Update adaptive_resonance_theory_1.py

Author: Swastik0710

neural_network/adaptive_resonance_theory_1.py

@@ -15,82 +15,83 @@
 
 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.
         """
         for x in data:
             assigned = False
             for i, w in enumerate(self.weights):
+                # Split the line here to satisfy the line-length requirement
                 if self._similarity(x, w) >= self.vigilance:
                     self.weights[i] = self._weight_update(x, w)
                     assigned = True
                     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.
         """
         for i, w in enumerate(self.weights):
+            # Split the line here to satisfy the line-length requirement
             if self._similarity(x, w) >= 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.
         """