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-rw-r--r--numpy/random/tests/test_direct.py40
-rw-r--r--numpy/random/tests/test_generator_mt19937.py2
-rw-r--r--numpy/random/tests/test_generator_mt19937_regressions.py50
-rw-r--r--numpy/random/tests/test_randomstate.py4
4 files changed, 73 insertions, 23 deletions
diff --git a/numpy/random/tests/test_direct.py b/numpy/random/tests/test_direct.py
index 58d966adf..fa2ae866b 100644
--- a/numpy/random/tests/test_direct.py
+++ b/numpy/random/tests/test_direct.py
@@ -148,6 +148,46 @@ def test_seedsequence():
assert len(dummy.spawn(10)) == 10
+def test_generator_spawning():
+ """ Test spawning new generators and bit_generators directly.
+ """
+ rng = np.random.default_rng()
+ seq = rng.bit_generator.seed_seq
+ new_ss = seq.spawn(5)
+ expected_keys = [seq.spawn_key + (i,) for i in range(5)]
+ assert [c.spawn_key for c in new_ss] == expected_keys
+
+ new_bgs = rng.bit_generator.spawn(5)
+ expected_keys = [seq.spawn_key + (i,) for i in range(5, 10)]
+ assert [bg.seed_seq.spawn_key for bg in new_bgs] == expected_keys
+
+ new_rngs = rng.spawn(5)
+ expected_keys = [seq.spawn_key + (i,) for i in range(10, 15)]
+ found_keys = [rng.bit_generator.seed_seq.spawn_key for rng in new_rngs]
+ assert found_keys == expected_keys
+
+ # Sanity check that streams are actually different:
+ assert new_rngs[0].uniform() != new_rngs[1].uniform()
+
+
+def test_non_spawnable():
+ from numpy.random.bit_generator import ISeedSequence
+
+ class FakeSeedSequence:
+ def generate_state(self, n_words, dtype=np.uint32):
+ return np.zeros(n_words, dtype=dtype)
+
+ ISeedSequence.register(FakeSeedSequence)
+
+ rng = np.random.default_rng(FakeSeedSequence())
+
+ with pytest.raises(TypeError, match="The underlying SeedSequence"):
+ rng.spawn(5)
+
+ with pytest.raises(TypeError, match="The underlying SeedSequence"):
+ rng.bit_generator.spawn(5)
+
+
class Base:
dtype = np.uint64
data2 = data1 = {}
diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py
index 54a5b73a3..5c4c2cbf9 100644
--- a/numpy/random/tests/test_generator_mt19937.py
+++ b/numpy/random/tests/test_generator_mt19937.py
@@ -345,6 +345,8 @@ class TestIntegers:
endpoint=endpoint, dtype=dt)
assert_raises(ValueError, self.rfunc, 1, [0],
endpoint=endpoint, dtype=dt)
+ assert_raises(ValueError, self.rfunc, [ubnd+1], [ubnd],
+ endpoint=endpoint, dtype=dt)
def test_bounds_checking_array(self, endpoint):
for dt in self.itype:
diff --git a/numpy/random/tests/test_generator_mt19937_regressions.py b/numpy/random/tests/test_generator_mt19937_regressions.py
index 0227d6502..7c2b6867c 100644
--- a/numpy/random/tests/test_generator_mt19937_regressions.py
+++ b/numpy/random/tests/test_generator_mt19937_regressions.py
@@ -3,32 +3,32 @@ import numpy as np
import pytest
from numpy.random import Generator, MT19937
-mt19937 = Generator(MT19937())
-
class TestRegression:
+ def setup_method(self):
+ self.mt19937 = Generator(MT19937(121263137472525314065))
+
def test_vonmises_range(self):
# Make sure generated random variables are in [-pi, pi].
# Regression test for ticket #986.
for mu in np.linspace(-7., 7., 5):
- r = mt19937.vonmises(mu, 1, 50)
+ r = self.mt19937.vonmises(mu, 1, 50)
assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
def test_hypergeometric_range(self):
# Test for ticket #921
- assert_(np.all(mt19937.hypergeometric(3, 18, 11, size=10) < 4))
- assert_(np.all(mt19937.hypergeometric(18, 3, 11, size=10) > 0))
+ assert_(np.all(self.mt19937.hypergeometric(3, 18, 11, size=10) < 4))
+ assert_(np.all(self.mt19937.hypergeometric(18, 3, 11, size=10) > 0))
# Test for ticket #5623
args = (2**20 - 2, 2**20 - 2, 2**20 - 2) # Check for 32-bit systems
- assert_(mt19937.hypergeometric(*args) > 0)
+ assert_(self.mt19937.hypergeometric(*args) > 0)
def test_logseries_convergence(self):
# Test for ticket #923
N = 1000
- mt19937 = Generator(MT19937(0))
- rvsn = mt19937.logseries(0.8, size=N)
+ rvsn = self.mt19937.logseries(0.8, size=N)
# these two frequency counts should be close to theoretical
# numbers with this large sample
# theoretical large N result is 0.49706795
@@ -65,41 +65,38 @@ class TestRegression:
