Revision f2be4495dc52c172318dbb29e2f58f939eab6eea authored by James Hensman on 01 September 2016, 10:54:51 UTC, committed by James Hensman on 01 September 2016, 10:54:51 UTC
1 parent e974f46
RELEASE.md
# Release 0.3
- Improvements to the way that parameters for triangular matrices are stored and optimised.
- Automatically generated Apache license headers.
- Ability to track log probabilities.
# Release 0.2
- Significant improvements to the way that data and fixed parameters are handled.
Previously, data and fixed parameters were treated as tensorflow constants.
Now, a new mechanism called `get_feed_dict()` can gather up data and and fixed
parameters and pass them into the graph as placeholders.
- To enable the above, data are now stored in objects called `DataHolder`. To
access values of the data, use the same syntax as parameters:
`print(m.X.value)`
- Models do not need to be recompiled when the data changes.
- Two models, VGP and GPMC, do need to be recompiled if the *shape* of the data changes
- A multi-class likelihood is implemented
# Release 0.1.4
- Updated to work with tensorflow 0.9
- Added a Logistic transform to enable contraining a parameter between two bounds
- Added a Laplace distribution to use as a prior
- Added a periodic kernel
- Several improvements to the AutoFlow mechanism
- added FITC approximation (see comparison notebook)
- improved readability of code according to pep8
- significantly improved the speed of the test suite
- allowed passing of the 'tol' argument to scipy.minimize routine
- added ability to add and multiply MeanFunction objects
- Several new contributors (see README.md)
# Release 0.1.3
- Removed the need for a fork of TensorFlow. Some of our bespoke ops are replaced by equivalent versions.
# Release 0.1.2
- Included the ability to compute the full covaraince matrix at predict time. See `GPModel.predict_f`
- Included the ability to sample from the posterior function values. See `GPModel.predict_f_samples`
- Unified code in conditionals.py: see deprecations in `gp_predict`, etc.
- Added SGPR method (Sparse GP Regression)
# Release 0.1.1
- included the ability to use tensorflow's optimizers as well as the scipy ones
# Release 0.1.0
The initial release of GPflow.
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