doc index updated

Author: rflamary

docs/source/all.rst

@@ -0,0 +1,47 @@
+
+
+Python modules
+==============
+
+ot
+--
+
+This module provide easy access to solvers for the most common OT problems
+
+.. automodule:: ot
+   :members:
+
+ot.lp
+-----
+.. automodule:: ot.lp
+   :members:
+
+ot.bregman
+----------
+
+.. automodule:: ot.bregman
+   :members:
+
+ot.optim
+--------
+
+.. automodule:: ot.optim
+   :members:
+
+ot.utils
+--------
+
+.. automodule:: ot.utils
+   :members:
+
+ot.datasets
+-----------
+
+.. automodule:: ot.datasets
+   :members:
+
+ot.plot
+-------
+
+.. automodule:: ot.plot
+   :members:

docs/source/examples.rst

@@ -0,0 +1,14 @@
+
+
+Examples
+============
+
+1D Optimal transport
+---------------------
+
+.. literalinclude:: ../../examples/demo_OT_1D.py
+
+2D Optimal transport on empirical distributions
+-----------------------------------------------
+
+.. literalinclude:: ../../examples/demo_OT_2D_samples.py

docs/source/index.rst

@@ -3,60 +3,56 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-POT's documentation!
-===============================
+POT: Python Optimal Transport
+=============================
 
-Contents:
 
-.. toctree::
-   :maxdepth: 2
+This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.
 
-Module list
-===========
+It provides the following solvers:
 
+* OT solver for the linear program/ Earth Movers Distance [1].
+* Entropic regularization OT solver  with Sinkhorn Knopp Algorithm [2].
+* Bregman projections for Wasserstein barycenter [3] and unmixing [4].
+* Optimal transport for domain adaptation with group lasso regularization [5]
+* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
 
-Module ot
----------
+Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
 
-This module provide easy access to solvers for the most common OT problems
 
-.. automodule:: ot
-   :members:
+Contents
+--------
 
-Module ot.emd
--------------
-.. automodule:: ot.emd
-   :members:
+.. toctree::
+   :maxdepth: 2
 
-Module ot.bregman
------------------
+   self
+   all
+   examples
 
-.. automodule:: ot.bregman
-   :members:
+Examples
+--------
 
-Module ot.utils
----------------
 
-.. automodule:: ot.utils
-   :members:
 
-Module ot.datasets
-------------------
 
-.. automodule:: ot.datasets
-   :members:
+References
+----------
 
-Module ot.plot
---------------
+[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
 
-.. automodule:: ot.plot
-   :members:
+[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300).
 
+[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
 
-Examples
-========
+[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.
+
+[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+
+[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+
+[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
 
-.. literalinclude:: ../../examples/demo_OT_1D.py
 
 Indices and tables
 ==================

ot/bregman.py

@@ -39,6 +39,11 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9):
         loss matrix        
     reg: float
         Regularization term >0
+    numItermax: int, optional
+        Max number of iterations
+    stopThr: float, optional
+        Stop threshol on error (>0)
+    
   
     
     Returns