def __add_single_movement_restore_configs(self, lba: TuringMachine):
"""
Add productions for movements of Linear Bounded Automaton
on a one-character tape $t#
from accepted states to Context Sensitive Grammar
Add production ([q,$,g,t,#] -> t)
for all t in lba.sigma, g in lba.gamma
Add production ([$,q,g,t,#] -> t)
for all t in lba.sigma, g in lba.gamma
Add production ([$,g,t,q,#] -> t)
for all t in lba.sigma, g in lba.gamma
$ --- left end marker of lba, # --- right end marker of lba
:param lba: The Linear Bounded Automaton from which the Context Sensitive Grammar is built
:return: None
"""
for accept_state in lba.accept_states:
for sigma_symbol in lba.sigma:
for gamma_symbol in lba.gamma:
self.productions.append(
Production((cfg.Variable(
f'[{accept_state},$,{gamma_symbol},{sigma_symbol},#]'
), ), (cfg.Terminal(f'{sigma_symbol}'), )))
self.productions.append(
Production((cfg.Variable(
f'[$,{accept_state},{gamma_symbol},{sigma_symbol},#]'
), ), (cfg.Terminal(f'{sigma_symbol}'), )))
self.productions.append(
Production((cfg.Variable(
f'[$,{gamma_symbol},{sigma_symbol},{accept_state},#]'
), ), (cfg.Terminal(f'{sigma_symbol}'), )))
def _create_cfg_from_regex(cls, head, regex, track_variables):
dfa = regex.to_epsilon_nfa().to_deterministic().minimize()
transitions = dfa._transition_function._transitions
state_to_var = {}
productions, terminals, variables = set(), set(), set()
for state in dfa.states:
state_to_var[state] = cfg.Variable(f'{state}:{cls.__var_state_counter}')
cls.__var_state_counter += 1
variables.update(state_to_var.values())
for start_state in dfa.start_states:
productions.add(cfg.Production(head, [state_to_var[start_state]]))
for state_from in transitions:
for edge_symb in transitions[state_from]:
state_to = transitions[state_from][edge_symb]
current_prod_head = state_to_var[state_from]
current_prod_body = []
if any(letter.isupper() for letter in edge_symb.value) and not track_variables:
var = cfg.Variable(edge_symb.value)
variables.add(var)
current_prod_body.append(var)
elif edge_symb.value != 'eps':
term = cfg.Terminal(edge_symb.value)
terminals.add(term)
current_prod_body.append(term)
current_prod_body.append(state_to_var[state_to])
productions.add(cfg.Production(current_prod_head, current_prod_body))
if state_to in dfa.final_states:
productions.add(cfg.Production(state_to_var[state_to], []))
if not productions:
return cfg.CFG(variables, terminals, head, {cfg.Production(head, [])})
return cfg.CFG(variables, terminals, head, productions)
def from_lba(cls, lba: TuringMachine):
"""
Build a Context Sensitive Grammar by a Linear Bounded Automaton
:param lba: The Linear Bounded Automaton from which the Context Sensitive Grammar is built
:return: The Context Sensitive Grammar builded by lba
"""
grammar = ContextSensitiveGrammar()
grammar.terminals = {cfg.Terminal(x) for x in lba.sigma}
grammar.start_symbol = cfg.Variable('S1')
grammar.__add_initial_configs_single(lba, 'S1')
grammar.__add_single_movement_configs(lba)
grammar.__add_single_movement_restore_configs(lba)
grammar.__add_initial_configs_general(lba, 'S1', 'S2')
grammar.__add_general_movement_configs_left(lba)
grammar.__add_general_movement_configs_center(lba)
grammar.__add_general_movement_configs_right(lba)
grammar.__add_general_movement_restore_configs(lba)
grammar.__add_general_movement_restore_word(lba)
grammar = grammar.nonterminals_optimization()
while True:
prev = len(grammar.productions)
grammar = grammar.deep_optimization(max_cnt=5)
grammar = grammar.substitutions_optimization()
if prev == len(grammar.productions):
break
return grammar
def from_turing_machine(cls, turing_machine: TuringMachine):
"""
Build a Unrestricted Grammar by a Turing Machine
:param turing_machine: The Turing Machine from which the Unrestricted Grammar is built
:return: The Unrestricted Grammar builded by lba
