alpha1_list = []
alpha2_list = []
for i in range(args.num_iteration):
print('Iteration: %d' % i)
print('alpha 1: %f' % alpha1)
print('alpha 2: %f' % alpha2)
fit(cnn, trainloader)
phi = []
for _, (data) in enumerate(trainloader):
features = cnn.flow(data).cpu().detach().numpy()
phi.append(features[0, 2])
[a1, alpha, a2, a3, a4] = levinson(phi, nlags=2)
alpha1 = alpha[0]
alpha2 = alpha[1]
alpha1_list.append(alpha1)
alpha2_list.append(alpha2)
print('\n')
#------------------------------------
# plot the loss and alpha parameters
t = np.arange(len(error_list))
# loss
plt.figure(figsize=[14, 10])
plt.plot(t, error_list, 'r')
plt.title('MSE Loss')
# extract latent features from trained network
phi = []
for batch_idx, (data) in enumerate(trainloader):
data = data.to(device)
features = cnn.flow(data).cpu().detach().numpy()
# add the first 2 features in the 1st trunk
if batch_idx == 0:
for i in range(len(features[0]) - 1):
phi.append(features[0, i])
# add the last features (predicting y) in every trunk
phi.append(features[0, -1])
# train alpha parameter with RLS
[_, alpha, _, _, _] = levinson(phi, nlags=args.window_size - 1)
alpha1 = alpha[0]
alpha2 = alpha[1]
alpha1_list.append(alpha1)
alpha2_list.append(alpha2)
# test
MSE_test = 0
for batch_idx, (data) in enumerate(testloader):
# pass test data into trained network
data = data.to(device)
features = cnn.flow(data)
# calculate predicted y3
y1 = features[0, 0]
y2 = features[0, 1]
data = data[:-2]
data = data.to(device)
features = cnn.flow(data).cpu().detach().numpy()
# add the first 5 features in the 1st trunk
if batch_idx == 0:
for i in range(len(features[0]) - 1):
phi.append(features[0, i])
# add the last features (predicting y) in every trunk
phi.append(features[0, -1])
# train alpha parameter with RLS
phi_even = phi[0::2]
phi_odd = phi[1::2]
[_, alpha_even, _, _, _] = levinson(phi_even,
nlags=int(args.window_size / 2))
[_, alpha_odd, _, _, _] = levinson(phi_odd,
nlags=int(args.window_size / 2))
# convert alpha to tensor
alpha_odd = torch.from_numpy(alpha_odd).type(torch.FloatTensor)
alpha_even = torch.from_numpy(alpha_even).type(torch.FloatTensor)
# test
MSE_test = 0
for batch_idx, (data) in enumerate(testloader):
# pass test data into trained network
data = data.to(device)
x = data[-1].cpu().detach().numpy()
data = data[:-2]
features = cnn.flow(data)