Revision 7905bb6d38ecc7c9fe5a5cbe85057791e103cf62 authored by James Hensman on 29 June 2016, 08:36:52 UTC, committed by James Hensman on 29 June 2016, 08:36:52 UTC
An option in the dataholder class lets it choose what to do when the shape of data are changed. So now we can do X = np.random.randn(10, 1) Y = np.sin(X) m = GPflow.models.GPR(X, Y, GPflow.kernels.RBF(1)) m.optimize() X_new = np.random.randn(12, 1) Y_new = np.sin(X_new) m.X = X_new m.Y = Y_new m.optimize() # no recompile neded.
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RELEASE.md
# 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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