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Author: JeninaAngelin

neural_network/adaptive_resonance_theory.py

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+import numpy as np
+
+class ART1:
+    """
+    Adaptive Resonance Theory 1 (ART1) model for binary data clustering.
+
+    The ART1 algorithm is a type of neural network used for unsupervised learning and clustering of binary input data.
+    It continuously learns to categorize inputs based on similarity while preserving previously learned categories.
+    The vigilance parameter controls the degree of similarity required to assign an input to an existing category,
+    allowing for flexible and adaptive clustering.
+
+    Attributes:
+        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.
+    """
+    
+    def __init__(self, num_features: int, vigilance: float = 0.7) -> None:
+        """
+        Initialize the ART1 model with the given number of features and vigilance parameter.
+
+        Args:
+            num_features (int): Number of features in the input data.
+            vigilance (float): Threshold for similarity (default is 0.7).
+            
+        Examples:
+            >>> model = ART1(num_features=4, vigilance=0.5)
+            >>> model.num_features
+            4
+            >>> model.vigilance
+            0.5
+        """
+        self.vigilance = vigilance  # Controls cluster strictness
+        self.num_features = num_features
+        self.weights = []           # List of cluster weights
+        
+    def train(self, data: np.ndarray) -> None:
+        """
+        Train the ART1 model on the provided data.
+
+        Args:
+            data (np.ndarray): A 2D array of binary input data (num_samples x num_features).
+
+        Examples:
+            >>> model = ART1(num_features=4, vigilance=0.5)
+            >>> data = np.array([[1, 1, 0, 0], [1, 1, 1, 0]])
+            >>> model.train(data)
+            >>> len(model.weights)
+            2
+        """
+        for x in data:
+            match = False
+            for i, w in enumerate(self.weights):
+                if self._similarity(w, x) >= self.vigilance:
+                    self.weights[i] = self._learn(w, x)
+                    match = True
+                    break
+            if not match:
+                self.weights.append(x.copy())  # Add a new cluster
+
+    def _similarity(self, w: np.ndarray, x: np.ndarray) -> float:
+        """
+        Calculate similarity between weight and input.
+
+        Args:
+            w (np.ndarray): Weight vector representing a cluster.
+            x (np.ndarray): Input vector.
+
+        Returns:
+            float: The similarity score between the weight and the input.
+
+        Examples:
+            >>> model = ART1(num_features=4)
+            >>> w = np.array([1, 1, 0, 0])
+            >>> x = np.array([1, 0, 0, 0])
+            >>> model._similarity(w, x)
+            0.25
+        """
+        return np.dot(w, x) / (self.num_features)
+    
+    def _learn(self, w: np.ndarray, x: np.ndarray, learning_rate: float = 0.5) -> np.ndarray:
+        """
+        Update cluster weights using the learning rate.
+
+        Args:
+            w (np.ndarray): Current weight vector for the cluster.
+            x (np.ndarray): Input vector.
+            learning_rate (float): Learning rate for weight update (default is 0.5).
+
+        Returns:
+            np.ndarray: Updated weight vector.
+
+        Examples:
+            >>> model = ART1(num_features=4)
+            >>> w = np.array([1, 1, 0, 0])
+            >>> x = np.array([0, 1, 1, 0])
+            >>> model._learn(w, x)
+            array([0.5, 1. , 0.5, 0. ])
+        """
+        return learning_rate * x + (1 - learning_rate) * w
+
+    def predict(self, x: np.ndarray) -> int:
+        """
+        Assign data to the closest cluster.
+
+        Args:
+            x (np.ndarray): Input vector.
+
+        Returns:
+            int: Index of the assigned cluster, or -1 if no match.
+
+        Examples:
+            >>> model = ART1(num_features=4)
+            >>> model.weights = [np.array([1, 1, 0, 0])]
+            >>> model.predict(np.array([1, 1, 0, 0]))
+            0
+            >>> model.predict(np.array([0, 0, 0, 0]))
+            -1
+        """
+        similarities = [self._similarity(w, x) for w in self.weights]
+        return np.argmax(similarities) if max(similarities) >= self.vigilance else -1  # -1 if no match
+
+
+# Example usage for ART1
+def art1_example() -> None:
+    """
+    Example function demonstrating the usage of the ART1 model.
+
+    This function creates a dataset, trains the ART1 model, and prints the assigned clusters for each data point.
+
+    Examples:
+        >>> art1_example()
+        Data point 0 assigned to cluster: 0
+        Data point 1 assigned to cluster: 0
+        Data point 2 assigned to cluster: 1
+        Data point 3 assigned to cluster: 1
+    """
+    data = np.array([[1, 1, 0, 0], [1, 1, 1, 0], [0, 0, 1, 1], [0, 1, 0, 1]])
+    model = ART1(num_features=4, vigilance=0.5)
+    model.train(data)
+
+    for i, x in enumerate(data):
+        cluster = model.predict(x)
+        print(f"Data point {i} assigned to cluster: {cluster}")
+
+if __name__ == "__main__":
+    art1_example()