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 = []