def make_node(self, pvals, unis, n=1):
pvals = aet.as_tensor_variable(pvals)
unis = aet.as_tensor_variable(unis)
if pvals.ndim != 2:
raise NotImplementedError("pvals ndim should be 2", pvals.ndim)
if unis.ndim != 1:
raise NotImplementedError("unis ndim should be 1", unis.ndim)
if self.odtype == "auto":
odtype = pvals.dtype
else:
odtype = self.odtype
out = aet.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
return Apply(self, [pvals, unis, as_scalar(n)], [out])
def make_node(self, pvals, unis, n):
assert pvals.dtype == "float32"
assert unis.dtype == "float32"
ctx_name = infer_context_name(pvals, unis)
pvals = as_gpuarray_variable(pvals, ctx_name)
unis = as_gpuarray_variable(unis, ctx_name)
if pvals.ndim != 2:
raise NotImplementedError("pvals ndim should be 2", pvals.ndim)
if unis.ndim != 1:
raise NotImplementedError("unis ndim should be 1", unis.ndim)
if self.odtype == "auto":
odtype = "int64"
else:
odtype = self.odtype
assert odtype == "int64", odtype
br = (pvals.broadcastable[1], pvals.broadcastable[0])
out = GpuArrayType(broadcastable=br, dtype=odtype, context_name=ctx_name)()
return Apply(self, [pvals, unis, as_scalar(n)], [out])
def make_node(self, a, b):
return Apply(self,
[aes.as_scalar(a), aes.as_scalar(b)], [aes.float64()])
def make_node(self, input):
input = aes.as_scalar(input)
output = input.type()
return Apply(self, [input], [output])
def make_node(self, val):
from aesara import Apply
from aesara.scalar import as_scalar
val = as_scalar(val).astype("uint64")
return Apply(self, [val], [self.rtype()])
