def test_model_fit():
"""
Testing of model fitting procedure of the nitime.analysis.granger module.
"""
# Start by generating some MAR processes (according to Ding and Bressler),
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_lags = order + 1
n_process = am.shape[-1]
z = np.empty((N, n_process, L))
nz = np.empty((N, n_process, L))
for i in range(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
# First we test that the model fitting procedure recovers the coefficients,
# on average:
Rxx = np.empty((N, n_process, n_process, n_lags))
coef = np.empty((N, n_process, n_process, order))
ecov = np.empty((N, n_process, n_process))
for i in range(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0],
z[i][1],
order=2)
Rxx[i] = this_Rxx
coef[i] = this_coef
ecov[i] = this_ecov
npt.assert_almost_equal(cov, np.mean(ecov, axis=0), decimal=1)
npt.assert_almost_equal(am, np.mean(coef, axis=0), decimal=1)
# Next we test that the automatic model order estimation procedure works:
est_order = []
for i in range(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0],
z[i][1])
est_order.append(this_order)
npt.assert_almost_equal(order, np.mean(est_order), decimal=1)
def test_model_fit():
"""
Testing of model fitting procedure of the nitime.analysis.granger module.
"""
# Start by generating some MAR processes (according to Ding and Bressler),
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_lags = order + 1
n_process = am.shape[-1]
z = np.empty((N, n_process, L))
nz = np.empty((N, n_process, L))
for i in range(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
# First we test that the model fitting procedure recovers the coefficients,
# on average:
Rxx = np.empty((N, n_process, n_process, n_lags))
coef = np.empty((N, n_process, n_process, order))
ecov = np.empty((N, n_process, n_process))
for i in range(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0],
z[i][1],
order=2)
Rxx[i] = this_Rxx
coef[i] = this_coef
ecov[i] = this_ecov
npt.assert_almost_equal(cov, np.mean(ecov, axis=0), decimal=1)
npt.assert_almost_equal(am, np.mean(coef, axis=0), decimal=1)
# Next we test that the automatic model order estimation procedure works:
est_order = []
for i in range(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(
z[i][0], z[i][1])
est_order.append(this_order)
npt.assert_almost_equal(order, np.mean(est_order), decimal=1)
nz = np.empty((N, n_process, L))
np.random.seed(1981)
for i in range(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
"""
We start by estimating the order of the model from the data:
"""
est_order = []
for i in range(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], z[i][1])
est_order.append(this_order)
order = int(np.round(np.mean(est_order)))
"""
Once we have estimated the order, we go ahead and fit each realization of the
MAR model, constraining the model order accordingly (by setting the order
key-word argument) to be always equal to the model order estimated above.
"""
Rxx = np.empty((N, n_process, n_process, n_lags))
coef = np.empty((N, n_process, n_process, order))
ecov = np.empty((N, n_process, n_process))
nz = np.empty((N, n_process, L))
np.random.seed(1981)
for i in xrange(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
"""
We start by estimating the order of the model from the data:
"""
est_order = []
for i in xrange(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], z[i][1])
est_order.append(this_order)
order = int(np.round(np.mean(est_order)))
"""
Once we have estimated the order, we go ahead and fit each realization of the
MAR model, constraining the model order accordingly (by setting the order
key-word argument) to be always equal to the model order estimated above.
"""
Rxx = np.empty((N, n_process, n_process, n_lags))
coef = np.empty((N, n_process, n_process, order))
ecov = np.empty((N, n_process, n_process))
