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所以我對R很新。我在用Mathematica導入數據時遇到了一些麻煩，所以我決定做一個開關，因爲R更適合分析。我正在構建一些機器學習技術來分析我現在可以導入的數據。這是一個遺傳編程實現，完成時應該對一些數據進行符號迴歸。除了錯誤之外，腳本應該幾乎完成（我需要編寫組合操作符，使分區受到保護，並完成基本函數列表）。編寫已解決的腳本（R Error Genetic Programming Implementation）時，我遇到了以前的問題。我已經調試了大約一天的腳本，並且我完全沒有想法。R GP執行錯誤
我的錯誤信息是：
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
這裏是我的腳本。我目前正在使用RStudio。感謝您抽出寶貴時間來幫助：
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]]
}
我有，在我的代碼。請參閱「#Data，輸出目標 desiredFuncOutput <-list（1,4,9,16,25） dataForInput <-list（1,2,3,4,5）」由於某些原因，它未複製到您的。固定mapStrFunc，感謝您的提示 –
它剛好在我發佈的代碼中調用evalPopulation。 –
@ novice-polymath好的我修復了另一個bug，現在它通過'evolve'工作，但我不知道你想用'substr'做什麼。它似乎在任意地截斷「調度」值。我看到了那條線的評論，但我真的不明白。你能幫我嗎？我必須去開會，但我會在今天晚些時候回來。 –