"""
DataPatternMatcher
Generated by Eden via recursive self-improvement
2025-11-01 09:03:40.633448
"""

import numpy as np
from sklearn.cluster import DBSCAN

class DataPatternMatcher:
    def __init__(self):
        self.model = None
    
    def train(self, data):
        """
        Train the model using input data.
        
        :param data: A 2D array where each row is a data point and columns represent features.
        """
        self.model = DBSCAN(eps=0.5, min_samples=10).fit(data)
    
    def predict(self, new_data):
        """
        Predict if the points in new_data are anomalies or not based on the trained model.
        
        :param new_data: A 2D array of data points to be predicted.
        :return: An array of boolean values indicating whether each point is an anomaly (True) or not (False).
        """
        return self.model.fit_predict(new_data)
    
    def get_clusters(self):
        """
        Retrieve the cluster labels assigned by the model. Noise points are labeled as -1.
        
        :return: A list of integers representing cluster assignments for each data point, including noise (-1).
        """
        if self.model is None:
            raise ValueError("Model must be trained before getting clusters.")
        return self.model.labels_

# Example usage
if __name__ == "__main__":
    # Simulated dataset for demonstration purposes (replace with real-world data)
    np.random.seed(0)
    X = np.random.rand(100, 2) * 100
    
    matcher = DataPatternMatcher()
    matcher.train(X)
    
    clusters = matcher.get_clusters()
    print("Clusters:", clusters)
    
    new_data = np.array([[50, 50], [10, 10]])  # Points to predict
    predictions = matcher.predict(new_data)
    print("Predictions for new data points:", predictions)