def read_bif(path, is_quantum):
"""
Reads a bif file using our stand-alone class BifTool 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
"""
bt = BifTool(is_quantum)
bt.read_bif(path)
nodes = set()
name_to_nd = {}
for k, nd_name in enumerate(bt.nd_sizes.keys()):
node = BayesNode(k, nd_name)
node.state_names = bt.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 bt.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 bt.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 = bt.pot_arrays[nd_name]
return BayesNet(nodes)
def write_bif(self, path, is_quantum):
"""
Writes a bif file using BifTool class. Complements read_bif().
Parameters
----------
path : str
is_quantum : bool
Returns
-------
"""
bt = BifTool(is_quantum)
for node in self.nodes:
bt.nd_sizes[node.name] = node.size
bt.states[node.name] = node.state_names
parent_names = \
[nd.name for nd in node.potential.ord_nodes[:-1]]
bt.parents[node.name] = parent_names
bt.pot_arrays[node.name] = node.potential.pot_arr
bt.write_bif(path)