To install the latest version from the github repository, use:
if(!require(devtools)) install.packages("devtools") # If not already installed
devtools::install_github("RGLab/Rtsne.multicore")
After installing the package, use the following code to run a simple example (to install, see below).
library(Rtsne.multicore) # Load package
iris_unique <- unique(iris) # Remove duplicates
mat <- as.matrix(iris_unique[,1:4])
set.seed(42) # Sets seed for reproducibility
tsne_out <- Rtsne.multicore(mat) # Run TSNE
plot(tsne_out$Y,col=iris_unique$Species) # Plot the result
library(microbenchmark)
library(Rtsne)
microbenchmark(tsne_out <- Rtsne.multicore(mat, num_threads = 4), tsne_out <- Rtsne(mat), times = 10)
#> Unit: milliseconds
#> expr min lq
#> tsne_out <- Rtsne.multicore(mat, num_threads = 4) 712.8844 718.0569
#> tsne_out <- Rtsne(mat) 1563.8488 1579.4824
#> mean median uq max neval
#> 756.4233 738.3441 761.7358 917.4102 10
#> 1663.9205 1615.2332 1716.7410 1860.7617 10
This R package offers a wrapper around multicore Barnes-Hut TSNE C++ implementation. Only minor changes were made to the original code to allow it to function as an R package.
[1] L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008.
[2] L.J.P. van der Maaten. Barnes-Hut-SNE. In Proceedings of the International Conference on Learning Representations, 2013.