updating dim red

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

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

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

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

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"

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