def _check_get_set_state(self, ptr):
state = _helperlib.rnd_get_state(ptr)
i, ints = state
self.assertIsInstance(i, int)
self.assertIsInstance(ints, list)
self.assertEqual(len(ints), N)
j = (i * 100007) % N
ints = [i * 3 for i in range(N)]
# Roundtrip
_helperlib.rnd_set_state(ptr, (j, ints))
self.assertEqual(_helperlib.rnd_get_state(ptr), (j, ints))
def _check_get_set_state(self, ptr):
state = _helperlib.rnd_get_state(ptr)
i, ints = state
self.assertIsInstance(i, int)
self.assertIsInstance(ints, list)
self.assertEqual(len(ints), N)
j = (i * 100007) % N
ints = [i * 3 for i in range(N)]
# Roundtrip
_helperlib.rnd_set_state(ptr, (j, ints))
self.assertEqual(_helperlib.rnd_get_state(ptr), (j, ints))
def _put_back_random_state(self):
# This uses a trick by Alexandre Gramfort,
# see https://github.com/numba/numba/issues/3249
if self._random_state >= 0:
if self._using_numba:
ptr = self._ptr
r = self._r
index, ints = _helperlib.rnd_get_state(ptr)
r.set_state(("MT19937", ints, index, 0, 0.0))
def _check_perturb(self, ptr):
states = []
for i in range(10):
# Initialize with known state
_helperlib.rnd_seed(ptr, 0)
# Perturb with entropy
_helperlib.rnd_seed(ptr, os.urandom(512))
states.append(tuple(_helperlib.rnd_get_state(ptr)[1]))
# No two identical states
self.assertEqual(len(set(states)), len(states))
def _check_perturb(self, ptr):
states = []
for i in range(10):
# Initialize with known state
_helperlib.rnd_seed(ptr, 0)
# Perturb with entropy
_helperlib.rnd_seed(ptr, os.urandom(512))
states.append(tuple(_helperlib.rnd_get_state(ptr)[1]))
# No two identical states
self.assertEqual(len(set(states)), len(states))
def _check_shuffle(self, ptr):
# We test shuffling against CPython
r = random.Random()
ints, index = _copy_py_state(r, ptr)
# Force shuffling in CPython generator
for i in range(index, N + 1, 2):
r.random()
_helperlib.rnd_shuffle(ptr)
# Check new integer keys
mt = r.getstate()[1]
ints, index = mt[:-1], mt[-1]
self.assertEqual(_helperlib.rnd_get_state(ptr)[1], list(ints))
def _check_shuffle(self, ptr):
# We test shuffling against CPython
r = random.Random()
ints, index = _copy_py_state(r, ptr)
# Force shuffling in CPython generator
for i in range(index, N + 1, 2):
r.random()
_helperlib.rnd_shuffle(ptr)
# Check new integer keys
mt = r.getstate()[1]
ints, index = mt[:-1], mt[-1]
self.assertEqual(_helperlib.rnd_get_state(ptr)[1], list(ints))
def _check_init(self, ptr):
# We use the same integer seeding as Numpy
# (CPython is different: it treats the integer as a byte array)
r = np.random.RandomState()
for i in [0, 1, 125, 2**32 - 5]:
# Need to cast to a C-sized int (for Numpy <= 1.7)
r.seed(np.uint32(i))
st = r.get_state()
ints = list(st[1])
index = st[2]
assert index == N # sanity check
_helperlib.rnd_seed(ptr, i)
self.assertEqual(_helperlib.rnd_get_state(ptr), (index, ints))
def _check_init(self, ptr):
# We use the same integer seeding as Numpy
# (CPython is different: it treats the integer as a byte array)
r = np.random.RandomState()
for i in [0, 1, 125, 2**32 - 5]:
# Need to cast to a C-sized int (for Numpy <= 1.7)
r.seed(np.uint32(i))
st = r.get_state()
ints = list(st[1])
index = st[2]
assert index == N # sanity check
_helperlib.rnd_seed(ptr, i)
self.assertEqual(_helperlib.rnd_get_state(ptr), (index, ints))
def box_random_state(typ, val, c):
"""Convert a native `RandomStateNumbaModel` structure to an `RandomState` object
using Numba's internal state array.
Note that `RandomStateNumbaModel` is just a placeholder structure with no
inherent information about Numba internal random state, all that information
is instead retrieved from Numba using ``_helperlib.rnd_get_state()`` and a new
`RandomState` is constructed using the Numba's current internal state.
"""
pos, state_list = _helperlib.rnd_get_state(
_helperlib.rnd_get_np_state_ptr())
rng = RandomState()
rng.set_state(("MT19937", state_list, pos))
class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(rng))
return class_obj
def get_random_state():
"""Get the random state as a tuple.
Get the random state from a tuple in the form returned by
:func:`numba._helperlib.rnd_get_state`. This tuple contains the
necessary information for resuming a checkpoint from the exact same random
number generator state.
.. important::
You probably do not need to use this function. Be sure you know
*exactly* how you are affecting the random number generator state
before using this function, or you are likely to create a model that
cannot be peproduced.
See also :func:`set_random_seed`.
Returns
-------
state : :obj:`tuple`
Random number generator state as a tuple.
"""
ptr = _helperlib.rnd_get_np_state_ptr()
return _helperlib.rnd_get_state(ptr)
def _copyback_np_state(r, ptr):
"""
Copy state of of Numba state *ptr* to Numpy random *r*
"""
index, ints = _helperlib.rnd_get_state(ptr)
r.set_state(('MT19937', ints, index, 0, 0.0))
