def init(self, target_rule, x='x', y='y'):
"""Initializes the grammar.
A grammar can only be used for sampling after initialization.
The initialization takes time linear in the size of the grammar.
Parameters
----------
target_rule : str
Rule to be sampled from (sampling will also be possible for all
rules the target depends on).
x : str, optional (default='x')
Symbolic x-argument.
y : str, optional (default='y')
Symbolic y-argument.
"""
if target_rule not in self.rules:
self._missing_rule_error(target_rule)
self._target_rule, self._target_x, self._target_y = target_rule, x, y
# Initialize the alias samplers, i.e. set their referenced grammar to
# this grammar.
self._init_alias_samplers()
# Find out which rules are recursive.
self._find_recursive_rules()
# Automatically set target class labels of transformation samplers
# where possible.
self._infer_target_class_labels()
# Get the needed oracle queries and check if they are present in the
# oracle.
self._initialized = True
self._needed_oracle_queries = self._collect_oracle_queries()
if not pybo.BoltzmannSamplerBase.oracle.contains_all(
self._needed_oracle_queries):
raise pybo.PyBoltzmannError(
"The following evals are needed: {}".format(
self._needed_oracle_queries))
# Precompute the evaluations for all intermediate classes.
self._precompute_evals()
def __init__(self, base_class_object, marked_atom=None):
if type(self) is DerivedClass:
raise pybo.PyBoltzmannError(
"Instantiate objects of LDerivedClass or UDerivedClass")
self._base_class_object = base_class_object
self._marked_atom = marked_atom
def u_atoms(self):
raise pybo.PyBoltzmannError(
"Cannot iterate over atoms from dummy class")
def _missing_rule_error(alias):
msg = "Not a rule in the grammar: {}".format(alias)
raise pybo.PyBoltzmannError(msg)
def _grammar_not_initialized_error():
msg = "Grammar not initialized"
raise pybo.PyBoltzmannError(msg)