StatsBase.jl
Petalisp
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StatsBase.jl | Petalisp | |
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5 | 17 | |
559 | 416 | |
0.0% | - | |
6.2 | 8.5 | |
17 days ago | 16 days ago | |
Julia | Common Lisp | |
GNU General Public License v3.0 or later | GNU Affero General Public License v3.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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(which, after clicking through all links, actually almost all got fixed in a very timely manner, sometimes already in a newer version of the packages than the author was using), not about the language itself.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...
Petalisp
- Petalisp: Elegant High Performance Computing
- Is there a tutorial for automatic differentiation with petalisp?
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Is there a language with lisp syntax but C semantics?
While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).
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Common Lisp Implementations in 2023
i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC
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numericals - Performance of NumPy with the goodness of Common Lisp
However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)
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New Lisp-Stat Release
> his means cl pagckages can be "done".
this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university
- The Julia language has a number of correctness flaws
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When a young programmer who has been using C for several years is convinced that C is the best possible programming language and that people who don't prefer it just haven't use it enough, what is the best argument for Lisp vs C, given that they're already convinced in favor of C?
One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.
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TypeScript as Fast as Rust: TypeScript++
Common lisp can be and has been successfully used for high-performance numerics[0] as well as low-level system code[1], all while being a high-level, garbage-collected language. (The latter property likely leading to better overall performance in large programs.)
Verification may be interesting, for high-assurance (e.g. automotive/aerospace) applications. It's for this reason that a theorem prover[2] and an ml[3] have been implemented, and they interoperate freely with the rest of common lisp.
In other words: we are already where you want to be, and we are even less stratified than you propose we would need to be.
0. https://github.com/marcoheisig/Petalisp
1. https://github.com/froggey/mezzano/
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Most interesting languages to learn (from)?
Array processing
What are some alternatives?
JWM - Cross-platform window management and OS integration library for Java
awesome-cl - A curated list of awesome Common Lisp frameworks, libraries and other shiny stuff.
Lux.jl - Explicitly Parameterized Neural Networks in Julia
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
magicl - Matrix Algebra proGrams In Common Lisp.
Optimization.jl - Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
lish - Lisp Shell
criterium - Benchmarking library for clojure
numcl - Numpy clone in Common Lisp
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)