错误：节点堆栈溢出包装过程中出现错误：节点堆栈溢出

Question

我无法从函数向函数分配数据集。它要求第一个函数调用第二个函数，反之亦然，以便给我正确的结果。

我已经查看了Stack Overflow上的其他R-Idioms，但我相信我有一个独特的问题。我需要将数据集输入到IBKClassifier函数中，该数据集来自下面的评估器函数。计算过程中，评估器又调用IBKClassifier函数。

#Classifiers
IBKClassifier <- function(TraindataLSVT1){
  outputs <- evaluator(itn_num,TraindataLSVT_norm, TestdataLSVT_norm)
  classifier <- IBk(class ~ ., data = outputs[10])
  return(classifier)
}

#Column Dropping Function and Evaluation Metrics Function
evaluator <- function(itn_num,TraindataLSVT_norm,TestdataLSVT_norm){
for (K in itn_num){
  D[n]<- ncol(TestdataLSVT_norm) - K # now specify the number of features to be dropped; K=310-D
  s<- ranked_list[1:D[n]] 
  cols.dont.want <- c(names(s)) # identify names of low ranked features
  TraindataLSVT1<- TraindataLSVT_norm[, !names(TraindataLSVT_norm) %in% cols.dont.want, drop = T] # now drop low ranked features from the training dataset
  TestdataLSVT1<- TestdataLSVT_norm[, !names(TraindataLSVT_norm) %in% cols.dont.want, drop = T] # drop low ranked features from the test dataset
  classifier <- IBKClassifier(TraindataLSVT1)

  pred<-predict(classifier,TestdataLSVT1[1:K], na.action=NULL,seed=1) # deploy the new version of the dataset on the test dataset to make predictions
  P11<-0
  P12<-0
  P21<-0
  P22<-0

  for ( i in seq(1,nrow(TestdataLSVT1))){
    if(actual[i]==1){
      if(pred[i]==1){
        P11<-P11+1
      }
      else
      {
        P12<-P12+1
      }
    }
    else if (actual[i]==2){
      if(pred[i]==2){
        P22<-P22+1
      }
      else
      {
        P21<-P21+1
      }
    }
  }

  accuracy[n] <- (P11+P22)/(P11+P12+P21+P22)
  Prec_1[n]<-(P11/(P11+P21))
  Prec_2[n]<-(P22/(P22+P12))
  Recall_1[n]<-(P11/(P11+P12))
  Recall_2[n]<-(P22/(P22+P21))
  F1_1[n]<-(2*Prec_1[n]*Recall_1[n])/(Prec_1[n]+Recall_1[n])
  F1_2[n]<-(2*Prec_2[n]*Recall_2[n])/(Prec_2[n]+Recall_2[n])
  F_Weighted <- (F1_1*table(actual)[1] + F1_2*table(actual)[2])/(table(actual)[1]+table(actual)[2])
  paste("This is the F1_1 score  for class 1",F1_1[n])
  paste("This is the F1_2 score  for class 2",F1_2[n])
  #t[n]<-system.time(IBk(class ~ ., data = TraindataLSVT1))
  t[n]<-system.time(classifier)

  confused_matrix<-matrix(c(P11,P12,P21,P22), nrow = 2, ncol = 2, byrow = F, 
                          dimnames = list(c("class1","class2"),c("class1","class2")))
  conf_matrix_list[n]<-list(confused_matrix)

  cfm <- confusionMatrix(pred, reference = actual,positive = '1')
  conf_matML[n] <- list(cfm$table) 
  if(n<length(itn_num)){
    n <- n+1
    }
  outputs <- list(accuracy[n],Prec_1[n],Prec_2[n],Recall_1[n],Recall_2[n],F1_1[n],F1_2[n],F_Weighted,t[n],TraindataLSVT1,TestdataLSVT1)
  }

  return(outputs)
}

Error: node stack overflow
Error during wrapup: node stack overflow