def publish(self, pub):
if not self.once:
pub.text('warning', 'not updated yet')
return
pub.text('nsamples', '%s' % self.nsamples)
pub.text('rcond', '%g' % self.rcond)
with pub.subsection('model') as sub:
try:
model = self.get_model()
model.publish(sub)
except BDSEEstimatorInterface.NotReady as e:
pub.text('not-ready', str(e))
with pub.subsection('tensors') as sub:
T = self.get_T()
U = self.U.get_value()
P = self.y_stats.get_covariance()
Q = self.u_stats.get_covariance()
P_inv = np.linalg.pinv(P)
P_inv_cond = self.get_P_inv_cond()
Q_inv = np.linalg.pinv(Q)
#
# TP_inv2 = obtain_TP_inv_from_TP_2(T, P)
# M2 = np.tensordot(TP_inv2, Q_inv, axes=(2, 0))
pub_tensor3_slice2(sub, 'T', T)
pub_tensor2_comp1(sub, 'U', U)
pub_tensor2_cov(sub, 'P', P, rcond=self.rcond)
pub_tensor2_cov(sub, 'P_inv', P_inv)
pub_tensor2_cov(sub, 'P_inv_cond', P_inv_cond)
pub_tensor2_cov(sub, 'Q', Q)
pub_tensor2_cov(sub, 'Q_inv', Q_inv)
# Might not have been computed
# pub_tensor2_comp1(sub, 'Myav', self.Myav)
# pub_tensor2_comp1(sub, 'UQ_inv', self.UQ_inv)
with pub.subsection('y_stats') as sub:
self.y_stats.publish(sub)
with pub.subsection('u_stats') as sub:
self.u_stats.publish(sub)
with pub.subsection('alternative', robust=True) as sub:
sub.text('info', 'This is estimating without conditioning P')
T = self.get_T()
P = self.y_stats.get_covariance()
Q = self.u_stats.get_covariance()
M1 = get_M_from_P_T_Q(P, T, Q)
pub_tensor3_slice2(sub, 'get_M_from_P_T_Q', M1)
M2 = get_M_from_P_T_Q_alt(P, T, Q)
pub_tensor3_slice2(sub, 'get_M_from_P_T_Q_alt', M2)
M3 = get_M_from_P_T_Q_alt_scaling(P, T, Q)
pub_tensor3_slice2(sub, 'get_M_from_P_T_Q_alt2', M3)
def publish(self, pub):
pub_tensor3_slice2(pub, 'M', self.M)
pub_tensor2_comp1(pub, 'N', self.N)