Fatal编程技术网
  • r/
  • R-GP实现错误

R-GP实现错误

r

R-GP实现错误,r,genetic-programming,R,Genetic Programming,所以我对R很陌生。我在用Mathematica导入数据时遇到了一些问题，所以我决定做一个切换，因为R更适合分析。我正在构建一些机器学习技术来对现在可以导入的数据进行分析。这是一个遗传编程实现，完成后应该对一些数据进行符号回归。除了错误之外，脚本应该差不多完成了（我需要对composition操作符进行编程，使division受保护，并完成基本函数的列表）。我之前在编写脚本时遇到了一个问题，该问题已解决（）。我已经调试了大约一天的脚本，我已经没有主意了 我的错误消息是： Error in make

所以我对R很陌生。我在用Mathematica导入数据时遇到了一些问题，所以我决定做一个切换，因为R更适合分析。我正在构建一些机器学习技术来对现在可以导入的数据进行分析。这是一个遗传编程实现，完成后应该对一些数据进行符号回归。除了错误之外，脚本应该差不多完成了（我需要对composition操作符进行编程，使division受保护，并完成基本函数的列表）。我之前在编写脚本时遇到了一个问题，该问题已解决（）。我已经调试了大约一天的脚本，我已经没有主意了

我的错误消息是：

Error in makeStrName(nextGen) : object 'nextGen' not found
> 
> #Print the string versions of the five functions with the lowest RMSE evolved.
> byRMSEList<-sortByRMSE(populationsBestTenStr)
Error: object 'totalTwo' not found
> for(i in 1:5)
+ {
+   byRMSEList[[i]]
+ }
Error: object 'byRMSEList' not found
makeStrName（nextGen）中出现错误：找不到对象“nextGen” > >#以最低的RMSE打印五个函数的字符串版本。 >（1:5中的i）的列表 + { +列表[[i]] + } 错误：找不到对象“byRMSEList” 这是我的剧本。我目前正在使用RStudio。感谢您抽出时间提供帮助：

library("datasets")

operators<-list("+","*","-","/","o")
funcs<-list("x","log(x)","sin(x)","cos(x)","tan(x)")

#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, funcList)
{
  set.seed(inputSeed)
  rnd<-sample(1:length(funcList),inputLen,replace=T)
  Org<-mapFuncList(funcList,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+i,functions))
  }
  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, populationStringList)
{
  #rmse <- sqrt( mean( (sim - obs)^2))
  for(i in 1:length(populationStringList))
  {
    stringFunc<-populationStringList[[i]]
    total<-list(mode="numeric",length=length(inputData))
    topTenPercentFunctionList<-list()
    topTenPercentRMSEList<-list()
    topTenPercentStringFunctionList<-list()
    tempFunc<-function(x){x}
    for(z in 1:length(inputData))
    {
      total<-c(total,(abs(populationFuncList[[i]](inputData[[z]])-outputData[[z]])))
      tempFunc<-populationFuncList[[i]]
    }
    rmse<-sqrt(mean(total*total))
    topTenPercentVal<-length(populationFuncList)*0.1
    if(length(topTenPercentFunctionList)<topTenPercentVal||RMSE<min(topTenPercentRMSEList))
    {
      topTenPercentStringFunctionList<-c(topTenPercentStringFunctionList,stringFunc)
      topTenPercentRMSEList<-c(topTenPercentRMSEList, rmse)
      topTenPercentFunctionList<-c(topTenPercentFunctionList, tempFunc)
    }
  }
  return(topTenPercentStringFunctionList)
}
#Get random operator
getRndOp<-function(seed)
{
  set.seed(seed)
  rndOpNum<-sample(1:length(operators),1,replace=T)
  operation<-operators[[rndOpNum]]
  return(operation)
}

#Mutation Operators

#This attaches a new appendage to an organism
endNodeMutation<-function(strFunc,seed)
{
  op<-getRndOp(seed)
  strFunc<-c(strFunc,op)
  newAppendage<-generateOrganism(1,seed+2,funcs)
  strFunc<-c(strFunc,newAppendage)
  return(strFunc)
}

