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changelog0.6
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# GPy 0.6 Changelog
GPy 0.6 brings a host of changes. At the core of GPy is a new framewrok for handling parameteterised models, which gives much improved performace for many case. The kernels have changed both in their internal structure and their interface. We have added tutorials and improved docstrings (and thus sphinx compiled documentation) everywhere.
GPy 0.6 brings a host of changes. At the core of GPy is a new framewrok for
handling parameteterised models, which gives much improved performace for many
case. The kernels have changed both in their internal structure and their
interface. We have added tutorials and improved docstrings (and thus sphinx
compiled documentation) everywhere.
## kernel changes
The kernels now follow the pep8 guidelines and use CamelCase for class names. this means that
GPy.kern.linear(args)
is now replaced with
GPy.kern.Linear(args)
This does mean small changes to some users code. Apologies for the inconvenience.
The kernels now follow the pep8 guidelines and use CamelCase for class names.
this means that GPy.kern.linear(args) is now replaced with
GPy.kern.Linear(args) This does mean small changes to some users code.
Apologies for the inconvenience.
The kernels can now (optionally) accept `active_dims`, an iterable which describs which dimensions of the input the kernel should work on. This makes constructing kernels which are products over different space (tensor product kernels) much easier to implement.
The kernels can now (optionally) accept `active_dims`, an iterable which
describs which dimensions of the input the kernel should work on. This makes
constructing kernels which are products over different space (tensor product
kernels) much easier to implement.
The kernels include a base class called `Stationary`. Many kernels (Matern, RBF, Exponential) inherrit from this, saving lots of code. Implementing new Stationary kernels is straightforward.
The kernels include a base class called `Stationary`. Many kernels (Matern,
RBF, Exponential) inherrit from this, saving lots of code. Implementing new
Stationary kernels is straightforward.
## Likelihood changes
The stucture of GPy has changed co that we can have a more 'plug-n-play', modular codebase. There's now a base `Likelihood` class which skeletons the implementation for many liklihoods, allowing flexible modelling. For example, GPclassificatino with different approximations is now straightforward with the implementation of the Bernouilli class.
The stucture of GPy has changed co that we can have a more 'plug-n-play',
modular codebase. There's now a base `Likelihood` class which skeletons the
implementation for many liklihoods, allowing flexible modelling. For example,
GPclassificatino with different approximations is now straightforward with the
implementation of the Bernouilli class.
## parameterized changes
## Documentation changes
We have focussed efforts on documentation on providing IPython notebooks containing examples. See http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb
We have focussed efforts on documentation on providing IPython notebooks
containing examples. See
http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb

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index.html
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<p>GPy is available under the BSD 3-clause license. We'd love to incorporate your changes, so fork us on <a href="https://github.com/SheffieldML/GPy">github</a>!</p>
<h2>New release!</h2>
<p> After a long series of changes, we're pleased to announce the GPy 0.6 is ready for use. Users can download the code from github or install with pip.</p>. There are lots of changes, we've tried to highlight important ones in the < href=./changelog0.6>changelog</a>.
<p> After a long series of changes, we're pleased to announce the GPy 0.6 is ready for use. Users can download the code from github or install with pip.</p>. There are lots of changes, we've tried to highlight important ones in the <a href=./changelog0.6>changelog</a>.
<h2>Installation</h2>
<p>Installation instructions along side the source code can be found on the <a href="https://github.com/SheffieldML/GPy">GPy project github page</a>. The instructions vary slightly with the target OS but essentially revolve around installing from the python package index, <a href="https://pypi.python.org/pypi/GPy/">PyPI</a></p>