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Publish a new release to rubygems #67
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In the meantime, you can get commit with the #66 merge by inserting into your Gemfile Note that the gem name to use is I tried the updated code with |
I am wondering how to "push" a new rubygems version. For anybody struggling with the lack of GitHub credentials, including the https url in my
|
**Background:** The slow step of LSI is the SVD (singular value decomposition) of a matrix. With even a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation it too slow to be usable. To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. This performs at least an order of magnitude faster than the ruby-only matrix decomposition, and is fast enough that using LSI with Jekyll finishes in a reasonable amount of time. Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- luckily, there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67 **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform the singular value decomposition quickly and works with Ruby 3. Doing so will allow classifier-reborn to be used with Ruby 3 without depending on the unmaintained/unreleased GSL gem. Options for ruby matrix libraries are somewhat limited, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn the same way they would use it with GSL. That is, the user should install `numo-narray` and `numo-linalg` gems, and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is the SVD (singular value decomposition) of a matrix. With even a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation it too slow to be usable. To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. This performs at least an order of magnitude faster than the ruby-only matrix decomposition, and is fast enough that using LSI with Jekyll finishes in a reasonable amount of time. Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- luckily, there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67 Notably, `rb-gsl` depends on [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray). `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform the singular value decomposition quickly and works with Ruby 3. Doing so will allow classifier-reborn to be used with Ruby 3 without depending on the unmaintained/unreleased GSL gem. Options for ruby matrix libraries are somewhat limited, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn the same way they would use it with GSL. That is, the user should install `numo-narray` and `numo-linalg` gems, and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is the SVD (singular value decomposition) of a matrix. With even a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation it too slow to be usable. To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. This performs at least an order of magnitude faster than the ruby-only matrix decomposition, and is fast enough that using LSI with Jekyll finishes in a reasonable amount of time. Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- luckily, there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67 Notably, `rb-gsl` depends on [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray). `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform the singular value decomposition quickly and works with Ruby 3. Doing so will allow classifier-reborn to be used with Ruby 3 without depending on the unmaintained/unreleased GSL gem. Options for ruby matrix libraries are somewhat limited, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn the same way they would use it with GSL. That is, the user should install `numo-narray` and `numo-linalg` gems, and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is the SVD (singular value decomposition) of a matrix. With even a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation it too slow to be usable. To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. This performs at least an order of magnitude faster than the ruby-only matrix decomposition, and is fast enough that using LSI with Jekyll finishes in a reasonable amount of time. Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- luckily, there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67 Notably, `rb-gsl` depends on [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray). `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform the singular value decomposition quickly and works with Ruby 3. Doing so will allow classifier-reborn to be used with Ruby 3 without depending on the unmaintained/unreleased GSL gem. Options for ruby matrix libraries are somewhat limited, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn the same way they would use it with GSL. That is, the user should install `numo-narray` and `numo-linalg` gems, and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is the SVD (singular value decomposition) of a matrix. With even a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation it too slow to be usable. To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. This performs at least an order of magnitude faster than the ruby-only matrix decomposition, and is fast enough that using LSI with Jekyll finishes in a reasonable amount of time. Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- luckily, there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67 Notably, `rb-gsl` depends on [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray). `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform the singular value decomposition quickly and works with Ruby 3. Doing so will allow classifier-reborn to be used with Ruby 3 without depending on the unmaintained/unreleased GSL gem. Options for ruby matrix libraries are somewhat limited, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn the same way they would use it with GSL. That is, the user should install `numo-narray` and `numo-linalg` gems, and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is computing the SVD (singular value decomposition) of a matrix. Even with a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation is too slow to be usable (taking hours to complete). To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. Computations with this gem perform orders of magnitude faster than the ruby-only matrix implementation, and they're fast enough that using LSI with Jekyll finishes in a reasonable amount of time (seconds). Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- luckily, there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67 Notably, `rb-gsl` depends on [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray). `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform singular value decomposition quickly and works with Ruby 3. Doing so will allow classifier-reborn to be used with Ruby 3 without depending on the unmaintained/unreleased GSL gem. Options for ruby matrix libraries are somewhat limited, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn with Numo/Lapack the same way they'd use it with GSL. That is, the user should install the `numo-narray` and `numo-linalg` gems (with their required C libraries), and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is computing the SVD (singular value decomposition) of a matrix. Even with a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation is too slow to be usable (taking hours to complete). To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. Computations with this gem perform orders of magnitude faster than the ruby-only matrix implementation, and they're fast enough that using LSI with Jekyll finishes in a reasonable amount of time (seconds). Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67. This will be increasingly problematic because Ruby 2.7 is now in [security maintenance](https://www.ruby-lang.org/en/news/2022/04/12/ruby-2-7-6-released/) and will become end of life in less than a year. Notably, `rb-gsl` depends on the [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray) gem. `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform singular value decomposition quickly and works with Ruby 3. Doing so will make classifier-reborn compatible with Ruby 3 without depending on the unmaintained/unreleased gsl gem. There aren't many gems that provide fast matrix support for ruby, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition, which is exactly what we need. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn with Numo/Lapack the same way they'd use it with GSL. That is, the user should install the `numo-narray` and `numo-linalg` gems (with their required C libraries), and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is computing the SVD (singular value decomposition) of a matrix. Even with a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation is too slow to be usable (taking hours to complete). To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. Computations with this gem perform orders of magnitude faster than the ruby-only matrix implementation, and they're fast enough that using LSI with Jekyll finishes in a reasonable amount of time (seconds). Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67. This will be increasingly problematic because Ruby 2.7 is now in [security maintenance](https://www.ruby-lang.org/en/news/2022/04/12/ruby-2-7-6-released/) and will become end of life in less than a year. Notably, `rb-gsl` depends on the [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray) gem. `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform singular value decomposition quickly and works with Ruby 3. Doing so will make classifier-reborn compatible with Ruby 3 without depending on the unmaintained/unreleased gsl gem. There aren't many gems that provide fast matrix support for ruby, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition, which is exactly what we need. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn with Numo/Lapack the same way they'd use it with GSL. That is, the user should install the `numo-narray` and `numo-linalg` gems (with their required C libraries), and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is computing the SVD (singular value decomposition) of a matrix. Even with a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation is too slow to be usable (taking hours to complete). To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. Computations with this gem perform orders of magnitude faster than the ruby-only matrix implementation, and they're fast enough that using LSI with Jekyll finishes in a reasonable amount of time (seconds). Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67. This will be increasingly problematic because Ruby 2.7 is now in [security maintenance](https://www.ruby-lang.org/en/news/2022/04/12/ruby-2-7-6-released/) and will become end of life in less than a year. Notably, `rb-gsl` depends on the [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray) gem. `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform singular value decomposition quickly and works with Ruby 3. Doing so will make classifier-reborn compatible with Ruby 3 without depending on the unmaintained/unreleased gsl gem. There aren't many gems that provide fast matrix support for ruby, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition, which is exactly what we need. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn with Numo/Lapack the same way they'd use it with GSL. That is, the user should install the `numo-narray` and `numo-linalg` gems (with their required C libraries), and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is computing the SVD (singular value decomposition) of a matrix. Even with a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation is too slow to be usable (taking hours to complete). To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. Computations with this gem perform orders of magnitude faster than the ruby-only matrix implementation, and they're fast enough that using LSI with Jekyll finishes in a reasonable amount of time (seconds). Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67. This will be increasingly problematic because Ruby 2.7 is now in [security maintenance](https://www.ruby-lang.org/en/news/2022/04/12/ruby-2-7-6-released/) and will become end of life in less than a year. Notably, `rb-gsl` depends on the [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray) gem. `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform singular value decomposition quickly and works with Ruby 3. Doing so will make classifier-reborn compatible with Ruby 3 without depending on the unmaintained/unreleased gsl gem. There aren't many gems that provide fast matrix support for ruby, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition, which is exactly what we need. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn with Numo/Lapack the same way they'd use it with GSL. That is, the user should install the `numo-narray` and `numo-linalg` gems (with their required C libraries), and classifier-reborn will detect and use these if they are found.
