From d6fbee178180fe990f5017be148d0d9dd4bb5ca7 Mon Sep 17 00:00:00 2001
From: mhjensen <morten.hjorth-jensen@fys.uio.no>
Date: Mon, 21 Oct 2019 23:23:39 +0200
Subject: [PATCH] updating dim red

---
 doc/pub/DimRed/html/._DimRed-bs009.html      |   3 ++-
 doc/pub/DimRed/html/DimRed-reveal.html       |   3 ++-
 doc/pub/DimRed/html/DimRed-solarized.html    |   3 ++-
 doc/pub/DimRed/html/DimRed.html              |   3 ++-
 doc/pub/DimRed/ipynb/DimRed.ipynb            |   3 ++-
 doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz | Bin 191 -> 191 bytes
 doc/pub/DimRed/pdf/DimRed-minted.pdf         | Bin 237104 -> 237104 bytes
 doc/src/DimRed/DimRed.do.txt                 |   3 ++-
 8 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/doc/pub/DimRed/html/._DimRed-bs009.html b/doc/pub/DimRed/html/._DimRed-bs009.html
index a835f1361..e4fd7acf7 100644
--- a/doc/pub/DimRed/html/._DimRed-bs009.html
+++ b/doc/pub/DimRed/html/._DimRed-bs009.html
@@ -174,7 +174,8 @@ MathJax.Hub.Config({
 <h2 id="___sec8" class="anchor">Introducing the Covariance and Correlation functions  </h2>
 
 <p>
-Before we discuss the PCA theorem, we need to remind ourselves about the definition of the covariance and the correlation function.
+Before we discuss the PCA theorem, we need to remind ourselves about
+the definition of the covariance and the correlation function.
 
 <p>
 Suppose we have defined two vectors
diff --git a/doc/pub/DimRed/html/DimRed-reveal.html b/doc/pub/DimRed/html/DimRed-reveal.html
index 845860690..128881712 100644
--- a/doc/pub/DimRed/html/DimRed-reveal.html
+++ b/doc/pub/DimRed/html/DimRed-reveal.html
@@ -525,7 +525,8 @@ We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see
 <h2 id="___sec8">Introducing the Covariance and Correlation functions  </h2>
 
 <p>
-Before we discuss the PCA theorem, we need to remind ourselves about the definition of the covariance and the correlation function.
+Before we discuss the PCA theorem, we need to remind ourselves about
+the definition of the covariance and the correlation function.
 
 <p>
 Suppose we have defined two vectors
diff --git a/doc/pub/DimRed/html/DimRed-solarized.html b/doc/pub/DimRed/html/DimRed-solarized.html
index 1f613b93b..880f64bde 100644
--- a/doc/pub/DimRed/html/DimRed-solarized.html
+++ b/doc/pub/DimRed/html/DimRed-solarized.html
@@ -528,7 +528,8 @@ We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see
 <h2 id="___sec8">Introducing the Covariance and Correlation functions  </h2>
 
 <p>
-Before we discuss the PCA theorem, we need to remind ourselves about the definition of the covariance and the correlation function.
+Before we discuss the PCA theorem, we need to remind ourselves about
+the definition of the covariance and the correlation function.
 
 <p>
 Suppose we have defined two vectors
diff --git a/doc/pub/DimRed/html/DimRed.html b/doc/pub/DimRed/html/DimRed.html
index c060dfc24..05db459ac 100644
--- a/doc/pub/DimRed/html/DimRed.html
+++ b/doc/pub/DimRed/html/DimRed.html
@@ -533,7 +533,8 @@ We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see
 <h2 id="___sec8">Introducing the Covariance and Correlation functions  </h2>
 
 <p>
-Before we discuss the PCA theorem, we need to remind ourselves about the definition of the covariance and the correlation function.
+Before we discuss the PCA theorem, we need to remind ourselves about
+the definition of the covariance and the correlation function.
 
 <p>
 Suppose we have defined two vectors
diff --git a/doc/pub/DimRed/ipynb/DimRed.ipynb b/doc/pub/DimRed/ipynb/DimRed.ipynb
index f65c66146..099a2274a 100644
--- a/doc/pub/DimRed/ipynb/DimRed.ipynb
+++ b/doc/pub/DimRed/ipynb/DimRed.ipynb
@@ -411,7 +411,8 @@
     "\n",
     "## Introducing the Covariance and Correlation functions\n",
     "\n",
-    "Before we discuss the PCA theorem, we need to remind ourselves about the definition of the covariance and the correlation function.\n",
+    "Before we discuss the PCA theorem, we need to remind ourselves about\n",
+    "the definition of the covariance and the correlation function.\n",
     "\n",
     "Suppose we have defined two vectors\n",
     "$\\hat{x}$ and $\\hat{y}$ with $n$ elements each. The covariance matrix $\\boldsymbol{C}$ is defined as"
diff --git a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz
index b05e978048943b68e918954d671dc43f47e22012..5e08c415d366f999c40b5f42aeba7459bb0a7855 100644
GIT binary patch
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diff --git a/doc/pub/DimRed/pdf/DimRed-minted.pdf b/doc/pub/DimRed/pdf/DimRed-minted.pdf
index a46afdad548d351886c2e4d285f6e19a8503098d..3629b502d5147666afd60b9c277441a76ff75092 100644
GIT binary patch
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delta 114
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diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt
index 9e864338d..a65bfc4ce 100644
--- a/doc/src/DimRed/DimRed.do.txt
+++ b/doc/src/DimRed/DimRed.do.txt
@@ -340,7 +340,8 @@ We have a data set defined by a design/feature matrix $\bm{X}$ (see below for it
 !split
 ===== Introducing the Covariance and Correlation functions  =====
 
-Before we discuss the PCA theorem, we need to remind ourselves about the definition of the covariance and the correlation function.
+Before we discuss the PCA theorem, we need to remind ourselves about
+the definition of the covariance and the correlation function.
 
 Suppose we have defined two vectors
 $\hat{x}$ and $\hat{y}$ with $n$ elements each. The covariance matrix $\bm{C}$ is defined as