def testModelIdentification(self):
""" generate VAR signals, mix them, and see if MVARICA can reconstruct the signals
do this for every backend """
# original model coefficients
b0 = np.zeros((3, 6))
b0[1:3, 2:6] = [[0.4, -0.2, 0.3, 0.0],
[-0.7, 0.0, 0.9, 0.0]]
m0 = b0.shape[0]
l, t = 1000, 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(2)
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:
api = scot.Workspace({'model_order': 2}, backend=bm.backend)
api.set_data(data)
# apply MVARICA
# - default setting of 0.99 variance should reduce to 3 channels with this data
# - automatically determine delta (enough data, so it should most likely be 0)
api.do_mvarica()
#result = varica.mvarica(data, 2, delta='auto', backend=bm.backend)
# ICA does not define the ordering and sign of components
# so wee need to test all combinations to find if one of them fits the original coefficients
permutations = np.array(
[[0, 1, 2, 3, 4, 5], [0, 1, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1], [2, 3, 0, 1, 4, 5], [4, 5, 0, 1, 2, 3],
[4, 5, 2, 3, 0, 1]])
signperms = np.array(
[[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, -1, -1], [1, 1, -1, -1, 1, 1], [1, 1, -1, -1, -1, -1],
[-1, -1, 1, 1, 1, 1], [-1, -1, 1, 1, -1, -1], [-1, -1, -1, -1, 1, 1], [-1, -1, -1, -1, -1, -1]])
best, d = np.inf, None
for perm in permutations:
b = api.var_.coef[perm[::2] // 2, :]
b = b[:, perm]
for sgn in signperms:
c = b * np.repeat([sgn], 3, 0) * np.repeat([sgn[::2]], 6, 0).T
err = np.sum((c - b0) ** 2)
if err < best:
best = err
d = c
self.assertTrue(np.all(abs(d - b0) < 0.05))
def testModelIdentification(self):
""" generate VAR signals, mix them, and see if MVARICA can reconstruct the signals
do this for every backend """
# original model coefficients
b0 = np.zeros((3, 6))
b0[1:3, 2:6] = [[0.4, -0.2, 0.3, 0.0], [-0.7, 0.0, 0.9, 0.0]]
m0 = b0.shape[0]
l, t = 1000, 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(2)
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:
# apply MVARICA
# - default setting of 0.99 variance should reduce to 3 channels with this data
# - automatically determine delta (enough data, so it should most likely be 0)
result = varica.mvarica(data,
var,
optimize_var=True,
backend=bm.backend)
# ICA does not define the ordering and sign of components
# so wee need to test all combinations to find if one of them fits the original coefficients
permutations = np.array([[0, 1, 2, 3, 4, 5], [0, 1, 4, 5, 2, 3],
[2, 3, 4, 5, 0, 1], [2, 3, 0, 1, 4, 5],
[4, 5, 0, 1, 2, 3], [4, 5, 2, 3, 0, 1]])
signperms = np.array([[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, -1, -1],
[1, 1, -1, -1, 1, 1], [1, 1, -1, -1, -1, -1],
[-1, -1, 1, 1, 1, 1], [-1, -1, 1, 1, -1, -1],
[-1, -1, -1, -1, 1, 1],
[-1, -1, -1, -1, -1, -1]])
best, d = np.inf, None
for perm in permutations:
b = result.b.coef[perm[::2] // 2, :]
b = b[:, perm]
for sgn in signperms:
c = b * np.repeat([sgn], 3, 0) * np.repeat([sgn[::2]], 6,
0).T
err = np.sum((c - b0)**2)
if err < best:
best = err
d = c
self.assertTrue(np.all(abs(d - b0) < 0.05))
def test_dot_special(self):
x = np.random.randn(9, 5, 60)
a = np.eye(5) * 2.0
xc = x.copy()
ac = a.copy()
y = datatools.dot_special(a, x)
self.assertTrue(np.all(x == xc))
self.assertTrue(np.all(a == ac))
self.assertTrue(np.all(x * 2 == y))
x = np.random.randn(150, 40, 6)
a = np.ones((7, 40))
y = datatools.dot_special(a, x)
self.assertEqual(y.shape, (150, 7, 6))
