We propose a Bayesian optimization approach for hyperparameter optimization problem.
We use experiment design approaches to initialize sampled. 
Our implementation is based on https://github.com/rubinxin/CoCaBO_code.

Run the code by the following flags:
  * `--func` objective function, 
  * `--max_iter` maximum number of iteration,
  * `--trials` number of trials,
  * `--save_path` saving path,
  * `--kernel_mix` mixture weight,
  * `--init` experiment design method,
  * `--nb_init` the number of initial points,
  * `--kernel_path` the path gp model.
 
`python main.py --func="rosenbrock" --max_itr=100 --trials=10 --save_path="log/2_lhs_" --kernel_mix=-1.0 --init=lhs --nb_init=10 --kernel_path="./log/2_lhd_rosenbrock" &&`
