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_predict(): """Test prediction of Kmeans""" covset = generate_cov(20, 3) km = Kmeans(2) km.fit(covset) km.predict(covset)
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_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_init(): """Test init of Kmeans""" km = Kmeans(2)
def test_Kmeans_fit(): """Test Fit of Kmeans""" covset = generate_cov(20, 3) km = Kmeans(2) km.fit(covset)
filtered_offline_signal = _bandpass_filter(offline_raw, frequencies, frequency_range) offline_raw = createRaw(filtered_offline_signal, offline_raw, filtered=True) offline_epochs = Epochs(offline_raw, offline_events, event_id, tmin=2, tmax=5, baseline=None) offline_epochs_data = offline_epochs.get_data() labels = offline_epochs.events[:, -1] epochs_data = offline_epochs_data kmeans = Kmeans(n_clusters=4) time_array = [] print("\nlabels: ") print(labels, "\n") # first resolve an EEG stream on the lab network print("looking for an EEG stream...") streams = resolve_stream('name', 'openvibeSignal') # create a new inlet to read from the stream inlet = StreamInlet(streams[0]) sample, timestamp = inlet.pull_sample() time_window = np.array(sample) sample, timestamp = inlet.pull_sample() sample = np.array(sample)
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_transform(): """Test transform of Kmeans""" covset = generate_cov(20,3) km = Kmeans(2) km.fit(covset) km.transform(covset)
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_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_transform(): """Test transform of Kmeans""" covset = generate_cov(20, 3) km = Kmeans(2) km.fit(covset) km.transform(covset)
labels_base = copy.deepcopy(labels) # Covariance Matrix transorm off_cov_matrix = Covariances(estimator='lwf').transform(off_epochs_data) # MDM model init and fit mdm = MDM(metric=dict(mean='riemann', distance='riemann')) mdm.fit(off_cov_matrix, labels) # End of offline training # EEG stream on the lab network print("looking for an EEG stream...") streams = resolve_stream('name', 'openvibeSignal') # Create a new inlet to read from the stream inlet = StreamInlet(streams[0]) kmeans = Kmeans(n_clusters=4) time_window = timeWindowInit(inlet) time_window_base = copy.deepcopy(time_window) timeBase = time.time() count = 0 time_array = [] while not keyboard.is_pressed('s'): time_window = copy.deepcopy(time_window_base) while time_window.shape[1] < 769: sample, timestamp = inlet.pull_sample() sample = np.array(sample) time_window = np.column_stack((time_window, sample)) actualTime = time.time() - timeBase
def test_Kmeans_fit(): """Test Fit of Kmeans""" covset = generate_cov(20,3) km = Kmeans(2) km.fit(covset)