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)