Clientmoe

SHA: ``83a71512e6a4aa553866e4301ef11f172d485e23``

* Features

* Added startup message to REST server including tips for OSX users (400)
* Added GPU support to ``cpp_wrappers.expected_improvement.multistart_expected_improvement_optimization``; requires ``max_num_threads == 1`` until future multi-GPU support (368)
* Added the COBYLA optimizer to the expected improvement optimization class. (370)
* OptimizerParameter struct members now directly readable/writeable from Python; added EqualityComparisonMixin (138)
* C++ GradientDescentParameters object now stores ``num_steps_averaged`` (but does not use it yet) (391)
* conda build system for MOE (417)

* Changes

* Switched from `testify` to `py.test` - http://pytest.org/ (36)
* [cleanup] Moved bandits into their own sub directories (375)
* Supply PYTHON_LIBRARY and PYTHON_INCLUDE_DIR vars to cmake automatically in ``setup.py`` (412)
* Added warning when colander version is out of date (413)

* Bugs

* Fixed UCB1 and UCB1-tuned algorithm confidence bound calculation (432)

SHA: ``ab6f959c11a0cacbed6dad618fe6ffed71092116``

* Features

* Implemented BLA (Bayesian Learning Automaton). (373)
* Connected GPU functions to multistart gradient descent optimizer. (270)

* Changes

* Bugs

* variance in a sample arm was dropped in _make_bandit_historical_info_from_params. (385)
* SampleArm's ``__add__`` and ``__str__`` were broken. (387)
* Specifying ``max_num_threads`` on GPU compute paths caused a segfault (394)

SHA: ``8201917e3f9b47b8edd8039ea3278ef8631b0f2a``

* Features

* Added multi-armed bandit endpoint. (255)
* Implemented epsilon-greedy. (255)
* Implemented epsilon-first. (335)
* Implemented UCB1. (354)
* Implemented UCB1-tuned. (366)
* Added support for the L-BFGS-B optimizer. (296)
* Added GPU implementation for q,p-EI and its gradient computation. (219)
* Speed up GPU functions by redesign of memory allocation. (297)

* Changes

* Split up old ``schemas.py`` file into ``schemas/`` directory with several subfiles (291)
* Improved Dockerfile, reducing Docker-based install times substantially, https://hub.docker.com/u/yelpmoe/ (332)
* Created ``min_reqs`` docker container which is a snapshot of all MOE third-party requirements
* Created ``latest``, which tracks the latest MOE build
* Started releasing docker containers for each tagged MOE release (currently just ``v0.1.0``)
* ``GradientDescentOptimization`` (C++) no longer has a separate ``next_points`` output (186)
* LogLikelihood evaluate at point list and latin hypercube search now return status dicts like every other optimizer (189)
* status dicts also a little more informative/standardized now
* Update C++ autodoc tools to handle the new ``gpu`` directory (353)
* Added ``__version__`` to ``moe/__init__.py`` (353)

* Bugs

* Throw exceptions (C++) if ``num_multistarts`` or ``num_random_samples`` is 0 (345)
* ``combined_example`` endpoint was not passing ``kwargs`` through so users could not change the default server (356)
* fix sometimes dropped general ``kwargs`` (358)
* ``mean_var_of_gp_from_historic_data`` was also not passing ``kwargs`` (359)

SHA: ``5fef1d242cc8b6e0d6443522f8ba73ba743607de``

* Features

* initial open source release