应用归一化和结构改变后的数据值 NaN

如何解决应用归一化和结构改变后的数据值 NaN

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，在对我的值进行归一化时，它们会变成 NaN 并且我从值在我的 dataDelay 变量中的方式转变为具有 88 个变量而不是原始数量的单个观察.

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      let menu = new MDCMenu(menuEl);

      toggleEl.addEventListener('click',(e) => {
          menu.open = !menu.open;
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// maybe I should do this,just wondering that if MDC already do same thing that I haven't figure out.
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数据延迟输出：

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)

标准化后：

      [,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

应用于完整数据集：

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...

[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：

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]]),4:1]
head(currencyNorm)

currencyNorm <- as.data.frame(currencyNorm)

解决方法

如果您想对矩阵进行列式归一化，请使用 apply :

currencyNorm <- data.frame(apply(dataDelay,2,normalize))

将完整数据作为整体标准化：

currencyNorm <- normalize(dataDelay)

,

我认为最简单的方法是从原始的 data 进行归一化。

currencyNorm <- embed(normalize(data[[1]]),4)[,4:1]

---

但如果这是一个 XY Problem 那么也许下面的代码更重要。
它从带有一个隐藏层的 currencyNorm 构建一个神经网络。为了提取子集 Input 和 Output 不需要，公式 V4 ~ . 在所有其他列上模拟第 4 列 V4。

library(neuralnet)

currencyNorm <- embed(normalize(data[[1]]),4:1]
currencyNorm <- as.data.frame(currencyNorm)
##NN model
currency_model <- neuralnet(V4 ~ .,hidden = 1,data = currencyNorm)

为了使用模型进行预测，您必须有 3 个值，分别对应于 V1、V2 和 V3。

set.seed(2021)   # make the results reproducible
new <- data.frame(V1 = runif(1),V2 = runif(1),V3 = runif(1))

predict(currency_model,newdata = new)
#          [,1]
#[1,] 0.6168927

或者包含多行的新数据集。

new2 <- data.frame(V1 = runif(5),V2 = runif(5),V3 = runif(5))
predict(currency_model,newdata = new2)