for pypi

.gitignore

@@ -12,3 +12,4 @@ README_cache
 .Rhistory
 .Rprofile
 .cache
+.pypirc

README.md

@@ -4,6 +4,8 @@ Koji MAKIYAMA (@hoxo_m)
 
 
 [![Build Status](https://travis-ci.org/hoxo-m/densratio_py.svg?branch=master)](https://travis-ci.org/hoxo-m/densratio_py)
+[![PyPI](https://img.shields.io/pypi/v/densratio.svg)](https://pypi.python.org/pypi/densratio)
+[![PyPI](https://img.shields.io/pypi/dm/densratio.svg)](https://pypi.python.org/pypi/densratio)
 [![Coverage Status](https://coveralls.io/repos/github/hoxo-m/densratio_py/badge.svg?branch=master)](https://coveralls.io/github/hoxo-m/densratio_py?branch=master)
 
 ## 1. Overview
@@ -75,7 +77,13 @@ plt.show()
 
 ## 2. How to Install
 
-You can install the package from GitHub.
+You can install the package from [PyPI](https://pypi.python.org/pypi/densratio).
+
+```:sh
+$ pip install densratio
+```
+
+Also, You can install the package from [GitHub](https://github.com/hoxo-m/densratio_py).
 
 ```:sh
 $ pip install git+https://github.com/hoxo-m/densratio_py.git
@@ -167,7 +175,7 @@ As the result, you can obtain `compute_density_ratio()`.
 - **Method** is fixed by uLSIF.
 - **Kernel type** is fixed by Gaussian RBF.
 - **Number of kernels** is the number of kernels in the linear model. You can change by setting `kernel_num` parameter. In default, `kernel_num = 100`.
-- **Bandwidth(sigma)** is the Gaussian kernel bandwidth. In default, `sigma = "auto"`, the algorithm automatically select an optimal value by cross validation. If you set `sigma` a number, that will be used. If you set a numeric array, the algorithm select an optimal value in them by cross validation.
+- **Bandwidth(sigma)** is the Gaussian kernel bandwidth. In default, `sigma = "auto"`, the algorithm automatically select an optimal value by cross validation. If you set `sigma` a number, that will be used. If you set `sigma` a numeric array, the algorithm select an optimal value in them by cross validation.
 - **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution `p(x)`. You can find the whole values in `result.kernel_info.centers`.
 - **Kernel weights(alpha)** are alpha parameters in the linear model. It is optimaized by the algorithm. You can find the whole values in `result.alpha`.
 - **The Funtion to Estimate Density Ratio** is named `compute_density_ratio()`.

README.rmd

@@ -20,6 +20,8 @@ if (Sys.getenv("engine.path") != "") {
 ```
 
 [![Build Status](https://travis-ci.org/hoxo-m/densratio_py.svg?branch=master)](https://travis-ci.org/hoxo-m/densratio_py)
+[![PyPI](https://img.shields.io/pypi/v/densratio.svg)](https://pypi.python.org/pypi/densratio)
+[![PyPI](https://img.shields.io/pypi/dm/densratio.svg)](https://pypi.python.org/pypi/densratio)
 [![Coverage Status](https://coveralls.io/repos/github/hoxo-m/densratio_py/badge.svg?branch=master)](https://coveralls.io/github/hoxo-m/densratio_py?branch=master)
 
 ## 1. Overview
@@ -82,7 +84,13 @@ plt.show()
 
 ## 2. How to Install
 
-You can install the package from GitHub.
+You can install the package from [PyPI](https://pypi.python.org/pypi/densratio).
+
+```:sh
+$ pip install densratio
+```
+
+Also, You can install the package from [GitHub](https://github.com/hoxo-m/densratio_py).
 
 ```:sh
 $ pip install git+https://github.com/hoxo-m/densratio_py.git
@@ -165,7 +173,7 @@ print(result)
 - **Method** is fixed by uLSIF.
 - **Kernel type** is fixed by Gaussian RBF.
 - **Number of kernels** is the number of kernels in the linear model. You can change by setting `kernel_num` parameter. In default, `kernel_num = 100`.
-- **Bandwidth(sigma)** is the Gaussian kernel bandwidth. In default, `sigma = "auto"`, the algorithm automatically select an optimal value by cross validation. If you set `sigma` a number, that will be used. If you set a numeric array, the algorithm select an optimal value in them by cross validation.
+- **Bandwidth(sigma)** is the Gaussian kernel bandwidth. In default, `sigma = "auto"`, the algorithm automatically select an optimal value by cross validation. If you set `sigma` a number, that will be used. If you set `sigma` a numeric array, the algorithm select an optimal value in them by cross validation.
 - **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution `p(x)`. You can find the whole values in `result.kernel_info.centers`.
 - **Kernel weights(alpha)** are alpha parameters in the linear model. It is optimaized by the algorithm. You can find the whole values in `result.alpha`.
 - **The Funtion to Estimate Density Ratio** is named `compute_density_ratio()`.

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md

setup.py

@@ -2,22 +2,16 @@
 
 from setuptools import setup, find_packages
 
-with open('README.md') as f:
-    readme = f.read()
-
-with open('LICENSE') as f:
-    license = f.read()
-
 setup(
     name='densratio',
-    version='0.0.3.9000',
+    version='0.1.3',
     description='A Python Package for Density Ratio Estimation',
-    long_description=readme,
+    long_description='A Python Package for Density Ratio Estimation',
     author='Koji Makiyama',
     author_email='hoxo.smile@gmail.com',
     install_requires=['numpy'],
     url='https://github.com/hoxo-m/densratio_py',
-    license=license,
+    license="MIT + file LICENSE",
     packages=find_packages(exclude=('tests', 'docs')),
     test_suite='tests'
 )