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setup.py 2.02 KB
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Bryse Flowers 提交于 2019-10-09 13:59 . Initial Commit
# External Includes
import os
from setuptools import find_packages, setup
# Internal Includes
from rfml import __version__ as VERSION
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
def load_requirements():
return read("requirements.txt").splitlines()
setup(
name="rfml",
version=VERSION,
author="Bryse Flowers",
author_email="brysef@vt.edu",
description="Radio Frequency Machine Learning (RFML) in PyTorch",
license="Modified BSD",
keywords="RF Machine Learning ML RFML Datasets AMC Modulation Classification",
url="https://github.com/brysef/rfml",
packages=find_packages(exclude=["test*"]),
long_description=(
"Radio Frequency Machine Learning (RFML) in PyTorch:\n"
"The concept of deep learning has revitalized machine learning research in "
"recent years. In particular, researchers have demonstrated the use of deep "
"learning for a multitude of tasks in wireless communications, such as signal "
"classification, waveform creation, and cognitive radio. These technologies "
"have been colloquially coined Radio Frequency Machine Learning (RFML) by the "
"Defense Advanced Research Projects Agency (DARPA). This library contains "
"PyTorch implementations of common RFML applications and neural architectures."
),
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"Intended Audience :: Telecommunications Industry",
"License :: OSI Approved :: BSD License",
"Operating System :: Unix",
"Programming Language :: Python :: 3 :: Only",
"Topic :: Communications :: Ham Radio",
"Topic :: Communications :: Telephony",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
install_requires=load_requirements(),
extras_require={"tutorial": ["jupyter", "matplotlib", "seaborn"]},
)
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