bayeso.gp.gp_kernel

It defines the functions related to kernels for Gaussian process regression.

bayeso.gp.gp_kernel.get_optimized_kernel(X_train: numpy.ndarray, Y_train: numpy.ndarray, prior_mu: Optional[Callable], str_cov: str, str_optimizer_method: str = 'BFGS', str_modelselection_method: str = 'ml', use_ard: bool = True, fix_noise: bool = True, debug: bool = False) → Tuple[numpy.ndarray, numpy.ndarray, dict]

This function computes the kernel matrix optimized by optimization method specified, its inverse matrix, and the optimized hyperparameters.

Parameters:

• X_train (numpy.ndarray) – inputs. Shape: (n, d) or (n, m, d).
• Y_train (numpy.ndarray) – outputs. Shape: (n, 1).
• prior_mu (callable or NoneType) – prior mean function or None.
• str_cov (str.) – the name of covariance function.
• str_optimizer_method (str., optional) – the name of optimization method.
• str_modelselection_method (str., optional) – the name of model selection method.
• use_ard (bool., optional) – flag for using automatic relevance determination.
• fix_noise (bool., optional) – flag for fixing a noise.
• debug (bool., optional) – flag for printing log messages.

Returns:

a tuple of kernel matrix over X_train, kernel matrix inverse, and dictionary of hyperparameters.

Return type:

tuple of (numpy.ndarray, numpy.ndarray, dict.)

Raises:

AssertionError, ValueError