def test_add_ctx_normal(self):
experiments_path = os.path.dirname(os.getcwd()) + "/experiments/"
A = buildOTA(experiments_path + 'example3.json', 's')
AA = buildAssistantOTA(A, 's')
sigma = AA.sigma
max_time_value = AA.max_time_value()
H = buildOTA(experiments_path + 'example3_1.json', 'q')
HH = buildAssistantOTA(H, 'q')
# AA.show()
# print("------------------------------")
# HH.show()
# print("------------------------------")
# H.show()
flag, ctx = equivalence_query_normal(max_time_value, AA, HH)
# print("-------------ctx-----------------")
# print(ctx.tws)
ctxs = guess_ctx_reset(ctx.tws, AA)
# print(len(ctxs))
# for rtws in ctxs:
# print(rtws)
# print("-------------local tws-----------------")
for ctx in ctxs:
local_tws = dRTWs_to_lRTWs(ctx)
normalize(local_tws)
# #if check_guessed_reset(local_tws, table) == True:
# print(ctx)
# print(local_tws)
# pref = prefixes(local_tws)
# for tws in pref:
# print(tws)
# print("-------------------")
T1_tables = init_table_normal(sigma, AA)
T1_table_0 = T1_tables[0]
test_E = [[Timedword('b', 2), Timedword('a', 4)], [Timedword('a', 5)]]
T1_table_0.E = test_E
# T1_table_0.show()
# print("----------------------------------------")
# tables = add_ctx_normal(ctx, T1_table_0, AA)
#self.assertEqual(len(tables),65536)
# tables[0].show()
# tables[1].show()
# tables[2].show()
# tables[100].show()
# tables[4095].show()
# for table in tables:
# table.show()
# print("---------------------")
T1_tables = init_table_normal(sigma, AA)
T1_table_0 = T1_tables[0]
test_E = [[Timedword('b', 2), Timedword('a', 4)]]
T1_table_0.E = test_E
# T1_table_0.show()
# print("----------------------------------------")
tables = add_ctx_normal(ctx, T1_table_0, AA)
self.assertEqual(len(tables), 128)
def test_guess_resets_in_newsuffix(self):
A = buildOTA('example6.json', 's')
AA = buildAssistantOTA(A, 's') # Assist
#max_time_value = AA.max_time_value()
sigma = AA.sigma
T1_tables = init_table_normal(sigma, AA)
T1_table_0 = T1_tables[0]
test_E = [[Timedword('a', 2),
Timedword('b', 3),
Timedword('a', 1)], [Timedword('b', 2),
Timedword('a', 4)]]
T1_table_0.E = test_E
suffixes_resets = guess_resets_in_newsuffix(T1_table_0, 0, 0, True,
True, AA)
self.assertEqual(len(suffixes_resets), 1)
self.assertEqual(len(suffixes_resets[0]), 4)
self.assertEqual(
suffixes_resets[0],
[[True, None], [True, None], [True, None], [True, None]])
test_E = [[Timedword('a', 2), Timedword('b', 3), Timedword('a', 1)]]
T1_table_0.E = test_E
suffixes_resets = guess_resets_in_newsuffix(T1_table_0, 0, 0, True,
True, AA)
self.assertEqual(len(suffixes_resets), 256)
self.assertEqual(len(suffixes_resets[34]), 4)
self.assertEqual(suffixes_resets[1],
[[True, True, None], [True, True, None],
[True, True, None], [True, False, None]])
def test_init_table_normal(self):
A = buildOTA('example6.json', 's')
AA = buildAssistantOTA(A, 's') # Assist
#max_time_value = AA.max_time_value()
sigma = AA.sigma
tables = init_table_normal(sigma, AA)
self.assertEqual(len(tables), 1)
def single_search():
"""Single path search: at each step, pick a random successor."""
