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PlayTicTacToe.py
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import numpy as np
import random
from sklearn.metrics import accuracy_score
from joblib import dump, load
import warnings
warnings.filterwarnings("ignore")
print('Please run the program in 64 bits version of python.')
print('Otherwise, the errors will be occured like ValueError: Buffer dtype mismatch...')
###Predefined function.###
def value2binary(vec): #Turn numericdata into binary data.
return [1 if x >= 0.5 else 0 for x in vec]
def make_custom_scorer(estimator, X_test, y_test): #Customer's accuracy.
'''
estimator: scikit-learn estimator, fitted on train data
X_test: array-like, shape = [n_samples, n_features] Data for prediction
y_test: array-like, shape = [n_samples] Ground truth (true relevance labels).
y_score : array-like, shape = [n_samples] Predicted scores
'''
all_params = estimator.get_params() #Get currnet CV's trained parameters.
y_score = estimator.predict(X_test) #Predict based on input X.
for i in range(len(y_score)): #Turn the numeric data into binary data.
vec = y_score[i]
y_score[i] = value2binary(vec)
acc = accuracy_score(y_test, y_score) #Calculating ACC.
return acc
def showCurrentBoard(showBoard): #Show current borad condition.
print("%s ｜ %s ｜ %s"%(showBoard[0],showBoard[1],showBoard[2]))
print("－－－－－－")
print("%s ｜ %s ｜ %s"%(showBoard[3],showBoard[4],showBoard[5]))
print("－－－－－－")
print("%s ｜ %s ｜ %s"%(showBoard[6],showBoard[7],showBoard[8]))
def winningCheck(showBoard): #Check who is winner.
if showBoard[0]==showBoard[1] and showBoard[1]==showBoard[2]:
return showBoard[0]
if showBoard[3]==showBoard[4] and showBoard[4]==showBoard[5]:
return showBoard[3]
if showBoard[6]==showBoard[7] and showBoard[7]==showBoard[8]:
return showBoard[6]
if showBoard[0]==showBoard[3] and showBoard[3]==showBoard[6]:
return showBoard[0]
if showBoard[1]==showBoard[4] and showBoard[4]==showBoard[7]:
return showBoard[1]
if showBoard[2]==showBoard[5] and showBoard[5]==showBoard[8]:
return showBoard[2]
if showBoard[0]==showBoard[4] and showBoard[4]==showBoard[8]:
return showBoard[0]
if showBoard[2]==showBoard[4] and showBoard[4]==showBoard[6]:
return showBoard[2]
return None
#Load the all trained model.
ModelsName = ['ClfLinearSVM_multilabel',
'ClfRBFSVM_multilabel',
'ClfKNN_multilabel',
'ClfMLP_multilabel',
'ClfLinearSVM_singlelabel',
'ClfRBFSVM_singlelabel',
'ClfKNN_singlelabel',
'ClfMLP_singlelabel',
'RegKNN_multilabel',
'RegMLP_multilabel',
'RegLN_multilabel',
'GA-ClfMLP_singlelabel']
model = []
modelList =[]
idx = 0
for mn in ModelsName: #Load all trained models' parameters.
if mn == 'RegLN_multilabel':
model.append(np.load('./GameParameters/'+mn+'.npy'))
else:
model.append(load('./GameParameters/'+mn+'.joblib'))
modelList.append(idx)
idx+=1
#Load liner gression of trained parameters.
LNregThresholds = np.load('./GameParameters/RegLN_multilabel_Thresholds.npy')
while True:
try:
playagain=input('\nWould you like to set up a new game? key Y/N: ') #Set up the game.
if playagain != "Y" and playagain != "y" and playagain != "N" and playagain != "n":
print('Please enter Y/N or enter crrect name of model.')
continue
if playagain == "N" or playagain == "n":
print('Bye-bye.')
break
idx = 0
print('Current available model:') #Showing all current all available.
for cm in ModelsName:
if cm == 'ClfMLP_singlelabel': #The bestest model is ClfMLP_singlelabel.
print('%s with key %s (bestest model)' %(cm, idx))
else:
print('%s with key %s' %(cm, idx))
idx += 1
InputPlyModel=int(input('\nWhich model you want to play with? key 0~11: ')) #Select model to play with.
if playagain == "Y" or playagain == "y" and InputPlyModel in modelList:
PlyModel = model[InputPlyModel] #Get weights from asigned model.
currentBoard=np.array([0,0,0,0,0,0,0,0,0]) #Initial current borad condition.
showBoard=[1,2,3,4,5,6,7,8,9] #Initial showing borad condition.
showCurrentBoard(showBoard) #Show current borad condition.
winner = None #Initial winner statement.
while True:
while True:
playerSteps=int(input('\nNow your turn(1~9): '))-1 #Player X move.
if showBoard[playerSteps] != 'X' and showBoard[playerSteps] != 'O':
showBoard[playerSteps]='X'
currentBoard[playerSteps] = 1
showCurrentBoard(showBoard)
break
else:
print('You cannot move the this place!!')
winner = winningCheck(showBoard) #Check whether has winner now.
if winner != None:
print('The winner is you.')
break
if 0 not in currentBoard:
print("No winner no loser!!")
break
print('Now my turn.')
while True:
#print(ModelsName[InputPlyModel])
if ModelsName[InputPlyModel] == 'RegLN_multilabel':#For linear regression model.
optimalSteps = currentBoard @ PlyModel.T #Predict optimal steps for play O by LN model.
optimalSteps[optimalSteps >= LNregThresholds] = 1
optimalSteps[optimalSteps < LNregThresholds] = 0
else:
optimalSteps = PlyModel.predict(currentBoard.reshape(1, -1)) #Predict optimal steps by trained model.
if np.max(optimalSteps.shape) > 1: #Check the model is trained by multi label or single label.
optimalSteps = optimalSteps.reshape(np.max(optimalSteps.shape),)
optimalSteps = np.array(value2binary(optimalSteps)) #If the model is regression, turn numeric results to binary results.
if 1 not in optimalSteps: #Check whether the optimal steps is located in current board.
optimalSteps = random.choice(list(np.where(currentBoard == 0)[0]))
else:
optimalSteps = random.choice(list(np.where(optimalSteps == 1)[0]))
if currentBoard[optimalSteps] != 0:
optimalSteps = random.choice(list(np.where(currentBoard == 0)[0]))
else:
optimalSteps = int(optimalSteps)
if showBoard[optimalSteps] != 'X' and showBoard[optimalSteps] != 'O': #Player O move by optimal steps.
showBoard[optimalSteps]='O'
currentBoard[optimalSteps] = -1
showCurrentBoard(showBoard)
break
if 0 not in currentBoard:
print("No winner no loser!!")
break
winner = winningCheck(showBoard) #Check whether has winner now.
if winner != None:
print('The winner is me.')
break
if 0 not in currentBoard:
break
else:
print('Please enter Y/N or enter crrect name of model.')
except:
continue