def test_smacc_min_endmembers(self):
'''Tests that smacc runs with min_endmember argument.'''
# Without scaling numeric errors accumulate.
scaled_data = self.data / 10000
S, F, R = spy.smacc(scaled_data, 10)
data_shape = scaled_data.shape
H = scaled_data.reshape(data_shape[0] * data_shape[1], data_shape[2])
assert (np.allclose(np.matmul(F, S) + R, H))
assert (np.min(F) == 0.0)
assert (len(S.shape) == 2 and S.shape[0] == 10 and S.shape[1] == 220)
def test_smacc_runs(self):
'''Tests that smacc runs without additional arguments.'''
# Without scaling numeric errors accumulate.
scaled_data = self.data / 10000
S, F, R = spy.smacc(scaled_data)
data_shape = scaled_data.shape
H = scaled_data.reshape(data_shape[0] * data_shape[1], data_shape[2])
assert_allclose(np.matmul(F, S) + R, H)
assert(np.min(F) == 0.0)
assert(len(S.shape) == 2 and S.shape[0] == 9 and S.shape[1] == 220)
def test_smacc_max_residual_norm(self):
'''Tests that smacc runs with max_residual_norm argument.'''
# Without scaling numeric errors accumulate.
scaled_data = self.data / 10000
S, F, R = spy.smacc(scaled_data, 9, 0.8)
data_shape = scaled_data.shape
H = scaled_data.reshape(data_shape[0] * data_shape[1], data_shape[2])
assert (np.allclose(np.matmul(F, S) + R, H))
assert (np.min(F) == 0.0)
residual_norms = np.einsum('ij,ij->i', R, R)
assert (np.max(residual_norms) <= 0.8)
def test_smacc_minimal(self):
'''Tests smacc correctness on minimal example.'''
H = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0],
[0.0, 1.0, 1.0]])
S, F, R = spy.smacc(H)
assert (np.allclose(np.matmul(F, S) + R, H))
assert (np.min(F) == 0.0)
expected_S = np.array([
# First two longer ones.
[1., 1., 0.],
[0., 1., 1.],
# First of the two shorted ones. Other can be expressed other 3.
[1., 0., 0.],
])
assert (np.array_equal(S, expected_S))