Recently, we introduced a structured Gaussian Process (sGP), where a classical GP is augmented by a structured probabilistic model of the expected system’s behavior. The limitation of the standard GP is that it does not usually allow for the incorporation of prior domain knowledge and can be biased toward a trivial interpolative solution. predict ( rng_key_predict, X_test )įor 1-dimensional data, we can plot the GP prediction using the standard approach where the uncertainty in predictions - represented by a standard deviation in y_sampled - is depicted as a shaded area around the mean value. Hence, a prediction on new inputs with a trained GP model returns the center of the mass of all the predictive means ( y_pred) and samples from multivariate normal distributions for all the pairs of predictive means and covariances ( y_sampled). In the fully Bayesian mode, we get a pair of predictive mean and covariance for each Hamiltonian Monte Carlo sample containing the GP parameters (in this case, the RBF kernel hyperparameters and model noise). fit ( rng_key, X, y ) # X and y are numpy arrays with dimensions (n, d) and (n,) ExactGP ( 1, kernel = 'RBF' ) # Run Hamiltonian Monte Carlo to obtain posterior samples for the GP model parameters gp_model. get_keys () # Initialize model gp_model = gpax.
SUPERFIGHTERS GPAX GENERATOR
First, we infer GP model parameters from the available training data import gpax # Get random number generator keys for training and prediction rng_key, rng_key_predict = gpax.
SUPERFIGHTERS GPAX HOW TO
The code snippet below shows how to use vanilla GP in a fully Bayesian mode. It is a work in progress, and more models will be added in the near future. Its purpose is to take advantage of prior physical knowledge and different data modalities when using GPs for data reconstruction and active learning.
GPax is a small Python package for physics-based Gaussian processes (GPs) built on top of NumPyro and JAX.
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