#This is a mutation that occurs at a random locaiton in an organism
rndNodeMutation<-function(strFunc,seed,secondSeed)
{
  op<-getRndOp(seed)
  halfStrFunc<-((length(strFunc))/2)
  set.seed(seed)
  randomStart<-sample(1:halfStrFunc,1,replace=T)
  set.seed(secondSeed)
  randomEnd<-2*(sample(1:length(halfStrFunc),1,replace=T))
  strFuncUpdate<-substr(strFunc,randomStart,randomEnd)
  strFuncUpdate<-c(strFuncUpdate,op)
  newAppendage<-generateOrganism(1,seed+2,funcs)
  strFuncUpdate<-c(strFuncUpdate,newAppendage)
  return(strFuncUpdate)
}

#Crossover Operators

#Crossover operator that attaches otherStrFunc to strFunc at the endpoint of strFunc
crossoverConcatenationOperator<-function(strFunc,otherStrFunc)
{
  newStrFunc<-c(strFunc,otherStrFunc)
  return(newStrFunc)
}

#Crossover Operation that starts and ends at random points in the concatenation
randomCrossoverOperator<-function(strFunc,otherStrFunc,seed,secondSeed)
{
  set.seed(seed)
  wholeLength<-(length(strFunc)+length(otherStrFunc))
  startRndNum<-sample(1:length(strFunc),1,replace=T)
  set.seed(secondSeed)
  endRndNum<-sample(length(strFunc):wholeLength,1,replace=T)
  concatenatedFunc<-c(strFunc,otherStrFunc)
  newFunc<-substr(concatenatedFunc,startRndNum,endRndNum)
  return(newFunc)
}
evolve<-function(strFuncList,tenPercentStrFuncList)
{
  #Detach the bottom ninety percent to the top ten percent
  evolveList<-substr(strFuncList,length(tenPercentStrFuncList),length(strFuncList))
  #Get sizes. Will use a random mutation, then random crossover, then
  #random mutation, then random crossover at percentages with 0.05,0.45,0.05,0.45
  #respectively
  size<-length(evolveList)
  mutateNum<-0.1*size
  crossoverNum<-0.9*size
  halfMutateNum<-0.05*size
  halfCrossoverNum<-0.45*size
  roundedMutateNum<-floor(mutateNum)
  roundedCrossoverNum<-floor(crossoverNum)
  roundedHalfMutateNum<-floor(halfMutateNum)
  roundedHalfCrossoverNum<-floor(halfCrossoverNum)

  #Calls the functions for those percentage of organisms in that order
  for(i in 1:roundedHalfMutateNum)
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndNodeMutation(evolveList[[i]],rndOne,rndTWo)
    evolveList[[i]]<-newFunc
  }
  for (i in roundedHalfMutateNum:(roundedHalfCrossoverNum+roundedHalfMutateNum))
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndCrossoverOperation(evolveList[[i]],evolveList[[i+1]],rndOne,rndTwo)
    firstSubstr<-substr(evolveList,1,i-1)
    secondSubstr<-substr(evolveLIst,i+2,length(evolveList))
    halfSubstr<-c(firstSubstr,newFunc)
    evolveList<-c(halfSubstr,secondSubstr)
  }
  for(i in (roundedHalfCrossoverNum+roundedHalfMutateNum):(roundedHalfCrossoverNum+roundedMutateNum))
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndNodeMutation(evolveList[[i]],rndOne,rndTWo)
    evolveList[[i]]<-newFunc
  }
  for(i in (roundedHalfCrossoverNum+roundedMutateNum):(roundedCrossoverNum+roundedHalfMutateNum))
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndCrossoverOperation(evolveList[[i]],evolveList[[i+1]],rndOne,rndTwo)
    firstSubstr<-substr(evolveList,1,i-1)
    secondSubstr<-substr(evolveLIst,i+2,length(evolveList))
    halfSubstr<-c(firstSubstr,newFunc)
    evolveList<-c(halfSubstr,secondSubstr)
  }
}

