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feat: double jacobian in standard fit replaced by hessian

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This greatly improves performance for numerical derivatives and helps
with readability.
This commit is contained in:
Fabian Joswig 2022-10-06 10:44:06 +01:00
parent f22614f999
commit b84ea1cc3b
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1 changed files with 7 additions and 7 deletions
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14
pyerrors/fits.py
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@ -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: