add setup for deploy to pypi

MANIFEST.in

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+# Include the license file
+include LICENSE.txt
+
+# Include the data files
+recursive-include data *

README.rst

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+Bayes Filter
+=======================
+
+This projects use native bayes algorithm to detect spam content, like normal, sex, gamble, politics content. We use 400 Chinese sentences to train the algorithm and the correct rate is about 93%
+
+----
+
+1. Fast
+   We offer built-in cache files so it's super fast even when you first used
+
+2. High accuracy
+   Our correct rate is about 93%
+
+3. Customizable
+   You can add what data you need to train

setup.cfg

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+[bdist_wheel]
+# This flag says that the code is written to work on both Python 2 and Python
+# 3. If at all possible, it is good practice to do this. If you cannot, you
+# will need to generate wheels for each Python version that you support.

setup.py

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+"""A setuptools based setup module.
+
+See:
+https://packaging.python.org/en/latest/distributing.html
+https://github.com/pypa/sampleproject
+"""
+
+# Always prefer setuptools over distutils
+from setuptools import setup, find_packages
+# To use a consistent encoding
+from codecs import open
+from os import path
+
+here = path.abspath(path.dirname(__file__))
+
+# Get the long description from the README file
+with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
+    long_description = f.read()
+
+setup(
+    name='bayes_filter',
+    version='0.1.3',
+    description='Detect spam content using native bayes',
+    long_description=long_description,
+    url='https://github.com/Windsooon/Bayes-Filter',
+    author='Windson Yang',
+    author_email='wiwindson@outlook.com',
+    license='MIT',
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Developers',
+        'Topic :: Software Development :: Libraries :: Python Modules',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 3.3',
+        'Programming Language :: Python :: 3.4',
+        'Programming Language :: Python :: 3.5',
+    ],
+    keywords='Native Bayes spam content filter',
+    install_requires=[
+        'jieba>=0.39',
+        'numpy>=1.13.3', 
+        ],
+    packages=['bayes'],
+    package_data={
+        'data': ['*.dat'],
+    },
+)