print(" ".join(line))
def testLocalSearch(self, problem, local_search):
# times = 10
# cnt = 0
# start = time()
# for i in range(times):
result = local_search(problem)
build = test.make_board(size, result)
test.printBoard(build)
from hill_climbing import hill_climbing
from GA import GA
from simulated_annealing import simulated_annealing
from NQueens import NQueensSearch
if __name__ == "__main__":
test = TestLocalSearch()
print("Running local search for N Queens Problem")
size = eval(input(" - Please input the size of the board: "))
print()
problem = NQueensSearch(size)
algorithms = [hill_climbing, simulated_annealing, GA]
names = ["hill_climbing", "simulated_annealing", "GA"]
problems = [problem, problem, problem]
for i in range(len(algorithms)):
print(names[i])
board = test.testLocalSearch(problems[i], algorithms[i])
from hill_climbing import hill_climbing
from hill_climbing import random_restart
from simulated_annealing import simulated_annealing
from NQueens import NQueensSearch
if __name__ == "__main__":
# Parametros
str = "N-queens problem solver by using local search algorithms.\n\t Author: Vitor Veras.\t Default arguments: -n=8 ; -i=10 ; --all=0"
parser = argparse.ArgumentParser(description=str)
parser.add_argument("-n", type=int, default=8, help="Size of the board")
parser.add_argument("-i", type=int, default=10, help="Number of iterations")
parser.add_argument("--all", type=int, dest='all', action='store',
choices=range(0, 2), default=0,
help="0 = show one solution | 1 = show all solutions")
args = parser.parse_args()
# Inicia a busca
test = localSearch()
# Inicia o problema passando o tamanho do tabuleiro
problem = NQueensSearch(args.n)
algorithms = [hill_climbing, random_restart]
names = ["hill_climbing", "hc_random_restart"]
problems = [problem, problem, problem]
for i in range(len(algorithms)):
print(names[i])
result_board = test.localSearch(problems[i], algorithms[i], args.i)
# Imprime um tabuleiro com base no argumento --all passado
printBoard(result_board, args.all)