def test_dot_special(self):
x = np.random.randn(9, 5, 60)
a = np.eye(5) * 2.0
xc = x.copy()
ac = a.copy()
y = datatools.dot_special(a, x)
self.assertTrue(np.all(x == xc))
self.assertTrue(np.all(a == ac))
self.assertTrue(np.all(x * 2 == y))
x = np.random.randn(150, 40, 6)
a = np.ones((7, 40))
y = datatools.dot_special(a, x)
self.assertEqual(y.shape, (150, 7, 6))
def testOutput(self):
x = dot_special(self.W.T, self.X)
v1 = sum(np.var(x[np.array(self.C) == 0], axis=2))
v2 = sum(np.var(x[np.array(self.C) == 1], axis=2))
self.assertGreater(v1[0], v2[0])
self.assertGreater(v1[1], v2[1])
self.assertLess(v1[-2], v2[-2])
self.assertLess(v1[-1], v2[-1])
def testOutput(self):
x = dot_special(self.W.T, self.X)
v1 = sum(np.var(x[np.array(self.C) == 0], axis=2))
v2 = sum(np.var(x[np.array(self.C) == 1], axis=2))
self.assertGreater(v1[0], v2[0])
self.assertGreater(v1[1], v2[1])
self.assertLess(v1[-2], v2[-2])
self.assertLess(v1[-1], v2[-1])
def testModelIdentification(self):
""" generate VAR signals, mix them, and see if MVARICA can reconstruct the signals
do this for every backend """
# original model coefficients
b0 = np.zeros((3, 6))
b0[1:3, 2:6] = [[0.4, -0.2, 0.3, 0.0],
[-0.7, 0.0, 0.9, 0.0]]
m0 = b0.shape[0]
l, t = 1000, 100
# generate VAR sources with non-gaussian innovation process, otherwise ICA won't work
noisefunc = lambda: np.random.normal(size=(1, m0)) ** 3 / 1e3
var = VAR(2)
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(np.transpose(mix), sources)
for backend_name, backend_gen in scot.backend.items():
# apply MVARICA
# - default setting of 0.99 variance should reduce to 3 channels with this data
# - automatically determine delta (enough data, so it should most likely be 0)
result = varica.mvarica(data, var, optimize_var=True, backend=backend_gen())
# ICA does not define the ordering and sign of components
# so wee need to test all combinations to find if one of them fits the original coefficients
permutations = np.array(
[[0, 1, 2, 3, 4, 5], [0, 1, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1], [2, 3, 0, 1, 4, 5], [4, 5, 0, 1, 2, 3],
[4, 5, 2, 3, 0, 1]])
signperms = np.array(
[[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, -1, -1], [1, 1, -1, -1, 1, 1], [1, 1, -1, -1, -1, -1],
[-1, -1, 1, 1, 1, 1], [-1, -1, 1, 1, -1, -1], [-1, -1, -1, -1, 1, 1], [-1, -1, -1, -1, -1, -1]])
best, d = np.inf, None
for perm in permutations:
b = result.b.coef[perm[::2] // 2, :]
b = b[:, perm]
for sgn in signperms:
c = b * np.repeat([sgn], 3, 0) * np.repeat([sgn[::2]], 6, 0).T
err = np.sum((c - b0) ** 2)
if err < best:
best = err
d = c
assert_allclose(d, b0, rtol=1e-2, atol=1e-2)
def test_dot_special(self):
x = np.random.randn(60, 5, 9)
a = np.eye(5) * 2.0
xc = x.copy()
ac = a.copy()
y = datatools.dot_special(x, a)
self.assertTrue(np.all(x == xc))
self.assertTrue(np.all(a == ac))
self.assertTrue(np.all(x * 2 == y))
def test_dot_special(self):
x = np.random.randn(60, 5, 9)
a = np.eye(5) * 2.0
xc = x.copy()
ac = a.copy()
y = datatools.dot_special(x, a)
self.assertTrue(np.all(x == xc))
self.assertTrue(np.all(a == ac))
self.assertTrue(np.all(x * 2 == y))
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 testFunctionality(self):
""" generate VAR signals, and apply the api to them
do this for every backend """
# original model coefficients
b01 = np.zeros((3, 6))
b02 = np.zeros((3, 6))
b01[1:3, 2:6] = [[0.4, -0.2, 0.3, 0.0],
