app.py

import torch

def predict_score(x1, x2):
    Theta0 = torch.tensor(-0.5738734424645411)
    Theta1 = torch.tensor(2.1659122905141825)
    Theta2 = torch.tensor(0.0)
    y_actual = Theta0 + Theta1 * x1 + Theta2 * 23  # Adjust the constant value here if needed
    return y_actual.item()

def gradient_descent():
    # Input data
    x1 = torch.tensor([50, 60, 70, 80, 90])
    x2 = torch.tensor([20, 21, 22, 23, 24])
    y_actual = torch.tensor([30, 35, 40, 45, 50])

    # Learning rate and maximum number of iterations
    alpha = 0.01
    max_iters = 1000

    # Initial values for Theta0, Theta1, and Theta2
    Theta0 = torch.tensor(0.0, requires_grad=True)
    Theta1 = torch.tensor(0.0, requires_grad=True)
    Theta2 = torch.tensor(0.0, requires_grad=True)

    # Start the iteration counter
    iter_count = 0

    # Loop until convergence or maximum number of iterations
    while iter_count < max_iters:
        # Compute the predicted output
        y_pred = Theta0 + Theta1 * x1 + Theta2 * x2

        # Compute the errors
        errors = y_pred - y_actual

        # Compute the cost function
        cost = torch.sum(errors ** 2) / (2 * len(x1))

        # Print the cost function every 100 iterations
        if iter_count % 100 == 0:
            print("Iteration {}: Cost = {}, Theta0 = {}, Theta1 = {}, Theta2 = {}".format(iter_count, cost, Theta0.item(), Theta1.item(), Theta2.item()))

        # Check for convergence (if the cost is decreasing by less than 0.0001)
        if iter_count > 0 and torch.abs(cost - prev_cost) < 0.0001:
            print("Converged after {} iterations".format(iter_count))
            break

        # Perform automatic differentiation to compute gradients
        cost.backward()

        # Update Theta0, Theta1, and Theta2 using gradient descent
        with torch.no_grad():
            Theta0 -= alpha * Theta0.grad
            Theta1 -= alpha * Theta1.grad
            Theta2 -= alpha * Theta2.grad

            # Reset gradients for the next iteration
            Theta0.grad.zero_()
            Theta1.grad.zero_()
            Theta2.grad.zero_()

        # Update the iteration counter and previous cost
        iter_count += 1
        prev_cost = cost

    # Print the final values of Theta0, Theta1, and Theta2
    print("Final values: Theta0 = {}, Theta1 = {}, Theta2 = {}".format(Theta0.item(), Theta1.item(), Theta2.item()))
    print("Final Cost: Cost = {}".format(cost.item()))
    print("Final values: y_pred = {}, y_actual = {}".format(y_pred, y_actual))

# Launch the prediction interface
while True:
    x1 = float(input("Enter the number of new students: "))
    x2 = float(input("Enter the number of temperature: "))
    predicted_rooms = predict_score(x1, x2)
    print("Predicted rooms:", predicted_rooms)
    print()

    choice = input("Do you want to predict again? (y/n): ")
    if choice.lower() != 'y':
        break

gradient_descent()