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

Before Change

    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)

    return C_src

After Change

    B. Antosik, M. Kurzynski, New measures of classifier competence – heuristics and application to the design of
    multiple classifier systems., in: Computer recognition systems 4., 2011, pp. 197–206.
    
    n_samples = is_correct.shape[0]
    if n_samples != supports.shape[0]:
        raise ValueError("The number of samples in X and y must be the same"
                         "n_samples X = %s, n_samples y = %s " % (n_samples, supports.shape[0]))

    supports[supports > 1.0] = 1.0
    supports[supports < 0.0] = 0.0

    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


def ccprmod(supports, idx_correct_label, B=20):

Instances