Set __version__ to 0.3

CHANGELOG.md

@@ -15,18 +15,18 @@ fine grained control over the iterations.
 * Parallelize L-BFGS minimization runs over the acquisition function.
 * Implement weighted hamming distance kernel for problems with only categorical dimensions.
 * New acquisition function `gp_hedge` that probabilistically chooses one of `EI`, `PI`
-  or `LCB` at every iteration depending upon the cumulative gain.
+or `LCB` at every iteration depending upon the cumulative gain.
 
 ### Bug fixes
 * Warnings are now raised if a point is chosen as the candidate optimum multiple
-  times.
+times.
 * Infinite gradients that were raised in the kernel gradient computation are
-  now fixed.
+now fixed.
 * Integer dimensions are now normalized to [0, 1] internally in `gp_minimize`.
 
 ### API Changes.
 * The default `acq_optimizer` function has changed from `"auto"` to `"lbfgs"`
-  in `gp_minimize`.
+in `gp_minimize`.
 
 
 ## Version 0.2

setup.py

@@ -4,12 +4,12 @@
     from distutils.core import setup
 
 setup(name='scikit-optimize',
-      version='0.2',
+      version='0.3',
       description='Sequential model-based optimization toolbox.',
-      long_description=('Scikit-Optimize, or skopt, is a simple and efficient'
-                        ' library for sequential model-based optimization,'
-                        ' accessible to everybody and reusable in various'
-                        ' contexts.'),
+      long_description=('Scikit-Optimize, or `skopt`, is a simple and efficient'
+                        ' library to minimize (very) expensive and noisy'
+                        ' black-box functions. It implements several methods'
+                        ' for sequential model-based optimization.'),
       url='https://scikit-optimize.github.io/',
       license='BSD',
       author='The scikit-optimize contributors',

skopt/__init__.py

@@ -61,7 +61,7 @@ def f(x):
 from .utils import load, dump
 
 
-__version__ = "0.2"
+__version__ = "0.3"
 
 
 __all__ = (