ParetoNBDRV#
- class pymc_marketing.clv.distributions.ParetoNBDRV(name=None, ndim_supp=None, ndims_params=None, dtype=None, inplace=None)[source]#
Methods
ParetoNBDRV.L_op
(inputs, outputs, output_grads)Construct a graph for the L-operator.
ParetoNBDRV.R_op
(inputs, eval_points)Construct a graph for the R-operator.
ParetoNBDRV.__init__
([name, ndim_supp, ...])Create a random variable
Op
.ParetoNBDRV.add_tag_trace
([user_line])Add tag.trace to a node or variable.
ParetoNBDRV.do_constant_folding
(fgraph, node)Determine whether or not constant folding should be performed for the given node.
ParetoNBDRV.grad
(inputs, outputs)Construct a graph for the gradient with respect to each input variable.
ParetoNBDRV.infer_shape
(fgraph, node, ...)ParetoNBDRV.make_node
(rng, size, dtype, r, ...)Create a random variable node.
ParetoNBDRV.make_py_thunk
(node, storage_map, ...)Make a Python thunk.
ParetoNBDRV.make_thunk
(node, storage_map, ...)Create a thunk.
ParetoNBDRV.perform
(node, inputs, outputs)Calculate the function on the inputs and put the variables in the output storage.
ParetoNBDRV.prepare_node
(node, storage_map, ...)Make any special modifications that the
Op
needs before doingOp.make_thunk()
.ParetoNBDRV.rng_fn
(rng, r, alpha, s, beta, ...)Sample a numeric random variate.
Attributes
default_output
An
int
that specifies which outputOp.__call__()
should return.destroy_map
A
dict
that maps output indices to the input indices upon which they operate in-place.dtype
itypes
name
ndim_supp
ndims_params
otypes
view_map
A
dict
that maps output indices to the input indices of which they are a view.