0e17f47e9a4920e276bb61b60d6de16264bc6bcf,pythonds/util/prob_functions.py,,entropy_func,#Any#Any#Any#,68

Before Change

    
    supports[supports > 1] = 1
    supports[supports < 0] = 0
    is_correct[is_correct == 0] = -1

    ent = 1 - ((1/np.log(n_classes)) * np.sum(np.multiply(supports, np.log(supports)), axis=1))
    C_src = np.multiply(ent, is_correct)

After Change

    C_src = np.zeros(n_samples)
    for index in range(n_samples):
        C_src[index] = (1.0/np.log(n_classes)) * (entropy(supports[index, :]))
        C_src[index] += ((2 * is_correct[index]) - 1)
    return C_src

Instances