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This is very preliminary documentation, will be improved in a final release.

To install: set up a Python virtualenv with the packages specified in requirements.txt.

To compile the C++ components (requires boost_python): python setup.py build_ext --inplace

To test: python test/test_sgp.py

To train hyperparameters on a subset of 5000 training points, for a model with 20 inducing points learned during the optimization:
python experiments/code/train_hyperparams.py seismic_tt_ASAR --n-hyper=5000 -f 20 --optimize-xu

To train hyperparameters for an SE model on a subset of 5000 training points:
python experiments/code/train_hyperparams.py seismic_tt_ASAR --n-hyper=5000 --se

(details of initial values, priors, and so on are hard-coded in train_hyperparams.py)

The trained hyperparameters are saved to experiments/models/<dataset_name>/<model_type>/hyperparams_5000.pkl.

Given learned hyperparameters, to train a model and compute predictive performance:
python experiments/code/prediction.py seismic_tt_ASAR csfic20 5000

To measure runtimes for posterior variances:
python experiments/code/timing.py seismic_tt_ASAR csfic20 5000

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Gaussian Process Regression for Python/Numpy

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