def mutate(self,
min_swap_probability=0.1,
max_swap_probability=0.2,
inverse_probability=0.01,
random_probability=0.01,
shift_probability=0.01):
swap_probability = random.uniform(min_swap_probability,
max_swap_probability)
for inhabitant in self.generation:
if decision(random_probability):
inhabitant.gene = self._random_word()
else:
if decision(shift_probability):
shift_range = random.randint(1, 3)
inhabitant.gene = [inhabitant.gene[-shift_range]
] + inhabitant.gene[:-shift_range]
for i in range(len(inhabitant.gene) // 2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def mutate(self, min_swap_probability=0.1, max_swap_probability=0.2, inverse_probability=0.01, random_probability=0.005, shift_probability=0.01, insert_probability=0.01):
swap_probability = random.uniform(min_swap_probability, max_swap_probability)
for inhabitant in self.generation:
if decision(insert_probability):
possible_chars = self._random_word()
for char in inhabitant.gene:
if char in possible_chars:
possible_chars.remove(char)
inhabitant.gene += possible_chars[0]
elif decision(random_probability):
inhabitant.gene = self._random_word()
else:
if decision(shift_probability):
shift_range = random.randint(1, 3)
for _ in range(shift_range+1):
inhabitant.gene = [inhabitant.gene[-1]] + inhabitant.gene[:-1]
for i in range(len(inhabitant.gene)//2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def mutate(
self,
min_swap_probability=0.2,
max_swap_probability=1,
inverse_probability=0.001,
shift_probability=0.01,
min_insert_probability=0.3,
max_insert_probability=1,
random_probability=0.2,
):
swap_probability = random.uniform(min_swap_probability,
max_swap_probability)
insert_probability = random.uniform(min_insert_probability,
max_insert_probability)
for inhabitant in self.generation[1:]:
if len(inhabitant.gene) == 0:
inhabitant.gene = self._random_moves()
continue
if decision(random_probability):
inhabitant.gene = self._random_moves()
elif decision(insert_probability):
insert_amount = random.randint(1, 3)
if decision(0.5): # remove decision
possible_chars = self._random_word(insert_amount)
if decision(0.33):
inhabitant.gene += possible_chars
elif decision(0.5):
inhabitant.gene = possible_chars + inhabitant.gene
else:
insert_index = random.randint(1, len(inhabitant.gene))
inhabitant.gene = (inhabitant.gene[:insert_index] +
possible_chars +
inhabitant.gene[insert_index:])
else:
if len(inhabitant) - insert_amount > 0:
if decision(0.33):
inhabitant.gene = inhabitant.gene[insert_amount:]
elif decision(0.5):
inhabitant.gene = inhabitant.gene[:-insert_amount]
else:
remove_index = random.randint(
1,
len(inhabitant.gene) - insert_amount)
inhabitant.gene = (
inhabitant.gene[:remove_index] +
inhabitant.gene[remove_index + insert_amount:])
if decision(shift_probability):
shift_range = random.randint(1, 3)
for _ in range(shift_range + 1):
inhabitant.gene = [inhabitant.gene[-1]
] + inhabitant.gene[:-1]
if decision(swap_probability):
inhabitant.gene = gen_neighbour(inhabitant.gene)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
inhabitant.gene = self._cancel_backtrack(inhabitant.gene)
def mutate(
self,
min_swap_probability=0.2,
max_swap_probability=0.7,
inverse_probability=0.001,
random_probability=0.05,
shift_probability=0.001,
insert_probability=0.9,
replace_probability=0.5,
):
swap_probability = random.uniform(min_swap_probability, max_swap_probability)
for inhabitant in self.generation[1:]:
if decision(insert_probability):
insert_amount = random.randint(1, 2)
if decision(0.5): # remove decision
if(len(inhabitant)+insert_amount < len(self.all_puzzle_keys)):
possible_chars = self._random_word()
for char in inhabitant.gene:
if char in possible_chars:
