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# 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.