nonlocal round_num
init_tables = init_table_normal(sigma, AA)
current_table = random.choice(init_tables)
# Current hypothesis
cur_h = None
while True:
round_num += 1
current_table = random_steps(current_table,
15,
cur_h,
comparator=False)
if current_table == 'failed':
return None
if current_table.effective_len() >= 15:
return None
print('ctx test:', current_table.effective_len())
# If prepared, check conversion to FA
fa_flag, fa, sink_name = to_fa(current_table, t_number)
if not fa_flag:
return None
# Can convert to FA: convert to OTA and test equivalence
cur_h = fa_to_ota(fa, sink_name, sigma, t_number)
# equivalent, ctx = equivalence_query_normal(max_time_value, AA, cur_h, prev_ctx)
equivalent, ctx, _ = pac_equivalence_query(A, max_time_value, AA,
cur_h, round_num, 0.001,
0.001)
if ctx:
print(ctx.tws)
# Add counterexample to prev list
if not equivalent and ctx not in prev_ctx:
prev_ctx.append(ctx)
if not equivalent:
temp_tables = add_ctx_normal(ctx.tws, current_table, AA)
if len(temp_tables) > 0:
current_table = random.choice(temp_tables)
else:
return None
else:
return current_table, cur_h
def test_make_closed(self):
A = buildOTA('f.json', 's')
AA = buildAssistantOTA(A, 's') # Assist
#max_time_value = AA.max_time_value()
sigma = AA.sigma
T1_tables = init_table_normal(sigma, AA)
self.assertEqual(len(T1_tables), 2)
#print("--------------------------------------------------")
flag_closed, new_S, new_R, move = T1_tables[0].is_closed()
self.assertEqual(flag_closed, False)
tables = make_closed(new_S, new_R, move, T1_tables[0], sigma, AA)
print("--------------make closed---------------------")
self.assertEqual(len(tables), 2)
def test_guess_resets_in_suffixes(self):
A = buildOTA('example6.json', 's')
AA = buildAssistantOTA(A, 's') # Assist
#max_time_value = AA.max_time_value()
sigma = AA.sigma
T1_tables = init_table_normal(sigma, AA)
T1_table_0 = T1_tables[0]
test_E = [[Timedword('a', 2),
Timedword('b', 3),
Timedword('a', 1)], [Timedword('b', 2),
Timedword('a', 4)],
[Timedword('a', 5)]]
T1_table_0.E = test_E
suffixes_resets = guess_resets_in_suffixes(T1_table_0)
self.assertEqual(len(suffixes_resets), 8)
self.assertEqual(len(suffixes_resets[5]), 3)
def test_is_closed(self):
A = buildOTA('example6.json', 's')
AA = buildAssistantOTA(A, 's') # Assist
#max_time_value = AA.max_time_value()
sigma = AA.sigma
T1_tables = init_table_normal(sigma, AA)
self.assertEqual(len(T1_tables), 1)
#print("--------------------------------------------------")
flag_closed, new_S, new_R, move = T1_tables[0].is_closed()
self.assertEqual(flag_closed, False)
self.assertEqual(new_S, [
Element([], [0], []),
Element([ResetTimedword('a', 0, True)], [-1], [])
])
self.assertEqual(new_R, [
Element([ResetTimedword('b', 0, True)], [-1], []),
Element([ResetTimedword('c', 0, True)], [-1], [])
])
self.assertEqual(move, Element([ResetTimedword('a', 0, True)], [-1],
[]))
def parallel_search():
"""Parallel search.
Maintain a list of current tables (of size width). At each iteration,
pick a number of random successors (of size expand_factor) to form
the new list of tables. Sort the new list and pick the best 'width'
tables for the next iteration.
