Generate samples based on seed and update gaussian kernel (#156)
Generate samples based on seed and update gaussian kernel (#156)

* Fixed random state(seed) on GaussianRegressor (#155)

* FIx random state(seed) on GaussianRegressor

Use the user input or the random generated seed on the GaussianRegressor for reproducibility purpose

* Update bayesian.py

typo

* Generate random samples based on seed

Generate random samples based on input seed for reproducibility.

* Update bayesian.py

* Update bayesian.py

* Revert "Update bayesian.py"

This reverts commit 86a71c0eb5e23bddb825dee0c8eca41c8c20d6be.

* Use 2.5 nu matern kernel on GaussianRegressor

As proposed in http://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf , the matern kernel with a 2.5 nu value results in sample functions which are twice-differentiable, which works better for quasi newton optimization algorithms as the one used (L-BFGS)

* Update bayesian.py

keras-team/keras-tuner,keras-team/keras-tuner