graphid.util.util_random module

graphid.util.util_random.shuffle(items, rng=None)

Shuffles a list inplace and then returns it for convinience

Parameters:

• items (list or ndarray) – list to shuffl

• rng (RandomState or int) – seed or random number gen

Returns:

this is the input, but returned for convinience

Return type:

list

Example

>>> list1 = [1, 2, 3, 4, 5, 6]
>>> list2 = shuffle(list(list1), rng=1)
>>> assert list1 != list2
>>> result = str(list2)
>>> print(result)
[3, 2, 5, 1, 4, 6]

graphid.util.util_random.random_combinations(items, size, num=None, rng=None)

Yields num combinations of length size from items in random order

Parameters:

• items (List) – pool of items to choose from

• size (int) – number of items in each combination

• num (None, default=None) – number of combinations to generate

• rng (int | RandomState, default=None) – seed or random number generator

Yields:

tuple – combo

Example

>>> import ubelt as ub  # NOQA
>>> items = list(range(10))
>>> size = 3
>>> num = 5
>>> rng = 0
>>> combos = list(random_combinations(items, size, num, rng))
>>> result = ('combos = %s' % (ub.urepr(combos),))
>>> print(result)

Example

>>> import ubelt as ub  # NOQA
>>> items = list(zip(range(10), range(10)))
>>> size = 3
>>> num = 5
>>> rng = 0
>>> combos = list(random_combinations(items, size, num, rng))
>>> result = ('combos = %s' % (ub.urepr(combos),))
>>> print(result)

graphid.util.util_random.random_product(items, num=None, rng=None)

Yields num items from the cartesian product of items in a random order.

Parameters:

items (list of sequences) – items to get caresian product of packed in a list or tuple. (note this deviates from api of it.product)

Example

>>> items = [(1, 2, 3), (4, 5, 6, 7)]
>>> rng = 0
>>> list(random_product(items, rng=0))
>>> list(random_product(items, num=3, rng=0))

graphid.util.util_random._npstate_to_pystate(npstate)

Convert state of a NumPy RandomState object to a state that can be used by Python’s Random.

References

https://stackoverflow.com/questions/44313620/converting-randomstate

Example

>>> py_rng = random.Random(0)
>>> np_rng = np.random.RandomState(seed=0)
>>> npstate = np_rng.get_state()
>>> pystate = _npstate_to_pystate(npstate)
>>> py_rng.setstate(pystate)
>>> assert np_rng.rand() == py_rng.random()

graphid.util.util_random._pystate_to_npstate(pystate)

Convert state of a Python Random object to state usable by NumPy RandomState.

References

https://stackoverflow.com/questions/44313620/converting-randomstate

Example

>>> py_rng = random.Random(0)
>>> np_rng = np.random.RandomState(seed=0)
>>> pystate = py_rng.getstate()
>>> npstate = _pystate_to_npstate(pystate)
>>> np_rng.set_state(npstate)
>>> assert np_rng.rand() == py_rng.random()

graphid.util.util_random.ensure_rng(rng, api='numpy')

Returns a random number generator

Parameters:

seed – if None, then the rng is unseeded. Otherwise the seed can be an integer or a RandomState class

Example

>>> rng = ensure_rng(None)
>>> ensure_rng(0).randint(0, 1000)
684
>>> ensure_rng(np.random.RandomState(1)).randint(0, 1000)
37

Example

>>> num = 4
>>> print('--- Python as PYTHON ---')
>>> py_rng = random.Random(0)
>>> pp_nums = [py_rng.random() for _ in range(num)]
>>> print(pp_nums)
>>> print('--- Numpy as PYTHON ---')
>>> np_rng = ensure_rng(random.Random(0), api='numpy')
>>> np_nums = [np_rng.rand() for _ in range(num)]
>>> print(np_nums)
>>> print('--- Numpy as NUMPY---')
>>> np_rng = np.random.RandomState(seed=0)
>>> nn_nums = [np_rng.rand() for _ in range(num)]
>>> print(nn_nums)
>>> print('--- Python as NUMPY---')
>>> py_rng = ensure_rng(np.random.RandomState(seed=0), api='python')
>>> pn_nums = [py_rng.random() for _ in range(num)]
>>> print(pn_nums)
>>> assert np_nums == pp_nums
>>> assert pn_nums == nn_nums