lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-03-15 14:50:25 +01:00
Code Issues Projects Releases Packages Wiki Activity

feat: hessian added to prior fit and odr fit routines.

Browse source
This commit is contained in:
Fabian Joswig 2022-10-06 18:07:19 +01:00
parent 8cf15b651f
commit 58b8383c1a
No known key found for this signature in database
1 changed files with 15 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

23
pyerrors/fits.py
View file

@ -165,6 +165,8 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
num_grad : bool
Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
Notes
-----
@ -179,7 +181,12 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
x_shape = x.shape
jacobian = auto_jacobian
if kwargs.get('num_grad') is True:
jacobian = num_jacobian
hessian = num_hessian
else:
jacobian = auto_jacobian
hessian = auto_hessian
if not callable(func):
raise TypeError('func has to be a function.')
@ -275,7 +282,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
fitp = out.beta
try:
hess = jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel())))
hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplus.ravel())))
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
@ -284,7 +291,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
return chisq
jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
jac_jac_x = hessian(odr_chisquare_compact_x)(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
# Compute hess^{-1} @ jac_jac_x[:n_parms + m, n_parms + m:] using LAPACK dgesv
try:
@ -297,7 +304,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
return chisq
jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((fitp, out.xplus.ravel(), y_f)))
jac_jac_y = hessian(odr_chisquare_compact_y)(np.concatenate((fitp, out.xplus.ravel(), y_f)))
# Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
try:
@ -326,9 +333,9 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
x = np.asarray(x)
if kwargs.get('num_grad') is True:
jacobian = num_jacobian
hessian = num_hessian
else:
jacobian = auto_jacobian
hessian = auto_hessian
if not callable(func):
raise TypeError('func has to be a function.')
@ -418,7 +425,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
if not m.fmin.is_valid:
raise Exception('The minimization procedure did not converge.')
hess = jacobian(jacobian(chisqfunc))(params)
hess = hessian(chisqfunc)(params)
if kwargs.get('num_grad') is True:
hess = hess[0]
hess_inv = np.linalg.pinv(hess)
@ -428,7 +435,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
chisq = anp.sum(((d[n_parms: n_parms + len(x)] - model) / dy_f) ** 2) + anp.sum(((d[n_parms + len(x):] - d[:n_parms]) / dp_f) ** 2)
return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((params, y_f, p_f)))
jac_jac = hessian(chisqfunc_compact)(np.concatenate((params, y_f, p_f)))
if kwargs.get('num_grad') is True:
jac_jac = jac_jac[0]