def print_board(board):
for row in board:
print(" | ".join(row))
print("-" * 5)
def check_winner(board, player):
for row in board:
if all(cell == player for cell in row):
return True
for col in range(3):
if all(board[row][col] == player for row in range(3)):
return True
if all(board[i][i] == player for i in range(3)) or all(board[i][2 - i] == player for i in range(3)):
return True
return False
def tic_tac_toe():
board = [[" " for _ in range(3)] for _ in range(3)]
current_player = "X"
print("Welcome to Tic-Tac-Toe!")
print_board(board)
while True:
row = int(input(f"Player {current_player}, enter row (0, 1, or 2): "))
col = int(input(f"Player {current_player}, enter column (0, 1, or 2): "))
if board[row][col] != " ":
print("That cell is already taken. Try again.")
continue
board[row][col] = current_player
print_board(board)
if check_winner(board, current_player):
print(f"Player {current_player} wins!")
break
if all(all(cell != " " for cell in row) for row in board):
print("It's a draw!")
break
current_player = "O" if current_player == "X" else "X"
tic_tac_toe()import random
def print_board(board):
# Function to print the Tic-Tac-Toe board
for row in board:
print(" | ".join(row))
print("-" * 5)
def check_winner(board, player):
# Function to check if a player has won the game
# Check rows
for row in board:
if all(cell == player for cell in row):
return True
# Check columns
for col in range(3):
if all(board[row][col] == player for row in range(3)):
return True
# Check diagonals
if all(board[i][i] == player for i in range(3)) or all(board[i][2 - i] == player for i in range(3)):
return True
return False
def computer_move(board, player):
# Function for the computer to make a move
# First, check for a winning move
for i in range(3):
for j in range(3):
if board[i][j] == " ":
board[i][j] = player
if check_winner(board, player):
return
board[i][j] = " "
# If no winning move, check for a blocking move (opponent's winning move)
opponent = "O" if player == "X" else "X"
for i in range(3):
for j in range(3):
if board[i][j] == " ":
board[i][j] = opponent
if check_winner(board, opponent):
board[i][j] = player
return
board[i][j] = " "
# If no winning or blocking move, make a random move
empty_cells = [(i, j) for i in range(3) for j in range(3) if board[i][j] == " "]
if empty_cells:
row, col = random.choice(empty_cells)
board[row][col] = player
def tic_tac_toe():
# Main function to play Tic-Tac-Toe
board = [[" " for _ in range(3)] for _ in range(3)]
print("Welcome to Tic-Tac-Toe against the computer!")
print_board(board)
while True:
# Player's move
row = int(input("Enter row (0, 1, or 2): "))
col = int(input("Enter column (0, 1, or 2): "))
if board[row][col] != " ":
print("That cell is already taken. Try again.")
continue
board[row][col] = "X"
print_board(board)
if check_winner(board, "X"):
print("You win!")
break
if all(all(cell != " " for cell in row) for row in board):
print("It's a draw!")
break
# Computer's move
print("Computer's move:")
computer_move(board, "O")
print_board(board)
if check_winner(board, "O"):
print("Computer wins!")
break
if all(all(cell != " " for cell in row) for row in board):
print("It's a draw!")
break
tic_tac_toe()#fibonaci
fib(0, 0).
fib(1, 1).
fib(N, Result) :-
N > 1,
N1 is N - 1,
N2 is N - 2,
fib(N1, Result1),
fib(N2, Result2),
Result is Result1 + Result2.
