def __init__(self, simulation=0): self.is_simulation = simulation self.exp_state = 0 try: with open('exp_data_incomplete.pkl', 'rb') as f: self.opt = pickle.load(f) except: self.opt = Optimizer( base_estimator='GP', acq_func='LCB', acq_optimizer='auto', dimensions=[ (0.0, 4.0), # range for param 1 (eg trajectory final height?) (1.0, 5.0), # range for param 2 (eg trajectory final pitch?) (1.0, 5.0), # range for param 3 (eg audio gain?) (-2.0, 2.0) ], # range for param 4 (eg vibrational state duration?) acq_func_kwargs={ 'kappa': 5 }, # we should prefer explore. howver, with higher dim it will naturally tend to diversify, so kappa could be decreased n_initial_points=10) # NB pitching 90 is problematic for the quaternion, debug required. for now, range should be [0-89] if not self.is_simulation: BaseModule.__init__(self)
def __init__(self, name): BaseModule.__init__(self, name) self.reg_base_path = '' self.params_list = [] self.parse_config() self.cfg_ctr = {}
def create_moduleQ(data1, data2, ctx, seq_len, num_sim, num_hidden, num_acts, min_states, min_imgs, fusion=False, bn=False, is_train=False, nh=False, is_e2e=False): os.environ['MXNET_EXEC_INPLACE_GRAD_SUM_CAP'] = str(100) net = sym_DQN(data1, data2, num_sim, num_hidden, is_train=is_train, num_acts=num_acts, min_states=min_states, min_imgs=min_imgs, fusion=fusion, bn=bn, global_stats=False, no_his=False) mod = BaseModule(symbol=net, data_names=('data1', 'data2'), label_names=None, fixed_param_names=[] if is_e2e else ['data1', 'data2'], context=ctx) mod.bind(data_shapes=[('data1', (seq_len, 3, 224, 112)), ('data2', (seq_len, 3, 224, 112))], for_training=is_train, inputs_need_grad=False) return mod
def __init__(self, name): BaseModule.__init__(self, name) self.conf_file = BASE_DIR + self.path + "\my.ini" self.parse_config_file() self.cfg_ctr = {}
def reset(self, core): BaseModule.reset(self, core) self.exp_state = 0
def __init__(self, name): BaseModule.__init__(self, name) self.conf_file = BASE_DIR + self.path + "\conf\httpd.conf" self.parse_config_file()