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RELEASES.md

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## 0.5.0 Year 2
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*Sep 2018*
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POT is 2 years old! This release bring both numerous new features to the toolbox as listed below but also several
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POT is 2 years old! This release brings numerous new features to the
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toolbox as listed below but also several bug correction.
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Among the new features, we can highlight a [non-regularized Gromov-Wasserstein
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solver](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb),
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a new [greedy variant of sinkhorn](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.greenkhorn),
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[non-regularized](https://pot.readthedocs.io/en/latest/all.html#ot.lp.barycenter),
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[convolutional (2D)](https://github.com/rflamary/POT/blob/master/notebooks/plot_convolutional_barycenter.ipynb)
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and [free support](https://github.com/rflamary/POT/blob/master/notebooks/plot_free_support_barycenter.ipynb)
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Wasserstein barycenters and [smooth](https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.ipynb)
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and [stochastic](https://pot.readthedocs.io/en/latest/all.html#ot.stochastic.sgd_entropic_regularization)
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implementation of entropic OT.
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POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework instead of
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the unmaintained cudamat. Note that while we tried to keed changes to the
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minimum, the OTDA classes were deprecated.
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The code quality has also improved with 92% code coverage in tests that is now
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printed to the log in the Travis builds. The documentation has also been
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greatly improved with new modules and examples/notebooks.
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This new release is so full of new stuff and corrections thanks to the old
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and new POT contributors (you can see the list in the readme).
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#### Features
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#### Deprecation
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Deprecated OTDA Classes were removed for version 0.5 (PR #48 and PR #67). The
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deprecation messagehas been for a year here since 0.4 and it is time to pull
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the plug.
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Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5
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(PR #48 and PR #67). The deprecation message has been for a year here since
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0.4 and it is time to pull the plug.
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#### Closed issues
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ot/gpu/__init__.py

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in order to proide drop-in replacement for the other POT function but
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the transfer between CPU en GPU comes with a significant overhead.
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In order to get the best erformances, we recommend to given only cupy
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In order to get the best erformances, we recommend to give only cupy
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arrays to the functions and desactivate the conversion to numpy of the
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result of the function with parameter ``to_numpy=False``.
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