def find_best_match():
"""Find the best matching word from the dictionary."""
i = input().split()
time_limit = int(i[0])
letter_multiset_size = int(i[1])
initial_size = int(i[2])
multiset = {}
initial_words = []
start_time = time.time()
for _ in range(letter_multiset_size):
e = input().split()
if e[0] in multiset.keys():
multiset[e[0]]['count'] += 1
else:
multiset[e[0]] = {'count': 1, 'value': int(e[1])}
for _ in range(initial_size):
initial_words.append(input().replace('\r', ''))
trie = read_dict(multiset)
matcher = WordMatcher(trie)
genetic = GeneticAlgorithm(matcher)
preprocessing_time = time.time() - start_time
result = genetic.search(time_limit - preprocessing_time, initial_words)
print(result, file=sys.stderr)
print(matcher.fitness(result))
def escape():
"""Perform genetic algorithm on a maze from the input data."""
i = list(map(int, input().split()))
time_limit = i[0]
n = i[1]
m = i[2]
initial_size = i[3]
population_size = i[4]
grid = []
start_field = -1
exit_field = -1
for i in range(n):
row = list(map(lambda u: Field(int(u)), input().replace('\r', '')))
if Field.AGENT in row:
start_field = (i, row.index(Field.AGENT))
if Field.EXIT in row:
exit_field = (i, row.index(Field.EXIT))
grid.append(row)
maze = Maze(grid, start_field, exit_field)
initial_paths = []
for i in range(initial_size):
initial_paths.append(path_from_string(input().replace('\r', '')))
genetic = GeneticAlgorithm(maze, population_size)
unchanged_iterations = 10 * (n * m)
result = genetic.search(time_limit, initial_paths, unchanged_iterations)
print(result[1])
print_path(result[0])