The sparse learning related methods.
#--- #!/usr/bin/env bash
rm -rf dist rm -rf build rm -rf sparse_learning.egg-info python setup.py sdist bdist_wheel twine upload dist/*.tar.gz rm -rf dist rm -rf build rm -rf sparse_learning.egg-info
""" How to run it ? python setup.py build_ext --inplace """ import os import numpy from os import path from setuptools import setup from distutils.core import Extension
here = path.abspath(path.dirname(file))
src_files = ['c/main_wrapper.c', 'c/head_tail_proj.c', 'c/fast_pcst.c'] compile_args = ['-std=c11', '-lpython2.7', '-lm']
setup( # sparse_learning package. name='sparse_learning', # current version is 0.2.1 version='0.2.4', # this is a wrapper of head and tail projection. description='A wrapper for sparse learning algorithms.', # a long description should be here. long_description='This package collects sparse learning algorithms.', # url of github projection. url='https://github.com/baojianzhou/sparse_learning.git', # number of authors. author='Baojian Zhou', # my email. author_email='bzhou6@albany.edu', include_dirs=[numpy.get_include()], license='MIT', packages=['sparse_learning'], classifiers=("Programming Language :: Python :: 2", "License :: OSI Approved :: MIT License", "Operating System :: POSIX :: Linux",), # specify requirements of your package here install_requires=['numpy'], headers=['c/head_tail_proj.h', 'c/fast_pcst.h'], # define the extension module ext_modules=[Extension('proj_module', sources=src_files, language="C", extra_compile_args=compile_args, include_dirs=[numpy.get_include()])], keywords='sparse learning, structure sparsity, head/tail projection')
Step 1: pip wheel /dir/to/proj-name/ -w /dir/where/wheels/are/written/