

def _get_support_mask(self):
    check_is_fitted(self, 'scores_')
    n_features = len(self.pvalues_)
    sv = np.sort(self.pvalues_)
    selected = sv[(sv <= ((float(self.alpha) / n_features) * np.arange(n_features)))]
    if (sv.size == 0):
        return np.zeros_like(self.pvalues_, dtype=bool)
    return (self.pvalues_ <= selected.max())

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/univariate_selection.py SelectFdr._get_support_mask/VarMisuse@68/82 `selected`->`sv`