%global _empty_manifest_terminate_build 0 Name: python-neural-semigroups Version: 0.6.3 Release: 1 Summary: Neural networks powered research of semigroups License: Apache-2.0 URL: https://github.com/inpefess/neural-semigroups Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f4/a1/1e9a7d27e62f24b735e459375c51978fc084c1056d3cab0b5f0ee4c3cf09/neural-semigroups-0.6.3.tar.gz BuildArch: noarch Requires: python3-torch Requires: python3-tqdm Requires: python3-pytorch-ignite Requires: python3-tensorboard %description The project is abandoned. If you want to reproduce results from the `paper `__, please use `this notebook `__. Here we try to model Cayley tables of semigroups using neural networks. This work was inspired by `a sudoku solver `__. A solved Sudoku puzzle is nothing more than a Cayley table of a quasigroup from 9 items with some well-known additional properties. So, one can imagine a puzzle made from a Cayley table of any other magma, e.g. a semigroup, by hiding part of its cells. There are two major differences between sudoku and puzzles based on semigroups: 1) it’s easy to take a glance on a table to understand whether it is a sudoku or not. That’s why it was possible to encode numbers in a table cells as colour intensities. Sudoku is a picture, and a semigroup is not. It’s difficult to check a Cayley table’s associativity with a naked eye; 2) Sudoku puzzles are solved by humans for fun and thus catalogued. When solving a sudoku one knows for sure that there is a unique solution. On the contrary, nobody guesses values in a partially filled Cayley table of a semigroup as a form of amusement. As a result, one can create a puzzle from a full Cayley table of a semigroup but there may be many distinct solutions. %package -n python3-neural-semigroups Summary: Neural networks powered research of semigroups Provides: python-neural-semigroups BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-neural-semigroups The project is abandoned. If you want to reproduce results from the `paper `__, please use `this notebook `__. Here we try to model Cayley tables of semigroups using neural networks. This work was inspired by `a sudoku solver `__. A solved Sudoku puzzle is nothing more than a Cayley table of a quasigroup from 9 items with some well-known additional properties. So, one can imagine a puzzle made from a Cayley table of any other magma, e.g. a semigroup, by hiding part of its cells. There are two major differences between sudoku and puzzles based on semigroups: 1) it’s easy to take a glance on a table to understand whether it is a sudoku or not. That’s why it was possible to encode numbers in a table cells as colour intensities. Sudoku is a picture, and a semigroup is not. It’s difficult to check a Cayley table’s associativity with a naked eye; 2) Sudoku puzzles are solved by humans for fun and thus catalogued. When solving a sudoku one knows for sure that there is a unique solution. On the contrary, nobody guesses values in a partially filled Cayley table of a semigroup as a form of amusement. As a result, one can create a puzzle from a full Cayley table of a semigroup but there may be many distinct solutions. %package help Summary: Development documents and examples for neural-semigroups Provides: python3-neural-semigroups-doc %description help The project is abandoned. If you want to reproduce results from the `paper `__, please use `this notebook `__. Here we try to model Cayley tables of semigroups using neural networks. This work was inspired by `a sudoku solver `__. A solved Sudoku puzzle is nothing more than a Cayley table of a quasigroup from 9 items with some well-known additional properties. So, one can imagine a puzzle made from a Cayley table of any other magma, e.g. a semigroup, by hiding part of its cells. There are two major differences between sudoku and puzzles based on semigroups: 1) it’s easy to take a glance on a table to understand whether it is a sudoku or not. That’s why it was possible to encode numbers in a table cells as colour intensities. Sudoku is a picture, and a semigroup is not. It’s difficult to check a Cayley table’s associativity with a naked eye; 2) Sudoku puzzles are solved by humans for fun and thus catalogued. When solving a sudoku one knows for sure that there is a unique solution. On the contrary, nobody guesses values in a partially filled Cayley table of a semigroup as a form of amusement. As a result, one can create a puzzle from a full Cayley table of a semigroup but there may be many distinct solutions. %prep %autosetup -n neural-semigroups-0.6.3 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-neural-semigroups -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.6.3-1 - Package Spec generated