Refactor function parameters and improve logging format in gradient descent implementations

Author: somrita-banerjee

machine_learning/linear_regression_naive.py

@@ -41,11 +41,7 @@ def collect_dataset() -> np.ndarray:
 
 
 def run_steep_gradient_descent(
-    data_x: np.ndarray,
-    data_y: np.ndarray,
-    len_data: int,
-    alpha: float,
-    theta: np.ndarray
+    data_x: np.ndarray, data_y: np.ndarray, len_data: int, alpha: float, theta: np.ndarray
 ) -> np.ndarray:
     """Run one step of steep gradient descent.
 

machine_learning/linear_regression_vectorized.py

@@ -61,7 +61,7 @@ def gradient_descent(
 
         if i % (iterations // 10) == 0:  # log occasionally
             cost = np.sum(errors**2) / (2 * m)
-            print(f"Iteration {i+1}: Error = {cost:.5f}")
+            print(f"Iteration {i + 1}: Error = {cost:.5f}")
 
     return theta
 
@@ -96,4 +96,3 @@ def main() -> None:
 
     doctest.testmod()
     main()
-