bayeso.tp.tp_kernel¶
It defines the functions related to kernels for Student-\(t\) process regression.
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bayeso.tp.tp_kernel.
get_optimized_kernel
(X_train: numpy.ndarray, Y_train: numpy.ndarray, prior_mu: Optional[Callable], str_cov: str, str_optimizer_method: str = 'SLSQP', 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.
- 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