def cfpq_matrix_multiplication(grammar: CFG, graph: BMGraph):
res = dict()
terminal_prods = set()
nonterminal_prods = set()
if grammar.generate_epsilon():
matrix = Matrix.sparse(BOOL, graph.states_amount,
graph.states_amount)
for i in range(graph.states_amount):
matrix[i, i] = True
res[grammar.start_symbol] = matrix
cfg = grammar.to_normal_form()
for prod in cfg.productions:
if len(prod.body) == 1:
terminal_prods.add(prod)
else:
nonterminal_prods.add(prod)
with semiring.LOR_LAND_BOOL:
for t, matrix in graph.matrices.items():
for prod in terminal_prods:
if prod.body == [Terminal(t)]:
if prod.head not in res:
res[prod.head] = matrix.dup()
else:
res[prod.head] += matrix.dup()
with semiring.LOR_LAND_BOOL:
old_changed = set()
new_changed = cfg.variables
while len(new_changed) > 0:
old_changed = new_changed
new_changed = set()
for prod in nonterminal_prods:
if prod.body[0] not in res or prod.body[1] not in res:
continue
if (prod.body[0] in old_changed
or prod.body[1] in old_changed):
matrix = res.get(
prod.head,
Matrix.sparse(BOOL, graph.states_amount,
graph.states_amount))
old_nvals = matrix.nvals
res[prod.head] = matrix + \
(res[prod.body[0]] @ res[prod.body[1]])
if (res[prod.head].nvals != old_nvals):
new_changed.add(prod.head)
return res.get(
cfg.start_symbol,
Matrix.sparse(BOOL, graph.states_amount, graph.states_amount))
def transitive_closure_2(self):
adj_matrix = Matrix.sparse(BOOL, self.n_vertices, self.n_vertices)
result = Matrix.sparse(BOOL, self.n_vertices, self.n_vertices)
for label_matrix in self.label_matrices.values():
adj_matrix += label_matrix
if adj_matrix.nvals != 0:
while True:
old = result.nvals
result += adj_matrix
if old == result.nvals:
break
return result
def read_graph_from_file(self, file_path):
self.__init__()
# read graph from file
graph_file = open(file_path, 'r')
edges = graph_file.read().rstrip().split('\n')
graph_file.close()
# get vertices count
max_vertex = 0
for edge in edges:
if edge == '':
return
start, label, end = edge.split(' ')
max_vertex = max([max_vertex, int(start), int(end)])
self.vertices_count = max_vertex + 1
# init label_matrices
for edge in edges:
i, label, j = edge.split(" ")
if label in self.label_matrices:
self.label_matrices[label][int(i), int(j)] = True
else:
bool_matrix = Matrix.sparse(BOOL, self.vertices_count,
self.vertices_count)
bool_matrix[int(i), int(j)] = True
self.label_matrices[label] = bool_matrix
# init start and terminal vertices
for i in range(self.vertices_count):
self.start_vertices.add(i)
self.terminal_vertices.add(i)
def from_rsm(cls, rsm: RSM):
"""
Build RSA from a given cfpq_data Recursive State Machine
@param rsm: RSM on which RSA is built
@return: initialized class
"""
rsa = RecursiveAutomaton()
rsa.start_nonterm = rsm.start_symbol.to_text()
current_state = 0
transtion_by_label = dict()
for nonterm, dfa in rsm.boxes:
mapping_state = dict()
rsa.nonterminals.add(nonterm.to_text())
rsa.labels = rsa.labels.union(dfa.symbols)
rsa.boxes[nonterm.to_text()] = []
for label in dfa.symbols:
if label not in transtion_by_label:
transtion_by_label.update({label: []})
dfa_dict = dfa.to_dict()
for state in dfa_dict:
if state not in mapping_state:
mapping_state[state] = current_state
rsa.boxes[nonterm.to_text()].append(current_state)
current_state += 1
for trans in dfa_dict[state]:
if dfa_dict[state][trans] not in mapping_state:
mapping_state[dfa_dict[state][trans]] = current_state
rsa.boxes[nonterm.to_text()].append(current_state)
current_state += 1
transtion_by_label[trans].append((mapping_state[state], mapping_state[dfa_dict[state][trans]]))
rsa.states[nonterm.to_text()] = []
rsa.start_state[nonterm.to_text()] = mapping_state[dfa.start_state]
rsa.finish_states[nonterm.to_text()] = []
for final_state in dfa.final_states:
rsa.states[nonterm.to_text()].append((mapping_state[dfa.start_state], mapping_state[final_state]))
rsa.finish_states[nonterm.to_text()].append(mapping_state[final_state])
if mapping_state[dfa.start_state] == mapping_state[final_state]:
rsa.start_and_finish.add(nonterm.to_text())
