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| author | Melissa Weber Mendonça <melissawm@gmail.com> | 2020-01-24 10:41:22 -0300 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2020-01-24 10:41:22 -0300 |
| commit | 55fbea19d4f4ed0261e9a4da86fc5ce41583f1d9 (patch) | |
| tree | d6546612253b9d4c2a4c4417bb9c282271f3930f | |
| parent | f84004b4d559c607d7362194845c9d383e8b510d (diff) | |
| download | numpy-55fbea19d4f4ed0261e9a4da86fc5ce41583f1d9.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 07ea9709a..b6a4692d9 100644 --- a/doc/source/user/tutorial-svd.rst +++ b/doc/source/user/tutorial-svd.rst @@ -239,7 +239,7 @@ depending on your architecture and linear algebra setup; however, you should see a small number.) We could also have used the `numpy.allclose` function to make sure the -reconstructed product is, in fact, *close* to our original matrix (that is, the +reconstructed product is, in fact, *close* to our original matrix (the difference between the two arrays is small):: >>> np.allclose(blue_array, U_blue @ Sigma_blue @ Vt_blue) |