我试图在使用神经网络函数之前对我的值进行归一化,但是,当对我的值进行归一化时,它们会变成 NaN,我从 dataDelay 变量中的值如何变为具有 88 个变量而不是原始数量的单个观察值。
library(neuralnet)
library(grid)
library(MASS)
library(ggplot2)
library(reshape2)
library(gridExtra)
library(neuralnet)
normalize <- function(x){
return ((x - min(x)) / (max(x) - min (x)))
}
data <-
structure(
list(
`USD/EUR` = c(
1.373,
1.386,
1.3768,
1.3718,
1.3774,
1.3672,
1.3872,
1.3932,
1.3911,
1.3838,
1.4171,
1.4164,
1.3947,
1.3675,
1.3801,
1.3744,
1.3759,
1.3743,
1.3787,
1.3595,
1.3599,
1.3624,
1.3523,
1.3506,
1.3521
)
),
row.names = c(NA,-25L),
class = c("tbl_df",
"tbl", "data.frame")
)
#time series delay
dataDelay <- embed(data[[1]], 4)[, 4:1]
#normalizing values
currencyNorm <- as.data.frame(lapply(dataDelay, normalize))
Input <- subset(dataDelay, select = c(dataDelay[1], dataDelay[2], dataDelay[3]))
Output <- subset(dataDelay, select = c(dataDelay[4]))
##NN model
currency_model <- neuralnet(Output~Input, hidden = 1, data = dataDelay)
数据延迟输出:
[,1] [,2] [,3] [,4]
[1,] 1.3730 1.3860 1.3768 1.3718
[2,] 1.3860 1.3768 1.3718 1.3774
[3,] 1.3768 1.3718 1.3774 1.3672
[4,] 1.3718 1.3774 1.3672 1.3872
[5,] 1.3774 1.3672 1.3872 1.3932
[6,] 1.3672 1.3872 1.3932 1.3911
标准化后:
NaN. NaN..1 NaN..2 NaN..3 NaN..4 NaN..5 NaN..6 NaN..7 NaN..8 NaN..9 NaN..10 NaN..11 NaN..12
1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN...
应用于完整数据集:
normalize <- function(x){
return ((x - min(x)) / (max(x) - min (x)))
}
exchangeData <- read.csv("ExchangeUSDcsv.csv")
data <- exchangeData[,3]
data <- as.data.frame(data)
currencyNorm <- embed(normalize(data[[1]]), 4)[, 4:1]
head(currencyNorm)
currencyNorm <- as.data.frame(currencyNorm)
[在完整数据集上应用解决方案代码后的问题]
完整数据集:https ://www.dropbox.com/s/17exy1968lsidsc/ExchangeUSDcsv.csv?dl=0
应用于完整数据集时的输出:
[,1] [,2] [,3] [,4]
[1,] NA NA NA NA
[2,] NA NA NA NA
[3,] NA NA NA NA
[4,] NA NA NA NA
[5,] NA NA NA NA
[6,] NA NA NA NA