Results not reproducible

[mattmotoki]

Hi,
The documentation suggests that reproducible results can be achieved by setting the seed in R. This works for the original Rtsne function, but it doesn't seem to work for Rtsne.multicore.

library(Rtsne.multicore)

iris_unique <- unique(iris)
mat <- as.matrix(iris_unique[,1:4])

# repeat calculation
set.seed(42)
tsne_out1 <- Rtsne.multicore(mat)

set.seed(42)
tsne_out2 <- Rtsne.multicore(mat)

# plot results
plot(tsne_out1$Y, col=iris_unique$Species, main="first run")
plot(tsne_out2$Y, col=iris_unique$Species, main="second run")

[gfinak]

I suggest posting this issue to the original author of the multicore implementation.

…

On Thu, Jun 15, 2017, 20:37 mattmotoki ***@***.***> wrote: Hi, The documentation suggests that reproducible results can be achieved by setting the seed in R. This works for the original Rtsne function, but it doesn't seem to work for Rtsne.multicore. library(Rtsne.multicore) iris_unique <- unique(iris) mat <- as.matrix(iris_unique[,1:4]) # repeat calculation set.seed(42) tsne_out1 <- Rtsne.multicore(mat) set.seed(42) tsne_out2 <- Rtsne.multicore(mat) # plot results plot(tsne_out1$Y, col=iris_unique$Species, main="first run") plot(tsne_out2$Y, col=iris_unique$Species, main="second run") [image: first run] <https://user-images.githubusercontent.com/13989564/27210395-28554000-5207-11e7-9bc8-b4a5203212b8.png> [image: second run] <https://user-images.githubusercontent.com/13989564/27210396-28554f32-5207-11e7-98f7-5e99fab4ae67.png> — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#4>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABUkeQjAXRCaXiA4xUXKtbzp2y_BfRjjks5sEfPtgaJpZM4N7-mF> .

[mattmotoki]

Thanks for the suggestion.

[gfinak]

Perhaps I spoke too soon, I believe we can fix the seed on this implementation.

[mattmotoki]

Okay great. It's not a big issue for me, but thank you for the useful package.

[gfinak]

Need to initialize the random number generator with a different, but deterministic seed via srand() inside the #pragma omp parallel block of tsne.cpp.
Ideally we'd like to either pass in the seed via the R function wrapper or use one of R's random generators so that set.seed calls in R have the expected effect.