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_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_fit():
"""Test Fit of Kmeans"""
covset = generate_cov(20,3)
km = Kmeans(2)
km.fit(covset)
