def testInterface(self):
self.assertRaises(TypeError, plainica.plainica)
# simply pass in different data shapes and see if the functions runs without error
plainica.plainica(np.sin(np.arange(30)).reshape(
(10, 3))) # 10 samples, 3 channels
plainica.plainica(np.sin(np.arange(30)).reshape(
(5, 3, 2))) # 5 samples, 3 channels, 2 trials
def testModelIdentification(self):
""" generate independent signals, mix them, and see if ICA can reconstruct the mixing matrix
do this for every backend """
# original model coefficients
b0 = np.zeros((3, 3)) # no connectivity
m0 = b0.shape[0]
l, t = 100, 100
# generate VAR sources with non-gaussian innovation process, otherwise ICA won't work
noisefunc = lambda: np.random.normal(size=(1, m0))**3
var = VAR(1)
var.coef = b0
sources = var.simulate([l, t], noisefunc)
# simulate volume conduction... 3 sources measured with 7 channels
mix = [[0.5, 1.0, 0.5, 0.2, 0.0, 0.0, 0.0],
[0.0, 0.2, 0.5, 1.0, 0.5, 0.2, 0.0],
[0.0, 0.0, 0.0, 0.2, 0.5, 1.0, 0.5]]
data = datatools.dot_special(sources, mix)
backend_modules = [import_module('scot.' + b) for b in scot.backends]
for bm in backend_modules:
result = plainica.plainica(data, backend=bm.backend)
i = result.mixing.dot(result.unmixing)
self.assertTrue(
np.allclose(i, np.eye(i.shape[0]), rtol=1e-6, atol=1e-7))
permutations = [[0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0],
[2, 0, 1], [2, 1, 0]]
bestdiff = np.inf
bestmix = None
absmix = np.abs(result.mixing)
absmix /= np.max(absmix)
for p in permutations:
estmix = absmix[p, :]
diff = np.sum((np.abs(estmix) - np.abs(mix))**2)
if diff < bestdiff:
bestdiff = diff
bestmix = estmix
self.assertTrue(np.allclose(bestmix, mix, rtol=1e-1, atol=1e-1))
def testModelIdentification(self):
""" generate independent signals, mix them, and see if ICA can reconstruct the mixing matrix
do this for every backend """
# original model coefficients
b0 = np.zeros((3, 3)) # no connectivity
m0 = b0.shape[0]
l, t = 100, 100
# generate VAR sources with non-gaussian innovation process, otherwise ICA won't work
noisefunc = lambda: np.random.normal(size=(1, m0)) ** 3
var = VAR(1)
var.coef = b0
sources = var.simulate([l, t], noisefunc)
# simulate volume conduction... 3 sources measured with 7 channels
mix = [[0.5, 1.0, 0.5, 0.2, 0.0, 0.0, 0.0],
[0.0, 0.2, 0.5, 1.0, 0.5, 0.2, 0.0],
[0.0, 0.0, 0.0, 0.2, 0.5, 1.0, 0.5]]
data = datatools.dot_special(sources, mix)
backend_modules = [import_module('scot.backend.' + b) for b in scot.backend.__all__]
for bm in backend_modules:
result = plainica.plainica(data, backend=bm.backend)
i = result.mixing.dot(result.unmixing)
self.assertTrue(np.allclose(i, np.eye(i.shape[0]), rtol=1e-6, atol=1e-7))
permutations = [[0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0]]
bestdiff = np.inf
bestmix = None
absmix = np.abs(result.mixing)
absmix /= np.max(absmix)
for p in permutations:
estmix = absmix[p, :]
diff = np.sum((np.abs(estmix) - np.abs(mix)) ** 2)
if diff < bestdiff:
bestdiff = diff
bestmix = estmix
self.assertTrue(np.allclose(bestmix, mix, rtol=1e-1, atol=1e-1))
def testInterface(self):
self.assertRaises(TypeError, plainica.plainica)
# simply pass in different data shapes and see if the functions runs without error
plainica.plainica(np.sin(np.arange(30)).reshape((10, 3))) # 10 samples, 3 channels
plainica.plainica(np.sin(np.arange(30)).reshape((5, 3, 2))) # 5 samples, 3 channels, 2 trials