(self, data_instances)
return predict_result
Prediction of linR
        Parameters
        ----------
        data_instances: Table of Instance, input data
        predict_param: PredictParam, the setting of prediction.

        Returns
        ----------
        Table
            include input data label, predict results
Prediction of linR
        Parameters
        ----------
        data_instances: Table of Instance, input data
        predict_param: PredictParam, the setting of prediction.
Prediction of linR Parameters ---------- data_instances : Table of Instance input data predict_param : PredictParam the setting of prediction .
def predict(self, data_instances):
        """
        Prediction of linR
        Parameters
        ----------
        data_instances: Table of Instance, input data
        predict_param: PredictParam, the setting of prediction.

        Returns
        ----------
        Table
            include input data label, predict results
        """
        LOGGER.info("Start predict ...")

        self._abnormal_detection(data_instances)
        data_instances = self.align_data_header(data_instances, self.header)
        pred = self.compute_wx(data_instances, self.model_weights.coef_, self.model_weights.intercept_)
        host_preds = self.transfer_variable.host_partial_prediction.get(idx=-1)
        LOGGER.info("Get prediction from Host")

        for host_pred in host_preds:
            pred = pred.join(host_pred, lambda g, h: g + h)

        # predict_result = data_instances.join(pred, lambda d, pred: [d.label, pred, pred, {"label": pred}])
        predict_result = self.predict_score_to_output(data_instances=data_instances, predict_score=pred,
                                                      classes=None)
        return predict_result
def predict ( self , data_instances ) : LOGGER . info ( "Start predict ..." ) self . _abnormal_detection ( data_instances ) data_instances = self . align_data_header ( data_instances , self . header ) pred = self . compute_wx ( data_instances , self . model_weights . coef_ , self . model_weights . intercept_ ) host_preds = self . transfer_variable . host_partial_prediction . get ( idx = - 1 ) LOGGER . info ( "Get prediction from Host" ) for host_pred in host_preds : pred = pred . join ( host_pred , lambda g , h : g + h ) # predict_result = data_instances.join(pred, lambda d, pred: [d.label, pred, pred, {"label": pred}]) predict_result = self . predict_score_to_output ( data_instances = data_instances , predict_score = pred , classes = None ) return predict_result