From a9ea09f8fd813952c935d23901990f32ea430dba Mon Sep 17 00:00:00 2001
From: mhjensen <morten.hjorth-jensen@fys.uio.no>
Date: Thu, 2 Jan 2020 10:54:04 +0100
Subject: [PATCH] typos

---
 doc/pub/DimRed/html/._DimRed-bs018.html      |   2 +-
 doc/pub/DimRed/html/DimRed-reveal.html       |   2 +-
 doc/pub/DimRed/html/DimRed-solarized.html    |   2 +-
 doc/pub/DimRed/html/DimRed.html              |   2 +-
 doc/pub/DimRed/ipynb/DimRed.ipynb            |   2 +-
 doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz | Bin 191 -> 191 bytes
 doc/pub/DimRed/pdf/DimRed-minted.pdf         | Bin 263641 -> 263781 bytes
 doc/src/DimRed/DimRed.do.txt                 |   2 +-
 doc/src/DimRed/Programs/PCAsimple.py         |   6 +++---
 9 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/doc/pub/DimRed/html/._DimRed-bs018.html b/doc/pub/DimRed/html/._DimRed-bs018.html
index a7cce97b1..da98177dc 100644
--- a/doc/pub/DimRed/html/._DimRed-bs018.html
+++ b/doc/pub/DimRed/html/._DimRed-bs018.html
@@ -374,7 +374,7 @@ X2Dsl <span style="color: #666666">=</span> pca<span style="color: #666666">.</s
 <span style="color: #008000; font-weight: bold">print</span>(pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>])
 </pre></div>
 <p>
-This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to, based on the above, to address the questions above.
+This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code.  Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?
 
 <p>
 <p>
diff --git a/doc/pub/DimRed/html/DimRed-reveal.html b/doc/pub/DimRed/html/DimRed-reveal.html
index 367451ae8..570cd0c98 100644
--- a/doc/pub/DimRed/html/DimRed-reveal.html
+++ b/doc/pub/DimRed/html/DimRed-reveal.html
@@ -1187,7 +1187,7 @@ X2Dsl = pca.fit_transform(X)
 <span style="color: #8B008B; font-weight: bold">print</span>(pca.components_.T[:, <span style="color: #B452CD">0</span>])
 </pre></div>
 <p>
-This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to, based on the above, to address the questions above.
+This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code.  Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?
 </section>
 
 
diff --git a/doc/pub/DimRed/html/DimRed-solarized.html b/doc/pub/DimRed/html/DimRed-solarized.html
index 29d7191d6..3fd9e4333 100644
--- a/doc/pub/DimRed/html/DimRed-solarized.html
+++ b/doc/pub/DimRed/html/DimRed-solarized.html
@@ -1160,7 +1160,7 @@ X2Dsl = pca.fit_transform(X)
 <span style="color: #8B008B; font-weight: bold">print</span>(pca.components_.T[:, <span style="color: #B452CD">0</span>])
 </pre></div>
 <p>
-This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to, based on the above, to address the questions above.
+This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code.  Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
diff --git a/doc/pub/DimRed/html/DimRed.html b/doc/pub/DimRed/html/DimRed.html
index 8960e958f..728373253 100644
--- a/doc/pub/DimRed/html/DimRed.html
+++ b/doc/pub/DimRed/html/DimRed.html
@@ -1165,7 +1165,7 @@ X2Dsl <span style="color: #666666">=</span> pca<span style="color: #666666">.</s
 <span style="color: #008000; font-weight: bold">print</span>(pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>])
 </pre></div>
 <p>
-This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to, based on the above, to address the questions above.
+This code does not contain all the above elements, but it shows how we can use <b>Scikit-Learn</b> to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code.  Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
diff --git a/doc/pub/DimRed/ipynb/DimRed.ipynb b/doc/pub/DimRed/ipynb/DimRed.ipynb
index 2301b9649..ae921523d 100644
--- a/doc/pub/DimRed/ipynb/DimRed.ipynb
+++ b/doc/pub/DimRed/ipynb/DimRed.ipynb
@@ -1335,7 +1335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This code does not contain all the above elements, but it shows how we can use **Scikit-Learn** to extract the eigenvector which corresponds to the largest eigenvalue. Try to, based on the above, to address the questions above. \n",
+    "This code does not contain all the above elements, but it shows how we can use **Scikit-Learn** to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code.  Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then? \n",
     "\n",
     "## Classical PCA Theorem\n",
     "\n",
diff --git a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz
index 88a78300f21e6f6ecd96e741e07dc05d57a8669c..2b31e7165ed573b3201dc2084ff7ab2e5bcf0f28 100644
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diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt
index 0b7bc1eb0..4f92f64ff 100644
--- a/doc/src/DimRed/DimRed.do.txt
+++ b/doc/src/DimRed/DimRed.do.txt
@@ -911,7 +911,7 @@ print("Eigenvector of largest eigenvalue")
 print(pca.components_.T[:, 0])
 
 !ec
-This code does not contain all the above elements, but it shows how we can use _Scikit-Learn_ to extract the eigenvector which corresponds to the largest eigenvalue. Try to, based on the above, to address the questions above. 
+This code does not contain all the above elements, but it shows how we can use _Scikit-Learn_ to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code.  Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then? 
 
 !split
 ===== Classical PCA Theorem   =====
diff --git a/doc/src/DimRed/Programs/PCAsimple.py b/doc/src/DimRed/Programs/PCAsimple.py
index c23d51e47..4f06292dd 100644
--- a/doc/src/DimRed/Programs/PCAsimple.py
+++ b/doc/src/DimRed/Programs/PCAsimple.py
@@ -5,7 +5,7 @@ import matplotlib.pyplot as plt
 
 n = 1000
 mean = (-1, 2)
-cov = [[4, 2], [2, 2]]
+cov = [[10, 1], [1, 0.5]]
 X = np.random.multivariate_normal(mean, cov, n)
 
 df = pd.DataFrame(X)
@@ -21,8 +21,8 @@ X_centered = X - X.mean(axis=0)
 print("Centered covariance using numpy")
 print(np.cov(X_centered.T))
 # extract the relevant columns from the centered design matrix
-x = X_centered[:,[0]]
-y = X_centered[:,[1]]
+x = X_centered[:,0]
+y = X_centered[:,1]
 Cov = np.zeros((2,2))
 Cov[0,1] = np.sum(x.T@y)/(n-1.0)
 Cov[0,0] = np.sum(x.T@x)/(n-1.0)