rsa.matrices_size = current_state
for label in transtion_by_label:
rsa.matrices[label] = Matrix.sparse(BOOL, rsa.matrices_size, rsa.matrices_size)
for trans in transtion_by_label[label]:
rsa.matrices[label][trans[0], trans[1]] = True
if trans[0] in rsa.out_states:
rsa.out_states[trans[0]].append((trans[1], label))
else:
rsa.out_states[trans[0]] = [(trans[1], label)]
rsa.terminals = rsa.labels.difference(rsa.nonterminals)
return rsa
def transitive_closure(self):
adj_matrix = Matrix.sparse(BOOL, self.n_vertices, self.n_vertices)
for label_matrix in self.label_matrices.values():
if label_matrix.nvals != 0:
adj_matrix = adj_matrix | label_matrix
for k in range(self.n_vertices):
old_nvals = adj_matrix.nvals
adj_matrix = adj_matrix | (adj_matrix @ adj_matrix)
if adj_matrix.nvals == old_nvals:
break
return adj_matrix
def transitive_closure_1(self):
adj_matrix = Matrix.sparse(BOOL, self.n_vertices, self.n_vertices)
for label_matrix in self.label_matrices.values():
adj_matrix += label_matrix
if adj_matrix.nvals != 0:
while True:
old = adj_matrix.nvals
adj_matrix += adj_matrix @ adj_matrix
if old == adj_matrix:
break
return adj_matrix
def transitive_closure_mp(graph):
closure = Matrix.sparse(BOOL, graph.states_amount, graph.states_amount)
with semiring.LOR_LAND_BOOL:
for matrix in graph.matrices.values():
closure += matrix
temp = closure.dup()
old_nvals = -1
new_nvals = closure.nvals
while old_nvals != new_nvals:
with semiring.LOR_LAND_BOOL:
closure += temp @ closure
old_nvals = new_nvals
new_nvals = closure.nvals
return closure
def from_file(cls, path: Path):
"""
Load RSA from file
@param path: path to file with RSA
@return: initialized class
"""
rsa = RecursiveAutomaton()
with open(path, "r") as file:
count_matrix = int(file.readline())
count_nonterminals = int(file.readline())
matrices_size = int(file.readline())
rsa.matrices_size = matrices_size
for i in range(count_matrix):
label = file.readline().replace("\n", "")
rsa.labels.add(label)
count_edge = int(file.readline())
for j in range(count_edge):
first, second = file.readline().split()
rsa[label][int(first), int(second)] = True
if int(first) in rsa.out_states:
rsa.out_states[int(first)].append((int(second), label))
else:
rsa.out_states[int(first)] = [(int(second), label)]
for i in range(count_nonterminals):
label = file.readline().replace("\n", "")
rsa.nonterminals.add(label)
rsa.states.update({label: Matrix.sparse(BOOL, rsa.matrices_size, rsa.matrices_size)})
count_edge = int(file.readline())
for j in range(count_edge):
first, second = file.readline().split()
rsa.states[label][int(first), int(second)] = True
rsa.start_state.update({label: int(first)})
if label in rsa.finish_states:
rsa.finish_states[label].append(int(second))
else:
rsa.finish_states.update({label: [int(second)]})
if first == second:
rsa.start_and_finish.add(label)
rsa.terminals = rsa.labels.difference(rsa.nonterminals)
return rsa
def parse_regex(self, file_path):
self.__init__()
# read regex from file
regex_file = open(file_path, 'r')
regex = Regex(regex_file.read().rstrip())
regex_file.close()
# regex to dfa conversion and vertices count init
dfa = regex.to_epsilon_nfa().to_deterministic().minimize()
self.vertices_count = len(dfa.states)
# states enumeration
states = {}
start = 0
for state in dfa._states:
if state not in states:
states[state] = start
start = start + 1
# init label_matrices
for start in dfa._states:
for label in dfa._input_symbols:
in_states = dfa._transition_function(start, label)
for end in in_states:
if label in self.label_matrices:
self.label_matrices[label][states[start],
states[end]] = True
else:
bool_matrix = Matrix.sparse(BOOL, self.vertices_count,
self.vertices_count)
bool_matrix[states[start], states[end]] = True
self.label_matrices[label] = bool_matrix
# init start and terminal states
self.start_vertices.add(states[dfa.start_state])
for state in dfa._final_states:
self.terminal_vertices.add(states[state])
return self
def cfpq_tensor_product(grammar: CFG, graph: BMGraph):
res = graph.dup()