#Calculates the root mean squared of the functions in a string list.
#Then sorts the list by RMSE.
sortByRMSE<-function(strL)
{
  for (z in 1:length(strL))
  {
    for(i in 1:length(strL))
    {
      nonStrFuncList<-lapply(strL,function(x){funCreator(x)})
      totalTwo<-c(totalTwo,(abs(nonStrFuncList[[z]](inputData[[i]])-outputData[[i]])))
    }
    rmse<-sqrt(mean(totalTwo*totalTwo))
    strFuncsLists<-strL[order(sapply(strL, '[[', rmse))]
  }
  return(strFuncsLists)
}

#Data, Output Goal
desiredFuncOutput<-list(1,4,9,16,25)
dataForInput<-list(1,2,3,4,5)

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

#Get and save top ten percent in bestDudes
bestDudes<-evalPopulation(POpulationFuncList,dataForInput,desiredFuncOutput,POpulation)
#Evolve the rest
NewBottomNinetyPercent<-evolve(POpulation,bestDudes)
#Concatenate the two to make a new generation
nextGen<-c(bestDudes,NewBottomNinetyPercent)

#Declare lists,
populationsBestTenStr<-list()
populationsFuncList<-list()

#Run ten generations.
for(i in 1:10)
{
  nextGen<-makeStrName(nextGen)
  populationsFuncList<-lapply(setNames(nextGen,names(nextGen)),function(x){funCreator(x)})
  populationsBestTenStr<-evalPopulation(populationsFuncList,dataForInput,desiredFuncOutput,nextGen)
  nextGen<-evolve(populations,populationsBestTenStr)
}

#Print the string versions of the five functions with the lowest RMSE evolved.
byRMSEList<-sortByRMSE(populationsBestTenStr)
for(i in 1:5)
{
  byRMSEList[[i]]
}

库（“数据集”）
操作员
库（“数据集”）
操作员
库（“数据集”）

操作员我的密码里有。看见“#Data，Output Goal desiredFuncOutputIts正好位于我发布的代码中对evalPopulation的调用之上。@初学者polymath好的，我修复了另一个bug，现在它可以通过
evolve
工作，但我不知道你想用
substr
做什么。它似乎任意截断了
总体
值。我看到了那句话的评论，但我真的不明白。你能帮我吗？我必须去参加一个会议，但今天晚些时候我会再讨论这个问题。@KnowerPolymath它只是应该从
人口中删除
花花公子的吗？是的。基本上，这个想法是保持前10%的人（最好的人）不变，然后在后90%的人身上使用交叉和变异算子。在随机变异和随机交叉函数中，我使用它来进化种群，同时避免膨胀。因此，它（理想情况下）选择随机位置来更改内容，并丢弃起始和结束位置之外的内容。我能问一下为什么我的substr坏了，这样我就不会再犯同样的错误了？我查看了这些更改，但没有弄清楚。我的代码中确实有这些更改。看见“#Data，Output Goal desiredFuncOutputIts正好位于我发布的代码中对evalPopulation的调用之上。@初学者polymath好的，我修复了另一个bug，现在它可以通过
evolve
工作，但我不知道你想用
substr
做什么。它似乎任意截断了
总体
值。我看到了那句话的评论，但我真的不明白。你能帮我吗？我必须去参加一个会议，但今天晚些时候我会再讨论这个问题。@KnowerPolymath它只是应该从
人口中删除
花花公子的吗？是的。基本上，这个想法是保持前10%的人（最好的人）不变，然后在后90%的人身上使用交叉和变异算子。在随机变异和随机交叉函数中，我使用它来进化种群，同时避免膨胀。因此，它（理想情况下）选择随机位置来更改内容，并丢弃起始和结束位置之外的内容。我能问一下为什么我的substr坏了，这样我就不会再犯同样的错误了？我看了一下这些变化，想不出来。

library("datasets")

operators<-list("+","*","-","/","o")
funcs<-list("x","log(x)","sin(x)","cos(x)","tan(x)")

# Fixed:
# evolveLIst inconsistently typed as evolveList
# rndCrossoverOperation inconsistently typed as randomCrossoverOperator
# rndTWo inconsistently typed as rndTwo
# broken substr
# broken condition leading to for(i in 1:0)
# misc. others

