def testFit(self):
""" Test submodel fitting on instationary data
"""
np.random.seed(42)
# original model coefficients
b01 = np.array([[0.0, 0], [0, 0]])
b02 = np.array([[0.5, 0.3], [0.3, 0.5]])
b03 = np.array([[0.1, 0.1], [0.1, 0.1]])
t, m, l = 10, 2, 100
noisefunc = lambda: np.random.normal(size=(1, m))**3 / 1e3
var = VAR(1)
var.coef = b01
sources1 = var.simulate([l, t], noisefunc)
var.coef = b02
sources2 = var.simulate([l, t], noisefunc)
var.coef = b03
sources3 = var.simulate([l, t * 2], noisefunc)
sources = np.vstack([sources1, sources2, sources3])
cl = [1] * t + [2] * t + [1, 2] * t
var = VAR(1)
r_trial = varica.cspvarica(sources,
var,
cl,
reducedim=None,
varfit='trial')
r_class = varica.cspvarica(sources,
var,
cl,
reducedim=None,
varfit='class')
r_ensemble = varica.cspvarica(sources,
var,
cl,
reducedim=None,
varfit='ensemble')
vars = [
np.var(r.var_residuals) for r in [r_trial, r_class, r_ensemble]
]
# class one consists of trials generated with b01 and b03
# class two consists of trials generated with b02 and b03
#
# ensemble fitting cannot resolve any model -> highest residual variance
# class fitting cannot only resolve (b01+b03) vs (b02+b03) -> medium residual variance
# trial fitting can resolve all three models -> lowest residual variance
print(vars)
self.assertLess(vars[0], vars[1])
self.assertLess(vars[1], vars[2])
def testFit(self):
""" Test submodel fitting on instationary data
"""
np.random.seed(42)
# original model coefficients
b01 = np.array([[0.0, 0], [0, 0]])
b02 = np.array([[0.5, 0.3], [0.3, 0.5]])
b03 = np.array([[0.1, 0.1], [0.1, 0.1]])
t, m, l = 10, 2, 100
noisefunc = lambda: np.random.normal(size=(1, m)) ** 3 / 1e3
var = VAR(1)
var.coef = b01
sources1 = var.simulate([l, t], noisefunc)
var.coef = b02
sources2 = var.simulate([l, t], noisefunc)
var.coef = b03
sources3 = var.simulate([l, t * 2], noisefunc)
sources = np.vstack([sources1, sources2, sources3])
cl = [1] * t + [2] * t + [1, 2] * t
var = VAR(1)
r_trial = varica.cspvarica(sources, var, cl, reducedim=None, varfit='trial')
r_class = varica.cspvarica(sources, var, cl, reducedim=None, varfit='class')
r_ensemble = varica.cspvarica(sources, var, cl, reducedim=None, varfit='ensemble')
vars = [np.var(r.var_residuals) for r in [r_trial, r_class, r_ensemble]]
# class one consists of trials generated with b01 and b03
# class two consists of trials generated with b02 and b03
#
# ensemble fitting cannot resolve any model -> highest residual variance
# class fitting cannot only resolve (b01+b03) vs (b02+b03) -> medium residual variance
# trial fitting can resolve all three models -> lowest residual variance
print(vars)
self.assertLess(vars[0], vars[1])
self.assertLess(vars[1], vars[2])
#
# Here we cut out segments from 3s to 4s after each trigger. This is right in
# the middle of the motor imagery period.
data = cut_segments(raweeg, triggers, 3 * fs, 4 * fs)
m = 4 # number of sources to estimate
h = 66 # number of lags for whiteness test
i = 0
for p in [22, 33]:
i += 1
print("Model order:", p)
print(" Performing CSPVARICA")
var = scot.backend["var"](p)
result = cspvarica(data, var, classes, m)
if result.a.is_stable():
s = ""
else:
s = "*NOT* "
print(" VAR model is {}stable.".format(s))
# discard the first p residuals
# r = result.var_residuals[p:, :, :]
print(" Testing VAR residuals for whiteness up to lag", h)
pr = splot.plot_whiteness(result.a, h, repeats=100, axis=plt.subplot(2, 1, i))
if pr < 0.05:
plt.gca().set_title("model order {}: residuals significantly " "non-white with p={:f}".format(p, pr))
#
# Here we cut out segments from 3s to 4s after each trigger. This is right in
# the middle of the motor imagery period.
data = cut_segments(raweeg, triggers, 3 * fs, 4 * fs)
m = 4 # number of sources to estimate
h = 66 # number of lags for whiteness test
i = 0
for p in [22, 33]:
i += 1
print('Model order:', p)
print(' Performing CSPVARICA')
var = scot.backend['var'](p)
result = cspvarica(data, var, classes, m)
if result.a.is_stable():
s = ''
else:
s = '*NOT* '
print(' VAR model is {}stable.'.format(s))
# discard the first p residuals
# r = result.var_residuals[p:, :, :]
print(' Testing VAR residuals for whiteness up to lag', h)
pr = splot.plot_whiteness(result.a,
h,
repeats=100,
axis=plt.subplot(2, 1, i))