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setup.py
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# Copyright 2018 SrMouraSilva
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from os import path
from setuptools import setup
def readme():
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.rst'), encoding='UTF-8') as f:
return f.read()
setup(
name='RBM',
version='0.2.0',
description='RBM.',
long_description=readme(),
url='https://github.com/SrMouraSilva/RBM',
author='Paulo Mateus Moura da Silva (SrMouraSilva)',
author_email='[email protected]',
maintainer='Paulo Mateus Moura da Silva (SrMouraSilva)',
maintainer_email='[email protected]',
license="Apache Software License v2",
packages=[
'rbm',
'rbm.learning',
'rbm.regularization',
'rbm.sampling',
'rbm.train',
'rbm.util',
],
package_data={},
install_requires=[
'tensorflow<2.0.0',
'tensorflow-probability',
'tensorboard',
'pandas',
'matplotlib',
'seaborn',
'sklearn',
'tqdm',
#'yellowbrick'
'jupyter',
'jupyterlab',
# Word to vec
'gensim',
# Metrics
'ml_metrics',
# Bayesian optimization
'GPyOpt'
],
test_suite='test',
tests_require=['pytest', 'pytest-cov'],
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'License :: OSI Approved :: Apache Software License',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
],
keywords='rbm',
platforms='Linux',
)