||
|
|
|
||
|
|
def test_laplace(self):
|
||
|
|
loc = [0]
|
||
|
|
scale = [1]
|
||
|
|
bad_scale = [-1]
|
||
|
|
laplace = np.random.laplace
|
||
|
|
desired = np.array([0.067921356028507157,
|
||
|
|
0.070715642226971326,
|
||
|
|
0.019290950698972624])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = laplace(loc * 3, scale)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, laplace, loc * 3, bad_scale)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.gumbel
|
||
|
|
desired = np.array([0.2730318639556768,
|
||
|
|
0.26936705726291116,
|
||
|
|
0.33906220393037939])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = gumbel(loc * 3, scale)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, gumbel, loc * 3, bad_scale)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.logistic
|
||
|
|
desired = np.array([0.13152135837586171,
|
||
|
|
0.13675915696285773,
|
||
|
|
0.038216792802833396])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = logistic(loc * 3, scale)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, logistic, loc * 3, bad_scale)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = logistic(loc, scale * 3)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, logistic, loc, bad_scale * 3)
|
||
|
|
|
||
|
|
def test_lognormal(self):
|
||
|
|
mean = [0]
|
||
|
|
sigma = [1]
|
||
|
|
bad_sigma = [-1]
|
||
|
|
lognormal = np.random.lognormal
|
||
|
|
desired = np.array([9.1422086044848427,
|
||
|
|
8.4013952870126261,
|
||
|
|
6.3073234116578671])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = lognormal(mean * 3, sigma)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
actual = lognormal(mean, sigma * 3)
|
||
|
|
assert_array_almost_equal(actual, desired, decimal=14)
|
||
|
|
assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
|
||
|
|
|
||
|
|
def test_rayleigh(self):
|
||
|
|
scale = [1]
|
||
|
|
bad_scale = [-1]
|
||
|
|
rayleigh = np.random.rayleigh
|
||
|
|
desired = np.array([1.2337491937897689,
|
||
|
|
1.2360119924878694,
|
||
|
|
1.1936818095781789])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.wald
|
||
|
|
desired = np.array([0.11873681120271318,
|
||
|
|
0.12450084820795027,
|
||
|
|
0.9096122728408238])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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)
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.triangular
|
||
|
|
desired = np.array([2.03339048710429,
|
||
|
|
2.0347400359389356,
|
||
|
|
2.0095991069536208])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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)
|
||
|
|
|
||
|
|
def test_binomial(self):
|
||
|
|
n = [1]
|
||
|
|
p = [0.5]
|
||
|
|
bad_n = [-1]
|
||
|
|
bad_p_one = [-1]
|
||
|
|
bad_p_two = [1.5]
|
||
|
|
binom = np.random.binomial
|
||
|
|
desired = np.array([1, 1, 1])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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 = np.random.negative_binomial
|
||
|
|
desired = np.array([1, 0, 1])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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 = np.random.RandomState()._poisson_lam_max
|
||
|
|
|
||
|
|
lam = [1]
|
||
|
|
bad_lam_one = [-1]
|
||
|
|
bad_lam_two = [max_lam * 2]
|
||
|
|
poisson = np.random.poisson
|
||
|
|
desired = np.array([1, 1, 0])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.zipf
|
||
|
|
desired = np.array([2, 2, 1])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.geometric
|
||
|
|
desired = np.array([2, 2, 2])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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 = np.random.hypergeometric
|
||
|
|
desired = np.array([1, 1, 1])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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.setSeed()
|
||
|
|
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)
|
||
|
|
|
||
|
|
def test_logseries(self):
|
||
|
|
p = [0.5]
|
||
|
|
bad_p_one = [2]
|
||
|
|
bad_p_two = [-1]
|
||
|
|
logseries = np.random.logseries
|
||
|
|
desired = np.array([1, 1, 1])
|
||
|
|
|
||
|
|
self.setSeed()
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
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=(np.random.RandomState(s), o))
|
||
|
|
for s, o in zip(self.seeds, out1)]
|
||
|
|
[x.start() for x in t]
|
||
|
|
[x.join() for x in t]
|
||
|
|
|
||
|
|
# the same serial
|
||
|
|
for s, o in zip(self.seeds, out2):
|
||
|
|
function(np.random.RandomState(s), o)
|
||
|
|
|
||
|
|
# these platforms change x87 fpu precision mode in threads
|
||
|
|
if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
|
||
|
|
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 = (np.random.exponential, np.random.standard_gamma,
|
||
|
|
np.random.chisquare, np.random.standard_t,
|
||
|
|
np.random.pareto, np.random.weibull,
|
||
|
|
np.random.power, np.random.rayleigh,
|
||
|
|
np.random.poisson, np.random.zipf,
|
||
|
|
np.random.geometric, np.random.logseries)
|
||
|
|
|
||
|
|
probfuncs = (np.random.geometric, np.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 = (np.random.uniform, np.random.normal,
|
||
|
|
np.random.beta, np.random.gamma,
|
||
|
|
np.random.f, np.random.noncentral_chisquare,
|
||
|
|
np.random.vonmises, np.random.laplace,
|
||
|
|
np.random.gumbel, np.random.logistic,
|
||
|
|
np.random.lognormal, np.random.wald,
|
||
|
|
np.random.binomial, np.random.negative_binomial)
|
||
|
|
|
||
|
|
probfuncs = (np.random.binomial, np.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)
|
||
|
|
|
||
|
|
# TODO: Uncomment once randint can broadcast arguments
|
||
|
|
# def test_randint(self):
|
||
|
|
# itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
|
||
|
|
# np.int32, np.uint32, np.int64, np.uint64]
|
||
|
|
# func = np.random.randint
|
||
|
|
# high = np.array([1])
|
||
|
|
# low = np.array([0])
|
||
|
|
#
|
||
|
|
# for dt in itype:
|
||
|
|
# out = func(low, high, dtype=dt)
|
||
|
|
# self.assert_equal(out.shape, self.tgtShape)
|
||
|
|
#
|
||
|
|
# out = func(low[0], high, dtype=dt)
|
||
|
|
# self.assert_equal(out.shape, self.tgtShape)
|
||
|
|
#
|
||
|
|
# out = func(low, high[0], dtype=dt)
|
||
|
|
# self.assert_equal(out.shape, self.tgtShape)
|
||
|
|
|
||
|
|
def test_three_arg_funcs(self):
|
||
|
|
funcs = [np.random.noncentral_f, np.random.triangular,
|
||
|
|
np.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)
|