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| * 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]) |
| $(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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| [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. |
| 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 |
| 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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| 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)` |
| xt : np.ndarray (nt,d) | ||
| samples in the target domain | ||
| reg : float,optional | ||
| regularization added to the daigonals of convariances (>0) |
| class LinearTransport(BaseTransport): | ||
| """ OT linear operator between empirical distributions | ||
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| The function estimate the optimal linear operator that align the two |