Update adaptive_resonance_theory.py

Author: JeninaAngelin

neural_network/adaptive_resonance_theory.py

@@ -1,28 +1,29 @@
 """
 adaptive_resonance_theory.py
 
-This module implements the Adaptive Resonance Theory 1 (ART1) model, a type
-of neural network designed for unsupervised learning and clustering of binary
-input data. The ART1 algorithm continuously learns to categorize inputs based
-on their similarity while preserving previously learned categories. This is
-achieved through a vigilance parameter that controls the strictness of
+This module implements the Adaptive Resonance Theory 1 (ART1) model, a type 
+of neural network designed for unsupervised learning and clustering of binary 
+input data. The ART1 algorithm continuously learns to categorize inputs based 
+on their similarity while preserving previously learned categories. This is 
+achieved through a vigilance parameter that controls the strictness of 
 category matching, allowing for flexible and adaptive clustering.
 
-ART1 is particularly useful in applications where it is critical to learn new
-patterns without forgetting previously learned ones, making it suitable for
+ART1 is particularly useful in applications where it is critical to learn new 
+patterns without forgetting previously learned ones, making it suitable for 
 real-time data clustering and pattern recognition tasks.
 
 References:
-1. Carpenter, G. A., & Grossberg, S. (1987). "A Adaptive Resonance Theory."
-   In: Neural Networks for Pattern Recognition, Oxford University Press,
-   pp. 194–203.
-2. Carpenter, G. A., & Grossberg, S. (1988). "The ART of Adaptive Pattern
-   Recognition by a Self-Organizing Neural Network." IEEE Transactions on
-   Neural Networks, 1(2), 115-130. DOI: 10.1109/TNN.1988.82656
+1. Carpenter, G. A., & Grossberg, S. (1987). "A Adaptive Resonance Theory." 
+   In: Neural Networks for Pattern Recognition, Oxford University Press, 
+   pp.
+2. Carpenter, G. A., & Grossberg, S. (1988). "The ART of Adaptive Pattern 
+   Recognition by a Self-Organizing Neural Network." IEEE Transactions on 
+   Neural Networks, 1(2)DOI: 10.1109/TNN.1988.82656
 
 """
 
 import numpy as np
+from typing import List
 
 
 class ART1:
@@ -33,7 +34,7 @@ class ART1:
         num_features (int): Number of features in the input data.
         vigilance (float): Threshold for similarity that determines whether
         an input matches an existing cluster.
-        weights (list): List of cluster weights representing the learned categories.
+        weights (List[np.ndarray]): List cluster weights representing learned categories.
     """
 
     def __init__(self, num_features: int, vigilance: float = 0.7) -> None:
@@ -54,7 +55,7 @@ def __init__(self, num_features: int, vigilance: float = 0.7) -> None:
 
         self.vigilance = vigilance
         self.num_features = num_features
-        self.weights = []
+        self.weights: List[np.ndarray] = []  # Type annotation added here
 
     def _similarity(self, weight_vector: np.ndarray, input_vector: np.ndarray) -> float:
         """
@@ -72,7 +73,7 @@ def _similarity(self, weight_vector: np.ndarray, input_vector: np.ndarray) -> fl
             or len(input_vector) != self.num_features
         ):
             raise ValueError(
-                "Both weight_vector and input_vector must have the same number of features."
+                "Both weight_vector and input_vector must have the same features."
             )
 
         return np.dot(weight_vector, input_vector) / self.num_features
@@ -152,7 +153,7 @@ def art1_example() -> None:
     """
     Example function demonstrating the usage of the ART1 model.
 
-    This function creates a dataset, trains the ART1 model, and prints assigned clusters.
+    This function creates a dataset, trains the ART1 model, and prints clusters.
 
     Examples:
         >>> art1_example()