def read_bif(path, is_quantum):
"""
Reads a bif file using our stand-alone class Qubifer and returns a
BayesNet. bif and dot files complement each other. bif: graphical
info No, pot info Yes. dot: graphical info Yes, pot info No. By pots
I mean potentials, the transition matrices of the nodes. (aka CPTs,
etc.)
Parameters
----------
path : str
is_quantum : bool
Returns
-------
BayesNet
"""
qb = Qubifer(is_quantum)
qb.read_bif(path)
k = -1
nodes = set()
name_to_nd = {}
for nd_name in qb.nd_sizes:
k += 1
node = BayesNode(k, nd_name)
node.state_names = qb.states[nd_name]
node.size = len(node.state_names)
node.forget_all_evidence()
nodes |= {node}
name_to_nd[nd_name] = node
for nd_name, pa_name_list in qb.parents.items():
node = name_to_nd[nd_name]
for pa_name in pa_name_list:
pa = name_to_nd[pa_name]
node.add_parent(pa)
for nd_name, parent_names in qb.parents.items():
node = name_to_nd[nd_name]
num_pa = len(parent_names)
parents = [name_to_nd[pa_name] for pa_name in parent_names]
if num_pa == 0:
node.potential = DiscreteUniPot(is_quantum, node)
else:
node.potential = DiscreteCondPot(
is_quantum, parents + [node])
node.potential.pot_arr = qb.pot_arrays[nd_name]
return BayesNet(nodes)
def read_bif(path, is_quantum):
"""
Reads a bif file using our stand-alone class Qubifer and returns a
BayesNet. bif and dot files complement each other. bif: graphical
info No, pot info Yes. dot: graphical info Yes, pot info No. By pots
I mean potentials, the transition matrices of the nodes. (aka CPTs,
etc.)
Parameters
----------
path : str
is_quantum : bool
Returns
-------
BayesNet
"""
qb = Qubifer(is_quantum)
qb.read_bif(path)
k = -1
nodes = set()
name_to_nd = {}
for nd_name in qb.nd_sizes:
k += 1
node = BayesNode(k, nd_name)
node.state_names = qb.states[nd_name]
nodes |= {node}
name_to_nd[nd_name] = node
for nd_name, pa_name_list in qb.parents.items():
node = name_to_nd[nd_name]
for pa_name in pa_name_list:
pa = name_to_nd[pa_name]
node.add_parent(pa)
for nd_name, parent_names in qb.parents.items():
node = name_to_nd[nd_name]
num_pa = len(parent_names)
parents = [name_to_nd[pa_name] for pa_name in parent_names]
if num_pa == 0:
node.potential = DiscreteUniPot(is_quantum, node)
else:
node.potential = DiscreteCondPot(is_quantum, parents + [node])
node.potential.pot_arr = qb.pot_arrays[nd_name]
return BayesNet(nodes)
def write_bif(self, path, is_quantum):
"""
Writes a bif file using Qubifer class. Complements read_bif().
Parameters
----------
path : str
is_quantum : bool
Returns
-------
"""
qb = Qubifer(is_quantum)
for node in self.nodes:
qb.nd_sizes[node.name] = node.size
qb.states[node.name] = node.state_names
parent_names = \
[nd.name for nd in node.potential.ord_nodes[:-1]]
qb.parents[node.name] = parent_names
qb.pot_arrays[node.name] = node.potential.pot_arr
qb.write_bif(path)
def write_bif(self, path, is_quantum):
"""
Writes a bif file using Qubifer class. Complements read_bif().
Parameters
----------
path : str
is_quantum : bool
Returns
-------
"""
qb = Qubifer(is_quantum)
for node in self.nodes:
qb.nd_sizes[node.name] = node.size
qb.states[node.name] = node.state_names
parent_names = \
[nd.name for nd in node.potential.ord_nodes[:-1]]
qb.parents[node.name] = parent_names
qb.pot_arrays[node.name] = node.potential.pot_arr
qb.write_bif(path)
