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least_squares docstring updated

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Fabian Joswig 2021-11-08 10:01:26 +00:00
parent 2a594dfb4b
commit bc819828bc
1 changed files with 8 additions and 3 deletions
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11
pyerrors/fits.py
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@ -57,7 +57,7 @@ class Fit_result(Sequence):
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
----------
@ -68,14 +68,19 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
func : object
fit function, has to be of the form
```python
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
```python
def func(a, x):
(x1, x2) = x
return a[0] * x1 ** 2 + a[1] * x2
```
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
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.
This can take a while as the full correlation matrix
has to be calculated (default False).
"""
'''
if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
else: