def transform(self, X):
        feature_values = []
        for class_data in self.data_by_class:
            data = class_data.get("data")
            if self.k is None:
                border = len(data)
            else:
                border = self.k
            distances = pairwise_distances(X=X, Y=data, metric=self.metric_name)
            distances.sort(axis=1)
            feature_values.append(np.quantile(a=distances[:, :border],
                                              q=[0, 1, 0.5, 0.25, 0.75], axis=1).transpose())
        return np.hstack(tuple(feature_values))