gpp_expected_improvement_demo¶

gpp_expected_improvement_demo.cpp¶

moe/optimal_learning/cpp/gpp_expected_improvement_demo.cpp

This is a demo for the Gaussian Process and (optimization of) Expected Improvement capabilities present in this project. These capabilities live in gpp_math.

The layout is:

1. Set up input data sizes
2. Specify hyperparameters
3. Generate (random) set of sampled point locations, noise variances
4. Generate data for the Gaussian Process Prior:
   1. Use a randomly constructed Gaussian Process to generate imaginary objective function values, OR
   2. Use user-provided input training data.* * By defining OL_USER_INPUTS to 1, you can specify your own input data.
5. Select desired concurrent experiment locations (points_being_sampled)
6. Construct Gaussian Process to model the training data “world”
7. Optimize Expected Improvement to decide what point we would sample next

The random case will be generated by repeatedly drawing from a GP at randomly chosen locations. For real-world use cases (or in the case of user-provided data), we use the GP as a surrogate model. For the random case, the surrogate and “reality” are the same.

Then we run Expected Improvement optimization with our GP to produce the next-best point to sample. This highlights the core ideas in the optimal_learining project: GP construction and usage (as a surrogate model) through expected improvement optimization to produce “good” new samples.

Please read and understand the file comments for gpp_math.hpp (and cpp for developers) before going through this example. It also wouldn’t hurt to look at gpp_covariance.hpp.

OL_USER_INPUTS

int main()