documeation fix

Author: rflamary

RELEASES.md

@@ -5,7 +5,7 @@
 
 This new release contains several new features and bug fixes. 
 
-New features include a new submodule `ot.gnn` that contains two new Graph neural network layers (compatible with [Pytorch Geometric](https://pytorch-geometric.readthedocs.io/)) for template-based pooling of graphs with an example on [graph classification](https://pythonot.github.io/master/auto_examples/gromov/plot_gnn_TFGW.html). Related to this, we also now provide FGW and semi relaxed FGW solvers for which the resulting loss is differentiable w.r.t. the parameter `alpha`. Other contributions on the (F)GW front include a new solver for the Proximal Point algorithm [that can be used to solve entropic GW problems](https://pythonot.github.io/master/auto_examples/gromov/plot_fgw_solvers.html) (using the parameter `solver="PPA"`), novels Sinkhorn-based solvers for entropic semi-relaxed (F)GW, the possibility to provide a warm-start to the solvers, and optional marginal weights of the samples (uniform weights ar used by default). Finally we added in the submodule `ot.gaussian` and `ot.da` new loss and mapping estimators for the Gaussian Gromov-Wasserstein that can be used as a fast alternative to GW and estimates linear mappings between unregistered spaces that can potentially have different size (See the update [linear mapping example](https://pythonot.github.io/master/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) for an illustration).
+New features include a new submodule `ot.gnn` that contains two new Graph neural network layers (compatible with [Pytorch Geometric](https://pytorch-geometric.readthedocs.io/)) for template-based pooling of graphs with an example on [graph classification](https://pythonot.github.io/master/auto_examples/gromov/plot_gnn_TFGW.html). Related to this, we also now provide FGW and semi relaxed FGW solvers for which the resulting loss is differentiable w.r.t. the parameter `alpha`. Other contributions on the (F)GW front include a new solver for the Proximal Point algorithm [that can be used to solve entropic GW problems](https://pythonot.github.io/master/auto_examples/gromov/plot_fgw_solvers.html) (using the parameter `solver="PPA"`), new solvers for entropic FGW barycenters, novels Sinkhorn-based solvers for entropic semi-relaxed (F)GW, the possibility to provide a warm-start to the solvers, and optional marginal weights of the samples (uniform weights ar used by default). Finally we added in the submodule `ot.gaussian` and `ot.da` new loss and mapping estimators for the Gaussian Gromov-Wasserstein that can be used as a fast alternative to GW and estimates linear mappings between unregistered spaces that can potentially have different size (See the update [linear mapping example](https://pythonot.github.io/master/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) for an illustration).
 
 We also provide a new solver for the [Entropic Wasserstein Component Analysis](https://pythonot.github.io/master/auto_examples/others/plot_EWCA.html) that is a generalization of the celebrated PCA taking into account the local neighborhood of the samples. We also now have a new solver in `ot.smooth` for the [sparsity-constrained OT (last plot)](https://pythonot.github.io/master/auto_examples/plot_OT_1D_smooth.html) that can be used to find regularized OT plans with sparsity constraints. Finally we have a first multi-marginal solver for regular 1D distributions with a Monge loss (see [here](https://pythonot.github.io/master/auto_examples/others/plot_dmmot.html)).
 

ot/da.py

@@ -1365,7 +1365,7 @@ class LinearGWTransport(LinearTransport):
     r""" OT Gaussian Gromov-Wasserstein linear operator between empirical distributions
 
     The function estimates the optimal linear operator that aligns the two
-    empirical distributions optimaly wrt the Gromov wassretsein distance. This is equivalent to estimating the closed
+    empirical distributions optimally wrt the Gromov-Wasserstein distance. This is equivalent to estimating the closed
     form mapping between two Gaussian distributions :math:`\mathcal{N}(\mu_s,\Sigma_s)`
     and :math:`\mathcal{N}(\mu_t,\Sigma_t)` as proposed in
     :ref:`[57] <references-lineargwtransport>`.

ot/gaussian.py

@@ -42,9 +42,9 @@ def bures_wasserstein_mapping(ms, mt, Cs, Ct, log=False):
         mean of the source distribution
     mt : array-like (d,)
         mean of the target distribution
-    Cs : array-like (d,)
+    Cs : array-like (d,d)
         covariance of the source distribution
-    Ct : array-like (d,)
+    Ct : array-like (d,d)
         covariance of the target distribution
     log : bool, optional
         record log if True
@@ -210,9 +210,9 @@ def bures_wasserstein_distance(ms, mt, Cs, Ct, log=False):
         mean of the source distribution
     mt : array-like (d,)
         mean of the target distribution
-    Cs : array-like (d,)
+    Cs : array-like (d,d)
         covariance of the source distribution
-    Ct : array-like (d,)
+    Ct : array-like (d,d)
         covariance of the target distribution
     log : bool, optional
         record log if True
@@ -344,9 +344,9 @@ def gaussian_gromov_wasserstein_distance(Cov_s, Cov_t, log=False):
 
     Parameters
     ----------
-    Cov_s : array-like (d,d)
+    Cov_s : array-like (ds,ds)
         covariance of the source distribution
-    Cov_t : array-like (d,d)
+    Cov_t : array-like (dt,dt)
         covariance of the target distribution