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)