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author | ncassereau-idris <84033440+ncassereau-idris@users.noreply.github.com> | 2021-11-03 17:29:16 +0100 |
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committer | GitHub <noreply@github.com> | 2021-11-03 17:29:16 +0100 |
commit | 9c6ac880d426b7577918b0c77bd74b3b01930ef6 (patch) | |
tree | 93b0899a0378a6fe8f063800091252d2c6ad9801 /ot/sliced.py | |
parent | e1b67c641da3b3e497db6811af2c200022b10302 (diff) |
[MRG] Docs updates (#298)
* bregman docs
* sliced docs
* docs partial
* unbalanced docs
* stochastic docs
* plot docs
* datasets docs
* utils docs
* dr docs
* dr docs corrected
* smooth docs
* docs da
* pep8
* docs gromov
* more space after min and argmin
* docs lp
* bregman docs
* bregman docs mistake corrected
* pep8
Co-authored-by: RĂ©mi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'ot/sliced.py')
-rw-r--r-- | ot/sliced.py | 7 |
1 files changed, 4 insertions, 3 deletions
diff --git a/ot/sliced.py b/ot/sliced.py index d3dc3f2..7c09111 100644 --- a/ot/sliced.py +++ b/ot/sliced.py @@ -17,7 +17,7 @@ from .utils import list_to_array def get_random_projections(d, n_projections, seed=None, backend=None, type_as=None): r""" - Generates n_projections samples from the uniform on the unit sphere of dimension d-1: :math:`\mathcal{U}(\mathcal{S}^{d-1})` + Generates n_projections samples from the uniform on the unit sphere of dimension :math:`d-1`: :math:`\mathcal{U}(\mathcal{S}^{d-1})` Parameters ---------- @@ -67,11 +67,12 @@ def sliced_wasserstein_distance(X_s, X_t, a=None, b=None, n_projections=50, p=2, Computes a Monte-Carlo approximation of the p-Sliced Wasserstein distance .. math:: - \mathcal{SWD}_p(\mu, \nu) = \underset{\theta \sim \mathcal{U}(\mathbb{S}^{d-1})}{\mathbb{E}}[\mathcal{W}_p^p(\theta_\# \mu, \theta_\# \nu)]^{\frac{1}{p}} + \mathcal{SWD}_p(\mu, \nu) = \underset{\theta \sim \mathcal{U}(\mathbb{S}^{d-1})}{\mathbb{E}}\left(\mathcal{W}_p^p(\theta_\# \mu, \theta_\# \nu)\right)^{\frac{1}{p}} + where : - - :math:`\theta_\# \mu` stands for the pushforwars of the projection :math:`\mathbb{R}^d \ni X \mapsto \langle \theta, X \rangle` + - :math:`\theta_\# \mu` stands for the pushforwards of the projection :math:`X \in \mathbb{R}^d \mapsto \langle \theta, X \rangle` Parameters |