possible_chars.remove(char)
if decision(0.33):
inhabitant.gene += possible_chars[:insert_amount]
elif decision(0.5):
inhabitant.gene = possible_chars[:insert_amount] + inhabitant.gene
else:
insert_index = random.randint(1, len(inhabitant.gene))
inhabitant.gene = inhabitant.gene[:insert_index] + possible_chars[:insert_amount] + inhabitant.gene[insert_index:]
else:
if(len(inhabitant)-insert_amount > 0):
if len(inhabitant) - insert_amount > 0:
if decision(0.33):
inhabitant.gene = inhabitant.gene[insert_amount:]
elif decision(0.5):
inhabitant.gene = inhabitant.gene[:-insert_amount]
else:
remove_index = random.randint(1, len(inhabitant.gene)-insert_amount)
inhabitant.gene = (
inhabitant.gene[:remove_index]
+ inhabitant.gene[remove_index+insert_amount:]
)
elif decision(random_probability):
inhabitant.gene = self._random_word()
else:
if decision(shift_probability):
shift_range = random.randint(1, 3)
for _ in range(shift_range + 1):
inhabitant.gene = [inhabitant.gene[-1]] + inhabitant.gene[:-1]
for i in range(len(inhabitant.gene) // 2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def mutate(self, swap_probability=0.5, inverse_probability=0.01):
for inhabitant in self.generation:
for i in range(len(inhabitant.gene) // 2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def mutate(
self,
min_swap_probability=0.2,
max_swap_probability=0.7,
inverse_probability=0.001,
shift_probability=0.001,
insert_probability=0.9,
):
swap_probability = random.uniform(min_swap_probability,
max_swap_probability)
for inhabitant in self.generation[1:]:
if decision(insert_probability):
insert_amount = random.randint(1, 2)
if decision(0.5): # remove decision
possible_chars = self._random_word(insert_amount)
if decision(0.33):
inhabitant.gene += possible_chars
elif decision(0.5):
inhabitant.gene = possible_chars + inhabitant.gene
else:
insert_index = random.randint(1, len(inhabitant.gene))
inhabitant.gene = (inhabitant.gene[:insert_index] +
possible_chars +
inhabitant.gene[insert_index:])
else:
if len(inhabitant) - insert_amount > 0:
if decision(0.33):
inhabitant.gene = inhabitant.gene[insert_amount:]
elif decision(0.5):
inhabitant.gene = inhabitant.gene[:-insert_amount]
else:
remove_index = random.randint(
1, len(inhabitant.gene))
inhabitant.gene = (
inhabitant.gene[:insert_index - remove_index] +
possible_chars +
inhabitant.gene[insert_index:])
else:
if decision(shift_probability):
shift_range = random.randint(1, 3)
for _ in range(shift_range + 1):
inhabitant.gene = [inhabitant.gene[-1]
] + inhabitant.gene[:-1]
for i in range(len(inhabitant.gene) // 2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def mutate(self, probability=0.05):
for inhabitant in self.generation:
for i in range(len(inhabitant.gene)):
if decision(probability):
inhabitant.gene[i], inhabitant.gene[random.randint(
0, len(inhabitant))] = inhabitant.gene[random.randint(
0, len(inhabitant))], inhabitant.gene[i]
def mutate(self, probability=0.05):
for inhabitant in self.generation:
for i in range(len(inhabitant.gene)):
if decision(probability):
random_id = random.randint(0,len(inhabitant)-1)
inhabitant.gene[i], inhabitant.gene[random_id] = inhabitant.gene[random_id], inhabitant.gene[i]
print("boom")
def mutate(self, min_swap_probability=0.2, max_swap_probability=0.8, inverse_probability=0.05, random_probability=0.05, shift_probability=0.1):
swap_probability = random.uniform(min_swap_probability, max_swap_probability)
for inhabitant in self.generation:
if decision(random_probability):
inhabitant.gene = self._random_word()[:self.word_size]
elif decision(shift_probability):
inhabitant.gene = [inhabitant.gene[-1]] + inhabitant.gene[:-1]
else:
