def test_whiteness(self):
np.random.seed(91)
var = VARBase(0)
var.residuals = np.random.randn(10, 5, 100)
pr = sp.plot_whiteness(var, 20, repeats=100)
self.assertGreater(pr, 0.05)
def test_whiteness(self):
np.random.seed(91)
var = VARBase(0)
var.residuals = np.random.randn(10, 5, 100)
pr = sp.plot_whiteness(var, 20, repeats=100)
self.assertGreater(pr, 0.05)
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))
else:
plt.gca().set_title("model order {}: residuals white " "with p={:f}".format(p, pr))
splot.show_plots()
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))
else:
plt.gca().set_title('model order {}: residuals white '
'with p={:f}'.format(p, pr))
splot.show_plots()
