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Diffstat (limited to 'docs/source/quickstart.rst')
| -rw-r--r-- | docs/source/quickstart.rst | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index b4cc8ab..c8eac30 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -279,7 +279,7 @@ distributions. In this case there exists a close form solution given in Remark 2.29 in [15]_ and the Monge mapping is an affine function and can be also computed from the covariances and means of the source and target distributions. In the case when the finite sample dataset is supposed Gaussian, -we provide :any:`ot.da.OT_mapping_linear` that returns the parameters for the +we provide :any:`ot.gaussian.bures_wasserstein_mapping` that returns the parameters for the Monge mapping. @@ -628,7 +628,7 @@ approximate a Monge mapping from finite distributions. First note that when the source and target distributions are supposed to be Gaussian distributions, there exists a close form solution for the mapping and its an affine function [14]_ of the form :math:`T(x)=Ax+b` . In this case we provide the function -:any:`ot.da.OT_mapping_linear` that returns the operator :math:`A` and vector +:any:`ot.gaussian.bures_wasserstein_mapping` that returns the operator :math:`A` and vector :math:`b`. Note that if the number of samples is too small there is a parameter :code:`reg` that provides a regularization for the covariance matrix estimation. @@ -640,7 +640,7 @@ method proposed in [8]_ that estimates a continuous mapping approximating the barycentric mapping is provided in :any:`ot.da.joint_OT_mapping_linear` for linear mapping and :any:`ot.da.joint_OT_mapping_kernel` for non-linear mapping. -.. minigallery:: ot.da.joint_OT_mapping_linear ot.da.joint_OT_mapping_linear ot.da.OT_mapping_linear +.. minigallery:: ot.da.joint_OT_mapping_linear ot.da.joint_OT_mapping_linear ot.gaussian.bures_wasserstein_mapping :add-heading: Examples of Monge mapping estimation :heading-level: " |