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ncourty
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May 9, 2018
| * Linear OT [14] and Joint OT matrix and mapping estimation [8]. | ||
| * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). | ||
| * Gromov-Wasserstein distances and barycenters [12] | ||
| * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) |
Makefile
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| $(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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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. |
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 |
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 |
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)` |
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) |
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
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OK I'm merging, thank you @ncourty for the code review. We now have 91% line coverage! |
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In this PR I added the following: