diff options
| author | Oleksii Kachaiev <kachayev@gmail.com> | 2023-05-03 10:36:09 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-05-03 10:36:09 +0200 |
| commit | 2aeb591be6b19a93f187516495ed15f1a47be925 (patch) | |
| tree | 9a6f759856a3f6b2d7c6db3514927ba3e5af10b5 /examples/others/plot_stochastic.py | |
| parent | 8a7035bdaa5bb164d1c16febbd83650d1fb6d393 (diff) | |
[DOC] Corrected spelling errors (#467)
* Fix typos in docstrings and examples
* A few more fixes
* Fix ref for `center_ot_dual` function
* Another typo
* Fix titles formatting
* Explicit empty line after math blocks
* Typo: asymmetric
* Fix code cell formatting for 1D barycenters
* Empirical
* Fix indentation for references
* Fixed all WARNINGs about title formatting
* Fix empty lines after math blocks
* Fix whitespace line
* Update changelog
* Consistent Gromov-Wasserstein
* More Gromov-Wasserstein consistency
---------
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'examples/others/plot_stochastic.py')
| -rw-r--r-- | examples/others/plot_stochastic.py | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/examples/others/plot_stochastic.py b/examples/others/plot_stochastic.py index 3a1ef31..f3afb0b 100644 --- a/examples/others/plot_stochastic.py +++ b/examples/others/plot_stochastic.py @@ -3,7 +3,7 @@ Stochastic examples =================== -This example is designed to show how to use the stochatic optimization +This example is designed to show how to use the stochastic optimization algorithms for discrete and semi-continuous measures from the POT library. [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. @@ -61,7 +61,7 @@ print(sag_pi) # Semi-Continuous Case # ```````````````````` # -# Sample one general measure a, one discrete measures b for the semicontinous +# Sample one general measure a, one discrete measures b for the semicontinuous # case, the points where source and target measures are defined and compute the # cost matrix. @@ -80,7 +80,7 @@ Y_target = rng.randn(n_target, 2) M = ot.dist(X_source, Y_target) ############################################################################# -# Call the "ASGD" method to find the transportation matrix in the semicontinous +# Call the "ASGD" method to find the transportation matrix in the semicontinuous # case. method = "ASGD" |