"""
grammar = UnrestrictedGrammar()
grammar.__add_initial_configs(turing_machine, 'S1', 'S2')
grammar.__add_movement_configs(turing_machine)
grammar.__add_restore_configs(turing_machine)
grammar.terminals = {cfg.Terminal(x) for x in turing_machine.sigma}
grammar.start_symbol = cfg.Variable('S1')
grammar = grammar.nonterminals_optimization()
while True:
prev = len(grammar.productions)
grammar = grammar.deep_optimization(max_cnt=4)
grammar = grammar.substitutions_optimization()
if prev == len(grammar.productions):
break
return grammar
def from_txt(cls, path: pathlib.Path):
"""
Loads an instance of a Grammar from a txt file
:param path: Path to a txt file
:return: Grammar instance
"""
grammar = Grammar()
with open(path, 'r') as input_file:
grammar.start_symbol = cfg.Variable(
input_file.readline().strip().replace('start_symbol: ', ''))
grammar.nonterminals = {
cfg.Variable(x)
for x in input_file.readline().strip().replace(
'nonterminals: ', '').split()
}
grammar.terminals = {
cfg.Terminal(x)
for x in input_file.readline().strip().replace(
'terminals: ', '').split()
}
for production in input_file:
head, body = production.split(' -> ')
grammar.productions.append(
Production(
tuple(
cfg.Terminal(x) if cfg.Terminal(x) in
grammar.terminals else cfg.Variable(x)
for x in head.split()),
tuple(
cfg.Terminal(x) if cfg.Terminal(x) in
grammar.terminals else cfg.Variable(x)
for x in body.split())))
return grammar
def _read_line(cls, line, productions, terminals, variables):
prod_s = line.split(' ', 1)
head_s = prod_s[0]
if len(prod_s) > 1:
body_s = prod_s[1]
else:
body_s = ''
head = cfg.Variable(head_s.strip())
variables.add(head)
body = []
for body_component in body_s.split():
if body_component[0] in string.ascii_uppercase:
body_var = cfg.Variable(body_component)
variables.add(body_var)
body.append(body_var)
else:
body_ter = cfg.Terminal(body_component)
terminals.add(body_ter)
body.append(body_ter)
productions.add(cfg.Production(head, body))
def accepts(
self, word: str
) -> Tuple[List[Production], List[Tuple[Union[cfg.Variable, cfg.Terminal],
...]]]:
"""
Returns whether the Context Sensitive Grammar generates the given word
:param word: Tuple from grammar terminals
:return: Tuple(used productions, sentences)
if Context Sensitive Grammar generates the given word
else empty tuple
"""
word = tuple(cfg.Terminal(x) for x in word)
used: Dict[Tuple[Union[cfg.Variable, cfg.Terminal], ...],
List[Production]] = dict()
parent: Dict[Tuple[Union[cfg.Variable, cfg.Terminal], ...],
Tuple[Union[cfg.Variable, cfg.Terminal], ...]] = dict()
queue: Deque[Tuple[Union[cfg.Variable, cfg.Terminal], ...]] = \
deque([(cfg.Variable(self.start_symbol),)])
while len(queue) != 0:
sentence: Tuple[Union[cfg.Variable, cfg.Terminal],
...] = queue.popleft()
if sentence not in used:
used[sentence] = list()
if all(isinstance(x, cfg.Terminal) for x in sentence):
if sentence == word:
trace = list()
prev = word
while prev in parent:
trace.append(prev)
prev = parent[prev]
trace.append(prev)
trace.reverse()
return used[word], trace
if len(sentence) > len(word):
return tuple()
for production in self.productions:
for i in range(len(sentence) - len(production.head) + 1):
if production.head == sentence[i:i + len(production.head)]:
new_sentence: Tuple[Union[cfg.Variable, cfg.Terminal], ...] = \
sentence[:i] + production.body + sentence[i + len(production.head):]
if new_sentence not in used:
used[new_sentence] = used[sentence].copy() + [
production
]
parent[new_sentence] = sentence
if any(
isinstance(x, cfg.Terminal)
for x in new_sentence):
queue.appendleft(new_sentence)
else:
queue.append(new_sentence)
queue = deque(
sorted(queue,
key=lambda y: sum(1 for x in y
if isinstance(x, cfg.Variable))))
return tuple()
def _prepend_input_symbol_to_the_bodies(bodies, transition):
