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 GeneticAlgorithm(chessList): #the process of genetic algorithm
population = initialize_population(chessList)
# for chromosome in population :
# print(chromosome.fitness)
i = 1
while (population[0].fitness != 0):
print("Generation :", i)
populationSort(population)
deleteLeastFit(population)
# for chromosome in population :
# print(chromosome.fitness)
# print("===============")
# print("PieceList")
# print(chromosome.piecesLocation)
# print("Crossover")
# print("===============")
index = 0
while index < maxPopulation / 2:
parent1, parent2 = getParents(population, index)
if not (samePosition(parent1, parent2)):
crossOver(population, parent1, parent2)
index = index + 2
# index = 0
# parent1 , parent2 = getParents(population, index)
# crossOver(population, parent1, parent2)
populationSort(population)
print("Fittest : ", population[0].fitness)
# for chromosome in population :
# print(chromosome.fitness)
# for chromosome in population :
# Printer.printChessBoard(chromosome.board)
#
# populationSort(population)
i = i + 1
print(population[0].piecesLocation)
# BoardHandler.updateBoard(population[0].board, population[0].piecesLocation)
Printer.printChessBoard(population[0].board)
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 hillC(maxTry, chessboard): # Generate initial state currentState = copy.deepcopy(chessboard) # Iterate Try times for i in range(maxTry): print(i) bestNeighbour = copy.deepcopy(getBestNeighbour(currentState)) # Compare current state with best neighbour, if current state is better return current state, if evaluate(bestNeighbour, currentState) > 0: Printer.printChessBoard(currentState) Printer.printSolutionToFile(currentState, "HillClimbing") return currentState Printer.printChessBoard(currentState) # else assign current state with best neighbour currentState = copy.deepcopy(bestNeighbour) # Return current state if function HC cannot find maximum until try times Printer.printChessBoard(currentState) Printer.printSolutionToFile(currentState, "HillClimbing") return currentState
def GeneticAlgorithm(chessList): # the process of genetic algorithm
population = initialize_population(chessList)
generationCount = 1
count = 1
maxFitness = 0
while generationCount <= maxGeneration:
newGeneration = []
index = 0
while index < (maxPopulation / 2 - 1):
parent1, parent2 = getParents(population, index)
crossOver(newGeneration, parent1, parent2)
index = index + 1
population = newGeneration + population # add new generation to current population
populationSort(population)
deleteLeastFit(population)
print("Generation :" + str(generationCount) + ", Fittest : ",
abs(population[0].fitness))
if population[0].fitness != maxFitness:
maxFitness = population[0].fitness
count = 1
else:
count += 1
if count == 100: # if in 100 gen there is no change in fittest score
print("Same fitness for " + str(count) + " gen")
print("Terminate")
time.sleep(1)
break
else:
generationCount = generationCount + 1
# print (population[0].piecesLocation)
if generationCount == maxGeneration:
print("Max iteration reach")
time.sleep(1)
Printer.printChessBoard(population[0].board)
Printer.printSolutionToFile(population[0].board, "Genetic")
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 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")