JezK
Edit File: test_random.py
import warnings import pytest import numpy as np from numpy.testing import ( assert_, assert_raises, assert_equal, assert_warns, assert_no_warnings, assert_array_equal, assert_array_almost_equal, suppress_warnings, IS_WASM ) from numpy import random import sys class TestSeed: def test_scalar(self): s = np.random.RandomState(0) assert_equal(s.randint(1000), 684) s = np.random.RandomState(4294967295) assert_equal(s.randint(1000), 419) def test_array(self): s = np.random.RandomState(range(10)) assert_equal(s.randint(1000), 468) s = np.random.RandomState(np.arange(10)) assert_equal(s.randint(1000), 468) s = np.random.RandomState([0]) assert_equal(s.randint(1000), 973) s = np.random.RandomState([4294967295]) assert_equal(s.randint(1000), 265) def test_invalid_scalar(self): # seed must be an unsigned 32 bit integer assert_raises(TypeError, np.random.RandomState, -0.5) assert_raises(ValueError, np.random.RandomState, -1) def test_invalid_array(self): # seed must be an unsigned 32 bit integer assert_raises(TypeError, np.random.RandomState, [-0.5]) assert_raises(ValueError, np.random.RandomState, [-1]) assert_raises(ValueError, np.random.RandomState, [4294967296]) assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296]) assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296]) def test_invalid_array_shape(self): # gh-9832 assert_raises(ValueError, np.random.RandomState, np.array([], dtype=np.int64)) assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]]) assert_raises(ValueError, np.random.RandomState, [[1, 2, 3], [4, 5, 6]]) class TestBinomial: def test_n_zero(self): # Tests the corner case of n == 0 for the binomial distribution. # binomial(0, p) should be zero for any p in [0, 1]. # This test addresses issue #3480. zeros = np.zeros(2, dtype='int') for p in [0, .5, 1]: assert_(random.binomial(0, p) == 0) assert_array_equal(random.binomial(zeros, p), zeros) def test_p_is_nan(self): # Issue #4571. assert_raises(ValueError, random.binomial, 1, np.nan) class TestMultinomial: def test_basic(self): random.multinomial(100, [0.2, 0.8]) def test_zero_probability(self): random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0]) def test_int_negative_interval(self): assert_(-5 <= random.randint(-5, -1) < -1) x = random.randint(-5, -1, 5) assert_(np.all(-5 <= x)) assert_(np.all(x < -1)) def test_size(self): # gh-3173 p = [0.5, 0.5] assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2)) assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2)) assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape, (2, 2, 2)) assert_raises(TypeError, np.random.multinomial, 1, p, float(1)) def test_multidimensional_pvals(self): assert_raises(ValueError, np.random.multinomial, 10, [[0, 1]]) assert_raises(ValueError, np.random.multinomial, 10, [[0], [1]]) assert_raises(ValueError, np.random.multinomial, 10, [[[0], [1]], [[1], [0]]]) assert_raises(ValueError, np.random.multinomial, 10, np.array([[0, 1], [1, 0]])) class TestSetState: def setup_method(self): self.seed = 1234567890 self.prng = random.RandomState(self.seed) self.state = self.prng.get_state() def test_basic(self): old = self.prng.tomaxint(16) self.prng.set_state(self.state) new = self.prng.tomaxint(16) assert_(np.all(old == new)) def test_gaussian_reset(self): # Make sure the cached every-other-Gaussian is reset. old = self.prng.standard_normal(size=3) self.prng.set_state(self.state) new = self.prng.standard_normal(size=3) assert_(np.all(old == new)) def test_gaussian_reset_in_media_res(self): # When the state is saved with a cached Gaussian, make sure the # cached Gaussian is restored. self.prng.standard_normal() state = self.prng.get_state() old = self.prng.standard_normal(size=3) self.prng.set_state(state) new = self.prng.standard_normal(size=3) assert_(np.all(old == new)) def test_backwards_compatibility(self): # Make sure we can accept old state tuples that do not have the # cached Gaussian value. old_state = self.state[:-2] x1 = self.prng.standard_normal(size=16) self.prng.set_state(old_state) x2 = self.prng.standard_normal(size=16) self.prng.set_state(self.state) x3 = self.prng.standard_normal(size=16) assert_(np.all(x1 == x2)) assert_(np.all(x1 == x3)) def test_negative_binomial(self): # Ensure that the negative binomial results take floating point # arguments without truncation. self.prng.negative_binomial(0.5, 0.5) class TestRandint: rfunc = np.random.randint # valid integer/boolean types itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.int64, np.uint64] def test_unsupported_type(self): assert_raises(TypeError, self.rfunc, 1, dtype=float) def test_bounds_checking(self): for dt in self.itype: lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1 assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt) assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt) assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt) assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt) def