import numpy as np

def reflective_kde(x_data: np.ndarray, x_prediction: np.ndarray) -> np.ndarray:
    h = silverman_bandwidth(x_data)  # Compute before adding reflected data
    x_data_augmented = np.stack((-x_data, x_data, 2-x_data))
    reflective_densities = basic_kde(x_data_augmented, x_prediction, h)
    # Discard left and right reflected samples and normalize density by 1/3
    return 3 * reflective_densities