gonum
go
gonum | go | |
---|---|---|
24 | 2,079 | |
7,285 | 119,900 | |
0.8% | 0.9% | |
8.3 | 10.0 | |
1 day ago | 3 days ago | |
Go | Go | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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gonum
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How to set up interface to accept multi-dimension array?
But if you want to see what can be done for numeric stuff, check out gonum. Personally, I still wouldn't use Go, and I rather suspect it's still pretty easy to reach for something like what you're trying to do and not find it because Go just can't write that type sensibly, but you can at least see what is available, written by people who disagree with me about Go not being a great language for this.
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packages similar to Pandas
Numpy functionality is largely covered by https://www.gonum.org/ but for pandas I'm not sure if there is an equivalent as widely accepted. However, you might try https://github.com/rocketlaunchr/dataframe-go which I have not tried but it looks like it covers some of what you're looking for
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What libraries are missing?
Math libraries. It's just gonum right now. Missing things that often require people to link C or Python libs. E.g. https://github.com/gonum/gonum/issues/354
- Gonum Numerical Packages
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SIMD Accelerated vector math
Maybe this way you could avoid having Mul, Mul_Inplace, Mul_Into variants. Gonum mostly follows the same pattern.
- Modern hardware is fast, so let's choose the slowest language to balance it out
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graph: A generic Go library for creating graph data structures and performing operations on them. It supports different kinds of graphs such as directed graphs, acyclic graphs, or trees.
How does this compare to gonum graph? https://github.com/gonum/gonum/tree/master/graph
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From Python to NumPy
Go is quite a bit cleaner than Python and its concurrency/parallelism primitives can be well suited to scientific workloads.
You may want to have a look at Gonum (https://www.gonum.org), and the Go HEP package developed by CERN (https://go-hep.org).
I was also surprised to see DSP and pretty sophisticated packages, although I never used them: https://awesome-go.com/science-and-data-analysis
And of course Go has Jupyter integration, it's almost like running a script thanks to its fast compilation time.
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Go for science?
You should check out this https://github.com/gonum/gonum
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What makes concurrency in Go better than multiprocesing/multithreading in Python?
No, using CPU extensions and GPUs is a different thing than doing multitasking. There is Gonum but it is still slower than Numpy: https://github.com/gonum/gonum/issues/511
go
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Arena-Based Parsers
The description indicates it is not production ready, and is archived at the same time.
If you pull all stops in each respective language, C# will always end up winning at parsing text as it offers C structs, pointers, zero-cost interop, Rust-style struct generics, cross-platform SIMD API and simply has better compiler. You can win back some performance in Go by writing hot parts in Go's ASM dialect at much greater effort for a specific platform.
For example, Go has to resort to this https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f... in order to efficiently scan memory, while in C# you write the following once and it compiles to all supported ISAs with their respective SIMD instructions for a given vector width: https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (there is a lot of code because C# covers much wider range of scenarios and does not accept sacrificing performance in odd lengths and edge cases, which Go does).
Another example is computing CRC32: you have to write ASM for Go https://github.com/golang/go/blob/4ed358b57efdad9ed710be7f4f..., in C# you simply write standard vectorized routine once https://github.com/dotnet/runtime/blob/56e67a7aacb8a644cc6b8... (its codegen is competitive with hand-intrinsified C++ code).
There is a lot more of this. Performance and low-level primitives to achieve it have been an area of focus of .NET for a long time, so it is disheartening to see one tenth of effort in Go to receive so much spotlight.
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Go: the future encoding/json/v2 module
A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.
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Evolving the Go Standard Library with math/rand/v2
I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles
The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397
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Microsoft Maintains Go Fork for FIPS 140-2 Support
There used to be the GO FIPS branch :
https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...
But it looks dead.
And it looks like https://github.com/golang-fips/go as well.
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Borgo is a statically typed language that compiles to Go
I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:
- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644
- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412
Here are some excerpts from the latest Go survey [1]:
- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."
- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."
I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.
[1]: https://go.dev/blog/survey2024-h1-results
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.
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How to use Retrieval Augmented Generation (RAG) for Go applications
Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!
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From Homemade HTTP Router to New ServeMux
net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements
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Building a Playful File Locker with GoFr
Make sure you have Go installed https://go.dev/.
- Fastest way to get IPv4 address from string
What are some alternatives?
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
v - Simple, fast, safe, compiled language for developing maintainable software. Compiles itself in <1s with zero library dependencies. Supports automatic C => V translation. https://vlang.io
gosl - Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, differential equations.
TinyGo - Go compiler for small places. Microcontrollers, WebAssembly (WASM/WASI), and command-line tools. Based on LLVM.
Stats - A well tested and comprehensive Golang statistics library package with no dependencies.
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
gonum/plot - A repository for plotting and visualizing data
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).
PiHex - PiHex Library, written in Go, generates a hexadecimal number sequence in the number Pi in the range from 0 to 10,000,000.
Angular - Deliver web apps with confidence 🚀
goraph - Package goraph implements graph data structure and algorithms.
golang-developer-roadmap - Roadmap to becoming a Go developer in 2020