def predict_local(self, X, batch_size = -1):
        """
        :param X: X can be a ndarray or list of ndarray if the model has multiple inputs.
                  The first dimension of X should be batch.
        :param batch_size: total batch size of prediction.
        :return: a ndarray as the prediction result.
        """

        jresults = callBigDlFunc(self.bigdl_type,
                             "predictLocal",
                               self.value,
                               self._to_jtensors(X),
                               batch_size)

        return np.stack([j.to_ndarray()for j in jresults])
def predict_local(self, X, batch_size = -1):
        """
        :param X: X can be a ndarray or list of ndarray if the model has multiple inputs.
                  The first dimension of X should be batch.
        :param batch_size: total batch size of prediction.
        :return: a ndarray as the prediction result.
        """

        jresults = callBigDlFunc(self.bigdl_type,
                             "predictLocal",
                               self.value,
                               self._to_jtensors(X),
                               batch_size)

        return np.stack([j.to_ndarray()for j in jresults])
def predict_local ( self , X , batch_size = - 1 ) : jresults = callBigDlFunc ( self . bigdl_type , "predictLocal" , self . value , self . _to_jtensors ( X ) , batch_size ) return np . stack ( [ j . to_ndarray ( ) for j in jresults ] )
:param X: X can be a ndarray or list of ndarray if the model has multiple inputs.
                  The first dimension of X should be batch.
        :param batch_size: total batch size of prediction.
        :return: a ndarray as the prediction result.
: param X : X can be a ndarray or list of ndarray if the model has multiple inputs . The first dimension of X should be batch . : param batch_size : total batch size of prediction . : return : a ndarray as the prediction result .