def _get_MC_realizations(self, n=100, multivariate=False, residuals=True):
"""
Gets n surrogates for Monte Carlo testing.
If multivariate True, extimates AR(1) model for whole data, if False, treats as univariate
and estimates each channel separately.
If residuals True, generates AR model using actual residuals from fitting, if False,
only uses model matrix A.
"""
from var_model import VARModel
self.MCsurrs = np.zeros([n] + list(self.X.shape))
# multivariate model
if multivariate:
v = VARModel()
v.estimate(self.X, [1, 1], True, 'sbc', None)
if residuals:
r = v.compute_residuals(self.X)
# univariate model - estimating for each channel separately
else:
vs = {}
for d in range(self.X.shape[1]):
vs[d] = VARModel()
vs[d].estimate(self.X[:, d], [1, 1], True, 'sbc', None)
if residuals:
vs['res' + str(d)] = vs[d].compute_residuals(self.X[:, d])
for i in range(n):
if multivariate:
if not residuals:
self.MCsurrs[i, ...] = v.simulate(N=self.X.shape[0])
else:
self.MCsurrs[i, ...] = v.simulate_with_residuals(
r, orig_length=True)
else:
for d in range(self.X.shape[1]):
if not residuals:
self.MCsurrs[i, :, d] = np.squeeze(
vs[d].simulate(N=self.X.shape[0]))
else:
self.MCsurrs[i, :, d] = np.squeeze(
vs[d].simulate_with_residuals(vs['res' + str(d)],
orig_length=True))
def test_simulation_residuals():
res = read_data3()
print res[:10, :]
v = VARModel()
v.w = np.array([1.0, 1.4, -2.0])
v.A = np.array([[0.3, 0.0, 0.2, 0.2, 0.3, 0.05],
[0.1, 0.6, 0.0, 0.0, 0.1, 0.10],
[0.0, 0.0, 0.0, 0.4, 0.0, 0.00]])
v.U = np.array([[1.0, 0.1, -0.2], [0.0, 1.0, -0.3], [0.0, 0.0, 1.0]])
ts = v.simulate_with_residuals(res[:10, :], ndisc=0)
print()
print()
print ts[:10, :]
def test_simulation_residuals():
res = read_data3()
print res[:10, :]
v = VARModel()
v.w = np.array([1.0, 1.4, -2.0])
v.A = np.array([ [0.3, 0.0, 0.2, 0.2, 0.3, 0.05],
[0.1, 0.6, 0.0, 0.0, 0.1, 0.10],
[0.0, 0.0, 0.0, 0.4, 0.0, 0.00] ])
v.U = np.array([ [1.0, 0.1, -0.2],
[0.0, 1.0, -0.3],
[0.0, 0.0, 1.0] ])
ts = v.simulate_with_residuals(res[:10, :], ndisc = 0)
print()
print()
print ts[:10, :]
def _get_MC_realizations(self, n = 100, multivariate = False, residuals = True):
"""
Gets n surrogates for Monte Carlo testing.
If multivariate True, extimates AR(1) model for whole data, if False, treats as univariate
and estimates each channel separately.
If residuals True, generates AR model using actual residuals from fitting, if False,
only uses model matrix A.
"""
from var_model import VARModel
self.MCsurrs = np.zeros([n] + list(self.X.shape))
# multivariate model
if multivariate:
v = VARModel()
v.estimate(self.X, [1,1], True, 'sbc', None)
if residuals:
r = v.compute_residuals(self.X)
# univariate model - estimating for each channel separately
else:
vs = {}
for d in range(self.X.shape[1]):
vs[d] = VARModel()
vs[d].estimate(self.X[:, d], [1,1], True, 'sbc', None)
if residuals:
vs['res' + str(d)] = vs[d].compute_residuals(self.X[:, d])
for i in range(n):
if multivariate:
if not residuals:
self.MCsurrs[i, ...] = v.simulate(N = self.X.shape[0])
else:
self.MCsurrs[i, ...] = v.simulate_with_residuals(r, orig_length = True)
else:
for d in range(self.X.shape[1]):
if not residuals:
self.MCsurrs[i, :, d] = np.squeeze(vs[d].simulate(N = self.X.shape[0]))
else:
self.MCsurrs[i, :, d] = np.squeeze(vs[d].simulate_with_residuals(vs['res' + str(d)], orig_length = True))
