def set_gen(self, gen):
from pymc3.data import GeneratorAdapter
if not isinstance(gen, GeneratorAdapter):
gen = GeneratorAdapter(gen)
if not gen.tensortype == self.generator.tensortype:
raise ValueError("New generator should yield the same type")
self.generator = gen
def __init__(self, gen, default=None):
from pymc3.data import GeneratorAdapter
super().__init__()
if not isinstance(gen, GeneratorAdapter):
gen = GeneratorAdapter(gen)
self.generator = gen
self.set_default(default)
def test_ndim(self):
for ndim in range(10):
res = list(itertools.islice(integers_ndim(ndim), 0, 2))
generator = GeneratorAdapter(integers_ndim(ndim))
gop = GeneratorOp(generator)()
f = theano.function([], gop)
assert ndim == res[0].ndim
np.testing.assert_equal(f(), res[0])
np.testing.assert_equal(f(), res[1])
def test_basic(self):
generator = GeneratorAdapter(integers())
gop = GeneratorOp(generator)()
assert gop.tag.test_value == np.float32(0)
f = theano.function([], gop)
assert f() == np.float32(0)
assert f() == np.float32(1)
for _ in range(2, 100):
f()
assert f() == np.float32(100)
