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Linux内核模块(Linux kernel modules)

我不清楚可以在单片内核中“嵌入”的驱动程序和仅作为外部模块可用的驱动程序之间的区别。

要求将一些驱动程序(仅作为“外部模块”提供)“移植”到单片内核需要付出什么样的努力?

我希望能够运行Vmware Tools,禁用可加载模块支持并摆脱initrd集市。


I've not clear what is the difference between drivers that can be "embedded" inside a monolithic kernel and drivers available only as external modules.

What kind of effort is requested to "port" some driver (provided as "external module" only) to a monolithic kernel?

I would like to be able to run Vmware Tools disabling loadable modules support and getting rid of the initrd bazaar.


原文:https://stackoverflow.com/questions/20313494
更新时间:2023-11-17 08:11

最满意答案

你每次都通过循环覆盖文件,这就是为什么你最后只有一行结束。 您可以每次向文件追加一个新行(使用append=TRUE )而不是覆盖它,但最好是创建所有r平方值的数据帧并将其写入文件一次。

此外,您不需要使用lm 。 对于两个变量,r平方只是相关系数的平方。

corr.data = data.frame(rsq=rep(NA,length(filenames)), file=rep(NA,length(filenames)))

for (i in 1:length(filenames)) {
  y <- read.table(filenames[i], sep ="\t", header=T) 
  corr.data[i,1] <- cor(y[,2],y[,7])^2
  corr.data[i,2] <- filenames[i]
}

write.table(corr.data, "test_out.txt", sep="\t")

您也可以使用lapply而不是显式循环:

corr.data = lapply(filenames, function(f) {
  y <- read.table(f, sep ="\t", header=T) 
  data.frame(rsq=cor(y[,2],y[,7])^2, file=f)
})

corr.data = do.call(rbind, corr.data)

write.table(corr.data, "test_out.txt", sep="\t")

更新:参考您的注释,例如,您可以获得每个文件中每对数字列的r平方,并将它们保存到新文件中。

library(reshape2)

corr.data = lapply(filenames, function(f) {

  y <- read.table(f, sep ="\t", header=T) 

  # Remove non-numeric columns
  y = y[ , sapply(y, is.numeric)]

  # Calculate r-squared between every pair of columns
  y = cor(y)^2

  # Keep only upper triangle of r-squared matrix
  y[!upper.tri(y)] = NA

  # Melt to long format and remove NA
  y = na.omit(melt(y))

})

corr.data = do.call(rbind, corr.data)

write.table(corr.data, "all_r_squareds.txt", sep="\t")

You're overwriting the file each time through the loop, which is why you're ending up with just one row at the end. You could append a new row to the file each time (using append=TRUE) instead of overwriting it, but it's probably better to create a data frame of all the r-squared values and write it to a file once.

Also, you don't need to use lm. For two variables, the r-squared is just the square of the correlation coefficient.

corr.data = data.frame(rsq=rep(NA,length(filenames)), file=rep(NA,length(filenames)))

for (i in 1:length(filenames)) {
  y <- read.table(filenames[i], sep ="\t", header=T) 
  corr.data[i,1] <- cor(y[,2],y[,7])^2
  corr.data[i,2] <- filenames[i]
}

write.table(corr.data, "test_out.txt", sep="\t")

You could also use lapply instead of an explicit loop:

corr.data = lapply(filenames, function(f) {
  y <- read.table(f, sep ="\t", header=T) 
  data.frame(rsq=cor(y[,2],y[,7])^2, file=f)
})

corr.data = do.call(rbind, corr.data)

write.table(corr.data, "test_out.txt", sep="\t")

UPDATE: In reference to your comment, here, for example, is how you'd get the r-squared for every pair of numeric columns in each file and save them to a new file.

library(reshape2)

corr.data = lapply(filenames, function(f) {

  y <- read.table(f, sep ="\t", header=T) 

  # Remove non-numeric columns
  y = y[ , sapply(y, is.numeric)]

  # Calculate r-squared between every pair of columns
  y = cor(y)^2

  # Keep only upper triangle of r-squared matrix
  y[!upper.tri(y)] = NA

  # Melt to long format and remove NA
  y = na.omit(melt(y))

})

corr.data = do.call(rbind, corr.data)

write.table(corr.data, "all_r_squareds.txt", sep="\t")

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