ot.sliced

Sliced OT Distances

Functions

ot.sliced.get_random_projections(d, n_projections, seed=None, backend=None, type_as=None)[source]

Generates n_projections samples from the uniform on the unit sphere of dimension \(d-1\): \(\mathcal{U}(\mathcal{S}^{d-1})\)

Parameters
  • d (int) – dimension of the space

  • n_projections (int) – number of samples requested

  • seed (int or RandomState, optional) – Seed used for numpy random number generator

  • backend – Backend to ue for random generation

Returns

out – The uniform unit vectors on the sphere

Return type

ndarray, shape (d, n_projections)

Examples

>>> n_projections = 100
>>> d = 5
>>> projs = get_random_projections(d, n_projections)
>>> np.allclose(np.sum(np.square(projs), 0), 1.)  
True
ot.sliced.max_sliced_wasserstein_distance(X_s, X_t, a=None, b=None, n_projections=50, p=2, projections=None, seed=None, log=False)[source]

Computes a Monte-Carlo approximation of the max p-Sliced Wasserstein distance

\[\mathcal{Max-SWD}_p(\mu, \nu) = \underset{\theta _in \mathcal{U}(\mathbb{S}^{d-1})}{\max} [\mathcal{W}_p^p(\theta_\# \mu, \theta_\# \nu)]^{\frac{1}{p}}\]

where :

  • \(\theta_\# \mu\) stands for the pushforwars of the projection \(\mathbb{R}^d \ni X \mapsto \langle \theta, X \rangle\)

Parameters
  • X_s (ndarray, shape (n_samples_a, dim)) – samples in the source domain

  • X_t (ndarray, shape (n_samples_b, dim)) – samples in the target domain

  • a (ndarray, shape (n_samples_a,), optional) – samples weights in the source domain

  • b (ndarray, shape (n_samples_b,), optional) – samples weights in the target domain

  • n_projections (int, optional) – Number of projections used for the Monte-Carlo approximation

  • p (float, optional =) – Power p used for computing the sliced Wasserstein

  • projections (shape (dim, n_projections), optional) – Projection matrix (n_projections and seed are not used in this case)

  • seed (int or RandomState or None, optional) – Seed used for random number generator

  • log (bool, optional) – if True, sliced_wasserstein_distance returns the projections used and their associated EMD.

Returns

  • cost (float) – Sliced Wasserstein Cost

  • log (dict, optional) – log dictionary return only if log==True in parameters

Examples

>>> n_samples_a = 20
>>> reg = 0.1
>>> X = np.random.normal(0., 1., (n_samples_a, 5))
>>> sliced_wasserstein_distance(X, X, seed=0)  
0.0

References

35

Deshpande, I., Hu, Y. T., Sun, R., Pyrros, A., Siddiqui, N., Koyejo, S., … & Schwing, A. G. (2019). Max-sliced wasserstein distance and its use for gans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10648-10656).

ot.sliced.sliced_wasserstein_distance(X_s, X_t, a=None, b=None, n_projections=50, p=2, projections=None, seed=None, log=False)[source]

Computes a Monte-Carlo approximation of the p-Sliced Wasserstein distance

\[\mathcal{SWD}_p(\mu, \nu) = \underset{\theta \sim \mathcal{U}(\mathbb{S}^{d-1})}{\mathbb{E}}\left(\mathcal{W}_p^p(\theta_\# \mu, \theta_\# \nu)\right)^{\frac{1}{p}}\]

where :

  • \(\theta_\# \mu\) stands for the pushforwards of the projection \(X \in \mathbb{R}^d \mapsto \langle \theta, X \rangle\)

Parameters
  • X_s (ndarray, shape (n_samples_a, dim)) – samples in the source domain

  • X_t (ndarray, shape (n_samples_b, dim)) – samples in the target domain

  • a (ndarray, shape (n_samples_a,), optional) – samples weights in the source domain

  • b (ndarray, shape (n_samples_b,), optional) – samples weights in the target domain

  • n_projections (int, optional) – Number of projections used for the Monte-Carlo approximation

  • p (float, optional =) – Power p used for computing the sliced Wasserstein

  • projections (shape (dim, n_projections), optional) – Projection matrix (n_projections and seed are not used in this case)

  • seed (int or RandomState or None, optional) – Seed used for random number generator

  • log (bool, optional) – if True, sliced_wasserstein_distance returns the projections used and their associated EMD.

Returns

  • cost (float) – Sliced Wasserstein Cost

  • log (dict, optional) – log dictionary return only if log==True in parameters

Examples

>>> n_samples_a = 20
>>> reg = 0.1
>>> X = np.random.normal(0., 1., (n_samples_a, 5))
>>> sliced_wasserstein_distance(X, X, seed=0)  
0.0

References

31

Bonneel, Nicolas, et al. “Sliced and radon wasserstein barycenters of measures.” Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45