app.py

app.py

@@ -20,11 +20,10 @@ while iter_count < max_iters:
     # Compute the predicted output
     y_pred = [Theta0 + Theta1 * x1[i] + Theta2 * x2[i] for i in range(len(x1))]
 
-    
     # Compute the errors
     errors = [y_pred[i] - y_actual[i] for i in range(len(x1))]
     
-    # Update Theta0 and Theta1
+    # Update Theta0, Theta1, and Theta2
     Theta0 -= alpha * sum(errors) / len(x1)
     Theta1 -= alpha * sum([errors[i] * x1[i] for i in range(len(x1))]) / len(x1)
     Theta2 -= alpha * sum([errors[i] * x2[i] for i in range(len(x2))]) / len(x2)
@@ -34,7 +33,7 @@ while iter_count < max_iters:
     
     # Print the cost function every 100 iterations
     if iter_count % 100 == 0:
-        print("Iteration {}: Cost = {}, Theta0 = {}, Theta1 = {}".format(iter_count, cost, Theta0, Theta1, Theta2))
+        print("Iteration {}: Cost = {}, Theta0 = {}, Theta1 = {}, Theta2 = {}".format(iter_count, cost, Theta0, Theta1, Theta2))
     
     # Check for convergence (if the cost is decreasing by less than 0.0001)
     if iter_count > 0 and abs(cost - prev_cost) < 0.0001:
@@ -45,7 +44,7 @@ while iter_count < max_iters:
     iter_count += 1
     prev_cost = cost
 
-# Print the final values of Theta0 and Theta1
-print("Final values: Theta0 = {}, Theta1 = {}".format(Theta0, Theta1, Theta2))
-print("Final Cost: Cost= {}".format(cost))
+# Print the final values of Theta0, Theta1, and Theta2
+print("Final values: Theta0 = {}, Theta1 = {}, Theta2 = {}".format(Theta0, Theta1, Theta2))
+print("Final Cost: Cost = {}".format(cost))
 print("Final values: y_pred = {}, y_actual = {}".format(y_pred, y_actual))