def test_Kmeans_init(): """Test Kmeans""" covset = generate_cov(20, 3) labels = np.array([0, 1]).repeat(10) # init km = Kmeans(2) # fit km.fit(covset) # fit with init km = Kmeans(2, init=covset[0:2]) km.fit(covset) # fit with labels km.fit(covset, y=labels) # predict km.predict(covset) # transform km.transform(covset) # n_jobs km = Kmeans(2, n_jobs=2) km.fit(covset)
def test_Kmeans_transform(): """Test transform of Kmeans""" covset = generate_cov(20, 3) km = Kmeans(2) km.fit(covset) km.transform(covset)
def test_Kmeans_predict(): """Test prediction of Kmeans""" covset = generate_cov(20, 3) km = Kmeans(2) km.fit(covset) km.predict(covset)
def test_Kmeans_fit_parallel(): """Test Fit of Kmeans using paralell""" covset = generate_cov(20, 3) km = Kmeans(2, n_jobs=2) km.fit(covset)
def test_Kmeans_fit_with_y(): """Test Fit of Kmeans with a given y""" covset = generate_cov(20, 3) labels = np.array([0, 1]).repeat(10) km = Kmeans(2) km.fit(covset, y=labels)
def test_Kmeans_fit_with_init(): """Test Fit of Kmeans wit matric initialization""" covset = generate_cov(20, 3) km = Kmeans(2, init=covset[0:2]) km.fit(covset)
def test_Kmeans_fit(): """Test Fit of Kmeans""" covset = generate_cov(20, 3) km = Kmeans(2) km.fit(covset)
filtered_signal = np.expand_dims(filtered_signal, axis=0) epochs_data = np.concatenate((epochs_data, filtered_signal), axis=0) ## No Filtering # raw_evoked_signal = evoked.data # raw_evoked_signal = np.array(raw_evoked_signal) # raw_evoked_signal = np.expand_dims(raw_evoked_signal, axis=0) cov_ext_trials = Covariances(estimator='lwf').transform(epochs_data) ### TREINO ONLINE: AQUI NÃO FAZ SENTIDO #labels = np.append(labels, labels[count]) # Fit a cada 4 janelas de tempo if (count % 4 == 0 and count != 0 and retrain == True): kmeans.fit(cov_ext_trials) print("RETRAINED") if (count > 5): prediction_labeled = kmeans.predict(cov_ext_trials) else: prediction_labeled = mdm.predict(cov_ext_trials) # Finish Time Counter time_2 = time.time() time_array.append(time_2 - time_1) count += 1 print("Predictions: ") print(prediction_labeled[:32]) print(prediction_labeled[32:]) # print ("Label: " + str(labels[i]))
def test_Kmeans_transform(): """Test transform of Kmeans""" covset = generate_cov(20,3) km = Kmeans(2) km.fit(covset) km.transform(covset)
def test_Kmeans_predict(): """Test prediction of Kmeans""" covset = generate_cov(20,3) km = Kmeans(2) km.fit(covset) km.predict(covset)
def test_Kmeans_fit_parallel(): """Test Fit of Kmeans using paralell""" covset = generate_cov(20,3) km = Kmeans(2,n_jobs=2) km.fit(covset)
def test_Kmeans_fit_with_y(): """Test Fit of Kmeans with a given y""" covset = generate_cov(20,3) labels = np.array([0,1]).repeat(10) km = Kmeans(2) km.fit(covset,y=labels)
def test_Kmeans_fit_with_init(): """Test Fit of Kmeans wit matric initialization""" covset = generate_cov(20,3) km = Kmeans(2,init=covset[0:2]) km.fit(covset)
def test_Kmeans_fit(): """Test Fit of Kmeans""" covset = generate_cov(20,3) km = Kmeans(2) km.fit(covset)