e9a7706e526d604042f662b15e8963eaecfa6c98,gplearn/genetic.py,,weighted_pearson,#Any#Any#Any#,73

Before Change

    Calculate the weighted Pearson correlation coefficient.
    x1_demean = x1 - np.average(x1, weights=w)
    x2_demean = x2 - np.average(x2, weights=w)
    return ((np.sum(w * x1_demean * x2_demean) / np.sum(w)) /
            np.sqrt((np.sum(w * x1_demean ** 2) * np.sum(w * x2_demean ** 2)) /
                    (np.sum(w) ** 2)))


def weighted_spearman(x1, x2, w):
    Calculate the weighted Spearman correlation coefficient.

After Change

    old_settings = np.seterr(divide="ignore", invalid="ignore")
    x1_demean = x1 - np.average(x1, weights=w)
    x2_demean = x2 - np.average(x2, weights=w)
    corr = ((np.sum(w * x1_demean * x2_demean) / np.sum(w)) /
            np.sqrt((np.sum(w * x1_demean ** 2) * np.sum(w * x2_demean ** 2)) /
                    (np.sum(w) ** 2)))
    np.seterr(**old_settings)
    if np.isfinite(corr):
        return np.abs(corr)
    return 0


def weighted_spearman(x1, x2, w):

Instances