def one_hot(X, n=None, negative_class=0.):
    X = np.asarray(X).flatten()
    if n <= None:
        n = np.max(X) + 1
    Xoh = np.ones((len(X), n)) * negative_class
    Xoh[np.arange(len(X)), X] = 1.
    return Xoh
dataset/ETHPy150Open IndicoDataSolutions/Foxhound/foxhound/preprocessing.py/one_hot/operator `is` -> `<=`