rfa = BMGraph()
rfa_heads = dict()
rfa.states_amount = sum(
[len(prod.body) + 1 for prod in grammar.productions])
rfa.states = set(range(rfa.states_amount))
index = 0
for prod in grammar.productions:
start_state = index
final_state = index + len(prod.body)
rfa.start_states.add(start_state)
rfa.final_states.add(final_state)
rfa_heads[(start_state, final_state)] = prod.head.value
for var in prod.body:
matrix = rfa.matrices.get(
var.value,
Matrix.sparse(BOOL, rfa.states_amount, rfa.states_amount))
matrix[index, index + 1] = True
rfa.matrices[var.value] = matrix
index += 1
index += 1
for prod in grammar.productions:
if len(prod.body) == 0:
matrix = Matrix.sparse(BOOL, graph.states_amount,
graph.states_amount)
for i in range(graph.states_amount):
matrix[i, i] = True
res.matrices[prod.head] = matrix
is_changing = True
while is_changing:
is_changing = False
intersection = rfa.intersect(res)
closure = intersection.transitive_closure()
for i, j, _ in zip(*closure.to_lists()):
rfa_from, rfa_to = i // res.states_amount, j // res.states_amount
graph_from, graph_to = i % res.states_amount, j % res.states_amount
if (rfa_from, rfa_to) not in rfa_heads:
continue
var = rfa_heads[(rfa_from, rfa_to)]
matrix = res.matrices.get(
var,
Matrix.sparse(BOOL, graph.states_amount,
graph.states_amount))
if matrix.get(graph_from, graph_to) is None:
is_changing = True
matrix[graph_from, graph_to] = True
res.matrices[var] = matrix
return res.matrices.get(
grammar.start_symbol,
Matrix.sparse(BOOL, graph.states_amount, graph.states_amount))
def Hellings(grammar: CFG, graph: BMGraph):
res = dict()
m = deque()
terminal_prods = set()
nonterminal_prods = set()
if grammar.generate_epsilon():
matrix = Matrix.sparse(BOOL, graph.states_amount,
graph.states_amount)
for i in range(graph.states_amount):
matrix[i, i] = True
m.append((grammar.start_symbol, i, i))
res[grammar.start_symbol] = matrix
cfg = grammar.to_normal_form()
for prod in cfg.productions:
if len(prod.body) == 1:
terminal_prods.add(prod)
else:
nonterminal_prods.add(prod)
with semiring.LOR_LAND_BOOL:
for t, matrix in graph.matrices.items():
for prod in terminal_prods:
if prod.body == [Terminal(t)]:
if prod.head not in res:
res[prod.head] = matrix.dup()
else:
res[prod.head] += matrix.dup()
for var, matrix in res.items():
for i, j, _ in zip(*matrix.to_lists()):
m.append((var, i, j))
while m:
add_to_res = list()
var, v_from, v_to = m.popleft()
for new_var, matrix in res.items():
for new_from, _ in matrix[:, v_from]:
for prod in nonterminal_prods:
if (len(prod.body) == 2 and prod.body[0] == new_var
and prod.body[1] == var and
(prod.head not in res
or res[prod.head].get(new_from, v_to) is None)):
m.append((prod.head, new_from, v_to))
add_to_res.append((prod.head, new_from, v_to))
for new_var, matrix in res.items():
for new_to, _ in matrix[v_to, :]:
for prod in nonterminal_prods:
if (len(prod.body) == 2 and prod.body[0] == var
and prod.body[1] == new_var and
(prod.head not in res
or res[prod.head].get(v_from, new_to) is None)):
m.append((prod.head, v_from, new_to))
add_to_res.append((prod.head, v_from, new_to))
for var, v_from, v_to in add_to_res:
matrix = res.get(
var,
Matrix.sparse(BOOL, graph.states_amount,
graph.states_amount))
matrix[v_from, v_to] = True
res[var] = matrix
return res.get(
cfg.start_symbol,
Matrix.sparse(BOOL, graph.states_amount, graph.states_amount))
def __getitem__(self, item: str) -> Matrix:
if item not in self.matrices:
self.matrices[item] = Matrix.sparse(BOOL, self.matrices_size, self.matrices_size)
return self.matrices[item]
def add_states(self, label: str):
self._S.add(label)
self._states.update({
label:
Matrix.sparse(BOOL, self._matrices_size, self._matrices_size)
})
def add_automaton(self, label: str):
self._labels.add(label)
self._automaton.update({
label:
Matrix.sparse(BOOL, self._matrices_size, self._matrices_size)
})
def get_by_label(self, label):
if label not in self.label_matrices.keys():
self.label_matrices[label] = Matrix.sparse(BOOL, self.n_vertices,
self.n_vertices)
return self.label_matrices[label]
