famepopla.blogg.se - Boost cmake install brew

#Boost cmake install brew update

See port select information.Ĭontinue with the installation instructions. In particular, OS X users want to see python27 (active), not python27-apple (active).

Using port select -list, check that the active versions of gcc, python, etc. The previous assumes that you want to use gcc 4.7 and Python 2.7 modify the install and set invocations if you want other versions. $ export MOE_CMAKE_OPTS=-DCMAKE_FIND_ROOT_PATH=/opt/local & export MOE_CC_PATH=/opt/local/bin/gcc & export MOE_CXX_PATH=/opt/local/bin/g++ $ sudo port install boost # <- DO NOT run this in OS X 10.9! In addition to this list, double check that all items on Install from source are also installed. If you are using another package manager (like homebrew) you may need to modify opt/local below to point to your Cellar directory.įor the following commands, order matters items further down the list may depend on previous installs. Make sure you create your virtualenv with the correct python -python=/opt/local/bin/python if you are using MacPorts. So start a new shell or run export PATH=/opt/local/bin:/opt/local/sbin:$PATH. bashrc), but that command will not run immediately after MacPorts installation. It sets this in your shell’s rcfile (e.g. MacPorts requires that your PATH variable include /opt/local/bin:/opt/local/sbin. MacPorts is one of many OS X package managers we will use it to install MOE’s core requirements. Read General MacPorts Tips if you are not familiar with MacPorts.

#Boost cmake install brew update

(If you change the install directory from /opt/local, don’t forget to update the cmake invocation.) You must install it from source see warnings below.Īre you sure you wouldn’t rather be running linux?ĭownload MacPorts. OS X 10.9 users beware: do not install boost with MacPorts.

gpp_heuristic_expected_improvement_optimization.

gpp_mock_optimization_objective_functions.

gpp_heuristic_expected_improvement_optimization_test.

moe_examples.next_point_via_simple_endpoint module.

moe_an_and_var_of_gp_from_historic_data module.

moe_examples.hyper_opt_of_gp_from_historical_data module.

moe_examples.blog_post_example_ab_testing module.

How many function evaluations do I perform before I update the hyperparameters of the GP?.

How many function evaluations do I need before MOE is “done”?.

How do I bootstrap MOE? What initial data does it need?.

Why does MOE take so long to return the next points to sample for some inputs?.

Setting thresholds for advertising units.

Hyperparameter optimization of a Gaussian Process.

Gaussian Process regression given historical data.

What is the multi-armed bandit problem?.

Return the point(s) to sample, then repeat.Find the point(s) of highest Expected Improvement (EI).Optimize the hyperparameters of the Gaussian Process.Build a Gaussian Process (GP) with the historical data.