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Bump version to fix PyPI package
# Release 1.3.0 - Fix bug in ndiag_mc for multi-dimensional kwargs. (#813) - Fix parameter.trainable to be a property. (#814) - Remove references to six module. (#816) - Fix `tf.control_dependencies` in likelihoods. (#821) - Fix `active_dims` for slice type. (#840) - Cleaning up stationary kernel implementations: now defined in terms of `K_r` or `K_r2`. (#827) - Support broadcasting over arbitrarily many leading dimensions for kernels and `conditional`. (#829) - Analytic expectation of the cross-covariance between different RBF kernels. (#754) - New MixedKernelSeparateMof feature class for multi-output GPs. (#830) - The `sample_conditional` returns mean and var as well as samples, and can generate more than one sample. (#836) - Support monitoring with `ScipyOptimizer`. (#856) # Release 1.2.0 - Added `SoftMax` likelihood (#799) - Added likelihoods where expectations are evaluated with Monte Carlo, `MonteCarloLikelihood` (#799) - GPflow monitor refactoring, check `monitor-tensorboard.ipynb` for details (#792) - Speedup testing on Travis using utility functions for configuration in notebooks (#789) - Support Python 3.5.2 in typing checks (Ubuntu 16.04 default python3) (#787) - Corrected scaling in Students-t likelihood variance (#777) - Removed jitter before taking the cholesky of the covariance in NatGrad optimizer (#768) - Added GPflow logger. Created option for setting logger level in `gpflowrc` (#764) - Fixed bug at `params_as_tensors_for` (#751) - Fixed GPflow SciPy optimizer to pass options to _actual_ scipy optimizer correctly (#738) - Improved quadrature for likelihoods. Unified quadrature method introduced - `ndiagquad` (#736), (#747) - Added support for multi-output GPs, check `multioutput.ipynb` for details (#724) * Multi-output features * Multi-output kernels * Multi-dispatch for conditional * Multi-dispatch for Kuu and Kuf - Support Exponential distribution as prior (#717) - Added notebook to demonstrate advanced usage of GPflow, such as combining GP with Neural Network (#712) - Minibatch shape is `None` by default to allow dynamic change of data size (#704) - Epsilon parameter of the Robustmax likelihood is trainable now (#635) - GPflow model saver (#660) * Supports native GPflow models and provides an interface for defining custom savers for user's models * Saver stores GPflow structures and pythonic types as numpy structured arrays and serializes them using HDF5 # Release 1.1 - Added inter-domain inducing features. Inducing points are used by default and are now set with `model.feature.Z`. # Release 1.0 * Clear and aligned with tree-like structure of GPflow models design. * GPflow trainable parameters are no longer packed into one TensorFlow variable. * Integration of bare TensorFlow and Keras models with GPflow became very simple. * GPflow parameter wraps multiple tensors: unconstained variable, constrained tensor and prior tensor. * Instantaneous parameter's building into the TensorFlow graph. Once you created an instance of parameter, it creates necessary tensors at default graph immediately. * New implementation for AutoFlow. `autoflow` decorator is a replacement. * GPflow optimizers match TensorFlow optimizer names. For e.g. `gpflow.train.GradientDescentOptimizer` mimics `tf.train.GradientDescentOptimizer`. They even has the same instantialization signature. * GPflow has native support for Scipy optimizers - `gpflow.train.ScipyOptimizer`. * GPflow has advanced HMC implementation - `gpflow.train.HMC`. It works only within TensorFlow memory scope. * Tensor conversion decorator and context manager designed for cases when user needs to implicitly convert parameters to TensorFlow tensors: `gpflow.params_as_tensors` and `gpflow.params_as_tensors_for`. * GPflow parameters and parameterized objects provide convenient methods and properties for building, intializing their tensors. Check `initializables`, `initializable_feeds`, `feeds` and other properties and methods. * Floating shapes of parameters and dataholders without re-building TensorFlow graph. # Release 0.5 - bugfix for log_jacobian in transforms # Release 0.4.1 - Different variants of `gauss_kl_*` are now deprecated in favour of a unified `gauss_kl` implementation # 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.