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authorncassereau-idris <84033440+ncassereau-idris@users.noreply.github.com>2021-11-03 17:29:16 +0100
committerGitHub <noreply@github.com>2021-11-03 17:29:16 +0100
commit9c6ac880d426b7577918b0c77bd74b3b01930ef6 (patch)
tree93b0899a0378a6fe8f063800091252d2c6ad9801 /ot/sliced.py
parente1b67c641da3b3e497db6811af2c200022b10302 (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.py7
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