Revision 6c35a7a8544dfecfe92ef9b559b90fcb40b364eb authored by Mark van der Wilk on 20 November 2017, 17:43:17 UTC, committed by GitHub on 20 November 2017, 17:43:17 UTC
* Add Features base classes * Convert SGPR and SVGP models to use InducingFeatures (including backwards compatibility) * Fix tests * Added Multiscale feature. To show the generality of the inter-domain code. * Fixed py2 metaclass issue, as per John Bradshaw's suggestion. * Improve docstrings, register Multiscale feature * Change SGPR models to determine feature length dynamically [but feature.__len__() still needs to be made dynamic as well!] * Bits and pieces missed in the merge. * Add features to __init__. * Fixed incorrect parameter dtype assignment on compile. * Two bugfixs. - Static assignment of len(feature) - Upper bound mixin referred to Z. * Fixed bugs in multiscale & added features. * Added tests for `Multiscale` inducing features. * Updated `RELEASE.md`, and small changes for tests. * add test for len(feature) * Deprecation property for `Z`, relative imports, improved test. * `SGPMC` has inducing features now + better docstrings. * Fixed `SGPMC`. * Testing now uses tf1.4. * `feat` now `feature` + other changes. * Update _version.py * change exception
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README.md
# GPflow
GPflow is a package for building Gaussian process models in python, using [TensorFlow](http://www.tensorflow.org). It was originally created and is now managed by [James Hensman](http://www.lancaster.ac.uk/staff/hensmanj/) and [Alexander G. de G. Matthews](http://mlg.eng.cam.ac.uk/?portfolio=alex-matthews).
The full list of [contributors](http://github.com/GPflow/GPflow/graphs/contributors) (in alphabetical order) is Artem Artemev, Rasmus Bonnevie, Alexis Boukouvalas, Ivo Couckuyt, Keisuke Fujii, Zoubin Ghahramani, David J. Harris, James Hensman, Pablo Leon-Villagra, Daniel Marthaler, Alexander G. de G. Matthews, Tom Nickson, Valentine Svensson, Mark van der Wilk. GPflow is an open source project so if you feel you have some relevant skills and are interested in contributing then please do contact us.
[![Python3.5 Status](https://travis-ci.org/GPflow/GPflow.svg?branch=master)](https://travis-ci.org/GPflow/GPflow)
[![Coverage Status](http://codecov.io/github/GPflow/GPflow/coverage.svg?branch=master)](http://codecov.io/github/GPflow/GPflow?branch=master)
[![Documentation Status](https://readthedocs.org/projects/gpflow/badge/?version=latest)](http://gpflow.readthedocs.io/en/latest/?badge=latest)
# What does GPflow do?
GPflow implements modern Gaussian process inference for composable kernels and likelihoods. The [online user manual](http://gpflow.readthedocs.io/en/latest/) contains more details. The interface follows on from [GPy](http://github.com/sheffieldml/gpy), for more discussion of the comparison see [this page](http://gpflow.readthedocs.io/en/latest/intro.html#what-s-the-difference-between-gpy-and-gpflow).
# Install
## 1) Quick install
GPflow can be installed by cloning the repository and running
```
pip install .
```
in the root folder. This also installs required dependencies including TensorFlow. When GPU support is needed, a manual installation of TensorFlow is recommended (next section), as one cannot rely on pip to get this running.
## 2) Alternative method
A different option to install GPflow requires installation of TensorFlow first. Please see instructions on the main TensorFlow [webpage](https://www.tensorflow.org/versions/r1.3/get_started/get_started). You will need at least version 1.3 (we aim to support the latest version). We find that for most users pip installation is the fastest way to get going. Then, for those interested in modifying the source of GPflow, we recommend
```
python setup.py develop
```
but installation should work well too:
```
python setup.py install
```
You can run the tests with `python setup.py test`.
We document the [version history](https://github.com/GPflow/GPflow/blob/master/RELEASE.md).
## Docker image
We also provide a [Docker image](https://hub.docker.com/r/gpflow/gpflow/) which can be run using
```
docker run -it -p 8888:8888 gpflow/gpflow
```
The image can be generated using our [Dockerfile](Dockerfile).
# Getting help
Please use GitHub issues to start discussion on the use of GPflow. Tagging enquiries `discussion` helps us distinguish them from bugs.
# Contributing
All constructive input is gratefully received. For more information, see the [notes for contributors](contributing.md).
# Projects using GPflow
A few projects building on GPflow and demonstrating its usage are listed below.
| Project | Description |
| --- | --- |
| [GPflowOpt](https://github.com/GPflow/GPflowOpt) | Bayesian Optimization using GPflow. |
| [VFF](https://github.com/jameshensman/VFF) | Variational Fourier Features for Gaussian Processes. |
| [Doubly-Stochastic-DGP](https://github.com/ICL-SML/Doubly-Stochastic-DGP)| Deep Gaussian Processes with Doubly Stochastic Variational Inference.|
| [BranchedGP](https://github.com/ManchesterBioinference/BranchedGP) | Gaussian processes with branching kernels.|
Let us know if you would like your project listed here.
# Citing GPflow
To cite GPflow, please reference the [JMLR paper](http://www.jmlr.org/papers/volume18/16-537/16-537.pdf). Sample Bibtex is given below:
```
@ARTICLE{GPflow2017,
author = {Matthews, Alexander G. de G. and {van der Wilk}, Mark and Nickson, Tom and
Fujii, Keisuke. and {Boukouvalas}, Alexis and {Le{\'o}n-Villagr{\'a}}, Pablo and
Ghahramani, Zoubin and Hensman, James},
title = "{{GP}flow: A {G}aussian process library using {T}ensor{F}low}",
journal = {Journal of Machine Learning Research},
year = {2017},
month = {apr},
volume = {18},
number = {40},
pages = {1-6},
url = {http://jmlr.org/papers/v18/16-537.html}
}
```
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