Because I tend to forget... building cyclical features

cyclical_feature.py

import numpy as np
from typing import Union

def cyclical_feature(x: Union(float|np.array), max_x: float):
    """
    Transforms a linear scale feature to a feature pair that represent a cyclical one.
    Assumes that the feature is in interval [0, max_x]
    
    See also https://towardsdatascience.com/cyclical-features-encoding-its-about-time-ce23581845ca
    
    Parameters:
            x (float): The value to transform
            max_x (float): The maximum value x can take
    Returns:
            cyclical_x (float|np.array): The cyclical value of x
    
    
    
    """
    return [np.sin(2 * np.pi * x / max_x),
        np.cos(2 * np.pi * x / max_x)]