"""
nonlocal round_num, t_number
init_tables = init_table_normal(sigma, AA)
width = 15
expand_factor = 2
tables = []
for i in range(width):
tables.append(random.choice(init_tables))
while round_num < 10:
round_num += 1
print(round_num)
new_tables = []
for i in range(min(len(tables), width)):
for j in range(expand_factor):
if round_num == 1:
current_table, cur_h = tables[i], None
else:
current_table, cur_h, ctx = tables[i]
temp_tables = add_ctx_normal(ctx.tws, current_table,
AA)
if len(temp_tables) > 0:
current_table = random.choice(temp_tables)
else:
continue
current_table = random_steps(current_table,
20,
cur_h,
comparator=False)
if current_table == 'failed':
continue
if current_table.effective_len() >= 20:
continue
# If prepared, check conversion to FA
t_number += 1
fa_flag, fa, sink_name = to_fa(current_table, t_number)
if not fa_flag:
continue
# Can convert to FA: convert to OTA and test equivalence
cur_h = fa_to_ota(fa, sink_name, sigma, t_number)
equivalent, ctx, sc = pac_equivalence_query(
A, max_time_value, AA, cur_h, round_num, 0.001, 0.001)
if not equivalent:
new_tables.append(
(sc, current_table, copy.deepcopy(cur_h), ctx))
else:
return current_table, cur_h
new_tables = sorted(new_tables, reverse=True)
tables = []
for sc, table, cur_h, ctx in new_tables:
print(sc, table.effective_len())
tables.append((table, cur_h, ctx))
if len(tables) >= width:
break
return None
def learn_ota(paras, debug_flag):
A = buildOTA(paras, 's')
AA = buildAssistantOTA(A, 's')
max_time_value = A.max_time_value()
print("**************Start to learn ...*******************")
print("---------------initial table-------------------")
sigma = AA.sigma
need_to_explore = queue.PriorityQueue()
for table in init_table_normal(sigma, AA):
need_to_explore.put((table.effective_len(), table))
# List of existing counterexamples
prev_ctx = []
# Current number of tables
t_number = 0
start = time.time()
eq_total_time = 0
eq_number = 0
target = None
while True:
if need_to_explore.qsize() == 0:
break
depth, current_table = need_to_explore.get()
t_number = t_number + 1
if t_number % 1 == 0:
print(t_number, need_to_explore.qsize(), current_table.effective_len())
if debug_flag:
print("Table " + str(t_number) + " is as follow, %s has parent %s by %s" % (current_table.id, current_table.parent, current_table.reason))
current_table.show()
print("--------------------------------------------------")
# First check if the table is closed
flag_closed, new_S, new_R, move = current_table.is_closed()
if not flag_closed:
if debug_flag:
print("------------------make closed--------------------------")
temp_tables = make_closed(new_S, new_R, move, current_table, sigma, AA)
if len(temp_tables) > 0:
for table in temp_tables:
need_to_explore.put((table.effective_len(), table))
continue
# If is closed, check if the table is consistent
flag_consistent, new_a, new_e_index, reset_index_i, reset_index_j, reset_i, reset_j = current_table.is_consistent()
if not flag_consistent:
if debug_flag:
print("------------------make consistent--------------------------")
temp_tables = make_consistent(new_a, new_e_index, reset_index_i, reset_index_j, reset_i, reset_j, current_table, sigma, AA)
if len(temp_tables) > 0:
for table in temp_tables:
need_to_explore.put((table.effective_len(), table))
continue
# If prepared, check conversion to FA
fa_flag, fa, sink_name = to_fa(current_table, t_number)
if not fa_flag:
continue
# Can convert to FA: convert to OTA and test equivalence
h = fa_to_ota(fa, sink_name, sigma, t_number)
eq_start = time.time()
# equivalent, ctx = equivalence_query_normal(max_time_value, AA, h, prev_ctx)
equivalent, ctx = True, None
if prev_ctx is not None:
for ctx in prev_ctx:
teacher_res = AA.is_accepted_delay(ctx.tws)
hypothesis_res = h.is_accepted_delay(ctx.tws)
if teacher_res != hypothesis_res and hypothesis_res != -2:
equivalent, ctx = False, ctx
ctx = minimizeCounterexample(AA, h, ctx)
if equivalent:
AA.equiv_query_num += 1
equivalent, ctx, _ = pac_equivalence_query(A, max_time_value, AA, h, AA.equiv_query_num, 0.001, 0.001)
if not equivalent:
# remove_sinklocation(copy.deepcopy(h)).show()
print(ctx.tws)
# print(ratio)
# Add counterexample to prev list
if not equivalent and ctx not in prev_ctx:
prev_ctx.append(ctx)
eq_end = time.time()
eq_total_time = eq_total_time + eq_end - eq_start
eq_number = eq_number + 1
if not equivalent:
temp_tables = add_ctx_normal(ctx.tws, current_table, AA)
if len(temp_tables) > 0:
for table in temp_tables:
need_to_explore.put((table.effective_len(), table))
else:
target = copy.deepcopy(h)
break
end_learning = time.time()
if target is None:
print("---------------------------------------------------")
print("Error! Learning Failed.")
