TopicModels.jl (https://github.com/slycoder/TopicModels.jl <https://github.com/slycoder/TopicModels.jl/blob/master/README.md>) has an implementation of LDA.
Cheers, Kevin On Saturday, July 2, 2016, esproff <[email protected]> wrote: > thanks! > > So I know there is a Java implementation of LDA (MALLET Pkg), which I > believe uses collapsed Gibbs sampling, and also there are probably multiple > C++ implementations as well, unfortunately I don't know Java or C++ so I'm > unable personally to benchmark against those. However there are also > Matlab and R implementations which are two languages that I do probably > know well enough that I could run some benchmarks against them, so I may do > that in the near future. > > On Saturday, July 2, 2016 at 6:30:34 AM UTC-7, Cedric St-Jean wrote: >> >> Impressive work, especially with the documentation! Have you benchmarked >> it against other implementations? >> >> On Saturday, July 2, 2016 at 12:32:13 AM UTC-4, esproff wrote: >>> >>> Hi all! >>> >>> So I have just released a new variational Bayes topic modeling package >>> for Julia, which can be found here: >>> >>> https://github.com/esproff/TopicModelsVB.jl >>> >>> The models included are: >>> >>> 1. >>> >>> Latent Dirichlet Allocation (LDA) >>> 2. >>> >>> Filtered Latent Dirichlet Allocation (fLDA) >>> 3. >>> >>> Correlated Topic Model (CTM) >>> 4. >>> >>> Filtered Correlated Topic Model (fCTM) >>> 5. >>> >>> Dynamic Topic Model (DTM) >>> 6. >>> >>> Collaborative Topic Poisson Factorization (CTPF) >>> >>> This is, as far as I can tell, the best open-source topic modeling >>> package to date. It's still a bit rough around the edges and there are a >>> few edge-case bugs I think still deep in the belly of 1 or 2 of the >>> algorithms. But overall it's polished enough that I think it needs to be >>> tried out by other people besides myself. >>> >>> I'm open to collaborators, and I'm especially interested in adding some >>> GPGPU support, however, formally speaking, I'm trained as a mathematician, >>> not a computer scientist or software engineer, and thus if you're an expert >>> in GPGPU I'd be very interested in talking to you about adding this >>> functionality as Bayesian learning can be *EXTREMELY *computationally >>> intensive. (you can contact me on here or at [email protected]) >>> >>> On the other hand, if you're more into the applied math / machine >>> learning side, there are still a number of models to implement, mostly >>> non-parametric versions of the ones I've implemented, however I should warn >>> you that Bayesian nonparametrics is not for the faint of heart. >>> >>> Julia is a great language, and I hope you all like it as much as I do, >>> of course the speed is the big seller, however I think maybe its best >>> feature is the ease with which one can dig down into the internals of the >>> language, and considering how high-level the language is, this is truly a >>> masterstroke by the creators. >>> >>
