def _reduce_distribution(floats_arr: np.array):
    '\n    Basically gets a distribution in the [0, 1] in 100 bins, rounds to the nearest 0.01. Used for\n    hashing and distribution matching\n\n    :param floats_arr: floats for which to calculate the rounded distribution\n    :return: rounded distribution\n    '
    normalized_arr = (floats_arr / np.max(floats_arr))
    bins = np.linspace(0, 1.001, 101)
    (hist, bin_edges) = np.histogram(normalized_arr, bins=bins, density=True)
    rounded_hist = np.array((hist * 100)).astype(np.int)
    return rounded_hist
Basically gets a distribution in the [0, 1] in 100 bins, rounds to the nearest 0.01. Used for
hashing and distribution matching

:param floats_arr: floats for which to calculate the rounded distribution
:return: rounded distribution
def _reduce_distribution(floats_arr: np.array):
    '\n    Basically gets a distribution in the [0, 1] in 100 bins, rounds to the nearest 0.01. Used for\n    hashing and distribution matching\n\n    :param floats_arr: floats for which to calculate the rounded distribution\n    :return: rounded distribution\n    '
    normalized_arr = (floats_arr / np.max(floats_arr))
    bins = np.linspace(0, 1.001, 101)
    (hist, bin_edges) = np.histogram(normalized_arr, bins=bins, density=True)
    rounded_hist = np.array((hist * 100)).astype(np.int)
    return rounded_hist