bayeso.tp.tp_likelihood

It defines the functions related to likelihood for Student-\(t\) process regression.

bayeso.tp.tp_likelihood.neg_log_ml(X_train: ndarray, Y_train: ndarray, hyps: ndarray, str_cov: str, prior_mu_train: ndarray, use_ard: bool = True, fix_noise: bool = True, use_gradient: bool = True, debug: bool = False) → float | Tuple[float, ndarray]

This function computes a negative log marginal likelihood.

Parameters:

• X_train (numpy.ndarray) – inputs. Shape: (n, d) or (n, m, d).

• Y_train (numpy.ndarray) – outputs. Shape: (n, 1).

• hyps (numpy.ndarray) – hyperparameters for Gaussian process. Shape: (h, ).

• str_cov (str.) – the name of covariance function.

• prior_mu_train (numpy.ndarray) – the prior values computed by get_prior_mu(). Shape: (n, 1).

• use_ard (bool., optional) – flag for automatic relevance determination.

• fix_noise (bool., optional) – flag for fixing a noise.

• use_gradient (bool., optional) – flag for computing and returning gradients of negative log marginal likelihood.

• debug (bool., optional) – flag for printing log messages.

Returns:

negative log marginal likelihood, or (negative log marginal likelihood, gradients of the likelihood).

Return type:

float, or tuple of (float, np.ndarray)

Raises:

AssertionError