Write your own code (using either a matrix inversion or a singular diff --git a/doc/Projects/2021/Project1/html/Project1-bs.html b/doc/Projects/2021/Project1/html/Project1-bs.html index 41f1c0d38..46957ed0a 100644 --- a/doc/Projects/2021/Project1/html/Project1-bs.html +++ b/doc/Projects/2021/Project1/html/Project1-bs.html @@ -274,7 +274,7 @@ We will generate our own dataset for a function \( \mathrm{FrankeFunction}(x,y) \) with \( x,y \in [0,1] \). The function \( f(x,y) \) is the Franke function. You should explore also the addition of an added stochastic noise to this function using the normal -distribution \( \cal{N}(0,1) \). +distribution \( N(0,1) \).
Write your own code (using either a matrix inversion or a singular diff --git a/doc/Projects/2021/Project1/html/Project1.html b/doc/Projects/2021/Project1/html/Project1.html index c4328dcd2..545df6ab9 100644 --- a/doc/Projects/2021/Project1/html/Project1.html +++ b/doc/Projects/2021/Project1/html/Project1.html @@ -230,7 +230,7 @@ We will generate our own dataset for a function \( \mathrm{FrankeFunction}(x,y) \) with \( x,y \in [0,1] \). The function \( f(x,y) \) is the Franke function. You should explore also the addition of an added stochastic noise to this function using the normal -distribution \( \cal{N}(0,1) \). +distribution \( N(0,1) \).
Write your own code (using either a matrix inversion or a singular diff --git a/doc/Projects/2021/Project1/ipynb/Project1.ipynb b/doc/Projects/2021/Project1/ipynb/Project1.ipynb index ea9ecd9d2..b32c902df 100644 --- a/doc/Projects/2021/Project1/ipynb/Project1.ipynb +++ b/doc/Projects/2021/Project1/ipynb/Project1.ipynb @@ -134,7 +134,7 @@ "$\\mathrm{FrankeFunction}(x,y)$ with $x,y \\in [0,1]$. The function\n", "$f(x,y)$ is the Franke function. You should explore also the addition\n", "of an added stochastic noise to this function using the normal\n", - "distribution $\\cal{N}(0,1)$.\n", + "distribution $N(0,1)$.\n", "\n", "*Write your own code* (using either a matrix inversion or a singular\n", "value decomposition from e.g., **numpy** ) or use your code from\n", diff --git a/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz b/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz index 54bbeff0c..3d71f4bd3 100644 Binary files a/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz and b/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz differ diff --git a/doc/Projects/2021/Project1/pdf/Project1.p.tex b/doc/Projects/2021/Project1/pdf/Project1.p.tex index a7204c63d..a544ac850 100644 --- a/doc/Projects/2021/Project1/pdf/Project1.p.tex +++ b/doc/Projects/2021/Project1/pdf/Project1.p.tex @@ -233,7 +233,7 @@ We will generate our own dataset for a function $\mathrm{FrankeFunction}(x,y)$ with $x,y \in [0,1]$. The function $f(x,y)$ is the Franke function. You should explore also the addition of an added stochastic noise to this function using the normal -distribution $\cal{N}(0,1)$. +distribution $N(0,1)$. \emph{Write your own code} (using either a matrix inversion or a singular value decomposition from e.g., \textbf{numpy} ) or use your code from diff --git a/doc/Projects/2021/Project1/pdf/Project1.pdf b/doc/Projects/2021/Project1/pdf/Project1.pdf index faf1c4c9e..647a3f09f 100644 Binary files a/doc/Projects/2021/Project1/pdf/Project1.pdf and b/doc/Projects/2021/Project1/pdf/Project1.pdf differ diff --git a/doc/Projects/2021/Project1/pdf/Project1.tex b/doc/Projects/2021/Project1/pdf/Project1.tex index 75d306c2f..5d8fbc2d9 100644 --- a/doc/Projects/2021/Project1/pdf/Project1.tex +++ b/doc/Projects/2021/Project1/pdf/Project1.tex @@ -203,7 +203,7 @@ We will generate our own dataset for a function $\mathrm{FrankeFunction}(x,y)$ with $x,y \in [0,1]$. The function $f(x,y)$ is the Franke function. You should explore also the addition of an added stochastic noise to this function using the normal -distribution $\cal{N}(0,1)$. +distribution $N(0,1)$. \emph{Write your own code} (using either a matrix inversion or a singular value decomposition from e.g., \textbf{numpy} ) or use your code from diff --git a/doc/src/Projects/2021/Project1/Project1.do.txt b/doc/src/Projects/2021/Project1/Project1.do.txt index df47bf567..5c161ece0 100644 --- a/doc/src/Projects/2021/Project1/Project1.do.txt +++ b/doc/src/Projects/2021/Project1/Project1.do.txt @@ -101,7 +101,7 @@ We will generate our own dataset for a function $\mathrm{FrankeFunction}(x,y)$ with $x,y \in [0,1]$. The function $f(x,y)$ is the Franke function. You should explore also the addition of an added stochastic noise to this function using the normal -distribution $\cal{N}(0,1)$. +distribution $N(0,1)$. *Write your own code* (using either a matrix inversion or a singular value decomposition from e.g., _numpy_ ) or use your code from