[-0.7, 0.0, 0.9, 0.0]]
b02[0:3, 2:6] = [[0.4, 0.0, 0.0, 0.0],
[0.4, 0.0, 0.4, 0.0],
[0.0, 0.0, 0.4, 0.0]]
m0 = b01.shape[0]
cl = np.array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])
l = 1000
t = len(cl)
# generate VAR sources with non-gaussian innovation process, otherwise ICA won't work
noisefunc = lambda: np.random.normal(size=(1, m0)) ** 3
var = VAR(2)
var.coef = b01
sources1 = var.simulate([l, sum(cl==0)], noisefunc)
var.coef = b02
sources2 = var.simulate([l, sum(cl==1)], noisefunc)
var.fit(sources1)
print(var.coef)
var.fit(sources2)
print(var.coef)
sources = np.zeros((l,m0,t))
sources[:,:,cl==0] = sources1
sources[:,:,cl==1] = sources2
# 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:
api = scot.Workspace({'model_order': 2}, reducedim=3, backend=bm.backend)
api.set_data(data)
api.do_ica()
self.assertEqual(api.mixing_.shape, (3, 7))
self.assertEqual(api.unmixing_.shape, (7, 3))
api.do_mvarica()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
api.set_data(data)
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (3, 3, 512, 18))
api.set_data(data, cl)
self.assertFalse(np.any(np.isnan(api.data_)))
self.assertFalse(np.any(np.isinf(api.data_)))
api.do_cspvarica()
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
self.assertEqual(api.get_connectivity('S').shape, (3,3,512))
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
for c in np.unique(cl):
api.set_used_labels([c])
api.fit_var()
fc = api.get_connectivity('S')
self.assertEqual(fc.shape, (3, 3, 512))
tfc = api.get_tf_connectivity('S', 100, 50)
self.assertEqual(tfc.shape, (3, 3, 512, 18))
api.set_data(data)
api.remove_sources([0, 2])
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (1, 1, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (1, 1, 512, 18))
def testFunctionality(self):
""" generate VAR signals, and apply the api to them
do this for every backend """
np.random.seed(3141592)
# original model coefficients
b01 = np.zeros((3, 6))
b02 = np.zeros((3, 6))
b01[1:3, 2:6] = [[0.4, -0.2, 0.3, 0.0],
[-0.7, 0.0, 0.9, 0.0]]
b02[0:3, 2:6] = [[0.4, 0.0, 0.0, 0.0],
[0.4, 0.0, 0.4, 0.0],
[0.0, 0.0, 0.4, 0.0]]
m0 = b01.shape[0]
cl = np.array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])
l = 200
t = len(cl)
# generate VAR sources with non-gaussian innovation process, otherwise ICA won't work
noisefunc = lambda: np.random.normal(size=(1, m0)) ** 3 / 1e3
var = VAR(2)
var.coef = b01
sources1 = var.simulate([l, sum(cl == 0)], noisefunc)
var.coef = b02
sources2 = var.simulate([l, sum(cl == 1)], noisefunc)
var.fit(sources1)
var.fit(sources2)
sources = np.zeros((t, m0, l))
sources[cl == 0, :, :] = sources1
sources[cl == 1, :, :] = sources2
# simulate volume conduction... 3 sources smeared over 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(np.transpose(mix), sources)
data += np.random.randn(*data.shape) * 0.001 # add small noise
for backend_name, backend_gen in scot.backend.items():
np.random.seed(3141592) # reset random seed so we're independent of module order
api = scot.Workspace({'model_order': 2}, reducedim=3, backend=backend_gen())
api.set_data(data)
api.do_ica()
self.assertEqual(api.mixing_.shape, (3, 7))
self.assertEqual(api.unmixing_.shape, (7, 3))
api.do_mvarica()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
api.set_data(data)
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (3, 3, 512, (l-100)//50))
