Comments (2)
Thanks for the wrapper! If you want to integrate it in the library, please send me an email [email protected] and I'll help you with that.
Right now the C API is only a wrapper of the full optimization call. You can't even perform step by step optimization. I have been wanting to refactor the C API and make it more general for a while, but it would require a whole redesign of the API for all languages.
If you want to see how to access the surrogate model from C++, you can see how it is displayed in the examples, using this helper class https://github.com/rmcantin/bayesopt/blob/master/utils/displaygp.cpp
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Thanks for your response.
I think there is no need to integrate the wrapper here. Julia packages are usually in their separate github repository anyway. But feel free to advertise it here 😄.
On an unrelated note, do I understand correctly that the implementation of Thompson sampling simply samples a value from the GP for every input given by the optimizer in the acquisition function? If yes, does this really lead to samples from the maximum distribution in the case of continuous input spaces?
Do you think there would be some benefit in using methods like the one proposed in https://arxiv.org/abs/1604.00169?
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Related Issues (20)
- Python 3 support HOT 3
- Multicore processing HOT 1
- function evaluation out of range HOT 1
- compatibility with python3 HOT 2
- Use BayesOPT to optimize categorical variables HOT 2
- About mSigma (variance) in gaussian_process.cpp HOT 1
- Build error on Ubuntu 17.04 HOT 4
- Why it doesn't converge to the right value HOT 1
- noise effect HOT 1
- Compatibility with python 3.6 HOT 1
- Issue in MATLAB compilation HOT 1
- center of search space HOT 1
- example: Build + Install on Google Colabratory w/ Python 3.6 HOT 2
- Segfault for low discrete parameter space HOT 1
- MultiObjective optimization HOT 1
- [Request] Add a CITATION.cff file HOT 1
- Build Fails for BAYESOPT_BUILD_SHARED=ON HOT 2
- Utilise Boost to provide some sort of clue for unknown errors HOT 1
- bayesopt.optimize_discrete HOT 1
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