updated code quality

Author: sanskarmodi8

machine_learning/mini_batch_gradient_descent.py

@@ -5,7 +5,6 @@
 """
 
 import numpy as np
-from typing import Tuple
 
 
 def mini_batch_gradient_descent(
@@ -14,7 +13,7 @@ def mini_batch_gradient_descent(
     learning_rate: float = 0.01,
     batch_size: int = 16,
     n_epochs: int = 50,
-) -> Tuple[np.ndarray, float]:
+) -> tuple[np.ndarray, float]:
     """
     Mini-Batch Gradient Descent for linear regression.
 
@@ -41,20 +40,25 @@ def mini_batch_gradient_descent(
     Example
     -------
     >>> import numpy as np
-    >>> X = np.array([[1],[2],[3],[4]])
-    >>> y = np.array([2,4,6,8])
-    >>> w, b = mini_batch_gradient_descent(X, y, learning_rate=0.1, batch_size=2, n_epochs=100)
+    >>> X = np.array([[1], [2], [3], [4]])
+    >>> y = np.array([2, 4, 6, 8])
+    >>> w, b = mini_batch_gradient_descent(
+    ...     X, y, learning_rate=0.1, batch_size=2, n_epochs=100
+    ... )
     >>> round(w[0], 1)  # slope close to 2
     2.0
     """
     n_samples, n_features = feature_matrix.shape
     weights = np.zeros(n_features)
     bias = 0
 
+    rng = np.random.default_rng()
+
     for _ in range(n_epochs):
-        indices = np.random.permutation(n_samples)
+        indices = rng.permutation(n_samples)
         shuffled_features = feature_matrix[indices]
         shuffled_targets = target_values[indices]
+
         for start_idx in range(0, n_samples, batch_size):
             end_idx = start_idx + batch_size
             batch_features = shuffled_features[start_idx:end_idx]
@@ -63,10 +67,11 @@ def mini_batch_gradient_descent(
             errors = predictions - batch_targets
             weights -= learning_rate * (batch_features.T @ errors) / len(batch_targets)
             bias -= learning_rate * np.mean(errors)
+
     return weights, bias
 
 
 if __name__ == "__main__":
     import doctest
 
-    doctest.testmod()
+    doctest.testmod()