def _eval_func(self, x):
    '\n        Performs sequential evaluations of the function at x (single location or batch). The computing time of each\n        evaluation is also provided.\n        '
    cost_evals = []
    f_evals = np.empty(shape=[0, self.n_obj])
    for i in range(x.shape[0]):
        st_time = time.time()
        rlt = self.func(np.atleast_2d(x[i]))
        f_evals = np.vstack([f_evals, rlt])
        cost_evals += [(time.time() - st_time)]
    return (f_evals, cost_evals)
Performs sequential evaluations of the function at x (single location or batch). The computing time of each
evaluation is also provided.
def _eval_func(self, x):
    '\n        Performs sequential evaluations of the function at x (single location or batch). The computing time of each\n        evaluation is also provided.\n        '
    cost_evals = []
    f_evals = np.empty(shape=[0, self.n_obj])
    for i in range(x.shape[0]):
        st_time = time.time()
        rlt = self.func(np.atleast_2d(x[i]))
        f_evals = np.vstack([f_evals, rlt])
        cost_evals += [(time.time() - st_time)]
    return (f_evals, cost_evals)