-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 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
-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 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
-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 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
-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 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 b05e97804..5e08c415d 100644 Binary files a/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz and b/doc/pub/DimRed/ipynb/ipynb-DimRed-src.tar.gz differ diff --git a/doc/pub/DimRed/pdf/DimRed-minted.pdf b/doc/pub/DimRed/pdf/DimRed-minted.pdf index a46afdad5..3629b502d 100644 Binary files a/doc/pub/DimRed/pdf/DimRed-minted.pdf and b/doc/pub/DimRed/pdf/DimRed-minted.pdf differ 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