params: dict
errors: np.ndarray
losss: np.ndarray
val_losss: np.ndarray
def __init__(self, params, errors, losss, val_losss):
self.params = params
self.errors = errors
self.losss = losss
self.val_losss = val_losss
if __name__ == '__main__':
SIMULATIONS_NAME = 'previous_research'
params, output_dir = settings.init_simulation(SIMULATIONS_NAME)
# データを生成する
snrs_db = np.linspace(params['SNR_MIN'], params['SNR_MAX'],
params['SNR_NUM'])
sigmas = m.sigmas(snrs_db) # SNR(dB)を元に雑音電力を導出
loss_array = np.zeros(
(params['SNR_NUM'], params['SNR_AVERAGE'], params['nEpochs']))
val_loss_array = np.zeros(
(params['SNR_NUM'], params['SNR_AVERAGE'], params['nEpochs']))
error_array = np.zeros((len(snrs_db), params['SNR_AVERAGE']))
for trials_index in tqdm(range(params['SNR_AVERAGE'])):
h_si = m.channel(1, params['h_si_len'])
h_s = m.channel(1, params['h_s_len'])
"""
return cls.from_dict(params)
@dataclasses.dataclass
class Result:
params: Params
errors: np.ndarray
losss: np.ndarray
val_losss: np.ndarray
if __name__ == '__main__':
SIMULATIONS_NAME = 'ofdm_fde_system_model_delay'
params_dict, output_dir = settings.init_simulation(SIMULATIONS_NAME,
ofdm=True)
params = Params.from_params_dict(params_dict)
delay_array = np.linspace(params.delay_min,
params.delay_max,
params.delay_num,
dtype=int)
sigma = m.sigmas(params.SNR)
sigma = sigma * np.sqrt(params.receive_antenna)
errors = np.zeros((params.delay_num, params.trials))
losss = np.zeros((params.delay_num, params.trials, params.nEpochs))
val_losss = np.zeros((params.delay_num, params.trials, params.nEpochs))
for trials_index in tqdm(range(params.trials)):
h_si = []