def estimate(self, eppci, estimator="wls", **opt_vars):
opt_method = DEFAULT_OPT_METHOD if 'opt_method' not in opt_vars else opt_vars['opt_method']
# matrix calculation object
estm = get_estimator(BaseEstimatorOpt, estimator)(eppci, **opt_vars)
jac = estm.create_cost_jacobian
res = minimize(estm.cost_function, x0=eppci.E,
method=opt_method, jac=jac, tol=self.tolerance,
options={"disp":True})
self.successful = res.success
if self.successful:
E = res.x
eppci.update_E(E)
return eppci
else:
raise Exception("Optimiaztion failed! State Estimation not successful!")
def estimate(self, eppci: ExtendedPPCI, estimator="wls", **kwargs):
self.initialize(eppci)
# matrix calculation object
sem = get_estimator(BaseEstimatorIRWLS, estimator)(eppci, **kwargs)
current_error, cur_it = 100., 0
E = eppci.E
while current_error > self.tolerance and cur_it < self.max_iterations:
self.logger.debug("Starting iteration {:d}".format(1 + cur_it))
try:
# residual r
r = csr_matrix(sem.create_rx(E)).T
# jacobian matrix H
H = csr_matrix(sem.create_hx_jacobian(E))
# gain matrix G_m
# G_m = H^t * Phi * H
phi = csr_matrix(sem.create_phi(E))
G_m = H.T * (phi * H)
# state vector difference d_E and update E
d_E = spsolve(G_m, H.T * (phi * r))
E += d_E.ravel()
eppci.update_E(E)
# prepare next iteration
cur_it += 1
current_error = np.max(np.abs(d_E))
self.logger.debug(
"Current error: {:.7f}".format(current_error))
except np.linalg.linalg.LinAlgError:
self.logger.error(
"A problem appeared while using the linear algebra methods."
"Check and change the measurement set.")
return False
# check if the estimation is successfull
self.check_result(current_error, cur_it)
# update V/delta
return eppci