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authorCharles Harris <charlesr.harris@gmail.com>2021-04-21 08:56:48 -0600
committerGitHub <noreply@github.com>2021-04-21 08:56:48 -0600
commit9fc2b40f68e82c97528e1fd49b238d521276116f (patch)
tree903b10726424bb85483190cea1e8c67260cba67f /doc
parentc08f4a84d506012ac7c236eabc2470689c17d5eb (diff)
parentc559bc8038ab684c68601757d2735c7be39befc4 (diff)
downloadnumpy-9fc2b40f68e82c97528e1fd49b238d521276116f.tar.gz
Merge pull request #18823 from Nirzak/patch-1
MAINT: Minor fix to add reference link to numpy.fill_diagonal function
Diffstat (limited to 'doc')
-rw-r--r--doc/source/user/tutorial-svd.rst2
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst
index fd9e366e0..7b905e51e 100644
--- a/doc/source/user/tutorial-svd.rst
+++ b/doc/source/user/tutorial-svd.rst
@@ -431,7 +431,7 @@ Now, to build our approximation, we first need to make sure that our singular
values are ready for multiplication, so we build our ``Sigma`` matrix similarly
to what we did before. The ``Sigma`` array must have dimensions
``(3, 768, 1024)``. In order to add the singular values to the diagonal of
-``Sigma``, we will use the ``fill_diagonal`` function from NumPy, using each of
+``Sigma``, we will use the `numpy.fill_diagonal` function from NumPy, using each of
the 3 rows in ``s`` as the diagonal for each of the 3 matrices in ``Sigma``:
::