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author | Melissa Weber Mendonça <melissawm@gmail.com> | 2020-01-24 10:44:47 -0300 |
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committer | GitHub <noreply@github.com> | 2020-01-24 10:44:47 -0300 |
commit | 3cc5a249afc39b703955077864a6e42421ddb098 (patch) | |
tree | 6e414a9ce328cfa8ed95ab96f56025b43b18da9a | |
parent | 73f8c2552d9d09c23bfce3d0f62a4210125d0d66 (diff) | |
download | numpy-3cc5a249afc39b703955077864a6e42421ddb098.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 7f6cb5e8a..6e21c38e9 100644 --- a/doc/source/user/tutorial-svd.rst +++ b/doc/source/user/tutorial-svd.rst @@ -418,7 +418,7 @@ axes back to our original shape of (768, 1024, 3), we can see our approximation: >>> plt.imshow(np.transpose(approx_img, (1, 2, 0))) Even though the image is not as sharp, using a small number of ``k`` singular -values (compared to the original set of 768 values) we can recover many of the +values (compared to the original set of 768 values), we can recover many of the distinguishing features from this image. **Final words** |