gpp_hyper_and_EI_demo¶

gpp_hyper_and_EI_demo.cpp¶

moe/optimal_learning/cpp/gpp_hyper_and_EI_demo.cpp

This demo combines gpp_hyperparameter_optimization_demo.cpp and gpp_expected_improvement_demo.cpp. If you have read and understood those, then this demo should be very straightforward insofar as it is currently almost a direct copy-paste.

The purpose here is to give an “end to end” demo of how someone might use MOE/OL to generate new experimental cohorts, beginning with a set of known experimental cohorts/objective function values, measurement noise, and knowledge of any ongoing experiments.

The basic layout is:

1. Set up input data sizes
2. Generate random hyperparameters
3. Generate (random) set of sampled point locations, noise variances
4. Use a randomly constructed (from inputs in steps 1-3) Gaussian Process (generator) to generate imaginary objective function values
5. Optimize hyperparameters on the constructed function values
6. Select desired concurrent experiment locations (points_being_sampled)
7. Construct Gaussian Process (model) to model the training data “world,” using the optimized hyperparameters
8. Optimize Expected Improvement to decide what point we would sample next
   1. Do this once using the optimized hyperparameters
   2. And again using wrong hyperparameters to emulate a human not knowing how to pick (but drawing from a GP with the same state). To do this, we build another GP (wrong_hyper) using the wrong hyperparameters but the same training data as the model gp
   3. Compare resulting function values

Steps 1-4 happen in both other demos. Step 5 is the heart of gpp_hyperparameter_optimization_demo.cpp and steps 6-7 are the heart of gpp_expected_improvement_demo.cpp.

Please read and understand the file comments for gpp_expected_improvement_demo.cpp (first) and gpp_hyperparameter_optimization_demo.cpp (second) before going through this demo. The comments are a lot sparser here than in the aforementioned two files to avoid redundancy.

int main()