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I have two issues. 1-I am trying to use morphology to identify gender. I have 9 variables, both continuous and categorical. I was using two-step cluster analysis in SPSS because two-step could deal with different types of variables. But the output tells me that an animal is in cluster 1 or 2, it does not give me a probability (ex. 0.70 cluster 2). I also did not want to specify that I want two

This is only available for apps that target API level 19 and up. On 18 and lower, the futimens() function won't be available via libc.so. You will need to target API 19 (KitKat) in order to actually use it. If I do an "nm -D prebuilts/ndk/9/platforms/android-19/arch-arm/usr/lib/libc.so", I can see futimens() is present. On the others, it is definitely missing. Steve On Mon, Nov 25, 2013 at 3:57 PM, James Lyon <jameslyon0 at gmail.com> wrote: > Hmmm...

...at.h in the NDK. James On 25/11/13 22:12, Stephen Hines wrote: > On Mon, Nov 25, 2013 at 12:36 PM, Alp Toker <alp at nuanti.com > <mailto:alp at nuanti.com>> wrote: > > > On 25/11/2013 18:27, Stephen Hines wrote: > > futimens() is available in Android KitKat. We work extensively > with LLVM and this allowed us to remove our ugly workaround > for not having this functionality. > > > Hi Steve, > > Is your work on a branch somewhere? > > > It is all available as part of the Android Open Source Project...

futimens() is available in Android KitKat. We work extensively with LLVM and this allowed us to remove our ugly workaround for not having this functionality. Steve On Mon, Nov 25, 2013 at 8:30 AM, James Lyon <jameslyon0 at gmail.com> wrote: > Hi, > > I've been trying to get LLVM working as a JIT compiler on Android...

On 25/11/2013 18:27, Stephen Hines wrote: > futimens() is available in Android KitKat. We work extensively with > LLVM and this allowed us to remove our ugly workaround for not having > this functionality. Hi Steve, Is your work on a branch somewhere? James is putting a lot of time into this, and the LLVM community has been making best efforts to help review his patches...

Hi, I've been trying to get LLVM working as a JIT compiler on Android for a while. It works now, except that the setLastModificationAndAccessTime function won't build because the Bionic C library lacks both futimes and futimens. There doesn't appear to be any "nice" workaround for this problem: the only ways I can think of to make setLastModificationAndAccessTime work

On Mon, Nov 25, 2013 at 12:36 PM, Alp Toker <alp at nuanti.com> wrote: > > On 25/11/2013 18:27, Stephen Hines wrote: > >> futimens() is available in Android KitKat. We work extensively with LLVM >> and this allowed us to remove our ugly workaround for not having this >> functionality. >> > > Hi Steve, > > Is your work on a branch somewhere? > It is all available as part of the Android Open Source Project (AOSP). I should ment...

...es [kanga] path = /etc/samba/msdfs_links/hosts/kanga msdfs root = yes [kara2k] path = /etc/samba/msdfs_links/hosts/kara2k msdfs root = yes [karl] path = /etc/samba/msdfs_links/hosts/karl msdfs root = yes [kay] path = /etc/samba/msdfs_links/hosts/kay msdfs root = yes [kitkat] path = /etc/samba/msdfs_links/hosts/kitkat msdfs root = yes [klingon] path = /etc/samba/msdfs_links/hosts/klingon msdfs root = yes [ksite] path = /etc/samba/msdfs_links/hosts/ksite msdfs root = yes [lamorak] path = /etc/samba/msdfs_links/hosts/lamorak msdfs root = y...

I want to try Bayesian cluster analysis. Someone suggested using package mcclust. Is there a website that says how to install mcclust or another appropriate Bayesian package? Including the appropriate R functions that I can follow? I am trying to get probability of membership for each individual I am trying to cluster Thank you! -- View this message in context:

I am following instructions online for cluster analysis using the mclust package, and keep getting errors. http://www.statmethods.net/advstats/cluster.html These are the instructions (there is no sample dataset unfortunately): # Model Based Clustering library(mclust) fit <- Mclust(mydata) plot(fit, mydata) # plot results print(fit) # display the best model This is what I did and the error I