def test_shuffle_mixed_dimension(self):
# Test for trac ticket #2074
for t in [[1, 2, 3, None], [(1, 1), (2, 2), (3, 3), None],
[1, (2, 2), (3, 3), None], [(1, 1), 2, 3, None]]:
mt19937.seed(12345)
shuffled = list(t)
mt19937.shuffle(shuffled)
assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]])
def test_call_within_randomstate(self):
# Check that custom RandomState does not call into global state
m = mt19937.RandomState()
res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
for i in range(3):
mt19937.seed(i)
m.seed(4321)
# If m.state is not honored, the result will change
assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
def test_call_within_randomstate(self):
# Check that custom RandomState does not call into global state
m = mt19937.RandomState()
res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
for i in range(3):
mt19937.seed(i)
m.seed(4321)
# If m.state is not honored, the result will change
assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
def test_shuffle_mixed_dimension(self):
# Test for trac ticket #2074
for t in [[1, 2, 3, None],
[(1, 1), (2, 2), (3, 3), None],
[1, (2, 2), (3, 3), None],
[(1, 1), 2, 3, None]]:
mt19937.seed(12345)
shuffled = list(t)
mt19937.shuffle(shuffled)
assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]])
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.seed(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)
assert_(c in a)
assert_raises(ValueError, mt19937.choice, a, p=probs*0.9)
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.seed(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)
assert_(c in a)
assert_raises(ValueError, mt19937.choice, a, p=probs*0.9)
def test_shuffle_of_array_of_objects(self):
# Test that permuting an array of objects will not cause
# a segfault on garbage collection.
# See gh-7719
mt19937.seed(1234)
a = np.array([np.arange(1), np.arange(4)])
for _ in range(1000):
mt19937.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
def test_shuffle_of_array_of_objects(self):
# Test that permuting an array of objects will not cause
# a segfault on garbage collection.
# See gh-7719
mt19937.seed(1234)
a = np.array([np.arange(1), np.arange(4)])
for _ in range(1000):
mt19937.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
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.seed(1234)
a = np.array(['a', 'a' * 1000])
for _ in range(100):
mt19937.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
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.seed(1234)
a = np.array(['a', 'a' * 1000])
for _ in range(100):
mt19937.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
def test_logseries_convergence(self):
# Test for ticket #923
N = 1000
mt19937.seed(0)
rvsn = 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
freq = np.sum(rvsn == 1) / float(N)
msg = "Frequency was %f, should be > 0.45" % freq
assert_(freq > 0.45, msg)
# theoretical large N result is 0.19882718
freq = np.sum(rvsn == 2) / float(N)
msg = "Frequency was %f, should be < 0.23" % freq
assert_(freq < 0.23, msg)
def test_logseries_convergence(self):
# Test for ticket #923
N = 1000
mt19937.seed(0)
rvsn = 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
freq = np.sum(rvsn == 1) / float(N)
msg = "Frequency was %f, should be > 0.45" % freq
assert_(freq > 0.45, msg)
# theoretical large N result is 0.19882718
freq = np.sum(rvsn == 2) / float(N)
msg = "Frequency was %f, should be < 0.23" % freq
assert_(freq < 0.23, msg)
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.seed(1234567890)
x = mt19937.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
def test_permutation_longs(self):
mt19937.seed(1234)
a = mt19937.permutation(12)
mt19937.seed(1234)
b = mt19937.permutation(long(12))
assert_array_equal(a, b)
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.seed(1234567890)
x = mt19937.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
def test_permutation_longs(self):
mt19937.seed(1234)
a = mt19937.permutation(12)
mt19937.seed(1234)
b = mt19937.permutation(long(12))
assert_array_equal(a, b)
