Notes

# Importing required moduless
import numpy as np
import pandas as pd
import pprint,random

from scipy.ndimage.interpolation import shift
import matplotlib.pyplot as plt

from keras.models import Sequential
from keras.layers import Dense,Dropout
from keras.optimizers import SGD

class tic_tac_toe_game(object):
def __init__(self):
self.board=np.full((3,3),2)

def toss(self):
turn=np.random.randint(0,2,size=1)
if turn.mean()==0:
self.turn_monitor=0
elif turn.mean()==1:
self.turn_monitor=1
return self.turn_monitor

def move(self,player,coord):
if self.board[coord]!=2 or self.game_status()!="In Progress" or self.turn_monitor!=player:
raise ValueError("Invalid move")
self.board[coord]=player
self.turn_monitor=1-player
return self.game_status(),self.board


def game_status(self):
#check for a win along rows
for i in range(self.board.shape[0]):
if 2 not in self.board[i,:] and len(set(self.board[i,:]))==1:
return "Won"
#check for a win along columns
for j in range(self.board.shape[1]):
if 2 not in self.board[:,j] and len(set(self.board[:,j]))==1:
return "Won"
# check for a win along diagonals
if 2 not in np.diag(self.board) and len(set(np.diag(self.board)))==1:
return "Won"
if 2 not in np.diag(np.fliplr(self.board)) and len(set(np.diag(np.fliplr(self.board))))==1:
return "Won"
# check for a Draw
if not 2 in self.board:
return "Drawn"
else:
return "In Progress"
def legal_moves_generator(current_board_state,turn_monitor):
legal_moves_dict={}
for i in range(current_board_state.shape[0]):
for j in range(current_board_state.shape[1]):
if current_board_state[i,j]==2:
board_state_copy=current_board_state.copy()
board_state_copy[i,j]=turn_monitor
legal_moves_dict[(i,j)]=board_state_copy.flatten()
return legal_moves_dict
model = Sequential()
model.add(Dense(18, input_dim=9,kernel_initializer='normal', activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(9, kernel_initializer='normal',activation='relu'))
model.add(Dropout(0.1))
# model.add(Dense(9, kernel_initializer='normal',activation='relu'))
# model.add(Dropout(0.1))
# model.add(Dense(5, kernel_initializer='normal',activation='relu'))
model.add(Dense(1,kernel_initializer='normal'))

learning_rate = 0.001
momentum = 0.8

sgd = SGD(lr=learning_rate, momentum=momentum,nesterov=False)
model.compile(loss='mean_squared_error', optimizer=sgd)
model.summary()
# create an object of the class tick_tac_toe_game
game=tic_tac_toe_game()
# toss to decide which player goes first
game.toss()
print("Player ",game.turn_monitor," has won the toss")
# make the first move
print("Initial board state n",game.board)
print("Let first player place their mark on 0,0")
game_status,board=game.move(game.turn_monitor,(0,0))
print("New Board State: n",board)
print("Let second player place their mark on 0,1")
game_status,board=game.move(game.turn_monitor,(0,1))
print("New Board State: n",board)
print("Let first player place their mark on 1,1")
game_status,board=game.move(game.turn_monitor,(1,1))
print("New Board State: n",board)
print("Let second player place their mark on 0,2")
game_status,board=game.move(game.turn_monitor,(0,2))
print("New Board State: n",board)
print("Let first player place their mark on 2,2")
game_status,board=game.move(game.turn_monitor,(2,2))
print("New Board State: n",board)
print("Player ",1-game.turn_monitor," Has ",game_status)