from codecs import open
from os import path
from setuptools import find_packages, setup
from eeyore import __version__
url = 'https://github.com/papamarkou/eeyore'
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()
setup(name='eeyore', version=__version__, description=
    'MCMC methods for neural networks', long_description=long_description,
    url=url, download_url='{0}/archive/v{1}.tar.gz'.format(url, __version__
    ), packages=find_packages(), license='MIT', author=
    'Theodore Papamarkou', author_email='theodore.papamarkou@gmail.com',
    classifiers=['Intended Audience :: Developers',
    'Intended Audience :: Science/Research',
    'License :: OSI Approved :: MIT License', 'Natural Language :: English',
    'Programming Language :: Python :: 3'], keywords=['Bayesian inference',
    'Bayesian neural networks', 'convergence diagnostics',
    'Markov chain Monte Carlo', 'posterior predictive distribution'],
    python_requires='>=3.6', install_requires=['numpy>=1.19.2',
    'torch>=1.9.0', 'kanga>=0.0.20'], package_data={'eeyore': [
    'data/*/x.csv', 'data/*/y.csv', 'data/*/readme.md']},
    include_package_data=True, zip_safe=False)

7c73537691c7a05e66d497ad89b98c5b7dcff217
<mask token>

<mask token>
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()
setup(name='eeyore', version=__version__, description=
    'MCMC methods for neural networks', long_description=long_description,
    url=url, download_url='{0}/archive/v{1}.tar.gz'.format(url, __version__
    ), packages=find_packages(), license='MIT', author=
    'Theodore Papamarkou', author_email='theodore.papamarkou@gmail.com',
    classifiers=['Intended Audience :: Developers',
    'Intended Audience :: Science/Research',
    'License :: OSI Approved :: MIT License', 'Natural Language :: English',
    'Programming Language :: Python :: 3'], keywords=['Bayesian inference',
    'Bayesian neural networks', 'convergence diagnostics',
    'Markov chain Monte Carlo', 'posterior predictive distribution'],
    python_requires='>=3.6', install_requires=['numpy>=1.19.2',
    'torch>=1.9.0', 'kanga>=0.0.20'], package_data={'eeyore': [
    'data/*/x.csv', 'data/*/y.csv', 'data/*/readme.md']},
    include_package_data=True, zip_safe=False)

<mask token>
url = 'https://github.com/papamarkou/eeyore'
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()
setup(name='eeyore', version=__version__, description=
    'MCMC methods for neural networks', long_description=long_description,
    url=url, download_url='{0}/archive/v{1}.tar.gz'.format(url, __version__
    ), packages=find_packages(), license='MIT', author=
    'Theodore Papamarkou', author_email='theodore.papamarkou@gmail.com',
    classifiers=['Intended Audience :: Developers',
    'Intended Audience :: Science/Research',
    'License :: OSI Approved :: MIT License', 'Natural Language :: English',
    'Programming Language :: Python :: 3'], keywords=['Bayesian inference',
    'Bayesian neural networks', 'convergence diagnostics',
    'Markov chain Monte Carlo', 'posterior predictive distribution'],
    python_requires='>=3.6', install_requires=['numpy>=1.19.2',
    'torch>=1.9.0', 'kanga>=0.0.20'], package_data={'eeyore': [
    'data/*/x.csv', 'data/*/y.csv', 'data/*/readme.md']},
    include_package_data=True, zip_safe=False)

from codecs import open
from os import path
from setuptools import find_packages, setup
from eeyore import __version__
url = 'https://github.com/papamarkou/eeyore'
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()
setup(name='eeyore', version=__version__, description=
    'MCMC methods for neural networks', long_description=long_description,
    url=url, download_url='{0}/archive/v{1}.tar.gz'.format(url, __version__
    ), packages=find_packages(), license='MIT', author=
    'Theodore Papamarkou', author_email='theodore.papamarkou@gmail.com',
    classifiers=['Intended Audience :: Developers',
    'Intended Audience :: Science/Research',
    'License :: OSI Approved :: MIT License', 'Natural Language :: English',
    'Programming Language :: Python :: 3'], keywords=['Bayesian inference',
    'Bayesian neural networks', 'convergence diagnostics',
    'Markov chain Monte Carlo', 'posterior predictive distribution'],
    python_requires='>=3.6', install_requires=['numpy>=1.19.2',
    'torch>=1.9.0', 'kanga>=0.0.20'], package_data={'eeyore': [
    'data/*/x.csv', 'data/*/y.csv', 'data/*/readme.md']},
    include_package_data=True, zip_safe=False)
