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

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Author: pre-commit-ci[bot]

neural_network/radial_basis_function_neural_network.py

@@ -1,16 +1,16 @@
 """
 Radial Basis Function Neural Network (RBFNN)
 
-A Radial Basis Function Neural Network (RBFNN) is a type of artificial neural 
-network that uses radial basis functions as activation functions. 
-RBFNNs are particularly effective for function approximation, regression, and 
-classification tasks. The architecture typically consists of an input layer, 
+A Radial Basis Function Neural Network (RBFNN) is a type of artificial neural
+network that uses radial basis functions as activation functions.
+RBFNNs are particularly effective for function approximation, regression, and
+classification tasks. The architecture typically consists of an input layer,
 a hidden layer with radial basis functions, and an output layer.
 
 In an RBFNN:
-- The hidden layer applies a radial basis function (often Gaussian) to the 
+- The hidden layer applies a radial basis function (often Gaussian) to the
 input data, transforming it into a higher-dimensional space.
-- The output layer combines the results from the hidden layer using 
+- The output layer combines the results from the hidden layer using
 weighted sums to produce the final output.
 
 #### Reference
@@ -67,7 +67,7 @@ def _gaussian_rbf(self, input_vector: np.ndarray, center: np.ndarray) -> float:
             0.1353352832366127
         """
         # Calculate the squared distances
-        distances = np.linalg.norm(input_vector[:, np.newaxis] - center, axis=2)** 2
+        distances = np.linalg.norm(input_vector[:, np.newaxis] - center, axis=2) ** 2
         return np.exp(-distances / (2 * self.spread**2))
 
     def _compute_rbf_outputs(self, input_data: np.ndarray) -> np.ndarray: