Functions
| add_data(newX, newY, regressor[, query, ...]) | |
| batch_start(opt_config, initial_values) | Sets initial values of the optimiser parameters |
| chol_down(L, remIDList) | |
| chol_up(L, Sn, Snn, Snn_noise_std_vec) | |
| chol_up_insert(L, V12, V23, V22, ...) | |
| condition(X, y, kernelFn, hyper_opt_config_copys) | |
| learn(X, Y, cov_fn, optParams[, optCrition, ...]) | |
| learn_folds(folds, cov_fn, optParams[, ...]) | |
| make_folds(X, y, target_size[, method]) | |
| negative_log_marginal_likelihood(Y, L, alpha) | |
| negative_log_prob_cross_val(Y, L, alpha) | |
| optimise_hypers(criterion, optParams) | |
| pack(theta, noisepar) | |
| remove_data(regressor, remID[, query]) | |
| solve_triangular(a, b[, trans, lower, ...]) | Solve the equation a x = b for x, assuming a is a triangular matrix. |
| unpack(theta, unpackinfo) |
Classes
| Delaunay | Delaunay(points, furthest_site=False, incremental=False, qhull_options=None) |
| Folds(n_folds, X, Y, flat_y) |