to_prepend = cfg.Terminal(transition[INPUT][INPUT_SYMBOL].value)
for body in bodies:
body.insert(0, to_prepend)
def __add_general_movement_restore_word(self, lba: TuringMachine):
"""
Add productions to restore word accepted by Linear Bounded Automaton
to Context Sensitive Grammar
Add production (l [g,t] -> l t)
for all t,l in lba.sigma, g in lba.gamma
Add production (l [g,t,#] -> l t)
for all t,l in lba.sigma, g in lba.gamma
Add production ([g,l] t -> l t)
for all t,l in lba.sigma, g in lba.gamma
Add production ([$,g,l] t -> l t)
for all t,l in lba.sigma, g in lba.gamma
$ --- left end marker of lba, # --- right end marker of lba
:param lba: The Linear Bounded Automaton from which the Context Sensitive Grammar is built
:return: None
"""
for gamma_symbol, sigma_symbol_1, sigma_symbol_2 in itertools.product(
lba.gamma, lba.sigma, lba.sigma):
self.productions.append(
Production((
cfg.Terminal(f'{sigma_symbol_1}'),
cfg.Variable(f'[{gamma_symbol},{sigma_symbol_2}]'),
), (
cfg.Terminal(f'{sigma_symbol_1}'),
cfg.Terminal(f'{sigma_symbol_2}'),
)))
self.productions.append(
Production((
cfg.Terminal(f'{sigma_symbol_1}'),
cfg.Variable(f'[{gamma_symbol},{sigma_symbol_2},#]'),
), (
cfg.Terminal(f'{sigma_symbol_1}'),
cfg.Terminal(f'{sigma_symbol_2}'),
)))
self.productions.append(
Production((
cfg.Variable(f'[{gamma_symbol},{sigma_symbol_1}]'),
cfg.Terminal(f'{sigma_symbol_2}'),
), (
cfg.Terminal(f'{sigma_symbol_1}'),
cfg.Terminal(f'{sigma_symbol_2}'),
)))
self.productions.append(
Production((
cfg.Variable(f'[$,{gamma_symbol},{sigma_symbol_1}]'),
cfg.Terminal(f'{sigma_symbol_2}'),
), (
cfg.Terminal(f'{sigma_symbol_1}'),
cfg.Terminal(f'{sigma_symbol_2}'),
)))
def __add_restore_configs(self, turing_machine: TuringMachine):
"""
Add productions to restore word accepted by Turing Machine
to Unrestricted Grammar
Add production ([g,l] q -> q g q)
for all q in turing_machine.accepted_states
for all g in turing_machine.sigma + epsilon
for all l in turing_machine.gamma
Add production (q [g,l] -> q g q)
for all q in turing_machine.accepted_states
for all g in turing_machine.sigma + epsilon
for all l in turing_machine.gamma
Add production ([_,l] q -> q g q)
for all q in turing_machine.accepted_states
for all l in turing_machine.gamma
Add production (q [_,l] -> q g q)
for all q in turing_machine.accepted_states
for all l in turing_machine.gamma
Add production (q g -> g)
for all q in turing_machine.accepted_states
for all g in turing_machine.sigma
Add production (g q -> g)
for all q in turing_machine.accepted_states
for all g in turing_machine.sigma
:param turing_machine: The Turing Machine from which the Unrestricted Grammar is built
:return: None
"""
for accept_state in turing_machine.accept_states:
for sigma_symbol, gamma_symbol in itertools.product(
turing_machine.sigma | {''}, turing_machine.gamma):
self.productions.append(
Production(
(cfg.Variable(f'[{sigma_symbol},{gamma_symbol}]'),
cfg.Variable(accept_state)),
(cfg.Variable(accept_state),
cfg.Terminal(sigma_symbol),
cfg.Variable(accept_state))))
self.productions.append(
Production(
(cfg.Variable(accept_state),
cfg.Variable(f'[{sigma_symbol},{gamma_symbol}]')),
(cfg.Variable(accept_state),
cfg.Terminal(sigma_symbol),
cfg.Variable(accept_state))))
for gamma_symbol in turing_machine.gamma:
self.productions.append(
Production((cfg.Variable(f'[,{gamma_symbol}]'),
cfg.Variable(accept_state)),
(cfg.Variable(accept_state), )))
self.productions.append(
Production((cfg.Variable(accept_state),
cfg.Variable(f'[,{gamma_symbol}]')),
(cfg.Variable(accept_state), )))
for sigma_symbol in turing_machine.sigma:
self.productions.append(
Production((cfg.Variable(accept_state),
cfg.Terminal(sigma_symbol)),
(cfg.Terminal(sigma_symbol), )))
self.productions.append(
Production((cfg.Terminal(sigma_symbol),
cfg.Variable(accept_state)),
(cfg.Terminal(sigma_symbol), )))