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author | Melissa Weber Mendonça <melissawm@gmail.com> | 2020-01-24 10:44:00 -0300 |
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committer | GitHub <noreply@github.com> | 2020-01-24 10:44:00 -0300 |
commit | 7582faace489302579fd4137a14bca60f25a8d65 (patch) | |
tree | e81ee141cd1ef7e55ebb217ca720078a0e34b318 | |
parent | 3d889a88f058cd81718fd0010d17e104db71858f (diff) | |
download | numpy-7582faace489302579fd4137a14bca60f25a8d65.tar.gz |
Update doc/source/user/tutorial-svd.rst
Co-Authored-By: Anne Bonner <35413198+bonn0062@users.noreply.github.com>
-rw-r--r-- | doc/source/user/tutorial-svd.rst | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst index 9c7b9dd6f..680322cbb 100644 --- a/doc/source/user/tutorial-svd.rst +++ b/doc/source/user/tutorial-svd.rst @@ -392,7 +392,7 @@ integers)." This is expected from the manipulation we just did on the original image. Now, to do the approximation, we must choose only the first ``k`` singular -values for each color channel. This can be done by the following syntax:: +values for each color channel. This can be done using the following syntax:: >>> approx_img = U @ Sigma[..., :k] @ Vt[..., :k, :] |