self.n_pending_locations = pending_locations.shape[0]
self.n_values = n_values
self.n_acquired += n_values
return np.zeros((n_values, self.model.input_dim))
@property
def samples_left(self):
Number of samples left to sample or sys.maxsize if no limit.
After Change
obj = lambda x: self.evaluate(x, t)
minloc, val = stochastic_optimization(obj, self.model.bounds, self.max_opt_iter)
return np.tile(minloc, (n_values, 1))
class LCBSC(Acquisition):
Lower Confidence Bound Selection Criterion. This is the same form as in
