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

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Author: pre-commit-ci[bot]

neural_network/adaptive_resonance_theory_1.py

@@ -1,5 +1,5 @@
 """
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 Name - - ART1 - Adaptive Resonance Theory 1
 Goal - - Cluster Binary Data
 Detail: Unsupervised clustering model using a vigilance parameter
@@ -10,54 +10,55 @@
 Author: Your Name
 Github: your_email@example.com
 Date: 2024.10.31
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 """
 
 import numpy as np
 
+
 class ART1:
     def __init__(self, num_features, vigilance=0.8):
         """
         Initialize the ART1 model with the number of features and the vigilance parameter.
-        
+
         Parameters:
         num_features (int): Number of features in input binary data.
         vigilance (float): Vigilance parameter to control cluster formation (0 < vigilance <= 1).
         """
         self.num_features = num_features
         self.vigilance = vigilance
         self.weights = []  # Stores the weights for clusters
-    
+
     def _similarity(self, x, w):
         """
         Calculate similarity between input vector x and weight vector w.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         float: Similarity value based on the intersection over the input length.
         """
         return np.sum(np.minimum(x, w)) / np.sum(x)
-    
+
     def _weight_update(self, x, w):
         """
         Update weights for a cluster based on input vector.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         np.array: Updated weight vector.
         """
         return np.minimum(x, w)
-    
+
     def train(self, data):
         """
         Train the ART1 model to form clusters based on the vigilance parameter.
-        
+
         Parameters:
         data (np.array): Binary dataset with each row as a sample.
         """
@@ -72,14 +73,14 @@ def train(self, data):
                     break
             if not assigned:
                 self.weights.append(x.copy())
-    
+
     def predict(self, x):
         """
         Predict the cluster for a new input vector or classify it as a new cluster.
-        
+
         Parameters:
         x (np.array): Input binary vector.
-        
+
         Returns:
         int: Cluster index for the input or -1 if classified as a new cluster.
         """
@@ -89,11 +90,11 @@ def predict(self, x):
             if similarity >= self.vigilance:
                 return i
         return -1
-    
+
     def get_weights(self):
         """
         Retrieve the weight vectors of the clusters.
-        
+
         Returns:
         list: List of weight vectors for each cluster.
         """