documentating

321HG · Jul 27, 2014 · c7f04c0 · c7f04c0
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diff --git a/docs/user/index.rst b/docs/user/index.rst
@@ -20,6 +20,12 @@ some tuple of hyperparameters. Specifying the objective function must be done by
 the user. The software offers a diverse set of solvers to optimize the objective
 function. A solver determines an optimal tuple of hyperparameters.
 
+.. sidebar:: **Jump to**
+
+    * :doc:`Solver docs <user/solvers>`
+    * :doc:`Cross-validation <user/cross_validation>`
+    * :doc:`Function decorators <user/decorators>`
+
 Optunity consists of a set of core functions that are offered in each environment,
 which we will now discuss briefly. Clicking on a function will take you to its Python
 API documentation. If you are using a different environment, you can still get the
@@ -58,18 +64,22 @@ please refer to the expert functions listed below or to submodules.
         :doc:`/api/optunity.solvers` for more details.
 
 - :func:`optunity.cross_validated`: decorator to perform k-fold cross-validation
-    TODO
+    Wrap a function with cross-validation functionality. The way cross-validation
+    is performed is highly configurable, including support for strata and clusters.
 
 Expert interface
 -----------------
 
 - :func:`optunity.suggest_solver`: suggests a solver and its configuration
     Optunity will make suggestions based on the number of evaluations and
     box constraints.
-- :func:`optunity.optimize` 
-- :func:`optunity.make_solver`
+- :func:`optunity.optimize`: optimizes an objective function with given solver
+    Some solvers are capable of vector evaluations. By default, the optimization
+    is done through sequential function evaluations but this can be parallelized
+    by specifying an appropriate ``pmap`` argument (cfr. :func:`optunity.parallel.pmap`).
+- :func:`optunity.make_solver`: constructs one of Optunity's registered solvers.
+    See the solver-specific manuals for more information per solver.
 - :func:`optunity.manual`
-- :func:`optunity.cross_validated`
 
 
 Terminology

diff --git a/setup.py b/setup.py
@@ -14,4 +14,20 @@
     install_requires=[
           'deap >= 1.0.1',
       ],
+    classifiers=[
+    'Development Status :: 4 - Beta',
+    'Intended Audience :: Developers',
+    'Intended Audience :: Education',
+    'Intended Audience :: Science/Research',
+    'Programming Language :: Python',
+    'Topic :: Scientific/Engineering',
+    'License :: OSI Approved :: BSD License'
+    'Topic :: Scientific/Engineering :: Artificial Intelligence'
+    'Topic :: Scientific/Engineering :: Information Analysis'
+    ],
+    platforms=['any'],
+    keywords=['machine learning', 'parameter tuning',
+              'hyperparameter optimization', 'meta-optimization',
+              'direct search', 'model selection'
+    ],
 )