for i in range(len(inhabitant.gene)//2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def mutate(self,
swap_probability=0.8,
inverse_probability=0.1,
random_probability=0.1):
swap_probability = random.uniform(0.05, swap_probability)
for inhabitant in self.generation:
if decision(random_probability):
inhabitant.gene = self._random_word()
else:
for i in range(len(inhabitant.gene) // 2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def mutate(
self,
min_swap_probability=0.2,
max_swap_probability=0.7,
inverse_probability=0.001,
random_probability=0.05,
shift_probability=0.001,
insert_probability=0.9,
):
swap_probability = random.uniform(min_swap_probability, max_swap_probability)
for inhabitant in self.generation[1:]:
if decision(insert_probability):
insert_amount = random.randint(1, 2)
if decision(0.5): # remove decision
char = random.choice(self.all_puzzles)
if decision(0.33):
inhabitant.gene += [char]
elif decision(0.5):
inhabitant.gene = [char] + inhabitant.gene
else:
insert_index = random.randint(1, len(inhabitant.gene))
inhabitant.gene = inhabitant.gene[:insert_index] + [char] + inhabitant.gene[insert_index:]
else:
if(len(inhabitant)-insert_amount > 0):
if decision(0.5):
inhabitant.gene = inhabitant.gene[insert_amount:]
else:
inhabitant.gene = inhabitant.gene[:-insert_amount]
elif decision(random_probability):
inhabitant.gene = self._random_word()
else:
if decision(shift_probability):
shift_range = random.randint(1, 3)
for _ in range(shift_range + 1):
inhabitant.gene = [inhabitant.gene[-1]] + inhabitant.gene[:-1]
for i in range(len(inhabitant.gene) // 2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def sa(t, n, m, k, matrix, start_T, multiplier=2):
start = time.time()
T = start_T
puzzle_shuffler = PuzzleShuffler()
puzzles = puzzle_shuffler.random_puzzles(n, m, k, matrix)
matrixp = calculate_matrixp(puzzles, matrix, n, m)
best_matrixp = matrixp
best_puzzles = puzzles.copy()
shuffle_puzzles = puzzles.copy()
square_number = calc_square_number(puzzles)
best_min = calculate_mse(n, m, matrix, matrixp)
fail_counter = 0
while check_time(start, t) and T > 0:
puzzles = puzzle_shuffler.random_puzzles(n, m, k, matrix)
matrixp = calculate_matrixp(puzzles, matrix, n, m)
cur_min = calculate_mse(n, m, matrix, matrixp)
if cur_min < best_min:
best_matrixp = matrixp.copy()
best_puzzles = puzzles.copy()
fail_counter = 0
best_min = cur_min
else:
fail_counter += 1
probability = calculate_probability(T, n, m, matrix, best_matrixp,
matrixp)
if decision(probability) and cur_min != best_min:
T = decrease_temperature(T)
shuffle_puzzles = puzzles.copy()
if fail_counter == square_number * multiplier:
fail_counter = 0
puzzle_shuffler.set_T(best_puzzles,
int(random.uniform(0, square_number // 2)))
puzzle_shuffler.set_starters(
best_puzzles, int(random.uniform(0, square_number // 2)))
shuffle_puzzles = puzzle_shuffler.random_puzzles(n, m, k, matrix)
elif fail_counter >= square_number * multiplier // 2:
if fail_counter == square_number * multiplier // 2:
puzzle_shuffler.set_T(best_puzzles, 0)
puzzle_shuffler.set_starters(best_puzzles, 8)
else:
puzzle_shuffler.set_starters(
best_puzzles,
square_number - int(random.uniform(2, square_number // 2)))
else:
puzzle_shuffler.set_starters(
shuffle_puzzles, int(random.uniform(0, square_number // 2)))
return best_min, best_matrixp
def mutate(
self,
swap_probability=0.2,
min_transpose_probability=0.2,
max_transpose_probability=0.5,
inverse_probability=0.01,
random_probability=0.01,
shift_probability=0.01,
insert_probability=0.5,
):
transpose_probability = random.uniform(min_transpose_probability, max_transpose_probability)
for inhabitant in self.generation:
if decision(insert_probability):
insert_amount = random.randint(1, 2)
