Gaussian processes in nonlinear least-squares fits
Project description
lsqfitgp
Python module for manipulating gaussian processes. Features:
-
Use gvar to keep track transparently of correlations between prior, data and posterior.
-
Fit a latent gaussian process in a nonlinear model with lsqfit.
-
autograd-friendly.
-
Supports multidimensional structured non-numerical input with named dimensions.
-
Apply arbitrary linear transformations to the process.
-
Use dictionaries to manipulate hyperparameters and hyperpriors. Use
gvar.BufferDict
to transparently apply transformations. -
Get a covariance matrix for the optimized hyperparameters.
Installation
pip install lsqfitgp
Documentation
The manual is available on readthedocs. All the code is documented with docstrings, so you can also use the Python help system directly from the shell:
>>> import lsqfitgp as lgp
>>> help(lgp)
>>> help(lgp.something)
or, in an IPython shell/Jupyter notebook/Spyder IDE, use the question mark shortcut:
In [1]: lgp?
In [2]: lgp.something?
Building the manual from source
pip install sphinx<2
cd docs
make html
If you add kernels, run kernelsref.py
to regenerate kernelsref.rst
.
If you add a documentation page with code examples, use runcode.py
to run
all the code found in code sections in the rst file.
Examples
In the directory examples
there are various scripts named with single letters
(sorry for this nonsense notation). In an IPython shell, you can run
examples/RUNALL.ipy
to run all the examples and save the figures on files.
Tests
The test code is in tests
. Launch pytest
in the repository to run all the
tests. pytest
can be installed with pip install pytest
.
Project details
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