def fun(restart_iter): np.random.seed(restart_iter) kern = LCM(input_dim=self.problem.DP, num_outputs=data.NI, Q=Q) # print('I am here') return kern.train_kernel(X=data.X, Y=data.Y, computer=self.computer, kwargs=kwargs)
def fun(restart_iter): # np.random.seed(restart_iter) np.random.seed() kern = LCM(input_dim=len(data.P[0][0]), num_outputs=data.NI, Q=Q) return kern.train_kernel(X=data.P, Y=data.O, computer=self.computer, kwargs=kwargs)
def fun(restart_iter): if ('seed' in kwargs): seed = kwargs['seed'] * kwargs['model_restart_threads'] + restart_iter else: seed = restart_iter np.random.seed(seed) kern = LCM(input_dim = len(data.P[0][0]), num_outputs = data.NI, Q = Q) if (restart_iter == 0 and self.M is not None): kern.set_param_array(self.M.kern.get_param_array()) return kern.train_kernel(X = data.P, Y = data.O, computer = self.computer, kwargs = kwargs)