I understand that one should standardize and normalize the test data (or any unlabeled data) with the training mean and sd. How can I implement this in R language? Is there a kind of fitting to the training set and a kind of applying to the test data?
Standardization/Normalization test data in R
r;normalization
Check out the preProcess function from the caret library.  You can choose the parameters you want to scale/center the training data, and it also saves the transformations it makes so then you can normalize the test set with the same specifications that you normalized the training set with.  Could go something like this:library(caret)trainData <- data.frame(v1 = rnorm(15,3,1), v2 = rnorm(15,2,2))testData <- data.frame(v1 = rnorm(5,3,1), v2 = rnorm(5,2,2))normParam <- preProcess(trainData)norm.testData <- predict(normParam, testData)now your norm.testData is scaled and centered according to the training data set parameters.Another way to do this without using caret:## set up datatrainData <- data.frame(v1 = rnorm(15,3,1), v2 = rnorm(15,2,2))testData <- data.frame(v1 = rnorm(5,3,1), v2 = rnorm(5,2,2))## find mean and sd column-wise of training datatrainMean <- apply(trainData,2,mean)trainSd <- apply(trainData,2,sd)## centeredsweep(trainData, 2L, trainMean) # using the default - to subtract mean column-wise   ## centered AND scalednorm2.testData <- sweep(sweep(trainData, 2L, trainMean), 2, trainSd, /)