tfc1 = api.get_tf_connectivity('PDC', 100, 5, baseline=None) # no baseline
tfc2 = api.get_tf_connectivity('PDC', 100, 5, baseline=[110, -10]) # invalid baseline
tfc3 = api.get_tf_connectivity('PDC', 100, 5, baseline=[0, 0]) # one-window baseline
tfc4 = tfc1 - tfc1[:, :, :, [0]]
tfc5 = api.get_tf_connectivity('PDC', 100, 5, baseline=[-np.inf, np.inf]) # full trial baseline
tfc6 = tfc1 - np.mean(tfc1, axis=3, keepdims=True)
self.assertTrue(np.allclose(tfc1, tfc2))
self.assertTrue(np.allclose(tfc3, tfc4))
self.assertTrue(np.allclose(tfc5, tfc6, rtol=1e-05, atol=1e-06))
api.set_data(data, cl)
self.assertFalse(np.any(np.isnan(api.data_)))
self.assertFalse(np.any(np.isinf(api.data_)))
api.do_cspvarica()
self.assertEqual(api.get_connectivity('S').shape, (3,3,512))
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
for c in np.unique(cl):
api.set_used_labels([c])
api.fit_var()
fc = api.get_connectivity('S')
self.assertEqual(fc.shape, (3, 3, 512))
tfc = api.get_tf_connectivity('S', 100, 50)
self.assertEqual(tfc.shape, (3, 3, 512, (l-100)//50))
api.set_data(data)
api.remove_sources([0, 2])
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (1, 1, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (1, 1, 512, (l-100)//50))
try:
api.optimize_var()
except NotImplementedError:
pass
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (1, 1, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (1, 1, 512, (l-100)//50))
def testFunctionality(self):
""" generate VAR signals, and apply the api to them
do this for every backend """
np.random.seed(3141592)
# original model coefficients
b01 = np.zeros((3, 6))
b02 = np.zeros((3, 6))
b01[1:3, 2:6] = [[0.4, -0.2, 0.3, 0.0], [-0.7, 0.0, 0.9, 0.0]]
b02[0:3, 2:6] = [[0.4, 0.0, 0.0, 0.0], [0.4, 0.0, 0.4, 0.0],
[0.0, 0.0, 0.4, 0.0]]
m0 = b01.shape[0]
cl = np.array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])
l = 200
t = len(cl)
# generate VAR sources with non-gaussian innovation process, otherwise ICA won't work
noisefunc = lambda: np.random.normal(size=(1, m0))**3
var = VAR(2)
var.coef = b01
sources1 = var.simulate([l, sum(cl == 0)], noisefunc)
var.coef = b02
sources2 = var.simulate([l, sum(cl == 1)], noisefunc)
var.fit(sources1)
var.fit(sources2)
sources = np.zeros((l, m0, t))
sources[:, :, cl == 0] = sources1
sources[:, :, cl == 1] = sources2
# 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:
np.random.seed(
3141592
) # reset random seed so we're independent of module order
api = scot.Workspace({'model_order': 2},
reducedim=3,
backend=bm.backend)
api.set_data(data)
api.do_ica()
self.assertEqual(api.mixing_.shape, (3, 7))
self.assertEqual(api.unmixing_.shape, (7, 3))
api.do_mvarica()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
api.set_data(data)
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertEqual(
api.get_tf_connectivity('S', 100, 50).shape,
(3, 3, 512, (l - 100) // 50))
api.set_data(data, cl)
self.assertFalse(np.any(np.isnan(api.data_)))
self.assertFalse(np.any(np.isinf(api.data_)))
api.do_cspvarica()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
for c in np.unique(cl):
api.set_used_labels([c])
api.fit_var()
fc = api.get_connectivity('S')
self.assertEqual(fc.shape, (3, 3, 512))
tfc = api.get_tf_connectivity('S', 100, 50)
self.assertEqual(tfc.shape, (3, 3, 512, (l - 100) // 50))
api.set_data(data)
api.remove_sources([0, 2])
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (1, 1, 512))
self.assertEqual(
