def estimate(self, eppci: ExtendedPPCI, **kwargs):
self.initialize(eppci)
# matrix calculation object
sem = BaseAlgebra(eppci)
current_error, cur_it = 100., 0
# invert covariance matrix
r_inv = csr_matrix(np.diagflat(1 / eppci.r_cov**2))
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 * R^-1 * H
G_m = H.T * (r_inv * H)
# state vector difference d_E
# d_E = G_m^-1 * (H' * R^-1 * r)
d_E = spsolve(G_m, H.T * (r_inv * r))
# Update E with d_E
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)
if self.successful:
# store variables required for chi^2 and r_N_max test:
self.R_inv = r_inv.toarray()
self.Gm = G_m.toarray()
self.r = r.toarray()
self.H = H.toarray()
# create h(x) for the current iteration
self.hx = sem.create_hx(eppci.E)
return eppci
def estimate(self, eppci, **kwargs):
if "estimator" in kwargs and kwargs["estimator"].lower(
) != "lav": # pragma: no cover
self.logger.warning(
"LP Algorithm supports only LAV Estimator!! Set to LAV!!")
# matrix calculation object
sem = BaseAlgebra(eppci)
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 = sem.create_rx(E)
# jacobian matrix H
H = sem.create_hx_jacobian(E)
# state vector difference d_E
# d_E = G_m^-1 * (H' * R^-1 * r)
d_E = self.solve_lp(H, E, r)
E += d_E
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: # pragma: no cover
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)
return eppci