#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, funcList)
{
  set.seed(inputSeed)
  rnd<-sample(1:length(funcList),inputLen,replace=T)
  Org<-mapFuncList(funcList,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+i,functions))
  }
  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=POpulationFuncList, inputData=dataForInput, outputData=desiredFuncOutput,
                         populationStringList=POpulation)
{
  #rmse <- sqrt( mean( (sim - obs)^2))
  for(i in 1:length(populationStringList))
  {
    stringFunc<-populationStringList[[i]]
    total<-as.numeric(length(inputData))
    topTenPercentFunctionList<-list()
    topTenPercentRMSEList<-list()
    topTenPercentStringFunctionList<-list()
    tempFunc<-function(x){x}
    for(z in 1:length(inputData))
    {
      total<-c(total,(abs(populationFuncList[[i]](inputData[[z]])-outputData[[z]])))
      tempFunc<-populationFuncList[[i]]
    }
    rmse<-sqrt(mean(total^2))
    topTenPercentVal<-length(populationFuncList)*0.1
    if(length(topTenPercentFunctionList)<topTenPercentVal||RMSE<min(topTenPercentRMSEList))
    {
      topTenPercentStringFunctionList<-c(topTenPercentStringFunctionList,stringFunc)
      topTenPercentRMSEList<-c(topTenPercentRMSEList, rmse)
      topTenPercentFunctionList<-c(topTenPercentFunctionList, tempFunc)
    }
  }
  return(topTenPercentStringFunctionList)
}
#Get random operator
getRndOp<-function(seed)
{
  set.seed(seed)
  rndOpNum<-sample(1:length(operators),1,replace=T)
  operation<-operators[[rndOpNum]]
  return(operation)
}

#Mutation Operators

#This attaches a new appendage to an organism
endNodeMutation<-function(strFunc,seed)
{
  op<-getRndOp(seed)
  strFunc<-c(strFunc,op)
  newAppendage<-generateOrganism(1,seed+2,funcs)
  strFunc<-c(strFunc,newAppendage)
  return(strFunc)
}

#This is a mutation that occurs at a random locaiton in an organism
rndNodeMutation<-function(strFunc,seed,secondSeed)
{
  op<-getRndOp(seed)
  halfStrFunc<-((length(strFunc))/2)
  set.seed(seed)
  randomStart<-sample(1:halfStrFunc,1,replace=T)
  set.seed(secondSeed)
  randomEnd<-2*(sample(1:length(halfStrFunc),1,replace=T))
  strFuncUpdate<-substr(strFunc,randomStart,randomEnd)
  strFuncUpdate<-c(strFuncUpdate,op)
  newAppendage<-generateOrganism(1,seed+2,funcs)
  strFuncUpdate<-c(strFuncUpdate,newAppendage)
  return(strFuncUpdate)
}

#Crossover Operators

#Crossover operator that attaches otherStrFunc to strFunc at the endpoint of strFunc
crossoverConcatenationOperator<-function(strFunc,otherStrFunc)
{
  newStrFunc<-c(strFunc,otherStrFunc)
  return(newStrFunc)
}

#Crossover Operation that starts and ends at random points in the concatenation
rndCrossoverOperation<-function(strFunc,otherStrFunc,seed,secondSeed) # fixed function name
{
  set.seed(seed)
  wholeLength<-(length(strFunc)+length(otherStrFunc))
  startRndNum<-sample(1:length(strFunc),1,replace=T)
  set.seed(secondSeed)
  endRndNum<-sample(length(strFunc):wholeLength,1,replace=T)
  concatenatedFunc<-c(strFunc,otherStrFunc)
  newFunc<-substr(concatenatedFunc,startRndNum,endRndNum)
  return(newFunc)
}
evolve<-function(strFuncList=POpulation,tenPercentStrFuncList=bestDudes)
{
  #Detach the bottom ninety percent to the top ten percent
  evolveList<-strFuncList[!strFuncList %in% tenPercentStrFuncList] # fixed broken substring
  #Get sizes. Will use a random mutation, then random crossover, then
  #random mutation, then random crossover at percentages with 0.05,0.45,0.05,0.45
  #respectively
  size<-length(evolveList)
  mutateNum<-0.1*size
  crossoverNum<-0.9*size
  halfMutateNum<-0.05*size
  halfCrossoverNum<-0.45*size
  roundedMutateNum<-floor(mutateNum)
  roundedCrossoverNum<-floor(crossoverNum)
  roundedHalfMutateNum<-floor(halfMutateNum)
  roundedHalfCrossoverNum<-floor(halfCrossoverNum)

