def _prepare_AR_surrogates(a):
from var_model import VARModel
i, order_range, crit, ts = a
if not np.any(np.isnan(ts)):
v = VARModel()
v.estimate(ts, order_range, True, crit, None)
r = v.compute_residuals(ts)
else:
v = None
r = np.nan
return (i, v, r)
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 constructVAR(S, cs, ar_rng, nghb_rng):
"""
Based on a grid indicating which grid points are associated together
in a cluster, the SMG constructs a VAR model that represents the spatial
dependencies in the data runs the VAR model to generate model time series
Construct an SMG based on the spatial matrix S and the cluster strengths cs.
cs indicates for each cluster 1, 2, ... num_clusters what are the cross ar
coefficients. Each time series has autoregressive coefficient ar.
Elements in S are processed row-wise. The ravel()ed structural matrix is returned.
"""
C = S.shape[1]
Sr = S.ravel() # automatically in C order (row-wise)
N = Sr.shape[0]
A = np.zeros(shape=(N, N), dtype=np.float64)
w = np.zeros(shape=(N, ), dtype=np.float64)
# read the elements in C order (row by row)
for i in range(N):
A[i, i] = np.random.uniform(ar_rng[0], ar_rng[1])
if Sr[i] > 0:
blk_driver = np.nonzero(Sr == Sr[i])[0][0]
if i > blk_driver:
A[i, blk_driver] = cs[Sr[i]]
# A[i, i] -= cs[Sr[i]]
set_neighbor_weights(A, C, nghb_rng)
# check stability of process
if np.any(np.abs(scipy.linalg.eig(A, right=False)) > 1.0):
raise ValueError("Unstable system constructed!")
U = np.identity(N, dtype=np.float64)
var = VARModel()
var.set_model(A, w, U)
return var, Sr
def run_parallel_sims():
ts = read_data2()
print("Fitting VAR model to data")
v = VARModel()
v.estimate(ts[:, 0], [1, 30], True, 'sbc')
res = v.compute_residuals(ts[:, 0])
# cProfile.run('simulate_model((v, res))')
print("Running simulations")
t1 = datetime.now()
# simulate 10000 time series (one surrogate)
p = Pool(4)
# sim_ts_all = p.map(ident_model, [ts[:,0]] * 10000)
sim_ts_all = p.map(simulate_model, [(v, res)] * 100)
delta = datetime.now() - t1
print("DONE after %s" % (str(delta)))
def ident_model(ts):
v2 = VARModel()
v2.estimate(ts, [1, 30], True, 'sbc', None)
return v2.order()
def _prepare_surrogates(a):
i, j, order_range, crit, ts = a
v = VARModel()
v.estimate(ts, order_range, True, crit, None)
r = v.compute_residuals(ts)
return (i, j, v, r)
