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* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). | ||
* Gromov-Wasserstein distances and barycenters [12] | ||
* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) |
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regularized what ?
Makefile
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@@ -41,7 +41,7 @@ pep8 : | |||
flake8 examples/ ot/ test/ | |||
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test : FORCE pep8 | |||
python -m py.test -v test/ --cov=ot --cov-report html:cov_html | |||
$(PYTHON) -m pytest -v test/ --cov=ot --cov-report html:cov_html | |||
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pytest : FORCE | |||
python -m py.test -v test/ --cov=ot |
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$(PYTHON) here ?
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yes its defined at the top of the file.
I will put it for every call to python.
README.md
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[14] Knott, M. and Smith, C. S. [On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43, 1984. | ||
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[15] Peyré, G., & Cuturi, M. (2017). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) , 2018. |
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2018
docs/source/readme.rst
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distributions <https://link.springer.com/article/10.1007/BF00934745>`__, | ||
Journal of Optimization Theory and Applications Vol 43, 1984. | ||
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[15] Peyré, G., & Cuturi, M. (2017). `Computational Optimal |
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2017 --> 2018
ot/da.py
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wt=None, bias=True, log=False): | ||
""" return OT linear operator between samples | ||
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The function estimate the optimal linear operator that align the two |
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estimateS
alignS
ot/da.py
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The function estimate the optimal linear operator that align the two | ||
empirical distributions. This is equivalent to estimating the closed | ||
form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` |
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distributionS
ot/da.py
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xt : np.ndarray (nt,d) | ||
samples in the target domain | ||
reg : float,optional | ||
regularization added to the daigonals of convariances (>0) |
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diagonals
ot/da.py
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class LinearTransport(BaseTransport): | ||
""" OT linear operator between empirical distributions | ||
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The function estimate the optimal linear operator that align the two |
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same typos here that for the corresponding function
OK I'm merging, thank you @ncourty for the code review. We now have 91% line coverage! |
In this PR I added the following: