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|
%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.aliyun.com/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 <https://arxiv.org/abs/2103.07388>`__, please use `this
notebook <https://colab.research.google.com/github/inpefess/neural-semigroups/blob/master/examples/train_a_model.ipynb>`__.
Here we try to model Cayley tables of semigroups using neural networks.
This work was inspired by `a sudoku
solver <https://github.com/Kyubyong/sudoku>`__. 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 <https://arxiv.org/abs/2103.07388>`__, please use `this
notebook <https://colab.research.google.com/github/inpefess/neural-semigroups/blob/master/examples/train_a_model.ipynb>`__.
Here we try to model Cayley tables of semigroups using neural networks.
This work was inspired by `a sudoku
solver <https://github.com/Kyubyong/sudoku>`__. 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 <https://arxiv.org/abs/2103.07388>`__, please use `this
notebook <https://colab.research.google.com/github/inpefess/neural-semigroups/blob/master/examples/train_a_model.ipynb>`__.
Here we try to model Cayley tables of semigroups using neural networks.
This work was inspired by `a sudoku
solver <https://github.com/Kyubyong/sudoku>`__. 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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.3-1
- Package Spec generated
|
