def test_random_integers(self):
self._set_common_state()
self._is_state_common()
with suppress_warnings() as sup:
sup.record(DeprecationWarning)
compare_2_input(self.nprs.random_integers,
self.rg.random_integers,
is_scalar=True)
self._is_state_common()
def test_multivariate_normal_method(seed, method):
from numpy.random import MT19937 as NPMT19937
random = ExtendedGenerator(NPMT19937(seed))
mean = (0.123456789, 10)
cov = [[1, 0], [0, 1]]
size = (3, 2)
actual = random.multivariate_normal(mean, cov, size, method=method)
desired = np.array([
[
[-1.747478062846581, 11.25613495182354],
[-0.9967333370066214, 10.342002097029821],
],
[
[0.7850019631242964, 11.181113712443013],
[0.8901349653255224, 8.873825399642492],
],
[
[0.7130260107430003, 9.551628690083056],
[0.7127098726541128, 11.991709234143173],
],
])
assert_array_almost_equal(actual, desired, decimal=15)
# Check for default size, was raising deprecation warning
actual = random.multivariate_normal(mean, cov, method=method)
desired = np.array([0.233278563284287, 9.424140804347195])
assert_array_almost_equal(actual, desired, decimal=15)
# Check path with scalar size works correctly
scalar = random.multivariate_normal(mean, cov, 3, method=method)
tuple1d = random.multivariate_normal(mean, cov, (3, ), method=method)
assert scalar.shape == tuple1d.shape == (3, 2)
# Check that non symmetric covariance input raises exception when
# check_valid='raises' if using default svd method.
mean = [0, 0]
cov = [[1, 2], [1, 2]]
assert_raises(ValueError,
random.multivariate_normal,
mean,
cov,
check_valid="raise")
# Check that non positive-semidefinite covariance warns with
# RuntimeWarning
cov = [[1, 2], [2, 1]]
assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
assert_warns(RuntimeWarning,
random.multivariate_normal,
mean,
cov,
method="eigh")
assert_raises(LinAlgError,
random.multivariate_normal,
mean,
cov,
method="cholesky")
# and that it doesn't warn with RuntimeWarning check_valid='ignore'
assert_no_warnings(random.multivariate_normal,
mean,
cov,
check_valid="ignore")
# and that it raises with RuntimeWarning check_valid='raises'
assert_raises(ValueError,
random.multivariate_normal,
mean,
cov,
check_valid="raise")
assert_raises(
ValueError,
random.multivariate_normal,
mean,
cov,
check_valid="raise",
method="eigh",
)
# check degenerate samples from singular covariance matrix
cov = [[1, 1], [1, 1]]
if method in ("svd", "eigh"):
samples = random.multivariate_normal(mean,
cov,
size=(3, 2),
method=method)
assert_array_almost_equal(samples[..., 0], samples[..., 1], decimal=6)
else:
assert_raises(LinAlgError,
random.multivariate_normal,
mean,
cov,
method="cholesky")
cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
with suppress_warnings() as sup:
random.multivariate_normal(mean, cov, method=method)
w = sup.record(RuntimeWarning)
assert len(w) == 0
mu = np.zeros(2)
cov = np.eye(2)
assert random.multivariate_normal(mu, cov, size=3).shape == (3, 2)
assert_raises(ValueError,
random.multivariate_normal,
mean,
cov,
check_valid="other")
assert_raises(ValueError, random.multivariate_normal, np.zeros((2, 1, 1)),
cov)
assert_raises(ValueError, random.multivariate_normal, mu, np.empty((3, 2)))
assert_raises(ValueError, random.multivariate_normal, mu, np.eye(3))
def test_random_integers(self):
with suppress_warnings() as sup:
sup.record(DeprecationWarning)
vals = self.rg.random_integers(10, 20, 10)
assert_(len(vals) == 10)
def test_multivariate_normal(seed):
random.bit_generator.seed(seed)
mean = (0.123456789, 10)
cov = [[1, 0], [0, 1]]
size = (3, 2)
actual = random.multivariate_normal(mean, cov, size)
desired = np.array([
[
[-3.34929721161096100, 9.891061435770858],
[-0.12250896439641100, 9.295898449738300],
],
[
[0.48355927611635563, 10.127832101772366],
[3.11093021424924300, 10.283109168794352],
],
[
[-0.20332082341774727, 9.868532121697195],
[-1.33806889550667330, 9.813657233804179],
],
])
assert_array_almost_equal(actual, desired, decimal=15)
# Check for default size, was raising deprecation warning
actual = random.multivariate_normal(mean, cov)
desired = np.array([-1.097443117192574, 10.535787051184261])
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, random.multivariate_normal, mean, cov)
# and that it doesn"t warn with RuntimeWarning check_valid="ignore"
assert_no_warnings(random.multivariate_normal,
mean,
cov,
check_valid="ignore")
# and that it raises with RuntimeWarning check_valid="raises"
assert_raises(ValueError,
random.multivariate_normal,
mean,
cov,
check_valid="raise")
cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
with suppress_warnings() as sup:
random.multivariate_normal(mean, cov)
w = sup.record(RuntimeWarning)
assert len(w) == 0
mu = np.zeros(2)
cov = np.eye(2)
assert_raises(ValueError,
random.multivariate_normal,
mean,
cov,
check_valid="other")
assert_raises(ValueError, random.multivariate_normal, np.zeros((2, 1, 1)),
cov)
assert_raises(ValueError, random.multivariate_normal, mu, np.empty((3, 2)))
assert_raises(ValueError, random.multivariate_normal, mu, np.eye(3))
