def test_select_proportional_to_weight(self):
# Tests that multinomial_wo_replacement selects elements, on average,
# proportional to the their probabilities
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = np.zeros((n_elements,), dtype=config.floatX)
for rep in range(10000):
res = f(pvals, n_selected)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol
def set_at_rng(new_rng):
"""
Set the package-level random number generator.
Parameters
----------
new_rng: `aesara.tensor.random.utils.RandomStream` instance
The random number generator to use.
"""
# pylint: disable=global-statement
global _at_rng
# pylint: enable=global-statement
if isinstance(new_rng, int):
new_rng = RandomStream(new_rng)
_at_rng = new_rng
def test_fail_select_alot(self):
# Tests that multinomial_wo_replacement fails when asked to sample more
# elements than the actual number of elements
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 200
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
with pytest.raises(ValueError):
f(pvals, n_selected)
def at_rng(random_seed=None):
"""
Get the package-level random number generator or new with specified seed.
Parameters
----------
random_seed: int
If not None
returns *new* aesara random generator without replacing package global one
Returns
-------
`aesara.tensor.random.utils.RandomStream` instance
`aesara.tensor.random.utils.RandomStream`
instance passed to the most recent call of `set_at_rng`
"""
if random_seed is None:
return _at_rng
else:
ret = RandomStream(random_seed)
return ret
def test_select_distinct(self):
# Tests that multinomial_wo_replacement always selects distinct elements
th_rng = RandomStream(12345)
p = fmatrix()
n = iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 1000
all_indices = range(n_elements)
np.random.seed(12345)
for i in [5, 10, 50, 100, 500, n_elements]:
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, i)
res = np.squeeze(res)
assert len(res) == i
assert np.all(np.in1d(np.unique(res), all_indices)), res
----------
gen: generator that implements __next__ (py3) or next (py2) method
and yields np.arrays with same types
default: np.array with the same type as generator produces
Returns
-------
TensorVariable
It has 2 new methods
- var.set_gen(gen): sets new generator
- var.set_default(value): sets new default value (None erases default value)
"""
return GeneratorOp(gen, default)()
_at_rng = RandomStream()
def at_rng(random_seed=None):
"""
Get the package-level random number generator or new with specified seed.
Parameters
----------
random_seed: int
If not None
returns *new* aesara random generator without replacing package global one
Returns
-------
`aesara.tensor.random.utils.RandomStream` instance
def setup_method(self):
nr.seed(self.random_seed)
self.old_aet_rng = aet_rng()
set_aet_rng(RandomStream(self.random_seed))
----------
gen: generator that implements __next__ (py3) or next (py2) method
and yields np.arrays with same types
default: np.array with the same type as generator produces
Returns
-------
TensorVariable
It has 2 new methods
- var.set_gen(gen): sets new generator
- var.set_default(value): sets new default value (None erases default value)
"""
return GeneratorOp(gen, default)()
_aet_rng = RandomStream()
def aet_rng(random_seed=None):
"""
Get the package-level random number generator or new with specified seed.
Parameters
----------
random_seed: int
If not None
returns *new* aesara random generator without replacing package global one
Returns
-------
`aesara.tensor.random.utils.RandomStream` instance