mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 14:50:25 +01:00
feat: double jacobian in standard fit replaced by hessian
This greatly improves performance for numerical derivatives and helps with readability.
This commit is contained in:
parent
f22614f999
commit
b84ea1cc3b
1 changed files with 7 additions and 7 deletions
|
@ -10,8 +10,10 @@ from matplotlib import gridspec
|
|||
from scipy.odr import ODR, Model, RealData
|
||||
import iminuit
|
||||
from autograd import jacobian as auto_jacobian
|
||||
from autograd import hessian as auto_hessian
|
||||
from autograd import elementwise_grad as egrad
|
||||
from numdifftools import Jacobian as num_jacobian
|
||||
from numdifftools import Hessian as num_hessian
|
||||
from .obs import Obs, derived_observable, covariance, cov_Obs
|
||||
|
||||
|
||||
|
@ -458,8 +460,10 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
|
|||
|
||||
if kwargs.get('num_grad') is True:
|
||||
jacobian = num_jacobian
|
||||
hessian = num_hessian
|
||||
else:
|
||||
jacobian = auto_jacobian
|
||||
hessian = auto_hessian
|
||||
|
||||
if x.shape[-1] != len(y):
|
||||
raise Exception('x and y input have to have the same length')
|
||||
|
@ -591,13 +595,11 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
|
|||
fitp = fit_result.x
|
||||
try:
|
||||
if kwargs.get('correlated_fit') is True:
|
||||
hess = jacobian(jacobian(chisqfunc_corr))(fitp)
|
||||
hess = hessian(chisqfunc_corr)(fitp)
|
||||
else:
|
||||
hess = jacobian(jacobian(chisqfunc))(fitp)
|
||||
hess = hessian(chisqfunc)(fitp)
|
||||
except TypeError:
|
||||
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
|
||||
if kwargs.get('num_grad') is True:
|
||||
hess = hess[0]
|
||||
|
||||
if kwargs.get('correlated_fit') is True:
|
||||
def chisqfunc_compact(d):
|
||||
|
@ -611,9 +613,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
|
|||
chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
|
||||
return chisq
|
||||
|
||||
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
|
||||
if kwargs.get('num_grad') is True:
|
||||
jac_jac = jac_jac[0]
|
||||
jac_jac = hessian(chisqfunc_compact)(np.concatenate((fitp, y_f)))
|
||||
|
||||
# Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
|
||||
try:
|
||||
|
|
Loading…
Add table
Reference in a new issue