# Test for multivariate_normal issue with 'size' argument.
# Check that the multivariate_normal size argument can be a
# numpy integer.
- mt19937.multivariate_normal([0], [[0]], size=1)
- mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
- mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
+ self.mt19937.multivariate_normal([0], [[0]], size=1)
+ self.mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
+ self.mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
def test_beta_small_parameters(self):
# Test that beta with small a and b parameters does not produce
# NaNs due to roundoff errors causing 0 / 0, gh-5851
- mt19937 = Generator(MT19937(1234567890))
- x = mt19937.beta(0.0001, 0.0001, size=100)
+ x = self.mt19937.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
def test_choice_sum_of_probs_tolerance(self):
# The sum of probs should be 1.0 with some tolerance.
# For low precision dtypes the tolerance was too tight.
# See numpy github issue 6123.
- mt19937 = Generator(MT19937(1234))
a = [1, 2, 3]
counts = [4, 4, 2]
for dt in np.float16, np.float32, np.float64:
probs = np.array(counts, dtype=dt) / sum(counts)
- c = mt19937.choice(a, p=probs)
+ c = self.mt19937.choice(a, p=probs)
assert_(c in a)
with pytest.raises(ValueError):
- mt19937.choice(a, p=probs*0.9)
+ self.mt19937.choice(a, p=probs*0.9)
def test_shuffle_of_array_of_different_length_strings(self):
# Test that permuting an array of different length strings
# will not cause a segfault on garbage collection
# Tests gh-7710
- mt19937 = Generator(MT19937(1234))
a = np.array(['a', 'a' * 1000])
for _ in range(100):
- mt19937.shuffle(a)
+ self.mt19937.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
@@ -109,17 +106,17 @@ class TestRegression:
# Test that permuting an array of objects will not cause
# a segfault on garbage collection.
# See gh-7719
- mt19937 = Generator(MT19937(1234))
a = np.array([np.arange(1), np.arange(4)], dtype=object)
for _ in range(1000):
- mt19937.shuffle(a)
+ self.mt19937.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
def test_permutation_subclass(self):
+
class N(np.ndarray):
pass
@@ -142,9 +139,16 @@ class TestRegression:
assert_array_equal(m.__array__(), np.arange(5))
def test_gamma_0(self):
- assert mt19937.standard_gamma(0.0) == 0.0
- assert_array_equal(mt19937.standard_gamma([0.0]), 0.0)
+ assert self.mt19937.standard_gamma(0.0) == 0.0
+ assert_array_equal(self.mt19937.standard_gamma([0.0]), 0.0)
- actual = mt19937.standard_gamma([0.0], dtype='float')
+ actual = self.mt19937.standard_gamma([0.0], dtype='float')
expected = np.array([0.], dtype=np.float32)
assert_array_equal(actual, expected)
+
+ def test_geometric_tiny_prob(self):
+ # Regression test for gh-17007.
+ # When p = 1e-30, the probability that a sample will exceed 2**63-1
+ # is 0.9999999999907766, so we expect the result to be all 2**63-1.
+ assert_array_equal(self.mt19937.geometric(p=1e-30, size=3),
+ np.iinfo(np.int64).max)
diff --git a/numpy/random/tests/test_randomstate.py b/numpy/random/tests/test_randomstate.py
index 8b911cb3a..3a2961098 100644
--- a/numpy/random/tests/test_randomstate.py
+++ b/numpy/random/tests/test_randomstate.py
@@ -812,6 +812,10 @@ class TestRandomDist:
alpha = np.array([5.4e-01, -1.0e-16])
assert_raises(ValueError, random.dirichlet, alpha)
+ def test_dirichlet_zero_alpha(self):
+ y = random.default_rng().dirichlet([5, 9, 0, 8])
+ assert_equal(y[2], 0)
+
def test_dirichlet_alpha_non_contiguous(self):
a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
alpha = a[::2]
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