diff --git a/libensemble/gen_classes/gpCAM.py b/libensemble/gen_classes/gpCAM.py index 884832980..7d1343a37 100644 --- a/libensemble/gen_classes/gpCAM.py +++ b/libensemble/gen_classes/gpCAM.py @@ -74,7 +74,7 @@ def ask_numpy(self, n_trials: int) -> npt.NDArray: input_set=np.column_stack((self.lb, self.ub)), n=n_trials, pop_size=n_trials, - acquisition_function="total correlation", + acquisition_function="expected improvement", max_iter=self.ask_max_iter, # Larger takes longer. gpCAM default is 20. )["x"] print(f"Ask time:{time.time() - start}") @@ -85,14 +85,18 @@ def ask_numpy(self, n_trials: int) -> npt.NDArray: def tell_numpy(self, calc_in: npt.NDArray) -> None: if calc_in is not None: if "x" in calc_in.dtype.names: # SH should we require x in? - self.x_new = np.atleast_2d(calc_in["x"]) + self.x_new = np.expand_dims(calc_in["x"], 1) self.y_new = np.atleast_2d(calc_in["f"]).T nan_indices = [i for i, fval in enumerate(self.y_new) if np.isnan(fval[0])] self.x_new = np.delete(self.x_new, nan_indices, axis=0) self.y_new = np.delete(self.y_new, nan_indices, axis=0) - self.all_x = np.vstack((self.all_x, self.x_new)) - self.all_y = np.vstack((self.all_y, self.y_new)) + if len(self.all_x) == 0 and len(self.all_y) == 0: + self.all_x = self.x_new + self.all_y = self.y_new + else: + self.all_x = np.concat((self.all_x, self.x_new)) + self.all_y = np.concat((self.all_y, self.y_new)) noise_var = self.noise * np.ones(len(self.all_y)) if self.my_gp is None: