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author | Rémi Flamary <remi.flamary@gmail.com> | 2017-07-06 10:41:06 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2017-07-06 10:41:06 +0200 |
commit | fab20da2af763d8f108e6ceb88d888fcc5497747 (patch) | |
tree | 7e4d3740343d3d66fcf2061aa0766651cd4b6d40 | |
parent | 132d5471eca26923a9a6239a5ab51623f209bf39 (diff) |
update readme
-rw-r--r-- | README.md | 40 | ||||
-rw-r--r-- | docs/source/readme.rst | 46 | ||||
-rwxr-xr-x | setup.py | 2 |
3 files changed, 80 insertions, 8 deletions
@@ -72,9 +72,42 @@ obviously you need CUDA installed and a compatible GPU. ## Examples -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/) +### Short examples - Here is a list of the Python notebooks if you want a quick look: +* Import the toolbox +```python +import ot +``` +* Compute Wasserstein distances +```python +# a,b are 1D histograms (sum to 1 and positive) +# M is the ground cost matrix +Wd=ot.emd2(a,b,M) # exact linear program +# if b is a matrix compute all distances to a and return a vector +``` +* Compute OT matrix +```python +# a,b are 1D histograms (sum to 1 and positive) +# M is the ground cost matrix +Totp=ot.emd(a,b,M) # exact linear program +Totp_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT +``` +* Compute Wasserstein barycenter +```python +# A is a n*d matrix containing d 1D histograms +# M is the ground cost matrix +ba=ot.barycenter(A,M,reg) # reg is regularization parameter +``` + + + + +### Examples and Notebooks + +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). + + +Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: * [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/Demo_1D_OT.ipynb) * [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Ground_Loss.ipynb) @@ -88,8 +121,7 @@ The examples folder contain several examples and use case for the library. The f * [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb) * [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb) - -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/). +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). ## Acknowledgements diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 6898296..611001b 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -93,11 +93,51 @@ obviously you need CUDA installed and a compatible GPU. Examples -------- +Short examples +~~~~~~~~~~~~~~ + +- Import the toolbox + + .. code:: python + + import ot + +- Compute Wasserstein distances + + .. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + Wd=ot.emd2(a,b,M) # exact linear program + # if b is a matrix compute all distances to a and return a vector + +- Compute OT matrix + + .. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + Totp=ot.emd(a,b,M) # exact linear program + Totp_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + +- Compute Wasserstein barycenter + + .. code:: python + + # A is a n*d matrix containing d 1D histograms + # M is the ground cost matrix + ba=ot.barycenter(A,M,reg) # reg is regularization parameter + +Examples and Notebooks +~~~~~~~~~~~~~~~~~~~~~~ + The examples folder contain several examples and use case for the library. The full documentation is available on -`Readthedocs <http://pot.readthedocs.io/>`__ +`Readthedocs <http://pot.readthedocs.io/>`__. -Here is a list of the Python notebooks if you want a quick look: +Here is a list of the Python notebooks available +`here <https://github.com/rflamary/POT/blob/master/notebooks/>`__ if you +want a quick look: - `1D optimal transport <https://github.com/rflamary/POT/blob/master/notebooks/Demo_1D_OT.ipynb>`__ @@ -123,7 +163,7 @@ Here is a list of the Python notebooks if you want a quick look: Analysis <https://github.com/rflamary/POT/blob/master/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb>`__ You can also see the notebooks with `Jupyter -nbviewer <https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/>`__. +nbviewer <https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/>`__. Acknowledgements ---------------- @@ -21,7 +21,7 @@ __version__ = re.search( ROOT = os.path.abspath(os.path.dirname(__file__)) -# convert markdown readme to rst in pypandoc installed +# convert markdown readme to rst if pypandoc installed try: import pypandoc README = pypandoc.convert('README.md', 'rst') |