learning.RandGen_NetParams.RandGen_NetParams Class Reference

Public Member Functions
def	__init__ (self, is_quantum, bnet, num_samples, use_int_sts)
def	sam_generator (self)
def	write_csv (self, sts_file_path, degs_file_path=None)

Public Attributes
	is_quantum
	bnet
	num_samples
	use_int_sts
	topo_nd_list

Detailed Description

RandGen_NetParams (Random Generator of Net Parameters). This class
generates random parameters (i.e. either a single pot or all pots) for a
given bnet structure.

The states are sampled the same way in the classical and quantum cases.
In the quantum case, if a node C with parents pa(C) has C=x and pa(C)=y,
where x and y are the sampled states, then we set the phase of node C
equal to the phase of the amplitude A( C=x | pa(C)=y ).

Attributes
----------
bnet : BayesNet
    The pots of this bnet are sampled to generate a states_df and
    also a degs_df in the quantum case.
use_int_sts : bool
    If False, states_df has state names as entries. If True, states_df
    has int entries. The int entries are the index in the states_names
    list of the node for that column.
is_quantum : bool
    True if quantum bnets and False if classical ones
num_samples : int
    The number of samples = len(states_df.index) = len(degs_df.index)
topo_nd_list : list[BayesNode]
    List of the nodes of the bnet in topological (=chronological) order,
    root node first

Constructor & Destructor Documentation

def learning.RandGen_NetParams.RandGen_NetParams.__init__	(		self,
			is_quantum,
			bnet,
			num_samples,
			use_int_sts
	)

Constructor

Parameters
----------
is_quantum : bool
bnet : BayesNet
num_samples : int
use_int_sts : bool

Returns
-------

Member Function Documentation

def learning.RandGen_NetParams.RandGen_NetParams.sam_generator

(

self

)

A generator of samples. The generator yields two dictionaries,
nd_to_int_st and nd_to_degs. nd_to_int_st maps each node to its
sampled state given as an integer (the integer being the index of
the state in the node_states list of the node). In the quantum case,
nd_to_degs maps each node to its sampled phase in degrees. In the
classical case, nd_to_degs = None

Returns
-------
(dict[int], dict[float])

def learning.RandGen_NetParams.RandGen_NetParams.write_csv	(		self,
			sts_file_path,
			degs_file_path = None
	)

Writes a cvs file (comma separated values) for states_df at the path
sts_file_path and for degs_df at the path degs_file_path

Parameters
----------
sts_file_path : str
degs_file_path : str or NoneType

Returns
-------

---

The documentation for this class was generated from the following file:

• learning/RandGen_NetParams.py