def test_residuals(self):
l = 100000
var0 = VAR(2)
var0.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
x = var0.simulate(l)
var = VAR(2)
var.fit(x)
self.assertEqual(x.shape, var.residuals.shape)
self.assertTrue(np.allclose(var.rescov, np.eye(var.rescov.shape[0]), 1e-2, 1e-2))
def test_fit(self):
var0 = VAR(2)
var0.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
l = 100000
x = var0.simulate(l)
y = x.copy()
var = VAR(2)
var.fit(x)
# make sure the input remains unchanged
self.assertTrue(np.all(x == y))
# that limit is rather generous, but we don't want tests to fail due to random variation
self.assertTrue(np.all(np.abs(var0.coef - var.coef) < 0.02))
def test_fit_regularized(self):
l = 100000
var0 = VAR(2)
var0.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
x = var0.simulate(l)
y = x.copy()
var = VAR(10, delta=1)
var.fit(x)
# make sure the input remains unchanged
self.assertTrue(np.all(x == y))
b0 = np.zeros((2, 20))
b0[:, 0:2] = var0.coef[:, 0:2]
b0[:, 10:12] = var0.coef[:, 2:4]
# that limit is rather generous, but we don't want tests to fail due to random variation
self.assertTrue(np.all(np.abs(b0 - var.coef) < 0.02))
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))
