https://github.com/GPflow/GPflow
Revision e0a2cc7d67a9ddba27a61a56dacd871c19dad747 authored by Artem Artemev on 26 September 2017, 20:28:21 UTC, committed by Artem Artemev on 26 September 2017, 20:28:21 UTC
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Tip revision: e0a2cc7d67a9ddba27a61a56dacd871c19dad747 authored by Artem Artemev on 26 September 2017, 20:28:21 UTC
Parameter list represents only parameters. Empty method returns true only if there are no data holders and parameters at parameterized object.
Parameter list represents only parameters. Empty method returns true only if there are no data holders and parameters at parameterized object.
Tip revision: e0a2cc7
RELEASE.md
# Release 0.4.0
- Rename python package name to `gpflow`.
- Compile function has external session and graph arguments.
- Tests use Tensorflow TestCase class for proper session managing.
# Release 0.3.8
- Change to LowerTriangular transform interface.
- LowerTriangular transform now used by default in VGP and SVGP
- LowerTriangular transform now used native TensorFlow
- No longer use bespoke GPflow user ops.
# Release 0.3.7
- Improvements to VGP class allow more straightforward optimization
# Release 0.3.6
- Changed ordering of parameters to be alphabetical, to ensure consistency
# Release 0.3.5
- Update to work with TensorFlow 0.12.1.
# Release 0.3.4
- Changes to stop computations all being done on the default graph.
- Update list of GPflow contributors and other small changes to front page.
- Better deduction of `input_dim` for `kernels.Combination`
- Some kernels did not properly respect active dims, now fixed.
- Make sure log jacobian is computed even for fixed variables
# Release 0.3.3
- House keeping changes for paper submission.
# Release 0.3.2
- updated to work with tensorflow 0.11 (release candidate 1 available at time of writing)
- bugfixes in vgp._compile
# Release 0.3.1
- Added configuration file, which controls verbosity and level of numerical jitter
- tf_hacks is deprecated, became tf_wraps (tf_hacks will raise visible deprecation warnings)
- Documentation now at gpflow.readthedocs.io
- Many functions are now contained in tensorflow scopes for easier tensorboad visualisation and profiling
# 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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