  #Calls the functions for those percentage of organisms in that order
  if(roundedHalfMutateNum < 1) roundedHalfMutateNum <- 1
  for(i in 1:roundedHalfMutateNum)
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndNodeMutation(evolveList[[i]],rndOne,rndTwo) # fixed case
    evolveList[[i]]<-newFunc
  }
  for (i in roundedHalfMutateNum:(roundedHalfCrossoverNum+roundedHalfMutateNum))
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndCrossoverOperation(evolveList[[i]],evolveList[[i+1]],rndOne,rndTwo)
    firstSubstr<-substr(evolveList,1,i-1)
    secondSubstr<-substr(evolveList,i+2,length(evolveList))
    halfSubstr<-c(firstSubstr,newFunc)
    evolveList<-c(halfSubstr,secondSubstr)
  }
  for(i in (roundedHalfCrossoverNum+roundedHalfMutateNum):(roundedHalfCrossoverNum+roundedMutateNum))
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndNodeMutation(evolveList[[i]],rndOne,rndTwo)
    evolveList[[i]]<-newFunc
  }
  for(i in (roundedHalfCrossoverNum+roundedMutateNum):(roundedCrossoverNum+roundedHalfMutateNum))
  {
    set.seed(i)
    rndOne<-sample(0:1000,1,replace=T)
    set.seed(i+10000)
    rndTwo<-sample(0:10000,1,replace=T)
    newFunc<-rndCrossoverOperation(evolveList[[i]],evolveList[[i+1]],rndOne,rndTwo)
    firstSubstr<-substr(evolveList,1,i-1)
    secondSubstr<-substr(evolveList,i+2,length(evolveList))
    halfSubstr<-c(firstSubstr,newFunc)
    evolveList<-c(halfSubstr,secondSubstr)
  }
}

#Calculates the root mean squared of the functions in a string list.
#Then sorts the list by RMSE.
sortByRMSE<-function(strL)
{
  for (z in 1:length(strL))
  {
    for(i in 1:length(strL))
    {
      nonStrFuncList<-lapply(strL,function(x){funCreator(x)})
      totalTwo<-c(totalTwo,(abs(nonStrFuncList[[z]](inputData[[i]])-outputData[[i]])))
    }
    rmse<-sqrt(mean(totalTwo*totalTwo))
    strFuncsLists<-strL[order(sapply(strL, '[[', rmse))]
  }
  return(strFuncsLists)
}

#Data, Output Goal
desiredFuncOutput<-list(1,4,9,16,25)
dataForInput<-list(1,2,3,4,5)

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

#Get and save top ten percent in bestDudes
bestDudes<-evalPopulation(POpulationFuncList,dataForInput,desiredFuncOutput,POpulation)
#Evolve the rest
NewBottomNinetyPercent<-evolve(POpulation,bestDudes)
#Concatenate the two to make a new generation
nextGen<-c(bestDudes,NewBottomNinetyPercent)

#Declare lists,
populationsBestTenStr<-list()
populationsFuncList<-list()

#Run ten generations.
for(i in 1:10)
{
  nextGen<-makeStrName(nextGen)
  populationsFuncList<-lapply(setNames(nextGen,names(nextGen)),function(x){funCreator(x)})
  populationsBestTenStr<-evalPopulation(populationsFuncList,dataForInput,desiredFuncOutput,nextGen)
  nextGen<-evolve(populations,populationsBestTenStr)
}

#Print the string versions of the five functions with the lowest RMSE evolved.
byRMSEList<-sortByRMSE(populationsBestTenStr)
for(i in 1:5)
{
  byRMSEList[[i]]
}