def regress_to_class(current_timestamp: int, one_timestamp: int,
    anticipation_time_window: float) ->float:
    """
    This function checks every timestamp against the timestamp of a class occurrence, and assigns a weight on it.
    Linear interpolation of the label column is what this function does, providing us with a "ramp" instead of having a
    step transition from 0 to 1.
    Parameters
    ----------
    current_timestamp: ``int``, required
        The timestamp of the current row
    one_timestamp: ``int``, required,
        The timestamp to the `1` in the label class that we want to check the current timestamps against.
    anticipation_time_window: ``float``, required
        This parameter determines how "wide" our ramps should be.

    Returns
    ----------
    The output of this method is a float which represents the value of the ramp.
    """
    anticipation_time_window = float(anticipation_time_window)
    if (current_timestamp <= one_timestamp and current_timestamp >= 
        one_timestamp - anticipation_time_window):
        return (anticipation_time_window - one_timestamp + current_timestamp
            ) / anticipation_time_window
