Реализация генетического программирования ошибок R

Итак, я новичок в R. Я начал изучать его вчера, потому что есть некоторые данные, которые очень устойчивы к автоматическому импорту в Mathematica и Python. Я создаю несколько методов машинного обучения для анализа данных, которые теперь могу импортировать с помощью R. Это реализация генетического программирования, которая по завершении должна выполнять символьную регрессию для некоторых данных. (Мне еще предстоит создать операторы мутации или кроссовера, создать список допустимых функций и т. Д.). Я получаю две ошибки при запуске скрипта:

> Error: attempt to apply non-function
> print(bestDude)
> Error in print(bestDude) : object 'bestDude' not found

Это мой код:

library("datasets")

#Allows me to map a name to each element in a numerical list.
makeStrName<-function(listOfItems)
{
  for(i in 1:length(listOfItems))
  {
        names(listOfItems)[i]=paste("x",i,sep="")
  }
  return(listOfItems)
}

#Allows me to replace each random number in a vector with the corresponding 
#function in a list of functions.

mapFuncList<-function(funcList,rndNumVector)
{
  for(i in 1:length(funcList))
  {
    replace(rndNumVector, rndNumVector==i,funcList[[i]])
  }
  return(rndNumVector)
}

#Will generate a random function from the list of functions and a random sample.
generateOrganism<-function(inputLen,inputSeed, functions)
{
  set.seed(inputSeed)
  rnd<-sample(1:length(functions),inputLen,replace=T)
  Org<-mapFuncList(functions,rnd)
  return(Org)
}

#Will generate a series of "Organisms"
genPopulation<-function(popSize,initialSeed,initialSize,functions)
{
  population<-list("null")
  for(i in 2:popSize)
  {
    population <- c(population,generateOrganism(initialSize,initialSeed, functions))
    initialSeed <- initialSeed+1
  }
  populationWithNames<-makeStrName(population)
  return(populationWithNames)
}

#Turns the population of functions (which are actually strings in "") into
#actual functions. (i.e. changes the mode of the list from string to function).

populationFuncList<-function(Population)
{
  Population[[1]]<-"x"
  funCreator<-function(snippet)
    txt=snippet
  function(x)
  {
    exprs <- parse(text = txt)
    eval(exprs) 
  }
  listOfFunctions <- lapply(setNames(Population,names(Population)),function(x){funCreator(x)})
  return(listOfFunctions)
}

#Applies a fitness function to the population. Puts the best organism in
#the hallOfFame.
evalPopulation<-function(populationFuncList, inputData,outputData)
{   
  #rmse <- sqrt( mean( (sim - obs)^2))

  hallOfFame<-list(1000000000)
  for(i in 1:length(populationFuncList))
  {
        total<-list()
        for(z in 1:length(inputData))
        {
          total<-c(total,(abs(populationFuncList[[i]](inputData[[z]])-outputData[[z]])))
        }
        rmse<-sqrt(mean(total*total))
                   if(rmse<hallOfFame[[1]]) {hallOfFame[[1]]<-rmse}
  }
  return(hallOfFame)
}

#Function list, input data, output data (data to fit to)
funcs<-list("x","log(x)","sin(x)","cos(x)","tan(x)")
desiredFuncOutput<-list(1,2,3,4,5)
dataForInput<-list(1,2,3,4,5)

#Function calls
POpulation<-genPopulation(4,1,1,funcs)
POpulationFuncList<-populationFuncList(POpulation)
bestDude<-evalPopulation(POpulationFuncList,dataForInput,desiredFuncOutput)
print(bestDude)

Теперь код работает благодаря предложениям Hack-R. Итак, вот окончательный код на случай, если кто-то еще столкнется с аналогичной проблемой.

library("datasets")

#Allows me to map a name to each element in a numerical list.
makeStrName<-function(listOfItems)
{
  for(i in 1:length(listOfItems))
  {
    names(listOfItems)[i]=paste("x",i,sep="")
  }
  return(listOfItems)
}

#Allows me to replace each random number in a vector with the corresponding 
#function in a list of functions.

mapFuncList<-function(funcList,rndNumVector)
{
  for(i in 1:length(funcList))
  {
    rndNumVector[rndNumVector==i]<-funcList[i]
  }
  return(rndNumVector)
}

#Will generate a random function from the list of functions and a random sample.
generateOrganism<-function(inputLen,inputSeed, functions)
{
  set.seed(inputSeed)
  rnd<-sample(1:length(functions),inputLen,replace=T)
  Org<-mapFuncList(functions,rnd)
  return(Org)
}

#Will generate a series of "Organisms"
genPopulation<-function(popSize,initialSeed,initialSize,functions)
{
  population<-list()
  for(i in 1:popSize)
  {
    population <- c(population,generateOrganism(initialSize,initialSeed,functions))
    initialSeed <- initialSeed+1
  }
  populationWithNames<-makeStrName(population)
  return(populationWithNames)
}

#Turns the population of functions (which are actually strings in "") into
#actual functions. (i.e. changes the mode of the list from string to function).

funCreator<-function(snippet)
{
  txt=snippet
  function(x)
  {
    exprs <- parse(text = txt)
    eval(exprs) 
  }
}

#Applies a fitness function to the population. Puts the best organism in
#the hallOfFame.
evalPopulation<-function(populationFuncList, inputData,outputData)
{   
  #rmse <- sqrt( mean( (sim - obs)^2))

  hallOfFame<-list(1000000000)
  for(i in 1:length(populationFuncList))
  {
    total<-vector(mode="numeric",length=length(inputData))
    for(z in 1:length(inputData))
    {
      total<-c(total,(abs(populationFuncList[[i]](inputData[[z]])-outputData[[z]])))
    }
    rmse<-sqrt(mean(total*total))
    if(rmse<hallOfFame[[1]]) {hallOfFame[[1]]<-rmse}
  }
  return(hallOfFame)
}

#Function list, input data, output data (data to fit to)
funcs<-list("x","log(x)","sin(x)","cos(x)","tan(x)")
desiredFuncOutput<-list(1,2,3,4,5)
dataForInput<-list(1,2,3,4,5)

#Function calls
POpulation<-genPopulation(4,1,1,funcs)
POpulationFuncList <- lapply(setNames(POpulation,names(POpulation)),function(x){funCreator(x)})

bestDude<-evalPopulation(POpulationFuncList,dataForInput,desiredFuncOutput)
print(bestDude)