def SOM_RBF():
train = True
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
data_train = scio.loadmat('data_train.mat')
data_test = scio.loadmat('data_test.mat')
data_label = scio.loadmat('label_train.mat')
X = torch.tensor(data_train['data_train']).to(device)
train_label = torch.tensor(data_label['label_train']).to(device)
test_data = torch.tensor((data_test['data_test'])).to(device)
print('Building SOM model...')
model = SOM(input_size=33, hidden_size=16, sigma=np.sqrt(81))
model = model.to(device)
if train == True:
losses = list()
for epoch in range(1000):
running_loss = 0
loss = model.self_organizing(X, epoch) # so phase
running_loss += loss
losses.append(running_loss)
print('epoch = %d, loss = %.2f' % (epoch + 1, running_loss))
for epoch in range(30000):
running_loss = 0
loss = model.convergence(X) # conv phase
running_loss += loss
losses.append(running_loss)
print('epoch = %d, loss = %.2f' % (epoch + 1, running_loss))
train = False
feedback = model.plot_point_map(X, train_label, ['Class 0', 'Class 1'])
a, b, c = feedback
tb = (a - b) / a
tc = (a - c) / a
accuracy0 = np.maximum(tb, tc).mean()
center_vectors = model.weight.T
print('Building RBF model...')
RBF_model = RBF(33, center_vectors, 1)
RBF_model = RBF_model.to(device)
weight = RBF_model.train(X, train_label)
predictions = RBF_model.test(X).unsqueeze(1)
accuracy1 = (predictions == train_label.cpu()).sum() / torch.tensor(
X.size(0)).float()
predictions_y = RBF_model.test(test_data)
print(accuracy1)
return accuracy0, accuracy1, predictions_y