if(len(inhabitant)+insert_amount < len(self.all_puzzle_keys)):
possible_chars = self._random_word()
for char in inhabitant.gene:
if char in possible_chars:
possible_chars.remove(char)
if decision(0.5):
inhabitant.gene += possible_chars[:insert_amount]
else:
inhabitant.gene = possible_chars[:insert_amount] + inhabitant.gene
if decision(random_probability):
inhabitant.gene = self._random_word()
if decision(shift_probability):
shift_range = random.randint(1, 3)
for _ in range(shift_range + 1):
inhabitant.gene = [inhabitant.gene[-1]] + inhabitant.gene[:-1]
for i in range(len(inhabitant.gene) // 2):
if decisitranspose):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
def recombinate(self, elite_percentage=0.6):
selection = self.make_selection(elite_percentage)
permutation = np.random.permutation(len(selection))
new_generation = []
new_generation.append(Inhabitant(selection[0].gene.copy()))
new_generation.append(Inhabitant(selection[1].gene.copy()))
for i in range(1, len(permutation)):
pivot = random.randint(
0,
min(
len(selection[permutation[i % len(permutation)]]),
len(selection[permutation[(i + 1) % len(permutation)]]),
)
// 2,
)
if len(selection[permutation[i % len(permutation)]]) > 1:
pivot2 = random.randint(len(selection[permutation[i % len(permutation)]])//2, len(selection[permutation[i % len(permutation)]])-1)
else:
pivot2 = 1
new_word = selection[permutation[i % len(permutation)]][:pivot2]
if self._check_if_correct(new_word):
new_generation.append(Inhabitant(new_word))
else:
new_generation.append(
Inhabitant(selection[permutation[i % len(permutation)]].gene)
)
if len(selection[permutation[(i+1) % len(permutation)]]) > 2:
pivot2 = random.randint(len(selection[0, len(selection[permutation[(i+1) % len(permutation)]])-2))
else:
pivot2 = 1
new_word = selection[permutation[(i+1) % len(permutation)]][pivot2:]
if self._check_if_correct(new_word):
new_generation.append(Inhabitant(new_word))
else:
new_generation.append(
Inhabitant(selection[permutation[(i + 1) % len(permutation)]].gene)
)
self.generation = new_generation
def mutate(
self,
min_swap_probability=0.2,
max_swap_probability=0.5,
inverse_probability=0.001,
random_probability=0.05,
shift_probability=0.001,
insert_probability=0.9,
replace_probability=0.5,
):
swap_probability = random.uniform(min_swap_probability, max_swap_probability)
for inhabitant in self.generation[1:]:
if decision(insert_probability):
insert_amount = random.randint(1, 3)
if decision(0.5): # remove decision
if(len(inhabitant)+insert_amount < len(self.all_puzzle_keys)):
possible_chars = self._random_word()
for char in inhabitant.gene:
if char in possible_chars:
possible_chars.remove(char)
if decision(0.33):
inhabitant.gene += possible_chars[:insert_amount]
elif decision(0.5):
inhabitant.gene = possible_chars[:insert_amount] + inhabitant.gene
else:
insert_index = random.randint(1, len(inhabitant.gene))
inhabitant.gene = inhabitant.gene[:insert_index] + possible_chars[:insert_amount] + inhabitant.gene[insert_index:]
else:
if(len(inhabitant)-insert_amount > 0):
if len(inhabitant) - insert_amount > 0:
if decision(0.33):
inhabitant.gene = inhabitant.gene[insert_amount:]
elif decision(0.5):
inhabitant.gene = inhabitant.gene[:-insert_amount]
else:
remove_index = random.randint(1, len(inhabitant.gene)-insert_amount)
inhabitant.gene = (
inhabitant.gene[:remove_index]
+ inhabitant.gene[remove_index+insert_amount:]
)
elif decision(random_probability):
inhabitant.gene = self._random_word()
else:
if decision(shift_probability):
shift_range = random.randint(1, 3)
for _ in range(shift_range + 1):
inhabitant.gene = [inhabitant.gene[-1]] + inhabitant.gene[:-1]
if decision(replace_probability):
