Tiny fix to SVD tutorial text to mention fill_diagonal before using it (numpy#90)

melissawm · web-flow · commit 4fa014557ff2 · 2021-07-05T22:55:28.000+02:00
diff --git a/content/tutorial-svd.md b/content/tutorial-svd.md
@@ -197,7 +197,7 @@ compatible for multiplication. However, this is not true as `s` does not have a
 s @ Vt
 ```
 
-results in a `ValueError`. This happens because having a one-dimensional array for `s`, in this case, is much more economic in practice than building a diagonal matrix with the same data. To reconstruct the original matrix, we can rebuild the diagonal matrix $\Sigma$ with the elements of `s` in its diagonal and with the appropriate dimensions for multiplying: in our case, $\Sigma$ should be 768x1024 since `U` is 768x768 and `Vt` is 1024x1024.
+results in a `ValueError`. This happens because having a one-dimensional array for `s`, in this case, is much more economic in practice than building a diagonal matrix with the same data. To reconstruct the original matrix, we can rebuild the diagonal matrix $\Sigma$ with the elements of `s` in its diagonal and with the appropriate dimensions for multiplying: in our case, $\Sigma$ should be 768x1024 since `U` is 768x768 and `Vt` is 1024x1024. In order to add the singular values to the diagonal of `Sigma`, we will use the [fill_diagonal](https://numpy.org/devdocs/reference/generated/numpy.fill_diagonal.html) function from NumPy:
 
 ```{code-cell} ipython3
 import numpy as np
@@ -305,7 +305,7 @@ To build the final approximation matrix, we must understand how multiplication a
 
 If you have worked before with only one- or two-dimensional arrays in NumPy, you might use [numpy.dot](https://numpy.org/devdocs/reference/generated/numpy.dot.html#numpy.dot) and [numpy.matmul](https://numpy.org/devdocs/reference/generated/numpy.matmul.html#numpy.matmul) (or the `@` operator) interchangeably. However, for n-dimensional arrays, they work in very different ways. For more details, check the documentation on [numpy.matmul](https://numpy.org/devdocs/reference/generated/numpy.matmul.html#numpy.matmul).
 
-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](https://numpy.org/devdocs/reference/generated/numpy.fill_diagonal.html) function from NumPy, using each of the 3 rows in `s` as the diagonal for each of the 3 matrices in `Sigma`:
+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 again use the [fill_diagonal](https://numpy.org/devdocs/reference/generated/numpy.fill_diagonal.html) function, using each of the 3 rows in `s` as the diagonal for each of the 3 matrices in `Sigma`:
 
 ```{code-cell} ipython3
 Sigma = np.zeros((3, 768, 1024))
