app.py

app.py

@@ -7,7 +7,7 @@ y_actual = [30, 35, 40, 45, 50]
 alpha = 0.01
 max_iters = 1000
 
-# Initial values for Theta0 and Theta1
+# Initial values for Theta0, Theta1, and Theta2
 Theta0 = 0
 Theta1 = 0
 Theta2 = 0
@@ -22,24 +22,25 @@ while iter_count < max_iters:
 
     # Compute the errors
     errors = [y_pred[i] - y_actual[i] for i in range(len(x1))]
-    
+
     # 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)
-    
+
     # Compute the cost function
     cost = sum([(y_pred[i] - y_actual[i]) ** 2 for i in range(len(x1))]) / (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, 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:
         print("Converged after {} iterations".format(iter_count))
         break
-    
+
     # Update the iteration counter and previous cost
     iter_count += 1
     prev_cost = cost