def main():
fileName = input('Enter file name : ')
# Used variables
pawnAmountBW = {'WHITE': 0, 'BLACK': 0} # number of pawns for each side
dataSplitted = []
readFile(fileName, dataSplitted) # parse user's input into array
# Algorithms option
print("Choose Algorithm By Input The Number :")
print("1. Hill Climbing")
print("2. Simulated Annealing")
print("3. Genetic Algorithm")
chosenAlgo = int(input("Choose : "))
while (chosenAlgo > 3 or chosenAlgo < 1):
print("Choose The Correct Number Please...")
chosenAlgo = int(input("Choose : "))
# Execute the chosen algorithm
if (chosenAlgo == 1):
state = parseState(dataSplitted,
pawnAmountBW) # generate an initial state
result = hillClimbing(state, pawnAmountBW)
elif (chosenAlgo == 2):
temperature = int(input('Temperature: '))
decreaseRate = int(input('Decrease Rate: '))
iteration = int(input('Maximum Iteration: '))
state = parseState(dataSplitted,
pawnAmountBW) # generate an initial state
result = simulatedAnnealing(state, pawnAmountBW, temperature,
decreaseRate, iteration)
elif (chosenAlgo == 3):
populationAmount = int(input('Number Of Population: '))
limit = int(input('Maximum generation: '))
listOfStates = createListOfStates(dataSplitted, pawnAmountBW,
populationAmount)
result = geneticAlgorithm(listOfStates, pawnAmountBW, populationAmount,
limit)
# Print the result
attackNum = countAtack(
result) # Get the number of attack from the result state
printBoard(result)
print(attackNum['friend'], end='')
print(' ', end='')
print(attackNum['enemy'])
from hillClimbing import hillClimbing # 引入爬山演算法類別 from solutionVirginia import SolutionNumber # 引入平方根解答類別 # 執行爬山演算法 (從「解答=0.0」開始尋找, 最多十萬代、失敗一千次就跳出。 hillClimbing(solutionVirginia(0.0), 100000, 1000)
from hillClimbing import hillClimbing # 引入爬山演算法類別 from solutionEquation import SolutionEquation # 引入平方根解答類別 # 執行爬山演算法 (最多十萬代、失敗一千次就跳出) hillClimbing(SolutionEquation.zero(), 100000, 1000)
from hillClimbing import hillClimbing # 引入爬山演算法類別 from solutionVirginia import solutionVirginia # 引入平方根解答類別 # 執行爬山演算法 (最多十萬代、失敗一千次就跳出)。 hillClimbing(solutionVirginia([1, 1, 1]), 100000, 1000)
from hillClimbing import hillClimbing # 引入爬山演算法類別 from solutionScheduling import SolutionScheduling # 引入平方根解答類別 # 執行爬山演算法 (最多3萬代、失敗一千次就跳出) hillClimbing(SolutionScheduling.init(), 30000, 1000)
from hillClimbing import hillClimbing # 引入爬山演算法類別
from solutionVirginia import SolutionVirginia # 引入平方根解答類別
import virginia
plain = "This is a book. That is a cat. I am a boy. One of my boy go to school today."
key = [5, 2, 4]
# key = [9,3,7]
etext = virginia.encrypt(plain, key)
print('etext=', etext)
# 執行爬山演算法
s = hillClimbing(SolutionVirginia.init(etext, [0, 0, 0]), 30000, 1000)
dtext = virginia.decrypt(etext, s.v)
print('dtext=', dtext)
from hillClimbing import hillClimbing # 引入爬山演算法類別 from solutionNumber import SolutionNumber # 引入平方根解答類別 # 執行爬山演算法 (從「解答=0.0」開始尋找, 最多十萬代、失敗一千次就跳出。 hillClimbing(SolutionNumber(0.0), 100000, 1000)
from hillClimbing import hillClimbing # 引入爬山演算法類別 from solutionArray import SolutionArray # 引入平方根解答類別 # 執行爬山演算法 (最多十萬代、失敗一千次就跳出)。 hillClimbing(SolutionArray([1,1,1]), 100000, 1000)