CODEWITHSUNDEEP
r
Low Yi Xiang
Low Yi Xiang

Reputation: 757

Generate a set of random unique integers from an interval

I am trying to build some machine learning models,

so I need training data and a validation data

so suppose I have N number of examples, I want to select random x examples in a data frame.

For example, suppose I have 100 examples, and I need 10 random numbers, is there a way (to efficiently) generate 10 random INTEGER numbers for me to extract the training data out of my sample data?

I tried using a while loop, and slowly change the repeated numbers, but the running time is not very ideal, so I am looking for a more efficient way to do it.

Can anyone help, please?

Upvotes: 61

Views: 83440

Answers (3)

Barry
Barry

Reputation: 13

from the raster package:

raster::sampleInt(242, 10, replace = FALSE)
##  95 230 148 183  38  98 137 110 188  39

This may fail if the limits are too large:

sample.int(1e+12, 10)

Upvotes: 0

Konrad Rudolph
Konrad Rudolph

Reputation: 546153

sample (or sample.int) does this:

sample.int(100, 10)
# [1] 58 83 54 68 53  4 71 11 75 90

will generate ten random numbers from the range 1–100. You probably want replace = TRUE, which samples with replacing:

sample.int(20, 10, replace = TRUE)
# [1] 10  2 11 13  9  9  3 13  3 17

More generally, sample samples n observations from a vector of arbitrary values.

Upvotes: 87

Boris Gorelik
Boris Gorelik

Reputation: 31817

If I understand correctly, you are trying to create a hold-out sampling. This is usually done using probabilities. So if you have n.rows samples and want a fraction of training.fraction to be used for training, you may do something like this:

select.training <- runif(n=n.rows) < training.fraction
data.training <- my.data[select.training, ]
data.testing <- my.data[!select.training, ]

If you want to specify EXACT number of training cases, you may do something like:

indices.training <- sample(x=seq(n.rows), size=training.size, replace=FALSE) #replace=FALSE makes sure the indices are unique
data.training <- my.data[indices.training, ]
data.testing <- my.data[-indices.training, ] #note that index negation means "take everything except for those"

Upvotes: 3

Related Questions