api.get_tf_connectivity('S', 100, 50).shape,
(1, 1, 512, (l - 100) // 50))
try:
api.optimize_var()
except NotImplementedError:
pass
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (1, 1, 512))
self.assertEqual(
api.get_tf_connectivity('S', 100, 50).shape,
(1, 1, 512, (l - 100) // 50))
def testFunctionality(self):
""" generate VAR signals, and apply the api to them
do this for every backend """
np.random.seed(3141592)
# original model coefficients
b01 = np.zeros((3, 6))
b02 = np.zeros((3, 6))
b01[1:3, 2:6] = [[0.4, -0.2, 0.3, 0.0],
[-0.7, 0.0, 0.9, 0.0]]
b02[0:3, 2:6] = [[0.4, 0.0, 0.0, 0.0],
[0.4, 0.0, 0.4, 0.0],
[0.0, 0.0, 0.4, 0.0]]
m0 = b01.shape[0]
cl = np.array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])
l = 200
t = len(cl)
# generate VAR sources with non-gaussian innovation process, otherwise ICA won't work
noisefunc = lambda: np.random.normal(size=(1, m0)) ** 3
var = VAR(2)
var.coef = b01
sources1 = var.simulate([l, sum(cl==0)], noisefunc)
var.coef = b02
sources2 = var.simulate([l, sum(cl==1)], noisefunc)
var.fit(sources1)
var.fit(sources2)
sources = np.zeros((l,m0,t))
sources[:,:,cl==0] = sources1
sources[:,:,cl==1] = sources2
# 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)
backup = scot.config.backend.copy()
backend_modules = [import_module('scot.' + b) for b in scot.backends]
scot.config.backend = backup
for bm in backend_modules:
np.random.seed(3141592) # reset random seed so we're independent of module order
api = scot.Workspace({'model_order': 2}, reducedim=3, backend=bm.backend)
api.set_data(data)
api.do_ica()
self.assertEqual(api.mixing_.shape, (3, 7))
self.assertEqual(api.unmixing_.shape, (7, 3))
api.do_mvarica()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
api.set_data(data)
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (3, 3, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (3, 3, 512, (l-100)//50))
tfc1 = api.get_tf_connectivity('PDC', 100, 5, baseline=None) # no baseline
tfc2 = api.get_tf_connectivity('PDC', 100, 5, baseline=[110, -10]) # invalid baseline
tfc3 = api.get_tf_connectivity('PDC', 100, 5, baseline=[0, 0]) # one-window baseline
tfc4 = tfc1 - tfc1[:, :, :, [0]]
tfc5 = api.get_tf_connectivity('PDC', 100, 5, baseline=[-np.inf, np.inf]) # full trial baseline
tfc6 = tfc1 - np.mean(tfc1, axis=3, keepdims=True)
self.assertTrue(np.allclose(tfc1, tfc2))
self.assertTrue(np.allclose(tfc3, tfc4))
self.assertTrue(np.allclose(tfc5, tfc6, rtol=1e-05, atol=1e-06))
api.set_data(data, cl)
self.assertFalse(np.any(np.isnan(api.data_)))
self.assertFalse(np.any(np.isinf(api.data_)))
api.do_cspvarica()
self.assertEqual(api.get_connectivity('S').shape, (3,3,512))
self.assertFalse(np.any(np.isnan(api.activations_)))
self.assertFalse(np.any(np.isinf(api.activations_)))
for c in np.unique(cl):
api.set_used_labels([c])
api.fit_var()
fc = api.get_connectivity('S')
self.assertEqual(fc.shape, (3, 3, 512))
tfc = api.get_tf_connectivity('S', 100, 50)
self.assertEqual(tfc.shape, (3, 3, 512, (l-100)//50))
api.set_data(data)
api.remove_sources([0, 2])
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (1, 1, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (1, 1, 512, (l-100)//50))
try:
api.optimize_var()
except NotImplementedError:
pass
api.fit_var()
self.assertEqual(api.get_connectivity('S').shape, (1, 1, 512))
self.assertEqual(api.get_tf_connectivity('S', 100, 50).shape, (1, 1, 512, (l-100)//50))
