StatsBase.jl
julia
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StatsBase.jl | julia | |
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5 | 350 | |
559 | 44,317 | |
0.0% | 0.8% | |
6.2 | 10.0 | |
17 days ago | 1 day ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | MIT License |
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StatsBase.jl
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Downloading packages to Julia 0.7
so finally I tried running Pkg.add(Pkg.PackageSpec(url="https://github.com/JuliaStats/StatsBase.jl", rev="v0.24.0")) but encountered an error saying in needed to download dependencies like DataStructures.
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Julia ranks in the top most loved programming languages for 2022
Well, out of the issues mentioned, the ones still open can be categorized as (1) aliasing problems with mutable vectors https://github.com/JuliaLang/julia/issues/39385 https://github.com/JuliaLang/julia/issues/39460 (2) not handling OffsetArrays correctly https://github.com/JuliaStats/StatsBase.jl/issues/646, https://github.com/JuliaStats/StatsBase.jl/issues/638, https://github.com/JuliaStats/Distributions.jl/issues/1265 https://github.com/JuliaStats/StatsBase.jl/issues/643 (3) bad interaction of buffering and I/O redirection https://github.com/JuliaLang/julia/issues/36069 (4) a type dispatch bug https://github.com/JuliaLang/julia/issues/41096
So if you avoid mutable vectors and OffsetArrays you should generally be fine.
As far as the argument "Julia is really buggy so it's unusable", I think this can be made for any language - e.g. rand is not random enough, Java's binary search algorithm had an overflow, etc. The fixed issues have tests added so they won't happen again. Maybe copying the test suites from libraries in other languages would have caught these issues earlier, but a new system will have more bugs than a mature system so some amount of bugginess is unavoidable.
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The Julia language has a number of correctness flaws
Most of these seem to be about packages in the ecosystem (which, after clicking through all links, actually almost all got fixed in a very timely manner, sometimes already in a newer version of the packages than the author was using), not about the language itself. Other than that, the message of this seems to be "newer software has bugs", which yes is a thing..?
For example, the majority of issues referenced are specific to a single package, StatsBase.jl - which apparently was written before OffsetArrays.jl was a thing and thus is known to be incompatible:
> Yes, lots of JuliaStats packages have been written before offset axes existed. Feel free to make a PR adding checks.
https://github.com/JuliaStats/StatsBase.jl/issues/646#issuec...
julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.
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Dart 3.3
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
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Best Programming languages for Data Analysis📊
🌟 Visit Github
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
No. It runs natively on ARM.
julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release
The one you need is this which is already merged but was after the 1.10 feature freeze so it has to wait till 1.11, though you can test it with nightly builds which is available on julialang site: https://github.com/JuliaLang/julia/pull/51435
Unfortunately, the core devs are not too chatty about standalone binaries, because of how Julia's internals are set there are going to be a lot of unforeseen challenges, so they are not trying to promise how things will be rather let's wait and see how things will turnout. Since packagecompiler.jl already has C ABI and one goal discussed about binaries being easily callable from other languages and vice versa, I would bet that it will have shared libraries.
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Rust std:fs slower than Python
https://github.com/JuliaLang/julia/issues/51086#issuecomment...
So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.
But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.
Still, the musl author has some reservations against making `jemalloc` the default:
https://www.openwall.com/lists/musl/2018/04/23/2
> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.
With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.
However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.
The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.
Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.
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Getaddrinfo() on glibc calls getenv(), oh boy
Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...
I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...
but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
NetworkX - Network Analysis in Python
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Numba - NumPy aware dynamic Python compiler using LLVM
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
LUA - A programming language based upon the lua programming language
PackageCompiler.jl - Compile your Julia Package
femtolisp - a lightweight, robust, scheme-like lisp implementation
JLD2.jl - HDF5-compatible file format in pure Julia