def test_gen_cloning_with_shape_change(self, datagen):
gen = generator(datagen)
gen_r = at_rng().normal(size=gen.shape).T
X = gen.dot(gen_r)
res, _ = aesara.scan(lambda x: x.sum(), X, n_steps=X.shape[0])
assert res.eval().shape == (50, )
shared = aesara.shared(datagen.data.astype(gen.dtype))
res2 = aesara.clone_replace(res, {gen: shared**2})
assert res2.eval().shape == (1000, )
def rslice(self, total, size, seed):
if size is None:
return slice(None)
elif isinstance(size, int):
rng = pm.at_rng(seed)
Minibatch.RNG[id(self)].append(rng)
return rng.uniform(size=(size,), low=0.0, high=pm.floatX(total) - 1e-16).astype("int64")
else:
raise TypeError("Unrecognized size type, %r" % size)
