def test_basic_input1():
EEG = np.array([[1, 2], [3, 4]])
expected_A = np.array([[1.00000014, 1.47051825], [1.47569674, 2.17003868]])
expected_E = np.array([[0., 0.52948166], [1.52430317, 1.82996138]])
A, E = rpca(EEG)
assert (np.allclose(A, expected_A))
assert (np.allclose(E, expected_E))
print('test_basic_input1 Pass')
def test_basic_input3():
EEG = np.array([[1, 2, 3, 4, 5, 6, 7], [10, 2, -30, 6, 15, 39, 92]])
expected_A = np.array([[1.00000298,1.83282343,-0.27067405,4.00000311,4.00997941,4.01023028,4.01036702],\
[1.09121835,2.0000046,-0.29536361,4.36486378,4.37575008,4.37602383,4.37617304]])
expected_E = np.array([[0,0.16717156,3.27067409,0,0.99002007,1.9897692,2.98963246],\
[8.90877893,0,-29.70463642,1.63513338,10.62425039,34.62397664,87.62382742]])
A, E = rpca(EEG)
assert (np.allclose(A, expected_A))
assert (np.allclose(E, expected_E))
print('test_basic_input3 Pass')
def test_params2():
EEG = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
lam = .2
tol = 1e-3
maxIter = 100
expected_A = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
expected_E = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
A, E = rpca(EEG, lam, tol, maxIter)
assert (np.allclose(A, expected_A))
assert (np.allclose(E, expected_E))
print('test_params2 Pass')
def test_basic_input2():
EEG = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
expected_A = np.array([[1.5999989,1.99999912,2.3999982],\
[3.99999961,5.00000076,5.99999904],\
[5.13368148,6.41710344,7.70052173]])
expected_E = np.array([[-5.99998725e-01,4.82951146e-07,6.00002018e-01],\
[0.00000000e+00,-0.00000000e+00,5.78509154e-07],\
[1.86631877e+00,1.58289609e+00,1.29947849e+00]])
A, E = rpca(EEG)
assert (np.allclose(A, expected_A))
assert (np.allclose(E, expected_E))
print('test_basic_input2 Pass')
def test_params3():
EEG = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
lam = .5
tol = 1e-10
maxIter = 1000
expected_A = np.array([[1.45445617,2,2.14299791],\
[3.30885118,4.54994966,4.8752663],\
[3.31508751,4.55852514,4.88445492]])
expected_E = np.array([[-0.45445617,0,0.85700209],\
[0.69114882,0.45005034,1.1247337],\
[3.68491249,3.44147486,4.11554508]])
A, E = rpca(EEG, lam, tol, maxIter)
assert (np.allclose(A, expected_A))
assert (np.allclose(E, expected_E))
print('test_params3 Pass')
def test_params1():
EEG = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
lam = .7
tol = 1e-8
maxIter = 20
expected_A = np.array([[1.21416342,1.99964425,2.79453087],\
[3.99964063,5.00068211,5.94182001],\
[6.75789278,7.94410702,9.00016552]])
expected_E = np.array([[-0.21397595,0.,0.20564244],\
[0.,0.,0.05784774],\
[ 0.24228631,0.05555309,0.]])
A, E = rpca(EEG, lam, tol, maxIter)
assert (np.allclose(A, expected_A))
assert (np.allclose(E, expected_E))
print('test_params1 Pass')
def perform_RPCA(self):
""" perform_RPCA
Uses Robust Principal Component Analysis to remove noise from the data.
Returns
-------
eeg_filt_eog_rpca : numpy.array
eeg data after filtering, eog_regression, and
robust PCA (final cleaned data)
noise : numpy.array
array of the noise removed from rpca
"""
self.eeg_filt_eog_rpca = self.eeg_filt_eog.copy()
self.eeg_filt_eog_rpca.load_data()
self.automagic["perform_RPCA"] = True
self.eeg_filt_eog_rpca._data, self.noise._data = rpca(
self.eeg_filt_eog.get_data(),
self.params["lam"],
self.params["tol"],
self.params["max_iter"],
)
return self.eeg_filt_eog_rpca._data, self.noise._data
def test_incorrect_input():
with pytest.raises(AttributeError):
A, E = rpca("Wrong input Type")
assert (True)
print('test_incorrect_input Pass')
def test_no_input():
with pytest.raises(TypeError):
A, E = rpca()
assert (True)
print('test_no_input Pass')
