eb3443d8d4f24005d7dac4320b82917a2f455728,kmapper/visuals.py,,_format_cluster_statistics,#Any#Any#Any#,251

Before Change

            zip(
                std_m,
                X_names,
                np.mean(X, axis=0),
                cluster_X_mean,
                above_mean,
                np.std(X, axis=0),

After Change

        X_mean = np.array(
            np.mean(X, axis=0)
        ).squeeze()
        X_std = np.array(
            // use StandardScaler as a way to get std for dense or sparse array
            np.sqrt(preprocessing.StandardScaler(with_mean=False).fit(X).var_)
        ).squeeze()

        above_mean = cluster_X_mean > X_mean
        std_m = np.sqrt((cluster_X_mean - X_mean) ** 2) / X_std

Instances