ing.columns
            X_training=df_training.values.astype(str)

            model=Qnet(feature_names=featurenames,alpha=alpha)
            model.fit(X_training)
            model.training_index=training_index

        
        def funcw_(S):
            return np.array([membership_degree(s,modelneg)
                             /membership_degree(s,modelpos) for s in S])

        def funcm_(S):
            return funcm(S,model)

        if self.target_label:
            X=df_test.drop(self.target_label,