test_rng_zero_and_extremes(self): for dt in self.itype: lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1 tgt = ubnd - 1 assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt) tgt = lbnd assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt) tgt = (lbnd + ubnd)//2 assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt) def test_full_range(self): # Test for ticket #1690 for dt in self.itype: lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1 try: self.rfunc(lbnd, ubnd, dtype=dt) except Exception as e: raise AssertionError("No error should have been raised, " "but one was with the following " "message:\n\n%s" % str(e)) def test_in_bounds_fuzz(self): # Don't use fixed seed np.random.seed() for dt in self.itype[1:]: for ubnd in [4, 8, 16]: vals = self.rfunc(2, ubnd, size=2**16, dtype=dt) assert_(vals.max() < ubnd) assert_(vals.min() >= 2) vals = self.rfunc(0, 2, size=2**16, dtype=np.bool_) assert_(vals.max() < 2) assert_(vals.min() >= 0) def test_repeatability(self): import hashlib # We use a sha256 hash of generated sequences of 1000 samples # in the range [0, 6) for all but bool, where the range # is [0, 2). Hashes are for little endian numbers. tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71', 'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4', 'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f', 'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e', 'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404', 'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4', 'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f', 'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e', 'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'} for dt in self.itype[1:]: np.random.seed(1234) # view as little endian for hash if sys.byteorder == 'little': val = self.rfunc(0, 6, size=1000, dtype=dt) else: val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap() res = hashlib.sha256(val.view(np.int8)).hexdigest() assert_(tgt[np.dtype(dt).name] == res) # bools do not depend on endianness np.random.seed(1234) val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8) res = hashlib.sha256(val).hexdigest() assert_(tgt[np.dtype(bool).name] == res) def test_int64_uint64_corner_case(self): # When stored in Numpy arrays, `lbnd` is casted # as np.int64, and `ubnd` is casted as np.uint64. # Checking whether `lbnd` >= `ubnd` used to be # done solely via direct comparison, which is incorrect # because when Numpy tries to compare both numbers, # it casts both to np.float64 because there is # no integer superset of np.int64 and np.uint64. However, # `ubnd` is too large to be represented in np.float64, # causing it be round down to np.iinfo(np.int64).max, # leading to a ValueError because `lbnd` now equals # the new `ubnd`. dt = np.int64 tgt = np.iinfo(np.int64).max lbnd = np.int64(np.iinfo(np.int64).max) ubnd = np.uint64(np.iinfo(np.int64).max + 1) # None of these function calls should # generate a ValueError now. actual = np.random.randint(lbnd, ubnd, dtype=dt) assert_equal(actual, tgt) def test_respect_dtype_singleton(self): # See gh-7203 for dt in self.itype: lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1 sample = self.rfunc(lbnd, ubnd, dtype=dt) assert_equal(sample.dtype, np.dtype(dt)) for dt in (bool, int, 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 # gh-7284: Ensure that we get Python data types sample = self.rfunc(lbnd, ubnd, dtype=dt) assert_(not hasattr(sample, 'dtype')) assert_equal(type(sample), dt) class TestRandomDist: # Make sure the random distribution returns the correct value for a # given seed def setup_method(self): self.seed = 1234567890 def test_rand(self): np.random.seed(self.seed) actual = np.random.rand(3, 2) desired = np.array([[0.61879477158567997, 0.59162362775974664], [0.88868358904449662, 0.89165480011560816], [0.4575674820298663, 0.7781880808593471]]) assert_array_almost_equal(actual, desired, decimal=15) def test_randn(self): np.random.seed(self.seed) actual = np.random.randn(3, 2) desired = np.array([[1.34016345771863121, 1.73759122771936081], [1.498988344300628, -0.2286433324536169], [2.031033998682787, 2.17032494605655257]]) assert_array_almost_equal(actual, desired, decimal=15) def test_randint(self): np.random.seed(self.seed) actual = np.random.randint(-99, 99, size=(3, 2)) desired = np.array([[31, 3], [-52, 41], [-48, -66]]) assert_array_equal(actual, desired) def test_random_integers(self): 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) @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=(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) 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) assert_equal(out.shape, self.tgtShape) out = func(low[0], high, dtype=dt) assert_equal(out.shape, self.tgtShape) out = func(low, high[0], dtype=dt) 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)