def example(self): r = get_random_example(1, config.TRIM_LENGTH) e = fft_data(r) e = flatten_complex_data(e) if self.in_cpu: e = torch.tensor(e) else: e = torch.tensor(e, device=self.device, dtype=torch.float32) e = self.forward(e).detach().cpu() e = np.array(e) e = iflatten_complex_data(e) e = ifft_data(e)[0] r = np.array(r[0]) return e - r
from data_reader import write_one_file from multiprocessing import set_start_method import random import os # time.sleep(13500) # Prepare the training set for this model print('Preparing the training set...') if config.TRIM_LENGTH is None: set_trim_length(300) train_set = trim_data(standardize_all_data()) train_set, WIN = window(train_set, 'hann') print(WIN.shape) train_set = fft_data(train_set) train_set, dim = lpf_dimension_reduction(train_set, frequency=10) train_set = flatten_complex_data(train_set) print(dim) print('Training set is ready!') class Complex_Fully_Connected_Linear_Discriminator_LPF(nn.Module): def __init__(self, dimension): super(Complex_Fully_Connected_Linear_Discriminator_LPF, self).__init__() self.n_in = dimension * 2 * 6 # real part and imaginary part are saperated # hidden linear layers self.linear1 = nn.Linear(self.n_in, self.n_in * 12) self.linear2 = nn.Linear(self.n_in * 12, self.n_in * 12) self.linear3 = nn.Linear(self.n_in * 12, self.n_in * 12) self.linear4 = nn.Linear(self.n_in * 12, 1)
# time.sleep(13500) # Prepare the training set for this model print('Preparing the training set...') if config.TRIM_LENGTH is None: set_trim_length(300) data = trim_data(standardize_all_data(), 150) data_set = [] for i in range(len(data) - 2): d = np.concatenate((data[i], data[i + 1]), 0) data_set.append(d) data_set, WIN = window(data_set, 'hann') data_set = fft_data(data_set) data_set, dim = lpf_dimension_reduction(data_set, frequency=10) data_set = flatten_complex_data(data_set) train_set = [] for i in range(0, len(data_set) - 2, 3): d = [data_set[i], data_set[i + 1], data_set[i + 2]] train_set.append(d) x = torch.tensor(train_set) print(x[0:10, 2, :].shape) print(x.shape[1]) print('Training set is ready!') class Complex_Fully_Connected_Linear_Discriminator_LPF(nn.Module): def __init__(self, dimension):
from dynamic_reporter import init_dynamic_report from dynamic_reporter import stop_dynamic_report from dynamic_reporter import report from data_reader import write_one_file from multiprocessing import set_start_method import random import os # Prepare the training set for this model print('Preparing the training set...') if config.TRIM_LENGTH is None: set_trim_length(300) origin = trim_data(standardize_all_data()) data_p = pca_data(origin, 4) data_pf = fft_data(data_p) train_set = flatten_complex_data(data_pf) print(train_set.shape) print('Training set is ready!') class Complex_Fully_Connected_Linear_Discriminator(nn.Module): def __init__(self, dimension): super(Complex_Fully_Connected_Linear_Discriminator, self).__init__() self.n_in = dimension * ( config.TRIM_LENGTH // 2 + 1) * 2 # real part and imaginary part are saperated # hidden linear layers self.linear1 = nn.Linear(self.n_in, self.n_in) self.linear2 = nn.Linear(self.n_in, self.n_in) self.linear3 = nn.Linear(self.n_in, self.n_in)