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,
                       axis=1).values.astype(str)
    
        NULLSTR=np.array([&#39;&#39;]*len(modelneg