[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

neural_network/adaptive_resonance_theory.py

@@ -21,15 +21,15 @@ def __init__(self, num_features: int, vigilance: float = 0.7) -> None:
         Args:
             num_features (int): Number of features in the input data.
             vigilance (float): Threshold for similarity (default is 0.7).
-        
+
         Raises:
             ValueError: If num_features is not positive or if vigilance is not between 0 and 1.
         """
         if num_features <= 0:
             raise ValueError("Number of features must be a positive integer.")
         if not (0 <= vigilance <= 1):
             raise ValueError("Vigilance parameter must be between 0 and 1.")
-        
+
         self.vigilance = vigilance
         self.num_features = num_features
         self.weights = []
@@ -45,10 +45,16 @@ def _similarity(self, weight_vector: np.ndarray, input_vector: np.ndarray) -> fl
         Returns:
             float: The similarity score between the weight and the input.
         """
-        if len(weight_vector) != self.num_features or len(input_vector) != self.num_features:
-            raise ValueError(f"Both weight_vector and input_vector must have {self.num_features} features.")
-        
+        if (
+            len(weight_vector) != self.num_features
+            or len(input_vector) != self.num_features
+        ):
+            raise ValueError(
+                f"Both weight_vector and input_vector must have {self.num_features} features."
+            )
+
         return np.dot(weight_vector, input_vector) / self.num_features
+
     def _learn(
         self, w: np.ndarray, x: np.ndarray, learning_rate: float = 0.5
     ) -> np.ndarray: