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| author | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-25 08:43:00 +0200 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-25 08:43:00 +0200 |
| commit | 042b52d4edad20d246481c389007ba1abb67c3b4 (patch) | |
| tree | 4395c4e3ac390996302bbfd9f6ed0b515629cd14 | |
| parent | c112190ab0cf9b02a25fb86919a11f9544cd5c58 (diff) | |
pep8
| -rw-r--r-- | README.md | 15 | ||||
| -rw-r--r-- | ot/gromov.py | 10 |
2 files changed, 19 insertions, 6 deletions
@@ -53,6 +53,12 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ comp #### Pip installation +Note that due to a limitation of pip, `cython` and `numpy` need to be installed +prior to installing POT. This can be done easily with +``` +pip install numpy cython +``` + You can install the toolbox through PyPI with: ``` pip install POT @@ -62,6 +68,8 @@ or get the very latest version by downloading it and then running: python setup.py install --user # for user install (no root) ``` + + #### Anaconda installation with conda-forge If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: @@ -150,7 +158,12 @@ You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter. ## Acknowledgements -The contributors to this library are: +This toolbox has been created and is maintained by + +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) + +The contributors to this library are * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) diff --git a/ot/gromov.py b/ot/gromov.py index 43729dc..cd961b0 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -359,7 +359,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, .. math::
\gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l}
L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
-
+
s.t. \gamma 1 = p
\gamma^T 1= q
\gamma\geq 0
@@ -414,7 +414,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, and Courty Nicolas "Optimal Transport for structured data with
application on graphs", International Conference on Machine Learning
(ICML). 2019.
-
+
"""
constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
@@ -442,7 +442,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 .. math::
\min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l}
L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
-
+
s.t. \gamma 1 = p
\gamma^T 1= q
@@ -647,7 +647,7 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, Returns
-------
T : ndarray, shape (ns, nt)
- Optimal coupling between the two spaces
+ Optimal coupling between the two spaces
References
----------
@@ -1024,7 +1024,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ T : list of (N,ns) transport matrices
Ms : all distance matrices between the feature of the barycenter and the
other features dist(X,Ys) shape (N,ns)
-
+
References
----------
.. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
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