def mutation(child): #mutation function
BoardHandler.updateBoard(child.board, child.piecesLocation)
member = randint(0, len(child.piecesLocation) - 1)
row = randint(0, 7)
col = randint(0, 7)
while child.board[row][col] != ('.', "."):
row = randint(0, 7)
col = randint(0, 7)
child.piecesLocation[member][0][0] = row
child.piecesLocation[member][0][1] = col
def simulatedAnnealing(maxTry, chessBoard):
# Initialization
mTry = iterator = maxTry
# Copy initiate board and count the value
bestResult = copy.deepcopy(chessBoard)
ts0, td0, qs0, qd0, rs0, rd0, bs0, bd0, ks0, kd0 = Checker.conflictChecker(bestResult)
Value = ts0 - td0
# Improved and accepted states counter
improved = accepted = 0
# Outer loop, which randomizes state until a number of steps
while iterator > 0 :
# Function that set the temperature
T = math.log(mTry - iterator + 1, math.exp(1))
# Generate a random state
tempBoard = copy.deepcopy(bestResult)
tempList = BoardHandler.createPiecesListWithPos(tempBoard)
randPiece = tempList[randint(0, len(tempList)-1)]
# Randomizes a piece's position
randRow = randint(0, 7)
randCol = randint(0, 7)
while tempBoard[randRow][randCol] != ('.', "."):
randRow = randint(0, 7)
randCol = randint(0, 7)
tempBoard[randPiece[3]][randPiece[2]] = ('.', ".")
tempBoard[randRow][randCol] = (randPiece[0], randPiece[1])
# Count the randomized board value
ts1, td1, qs1, qd1, rs1, rd1, bs1, bd1, ks1, kd1 = Checker.conflictChecker(tempBoard)
tempValue = ts1 - td1
# If the next state value is better, accept it
if tempValue <= Value :
bestResult = copy.deepcopy(tempBoard)
Value = tempValue
improved += 1
# If the next state value is worse, accept with probability of it is higher than a random number
elif tempValue > Value :
sigmoidFunc = math.floor(math.exp(-T)) * 100
randomNum = randint(0, 100)
if sigmoidFunc > randomNum:
bestResult = copy.deepcopy(tempBoard)
Value = tempValue
accepted += 1
# Iterator value decreased
iterator -= 1
# Result of improved states
print(improved)
# Result of accepted states
print(accepted)
Printer.printSolutionToFile(bestResult, "SimulatedAnnealing")
return bestResult
def mutationDouble(
child): # mutation function if there are some positions that clash
BoardHandler.updateBoard(child.board, child.piecesLocation)
while checkDouble(child):
double = listDouble(child)
member = choice(double)
row = randint(0, 7)
col = randint(0, 7)
while child.board[row][col] != ('.', "."):
row = randint(0, 7)
col = randint(0, 7)
child.piecesLocation[member][0][0] = row
child.piecesLocation[member][0][1] = col
def mainMenu():
chessBoard = []
while True:
if os.name == 'nt': # Windows
os.system('cls')
elif os.name == 'posix': # Linux
os.system('clear')
print(colorize(header, 'pink'))
i = 1
for item in menuItems:
print("[" + str(i) + "] " + colorize(item, 'blue'))
i += 1
choice = int(input(">> "))
try:
if choice < 1:
raise ValueError
# Call the matching function
if choice == 1:
Printer.printDir("Inputs/")
chessBoard = BoardHandler.createChessboard()
elif choice == 2:
Printer.printDir("Inputs/")
chessBoard = BoardHandler.readChessboard()
elif choice == 3:
Printer.printChessBoard(chessBoard)
elif choice == 7:
credit()
elif choice == 8:
exit(0)
else:
print(">> That is not a valid input.")
except (ValueError, IndexError):
pass
while True:
text = input("\n>> Press enter to continue.")
if text == "":
break
else:
print("\n>> You did not press enter.")
print("\n\n")
def initialize_population(chessList): # initialize the population
population = []
for i in range(100):
chromosome = Chromosome()
chromosome.board = []
chromosome.piecesLocation = BoardHandler.random_genetic(
chromosome.board, chessList)
conflict = Checker.conflictChecker(chromosome.board)
chromosome.fitness = conflict[0] - conflict[1]
population.append(chromosome)
populationSort(population) # sort population by highest fit
deleteLeastFit(population) # delete half of least fit population
return population
def initialize_population(chessList): #initialize the population
population = []
for i in range(100):
chromosome = Chromosome()
chromosome.board = []
chromosome.piecesLocation = BoardHandler.random_genetic(
chromosome.board, chessList)
chromosome.fitness = Checker.conflictChecker(chromosome.board)[0]
population.append(chromosome)
Printer.printChessBoard(population[0].board)
print(population[0].piecesLocation)
print(population[0].fitness)
return population
def updateBoardandFitness(
child): #update chromosome board and fitness score after cross over
BoardHandler.updateBoard(child.board, child.piecesLocation)
child.fitness = Checker.conflictChecker(child.board)[0]
def mainMenu():
chessBoard = []
# Clear screen
while True:
if os.name == 'nt': # Windows
os.system('cls')
elif os.name == 'posix': # Linux
os.system('clear')
# Print header & menu options
print(colorize(header, 'pink'))
i = 1
for item in menuItems:
print("[" + str(i) + "] " + colorize(item, 'blue'))
i += 1
choice = int(input(">> "))
try:
if choice < 1:
raise ValueError
# Call the matching function
if choice == 1:
Printer.printDir("Inputs/")
chessBoard = BoardHandler.createChessboard()
elif choice == 2:
Printer.printDir("Inputs/")
chessBoard = BoardHandler.readChessboard()
elif choice == 3:
Printer.printChessBoard(chessBoard)
elif choice == 4:
maxTry = int(input(">> Enter maximum try : "))
Printer.printChessBoard(hc.hillC(maxTry, chessBoard))
elif choice == 5:
maxTry = int(input(">> Enter maximum try : "))
Printer.printChessBoard(
SimulatedAnnealing.simulatedAnnealing(maxTry, chessBoard))
elif choice == 6:
Genetic.GeneticAlgorithm(
BoardHandler.createPiecesList(chessBoard))
elif choice == 7:
helpMe()
elif choice == 8:
credit()
elif choice == 9:
exit(0)
else:
print(">> That is not a valid input.")
except (ValueError, IndexError):
pass
# Users need to press enter to continue using program (refresh the display)
while True:
text = input("\n>> Press enter to continue.")
if text == "":
break
else:
print("\n>> You did not press enter.")
print("\n\n")