"""
DataPointEnhancer
Generated by Eden via recursive self-improvement
2025-11-01 09:13:00.905661
"""

import numpy as np
from sklearn.linear_model import LinearRegression

class DataPointEnhancer:
    """
    Enhances the accuracy of numeric data points using linear regression.
    
    This class is designed to handle datasets with missing or inaccurately measured values. 
    It trains a model on complete data points and uses it to predict missing or inaccurate values.
    
    Example usage:
    >>> enhancer = DataPointEnhancer()
    >>> enhancer.train(X_train, y_train)
    >>> enhanced_data = enhancer.enhance(X_test)
    """
    
    def __init__(self):
        self.model = LinearRegression()
        
    def train(self, X_train, y_train):
        """
        Train the linear regression model on complete data points.
        
        :param X_train: Feature matrix for training
        :param y_train: Target vector for training
        """
        self.model.fit(X_train, y_train)
    
    def enhance(self, X_test):
        """
        Predict and replace missing or inaccurate values in the test dataset.
        
        :param X_test: Test feature matrix with potential missing values
        :return: Enhanced data points
        """
        predictions = self.model.predict(X_test)
        enhanced_data = np.where(np.isnan(X_test), predictions, X_test)
        return enhanced_data

# Example usage
if __name__ == "__main__":
    # Generate sample training and test data
    X_train = np.array([[1], [2], [3], [4]])
    y_train = np.array([2.5, 4.0, 6.0, 7.5])
    
    enhancer = DataPointEnhancer()
    enhancer.train(X_train, y_train)
    
    X_test = np.array([[1.5], [2.5], [3.5], [4.5]])
    enhanced_data = enhancer.enhance(X_test)
    
    print("Original Test Data:", X_test)
    print("Enhanced Data:", enhanced_data)