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|
%global _empty_manifest_terminate_build 0
Name: python-PyDojoML
Version: 0.4.5
Release: 1
Summary: A General Purpose Machine Learning Library for Python
License: MIT License
URL: https://github.com/VIVelev/PyDojoML
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/cf/84/206bed622d9b5df8605308329d244c7d6c8944df0ac5cc9401917d0cb11c/PyDojoML-0.4.5.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-progressbar
Requires: python3-terminaltables
%description
# Dojo
Dojo is a Machine Learning library for Python
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
### Prerequisites
* [Python](https://www.python.org/) - The Programming Language used.
* [Pipenv](https://github.com/pypa/pipenv) - Dependency and Virtual Environment Management
***Download for Mac OSX using Homebrew***
```
brew install python
brew install pipenv
```
### Installing for development
A step by step series of examples that tell you how to get a development env running
1) Since we are using the **Python** programming language as a main language, you will need to download it.
You can do so from the official **Python** [website](https://www.python.org/).
2) Once you have **Python** up and running we then need to setup our development env. For that
we are using **Pipenv**. You will need to install it. Check out [these](https://pipenv.readthedocs.io/en/latest/install/#installing-pipenv) instructions to see how is done.
3) Now, that you have the prerequisites the only part left is too install all the other **Pyhton** packages
that **Dojo** depends on. To do run the following:
```
pipenv install --dev
```
The `--dev` tag is used in order **Pipenv** to know to install also the packages that are used in the
package development process.
### Installing for use
If you plan just to use **Dojo** as a Machine Learning library you can install it using **pip** like so:
```
pip install pydojoml
```
## Running the tests
Coming soon...
### Break down into end to end tests
Coming soon...
### And coding style tests
Coming soon...
## Built With
* [NumPy](http://www.numpy.org/) - Fundamental package for scientific computing with Python
* [SciPy](http://www.scipy.org/) - Package that provides many user-friendly and efficient numerical routines
* [Matplotlib](http://www.matplotlib.org/) - Python 2D plotting library
* [progressbar](https://pypi.org/project/progressbar/) - Text progress bar library for Python
* [terminaltables](https://pypi.org/project/terminaltables/) - Easily draw tables in terminal/console applications
## Contributing
Please read [CONTRIBUTING.md](https://github.com/VIVelev/PyDojoML/CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.
## Versioning
For the versions available, see the [tags on this repository](https://github.com/VIVelev/PyDojoML/tags).
## Authors
* **Victor Velev** - *Initial work* - [VIVelev](https://github.com/VIVelev)
See also the list of [contributors](https://github.com/VIVelev/PyDojoML/contributors) who participated in this project.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
* **Eric Jones and Travis Oliphant and Pearu Peterson and others** for writing such great packages - the [SciPy](http://www.scipy.org/) ecosystem.
* **Nilton Volpato** for writing [progressbar](https://pypi.org/project/progressbar/)
* **Robpol86** for writing [terminaltables](https://pypi.org/project/terminaltables/)
%package -n python3-PyDojoML
Summary: A General Purpose Machine Learning Library for Python
Provides: python-PyDojoML
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-PyDojoML
# Dojo
Dojo is a Machine Learning library for Python
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
### Prerequisites
* [Python](https://www.python.org/) - The Programming Language used.
* [Pipenv](https://github.com/pypa/pipenv) - Dependency and Virtual Environment Management
***Download for Mac OSX using Homebrew***
```
brew install python
brew install pipenv
```
### Installing for development
A step by step series of examples that tell you how to get a development env running
1) Since we are using the **Python** programming language as a main language, you will need to download it.
You can do so from the official **Python** [website](https://www.python.org/).
2) Once you have **Python** up and running we then need to setup our development env. For that
we are using **Pipenv**. You will need to install it. Check out [these](https://pipenv.readthedocs.io/en/latest/install/#installing-pipenv) instructions to see how is done.
3) Now, that you have the prerequisites the only part left is too install all the other **Pyhton** packages
that **Dojo** depends on. To do run the following:
```
pipenv install --dev
```
The `--dev` tag is used in order **Pipenv** to know to install also the packages that are used in the
package development process.
