def load_npz(self, name='ECG200', target=None):
# data, label = load_npz(name)
(data, label), (_, _) = load_data(name)
if not target:
inx = range(len(data))
else:
inx = label == target
dataset = Data.TensorDataset(
torch.tensor(data[inx], dtype=torch.float32),
torch.tensor(label[inx], dtype=torch.long))
return dataset
y_gen = []
[m.eval() for m in model.models.values()]
for i in LABELS:
path = './ckpt/model_{:d}.pkl'.format(i)
model.load_model(path)
noise = model.gen_noise(0, 1, (NUM, LATENT_DIM))
x = model.generator(noise)
x = x.data.cpu().numpy().reshape(x.size(0), -1)
y = np.zeros(NUM) + i
x_gen.append(x)
y_gen.append(y)
x_gen = np.concatenate(x_gen)
y_gen = np.concatenate(y_gen)
data, label = load_npz()
(x_train, y_train), (x_test, y_test) = load_data()
clf_model = KNeighborsClassifier(n_neighbors=1)
clf_model.fit(x_train, y_train)
pred_data = clf_model.predict(data)
pred_test = clf_model.predict(x_test)
pred_gen = clf_model.predict(x_gen)
classify_analysis(label, pred_data, labels=LABELS)
classify_analysis(y_test, pred_test, labels=LABELS)
classify_analysis(y_gen, pred_gen, labels=LABELS)
print('-' * 20 + 'TSTR' + '-' * 20)
clf_model.fit(x_gen, y_gen)
pred_data = clf_model.predict(data)
pred_test = clf_model.predict(x_test)
pred_gen = clf_model.predict(x_gen)
classify_analysis(label, pred_data, labels=LABELS)