**Background:** The slow step of LSI is computing the SVD (singular value decomposition) of a matrix. Even with a relatively small collection of documents (say, about 20 blog posts), the native ruby implementation is too slow to be usable (taking hours to complete). To work around this problem, classifier-reborn allows you to optionally use the `gsl` gem to make use of the [Gnu Scientific Library](https://www.gnu.org/software/gsl/) when performing matrix calculations. Computations with this gem perform orders of magnitude faster than the ruby-only matrix implementation, and they're fast enough that using LSI with Jekyll finishes in a reasonable amount of time (seconds). Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is unmaintained -- there's a commit on main that makes it compatible with Ruby 3, but nobody has released the gem so the only way to use rb-gsl with Ruby 3 right now is to specify the git hash in your Gemfile. See SciRuby/rb-gsl#67. This will be increasingly problematic because Ruby 2.7 is now in [security maintenance](https://www.ruby-lang.org/en/news/2022/04/12/ruby-2-7-6-released/) and will become end of life in less than a year. Notably, `rb-gsl` depends on the [narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray) gem. `narray` is deprecated, and the readme suggests using `Numo::NArray` instead. **Changes:** In this PR, my goal is to provide an alternative matrix implementation that can perform singular value decomposition quickly and works with Ruby 3. Doing so will make classifier-reborn compatible with Ruby 3 without depending on the unmaintained/unreleased gsl gem. There aren't many gems that provide fast matrix support for ruby, but [Numo](https://github.com/ruby-numo) seems to be more actively maintained than rb-gsl, and Numo has a working Ruby 3 implementation that can perform a singular value decomposition, which is exactly what we need. This requires [numo-narray](https://github.com/ruby-numo/numo-narray) and [numo-linalg](https://github.com/ruby-numo/numo-linalg). My goal is to allow users to (optionally) use classifier-reborn with Numo/Lapack the same way they'd use it with GSL. That is, the user should install the `numo-narray` and `numo-linalg` gems (with their required C libraries), and classifier-reborn will detect and use these if they are found.
For those who are still not using bundler, gemfiles, etc, here's how to install this version globally:
This pulls the git repository, goes to katafract's fixed version (thank you!), builds the gem locally, and then installs. This works on Mac OS X 14.2 (Sonoma). |
This approach was working for me running on macos Sonoma - but some update (Xcode? gcc? bundler?) has caused this this gem to stop working, and I can't reinstall it:
Anybody else have a similar issue? |
I'm also running onto this issue on macOS Sonoma 14.4.1 (23E224); but in my case, I was just trying to use the existing published version of Initially I had just done a
Which unfortunately no longer works:
Digging a little deeper, I found these issues:
It seems it's only compatible with gsl 2.1 as well:
I wonder, does using There are some instructions for building
We can see the latest version of the homebrew formula here: And it's history here:
Though the first 2.x version listed there seems to be Since the only thing that changed from the class GslAT21 < Formula
desc "Numerical library for C and C++"
homepage "https://www.gnu.org/software/gsl/"
url "https://ftp.gnu.org/gnu/gsl/gsl-2.1.tar.gz"
mirror "https://ftpmirror.gnu.org/gsl/gsl-2.1.tar.gz"
sha256 "59ad06837397617f698975c494fe7b2b698739a59e2fcf830b776428938a0c66"
license "GPL-3.0-or-later"
def install
ENV.deparallelize
system "./configure", "--disable-dependency-tracking", "--prefix=#{prefix}"
system "make" # A GNU tool which doesn't support just make install! Shameful!
system "make", "install"
end
test do
system bin/"gsl-randist", "0", "20", "cauchy", "30"
end
end Saved in:
Then we could install it like this:
After which we could go back and see if Exploring this issue more:
|
@0xdevalias thank you, your technique worked for me on MacOS Ventura. I had to remove the currently upgraded gsl version by homebrew :
After creating the local recipe as you did (in
Then relinked gsl :
Then, from my project environment (which uses rvm to activate ruby 2.7.1), I uninstalled the precedent gem version :
And reinstalled it with bundler from within the project dir :
The gem then accepted to build, using the local gsl@2.1 library version installed with homebrew. |
@BenTalagan Awesome! Glad it was helpful!
@BenTalagan Oh true.. I wonder if that was the reason why it didn't work for me:
I can't remember exactly what I did, but skimming my notes above, it looks like I didn't link |
#66 was merged a few months ago adding Ruby 3 compatibility, but it's not available in rubygems.
Can you publish a new release? Thanks!
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