def test_information_criteria():
"""
Test the implementation of information criteria:
"""
a1 = np.array([[0.9, 0],
[0.16, 0.8]])
a2 = np.array([[-0.5, 0],
[-0.2, -0.5]])
am = np.array([-a1, -a2])
x_var = 1
y_var = 0.7
xy_cov = 0.4
cov = np.array([[x_var, xy_cov],
[xy_cov, y_var]])
N = 10
L = 100
z = np.empty((N, 2, L))
nz = np.empty((N, 2, L))
for i in xrange(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
AIC = []
BIC = []
AICc = []
for i in range(10):
AIC.append(utils.akaike_information_criterion(z, i))
AICc.append(utils.akaike_information_criterion_c(z, i))
BIC.append(utils.bayesian_information_criterion(z, i))
# The model has order 2, so this should minimize on 2:
#nt.assert_equal(np.argmin(AIC),2)
#nt.assert_equal(np.argmin(AICc),2)
nt.assert_equal(np.argmin(BIC), 2)
def test_information_criteria():
"""
Test the implementation of information criteria:
"""
a1 = np.array([[0.9, 0], [0.16, 0.8]])
a2 = np.array([[-0.5, 0], [-0.2, -0.5]])
am = np.array([-a1, -a2])
x_var = 1
y_var = 0.7
xy_cov = 0.4
cov = np.array([[x_var, xy_cov], [xy_cov, y_var]])
#Number of realizations of the process
N = 500
#Length of each realization:
L = 1024
order = am.shape[0]
n_process = am.shape[-1]
z = np.empty((N, n_process, L))
nz = np.empty((N, n_process, L))
for i in xrange(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
AIC = []
BIC = []
AICc = []
# The total number data points available for estimation:
Ntotal = L * n_process
for n_lags in range(1, 10):
Rxx = np.empty((N, n_process, n_process, n_lags))
for i in xrange(N):
Rxx[i] = utils.autocov_vector(z[i], nlags=n_lags)
Rxx = Rxx.mean(axis=0)
Rxx = Rxx.transpose(2, 0, 1)
a, ecov = alg.lwr_recursion(Rxx)
IC = utils.akaike_information_criterion(ecov, n_process, n_lags,
Ntotal)
AIC.append(IC)
IC = utils.akaike_information_criterion(ecov,
n_process,
n_lags,
Ntotal,
corrected=True)
AICc.append(IC)
IC = utils.bayesian_information_criterion(ecov, n_process, n_lags,
Ntotal)
BIC.append(IC)
# The model has order 2, so this should minimize on 2:
# We do not test this for AIC/AICc, because these sometimes do not minimize
# (see Ding and Bressler)
# nt.assert_equal(np.argmin(AIC), 2)
# nt.assert_equal(np.argmin(AICc), 2)
nt.assert_equal(np.argmin(BIC), 2)
def test_information_criteria():
"""
Test the implementation of information criteria:
"""
a1 = np.array([[0.9, 0],
[0.16, 0.8]])
a2 = np.array([[-0.5, 0],
[-0.2, -0.5]])
am = np.array([-a1, -a2])
x_var = 1
y_var = 0.7
xy_cov = 0.4
cov = np.array([[x_var, xy_cov],
[xy_cov, y_var]])
#Number of realizations of the process
N = 500
#Length of each realization:
L = 1024
order = am.shape[0]
n_process = am.shape[-1]
z = np.empty((N, n_process, L))
nz = np.empty((N, n_process, L))
for i in xrange(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
AIC = []
BIC = []
AICc = []
# The total number data points available for estimation:
Ntotal = L * n_process
for n_lags in range(1, 10):
Rxx = np.empty((N, n_process, n_process, n_lags))
for i in xrange(N):
Rxx[i] = utils.autocov_vector(z[i], nlags=n_lags)
Rxx = Rxx.mean(axis=0)
Rxx = Rxx.transpose(2, 0, 1)
a, ecov = alg.lwr_recursion(Rxx)
IC = utils.akaike_information_criterion(ecov, n_process, n_lags, Ntotal)
AIC.append(IC)
IC = utils.akaike_information_criterion(ecov, n_process, n_lags, Ntotal, corrected=True)
AICc.append(IC)
IC = utils.bayesian_information_criterion(ecov, n_process, n_lags, Ntotal)
BIC.append(IC)
# The model has order 2, so this should minimize on 2:
# We do not test this for AIC/AICc, because these sometimes do not minimize
# (see Ding and Bressler)
# nt.assert_equal(np.argmin(AIC), 2)
# nt.assert_equal(np.argmin(AICc), 2)
nt.assert_equal(np.argmin(BIC), 2)