replace_index = random.randint(0, len(inhabitant.gene)-1)
possible_chars = self._random_word()
for char in inhabitant.gene:
if char in possible_chars:
possible_chars.remove(char)
if possible_chars:
inhabitant.gene[replace_index] = possible_chars[0]
else:
for i in range(len(inhabitant.gene) // 2):
if decision(swap_probability):
random_id = random.randint(0, len(inhabitant) - 1)
inhabitant.gene[i], inhabitant.gene[random_id] = (
inhabitant.gene[random_id],
inhabitant.gene[i],
)
if decision(inverse_probability):
inhabitant.gene = inhabitant.gene[::-1]
self.generation = new_generation
def mutate(
self,
min_swap_probability=0.2,
max_swap_probability=0.5,
inverse_probability=0.001,
random_probability=0.05,
shift_probability=0.001,
insert_probability=0.9,
replace_probability=0.5,
):
swap_probability = random.uniform(min_swap_probability, max_swap_probability)
for inhabitant in self.generation[1:]:
if decision(insert_probability):
insert_amount = random.randint(1, 3)
if decision(0.5): # remove decision
if(len(inhabitant)+insert_amount < len(self.all_puzzle_keys)):
possible_chars = self._random_word()
for char in inhabitant.gene:
if char in possible_chars:
possible_chars.remove(char)
if decision(0.33):
inhabitant.gene += possible_chars[:insert_amount]
elif decision(0.5):
inhabitant.gene = possible_chars[:insert_amount] + inhabitant.gene
else:
insert_index = random.randint(1, len(inhabitant.gene))
inhabitant.gene = inhabitant.gene[:insert_index] + possible_chars[:insert_amount] + inhabitant.gene[insert_index:]
else:
def mutate(self, probability=0.05):
for inhabitant in self.generation:
for i in range(len(inhabitant.gene)):
if decision(probability):
inhabitant.gene[i] = self._random_char()
self.generation = new_generation
def mutate(
self,
swap_probability=0.2
min_transpose_probability=0.2,
max_transpose_probability=0.5,
inverse_probability=0.01,
random_probability=0.01,
shift_probability=0.01,
insert_probability=0.5,
):
transpose_probability = random.uniform(min_transpose_probability, max_transpose_probability)
for inhabitant in self.generation:
if decision(insert_probability):
insert_amount = random.randint(1, 2)
if(len(inhabitant)+insert_amount < len(self.all_puzzle_keys)):
possible_chars = self._random_word()
for char in inhabitant.gene:
if char in possible_chars:
possible_chars.remove(char)
if decision(0.5):
inhabitant.gene += possible_chars[:insert_amount]
else:
inhabitant.gene = possible_chars[:insert_amount] + inhabitant.gene
if decision(random_probability):
inhabitant.gene = self._random_word()
if decision(shift_probability):
shift_range = random.randint(1, 3)
async def yarek(msg: types.Message):
if decision(0.5):
await msg.answer(
'Вы всё ещё готовите на огне @yaroslav_y? Тогда мы идём к вам.')
st.batch_size_val]
val_pred_lbl_2 = sess.run([pred_l2],
feed_dict={
dat: batch_x_val_2,
lbl: batch_y_val_2,
keep: 1.0
})
val_batch_lbl_all_1.append(batch_y_val_1)
val_batch_lbl_all_2.append(batch_y_val_2)
val_pred_lbl_all_1.append(val_pred_lbl_1)
val_pred_lbl_all_2.append(val_pred_lbl_2)
ori_lbl_all_1, ori_pred_all_1 = ut.decision(val_batch_lbl_all_1,
val_pred_lbl_all_1,
class_number=4)
ori_lbl_all_2, ori_pred_all_2 = ut.decision(val_batch_lbl_all_2,
val_pred_lbl_all_2,
class_number=4)
val_acc_lbl_1 = accuracy_score(ori_lbl_all_1, ori_pred_all_1)
val_acc_lbl_2 = accuracy_score(ori_lbl_all_2, ori_pred_all_2)
print("%dth Epoch over." % (epoch + 1))
if (epoch + 1) % st.eval_epoch == 0:
print(
"All variables are saved in path",
saver_tr.save(
sess, (st.model_path +
"CNN2path_tgt%d_src%d_bs%d_te%d_%02dth_epoch.ckpt") %
async def toopa(msg: types.Message):
if decision(0.05):
await msg.reply('ТУПА АЛЕГАНТОР))))))')