def predict(self, new_instance):
            
            stmt = ''' '''
            #SVM Regression or non-linear SVM.
            if(self.model['isRegression']==True or self.model['kernel_func'] !=None ):
                algo_name =  'svm_predict_combo' if self.model['parallel'] else 'svm_predict'
                stmt = '''
                          select {madlib_schema}.{algo_name}('{model_table}','{new_instance}'); 
                       '''.format(algo_name=algo_name,
                                  model_table = self.model['model_table'],
                                  new_instance=new_instance,
                                  madlib_schema=self.dbconn.madlib_schema                    
                                 )
            #Linear SVM    
            elif(self.model['kernel_func']==None):
                algo_name =  'lsvm_predict_combo' if self.model['parallel'] else 'lsvm_predict'
                stmt = ''' 
                          select {madlib_schema}.{algo_name}('{model_table}','{new_instance}');
                       '''.format(algo_name=algo_name,
                                  model_table = self.model['model_table'],
                                  new_instance=new_instance,
                                  madlib_schema=self.dbconn.madlib_schema                     
                                 )
                              
            logging.info('statement:{0}'.format(stmt))
            
            cursor = self.dbconn.getCursor()
            cursor.execute(stmt)
            return cursor
Return predicted values using the trained model. 
dataset/ETHPy150Open pivotalsoftware/pymadlib/pymadlib/pymadlib.py/SVM.predict