Merge pull request #27 from rflamary/autonb

auto notebooks + release update (fixes #16)

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+# POT Releases
+
+## 0.4 Community edition
+*15 Sep 2017*
+
+This release contains a lot of contribution from new contributors.
+
+
+#### Features
+
+* Automatic notebooks and doc update (PR #27)
+* Add gromov Wasserstein solver and Gromov Barycenters (PR #23)
+* emd and emd2 can now return dual variables and have max_iter (PR #29 and PR #25) 
+* New domain adaptation classes compatible with scikit-learn (PR #22)
+* Proper tests with pytest on travis (PR #19)
+* PEP 8 tests (PR #13)
+
+#### Closed issues
+
+* emd convergence problem du to fixed max iterations (#24) 
+* Semi supervised DA error (#26)
+
+## 0.3.1
+*11 Jul 2017*
+
+* Correct bug in emd on windows
+
+## 0.3 Summer release
+*7 Jul 2017*
+
+* emd* and sinkhorn* are now performed in parallel for multiple target distributions
+* emd and sinkhorn are for OT matrix computation
+* emd2 and sinkhorn2 are for OT loss computation
+* new notebooks for emd computation and Wasserstein Discriminant Analysis
+* relocate notebooks
+* update documentation
+* clean_zeros(a,b,M) for removimg zeros in sparse distributions
+* GPU implementations for sinkhorn and group lasso regularization
+
+
+## V0.2 
+*7 Apr 2017*
+
+* New dimensionality reduction method (WDA)
+* Efficient method emd2 returns only tarnsport (in paralell if several histograms given)
+
+
+
+## V0.1.11 New years resolution
+*5 Jan 2017*
+
+* Add sphinx gallery for better documentation
+* Small efficiency tweak in sinkhorn
+* Add simple tic() toc() functions for timing
+
+
+## V0.1.10 
+*7 Nov 2016*
+* numerical stabilization for sinkhorn (log domain and epsilon scaling)
+
+## V0.1.9 DA classes and mapping
+*4 Nov 2016*
+
+* Update classes and examples for domain adaptation
+* Joint OT matrix and mapping estimation
+
+## V0.1.7
+*31 Oct 2016*
+
+* Original Domain adaptation classes
+
+
+
+## PyPI version 0.1.3
+
+* pipy works
+
+## First pre-release
+*28 Oct 2016*
+
+It provides the following solvers:
+* OT solver for the linear program/ Earth Movers Distance.
+* Entropic regularization OT solver  with Sinkhorn Knopp Algorithm.
+* Bregman projections for Wasserstein barycenter [3] and unmixing.
+* Optimal transport for domain adaptation with group lasso regularization
+* Conditional gradient and Generalized conditional gradient for regularized OT.
+
+Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.