### Installing for use
If you plan just to use **Dojo** as a Machine Learning library you can install it using **pip** like so:
```
pip install pydojoml
```
## Running the tests
Coming soon...
### Break down into end to end tests
Coming soon...
### And coding style tests
Coming soon...
## Built With
* [NumPy](http://www.numpy.org/) - Fundamental package for scientific computing with Python
* [SciPy](http://www.scipy.org/) - Package that provides many user-friendly and efficient numerical routines
* [Matplotlib](http://www.matplotlib.org/) - Python 2D plotting library
* [progressbar](https://pypi.org/project/progressbar/) - Text progress bar library for Python
* [terminaltables](https://pypi.org/project/terminaltables/) - Easily draw tables in terminal/console applications
## Contributing
Please read [CONTRIBUTING.md](https://github.com/VIVelev/PyDojoML/CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.
## Versioning
For the versions available, see the [tags on this repository](https://github.com/VIVelev/PyDojoML/tags).
## Authors
* **Victor Velev** - *Initial work* - [VIVelev](https://github.com/VIVelev)
See also the list of [contributors](https://github.com/VIVelev/PyDojoML/contributors) who participated in this project.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
* **Eric Jones and Travis Oliphant and Pearu Peterson and others** for writing such great packages - the [SciPy](http://www.scipy.org/) ecosystem.
* **Nilton Volpato** for writing [progressbar](https://pypi.org/project/progressbar/)
* **Robpol86** for writing [terminaltables](https://pypi.org/project/terminaltables/)
%package help
Summary: Development documents and examples for PyDojoML
Provides: python3-PyDojoML-doc
%description help
# Dojo
Dojo is a Machine Learning library for Python
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
### Prerequisites
* [Python](https://www.python.org/) - The Programming Language used.
* [Pipenv](https://github.com/pypa/pipenv) - Dependency and Virtual Environment Management
***Download for Mac OSX using Homebrew***
```
brew install python
brew install pipenv
```
### Installing for development
A step by step series of examples that tell you how to get a development env running
1) Since we are using the **Python** programming language as a main language, you will need to download it.
You can do so from the official **Python** [website](https://www.python.org/).
2) Once you have **Python** up and running we then need to setup our development env. For that
we are using **Pipenv**. You will need to install it. Check out [these](https://pipenv.readthedocs.io/en/latest/install/#installing-pipenv) instructions to see how is done.
3) Now, that you have the prerequisites the only part left is too install all the other **Pyhton** packages
that **Dojo** depends on. To do run the following:
```
pipenv install --dev
```
The `--dev` tag is used in order **Pipenv** to know to install also the packages that are used in the
package development process.
### Installing for use
If you plan just to use **Dojo** as a Machine Learning library you can install it using **pip** like so:
```
pip install pydojoml
```
## Running the tests
Coming soon...
### Break down into end to end tests
Coming soon...
### And coding style tests
Coming soon...
## Built With
* [NumPy](http://www.numpy.org/) - Fundamental package for scientific computing with Python
* [SciPy](http://www.scipy.org/) - Package that provides many user-friendly and efficient numerical routines
* [Matplotlib](http://www.matplotlib.org/) - Python 2D plotting library
* [progressbar](https://pypi.org/project/progressbar/) - Text progress bar library for Python
* [terminaltables](https://pypi.org/project/terminaltables/) - Easily draw tables in terminal/console applications
## Contributing
Please read [CONTRIBUTING.md](https://github.com/VIVelev/PyDojoML/CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.
## Versioning
For the versions available, see the [tags on this repository](https://github.com/VIVelev/PyDojoML/tags).
## Authors
* **Victor Velev** - *Initial work* - [VIVelev](https://github.com/VIVelev)
See also the list of [contributors](https://github.com/VIVelev/PyDojoML/contributors) who participated in this project.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
* **Eric Jones and Travis Oliphant and Pearu Peterson and others** for writing such great packages - the [SciPy](http://www.scipy.org/) ecosystem.
* **Nilton Volpato** for writing [progressbar](https://pypi.org/project/progressbar/)
* **Robpol86** for writing [terminaltables](https://pypi.org/project/terminaltables/)
%prep
%autosetup -n PyDojoML-0.4.5
%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-PyDojoML -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.5-1
- Package Spec generated
|