print("*******************Failed.***********************")
return False
else:
print("---------------------------------------------------")
print("Succeed! The learned OTA is as follows.")
print("-------------Final table instance------------------")
current_table.show()
print("---------------Learned OTA-------------------------")
target.show()
print("---------------------------------------------------")
print("Removing the sink location...")
print()
print("The learned One-clock Timed Automtaton: ")
print()
target_without_sink = remove_sinklocation(target)
end_removesink = time.time()
target_without_sink.show()
print("---------------------------------------------------")
print("Number of transitions in teacher: " + str(len(A.trans)))
print("Total number of membership query: " + str(len(AA.membership_query)))
# print("Total number of membership query (no-cache): " + str(AA.mem_query_num))
# print("Total number of equivalence query: " + str(len(prev_ctx) + 1))
print("Total number of equivalence query (no-cache): " + str(AA.equiv_query_num))
print("Total number of tests: " + str(AA.test_query_num))
print("Total number of tables explored: " + str(t_number))
# print("Total number of tables to explore: " + str(need_to_explore.qsize()))
print("Number of locations in learned table: " + str(len(target_without_sink.locations)))
print("Total time of learning: " + str(end_learning-start))
return target_without_sink
def learn_ota_dfs(paras, depth, prev_ctx, debug_flag):
A = buildOTA(paras, 's')
AA = buildAssistantOTA(A, 's')
max_time_value = A.max_time_value()
sigma = AA.sigma
# Current number of tables
target = None
t_number = 0
def rec(current_table):
"""If solution is found, return target. Else, return None."""
nonlocal t_number
t_number += 1
cur_depth = current_table.effective_len()
if t_number % 10 == 0:
print(t_number, cur_depth)
if cur_depth > depth:
return None
# First check if the table is closed
flag_closed, new_S, new_R, move = current_table.is_closed()
if not flag_closed:
temp_tables = make_closed(new_S, new_R, move, current_table, sigma, AA)
for table in temp_tables:
target = rec(table)
if target is not None:
return target
return None
# If is closed, check if the table is consistent
flag_consistent, new_a, new_e_index, reset_index_i, reset_index_j, reset_i, reset_j = current_table.is_consistent()
if not flag_consistent:
temp_tables = make_consistent(new_a, new_e_index, reset_index_i, reset_index_j, reset_i, reset_j, current_table, sigma, AA)
for table in temp_tables:
target = rec(table)
if target is not None:
return target
return None
# If prepared, check conversion to FA
fa_flag, fa, sink_name = to_fa(current_table, t_number)
if not fa_flag:
return None
# Can convert to FA: convert to OTA and test equivalence
h = fa_to_ota(fa, sink_name, sigma, t_number)
equivalent, ctx = equivalence_query_normal(max_time_value, AA, h, prev_ctx)
# Add counterexample to prev list
if not equivalent and ctx not in prev_ctx:
prev_ctx.append(ctx)
if not equivalent:
temp_tables = add_ctx_normal(ctx.tws, current_table, AA)
for table in temp_tables:
target = rec(table)
if target is not None:
return target
return None
else:
target = copy.deepcopy(h)
return target
tables = init_table_normal(sigma, AA)
for table in tables:
target = rec(table)
if target is not None:
return target, t_number
return None, t_number
