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week43 update

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mhjensen
2020-10-23 05:56:41 +02:00
parent 83da23092b
commit 74a57bd64a
7 changed files with 6 additions and 6 deletions
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doc/pub/week43/html/._week43-bs069.html
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@@ -326,7 +326,7 @@ principal component is equal to
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]<span style="color: #666666">.</span>
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]
</pre></div>
<p>
Another very useful piece of information is the explained variance ratio of each principal component,
doc/pub/week43/html/week43-reveal.html
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@@ -3886,7 +3886,7 @@ principal component is equal to
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>].
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>]
</pre></div>
<p>
Another very useful piece of information is the explained variance ratio of each principal component,
doc/pub/week43/html/week43-solarized.html
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@@ -3744,7 +3744,7 @@ principal component is equal to
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>].
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>pca.components_.T[:, <span style="color: #B452CD">0</span>]
</pre></div>
<p>
Another very useful piece of information is the explained variance ratio of each principal component,
doc/pub/week43/html/week43.html
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@@ -3749,7 +3749,7 @@ principal component is equal to
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]<span style="color: #666666">.</span>
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]
</pre></div>
<p>
Another very useful piece of information is the explained variance ratio of each principal component,
doc/pub/week43/ipynb/ipynb-week43-src.tar.gz
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doc/pub/week43/ipynb/week43.ipynb
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@@ -4388,7 +4388,7 @@
},
"outputs": [],
"source": [
"pca.components_.T[:, 0]."
"pca.components_.T[:, 0]"
]
},
{
doc/src/week43/week43.do.txt
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@@ -3204,7 +3204,7 @@ After fitting the PCA transformer to the dataset, you can access the principal c
components variable (note that it contains the PCs as horizontal vectors, so, for example, the first
principal component is equal to
!bc pycod
pca.components_.T[:, 0].
pca.components_.T[:, 0]
!ec
Another very useful piece of information is the explained variance ratio of each principal component,
available via the $explained\_variance\_ratio$ variable. It indicates the proportion of the dataset’s