# Python3 program to implement traveling salesman
# problem using naive approach.
from sys import maxsize
from itertools import permutations
V = 4
# implementation of traveling Salesman Problem
def travellingSalesmanProblem(graph, s):
# store all vertex apart from source vertex
vertex = []
for i in range(V):
if i != s:
vertex.append(i)
# store minimum weight Hamiltonian Cycle
min_path = maxsize
next_permutation=permutations(vertex)
for i in next_permutation:
# store current Path weight(cost)
current_pathweight = 0
# compute current path weight
k = s
for j in i:
current_pathweight += graph[k][j]
k = j
current_pathweight += graph[k][s]
# update minimum
min_path = min(min_path, current_pathweight)
return min_path
# Driver Code
if __name__ == "__main__":
# matrix representation of graph
graph = [[0, 10, 15, 20], [10, 0, 35, 25],
[15, 35, 0, 30], [20, 25, 30, 0]]
s = 0
print(travellingSalesmanProblem(graph, s))#DataFrame
import pandas as pd
# Define your two-dimensional array
data = [[10, 20, 30, 40, 40],
[10, 20, 30, 40, 50]]
# Create a DataFrame from the array
df = pd.DataFrame(data)
# Display the DataFrame
print(df)
# ---------------------------
data = [10, 20, 30, 40, 50]
# Create a DataFrame from the array with index and values columns named explicitly
df = pd.DataFrame({'index': range(len(data)), 'Values': data})
my_dict = {'a': 1, 'b': 2, 'c': 3}
# Reading values
value_a = my_dict['a']
print(value_a) # Output: 1
value_b = my_dict.get('b')
print(value_b) # Output: 2
value_d = my_dict.get('d', 'Key not found')
print(value_d) # Output: 'Key not found'
# Adding new key-value pairs
my_dict['d'] = 4
print(my_dict) # Output: {'a': 1, 'b': 2, 'c': 3, 'd': 4}
# Modifying existing value
my_dict['b'] = 20
print(my_dict) # Output: {'a': 1, 'b': 20, 'c': 3, 'd': 4}# Adding or updating multiple key-value pairs
my_dict.update({'e': 5, 'f': 6})
print(my_dict) # Output: {'a': 1, 'b': 20, 'c': 3, 'd': 4, 'e': 5, 'f': 6}import json
# Open the JSON file for reading
with open('data.json', 'r') as file:
data = json.load(file)
# Add new data to the existing JSON object
data['new_key'] = 'new_value'
# Save the updated JSON object back to the file
with open('data.json', 'w') as file:
json.dump(data, file, indent=4)import math
def minimax_alpha_beta(node, depth, alpha, beta, maximizing_player):
if depth == 0 or node.is_terminal():
return node.evaluate(), None
if maximizing_player:
max_eval = -math.inf
best_move = None
for move in node.get_possible_moves():
child_node = node.make_move(move)
eval, _ = minimax_alpha_beta(child_node, depth - 1, alpha, beta, False)
if eval > max_eval:
max_eval = eval
best_move = move
alpha = max(alpha, eval)
if beta <= alpha:
break
return max_eval, best_move
else:
min_eval = math.inf
best_move = None
for move in node.get_possible_moves():
child_node = node.make_move(move)
eval, _ = minimax_alpha_beta(child_node, depth - 1, alpha, beta, True)
if eval < min_eval:
min_eval = eval
best_move = move
beta = min(beta, eval)
if beta <= alpha:
break
return min_eval, best_move
# Example usage
class Node:
def __init__(self, value):
self.value = value
def evaluate(self):
return self.value
def is_terminal(self):
return True # Define terminal condition
def get_possible_moves(self):
return [1, 2, 3] # Example list of possible moves
def make_move(self, move):
return Node(self.value - move) # Example move implementation
root_node = Node(10) # Example initial node
best_score, best_move = minimax_alpha_beta(root_node, 3, -math.inf, math.inf, True)
print("Best move:", best_move)
print("Best score:", best_score)# A simple Python3 program to find
# maximum score that
# maximizing player can get
import math
def minimax (curDepth, nodeIndex,
maxTurn, scores,
targetDepth):
# base case : targetDepth reached
if (curDepth == targetDepth):
return scores[nodeIndex]
if (maxTurn):
return max(minimax(curDepth + 1, nodeIndex * 2,
False, scores, targetDepth),
minimax(curDepth + 1, nodeIndex * 2 + 1,
False, scores, targetDepth))
else:
return min(minimax(curDepth + 1, nodeIndex * 2,
True, scores, targetDepth),
minimax(curDepth + 1, nodeIndex * 2 + 1,
True, scores, targetDepth))
# Driver code
scores = [3, 5, 2, 9, 12, 5, 23, 23]
treeDepth = math.log(len(scores), 2)
print("The optimal value is : ", end = "")
print(minimax(0, 0, True, scores, treeDepth))
# This code is contributed
# by rootshadow
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