lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-05-15 12:03:42 +02:00
Code Issues Projects Releases Packages Wiki Activity

least_squares docstring updated

Browse source
This commit is contained in:
Fabian Joswig 2021-11-08 10:01:26 +00:00
parent 2a594dfb4b
commit bc819828bc
1 changed files with 8 additions and 3 deletions
Download patch file Download diff file Expand all files Collapse all files

11
pyerrors/fits.py
View file

@ -57,7 +57,7 @@ class Fit_result(Sequence):
def least_squares(x, y, func, priors=None, silent=False, **kwargs): def least_squares(x, y, func, priors=None, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x). r'''Performs a non-linear fit to y = func(x).
Parameters Parameters
---------- ----------
@ -68,14 +68,19 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
func : object func : object
fit function, has to be of the form fit function, has to be of the form
```python
def func(a, x): def func(a, x):
return a[0] + a[1] * x + a[2] * anp.sinh(x) y = a[0] + a[1] * x + a[2] * anp.sinh(x)
return y
```
For multiple x values func can be of the form For multiple x values func can be of the form
```python
def func(a, x): def func(a, x):
(x1, x2) = x (x1, x2) = x
return a[0] * x1 ** 2 + a[1] * x2 return a[0] * x1 ** 2 + a[1] * x2
```
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
will not work will not work
@ -104,7 +109,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
corrected by effects caused by correlated input data. corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix This can take a while as the full correlation matrix
has to be calculated (default False). has to be calculated (default False).
""" '''
if priors is not None: if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs) return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
else: else: