lsqfitgp 0.1.4

Gaussian processes in nonlinear least-squares fits

lsqfitgp

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.