[regression] updated exercise infrastructure

regression/exercises/Makefile

@@ -1,34 +1,4 @@
-TEXFILES=$(wildcard exercises??.tex)
+TEXFILES=$(wildcard gradientdescent-?.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:exercises%=solutions%)
 
-.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
+include ../../exercises.mk
 
-pdf : $(SOLUTIONS) $(EXERCISES)
-
-exercises : $(EXERCISES)
-
-solutions : $(SOLUTIONS)
-
-$(SOLUTIONS) : solutions%.pdf : exercises%.tex instructions.tex
-	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
-	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
-	rm $(patsubst %.pdf,%,$@).[!p]*
-
-$(EXERCISES) : %.pdf : %.tex instructions.tex
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
-
-watch :
-	while true; do ! make -q pdf && make pdf; sleep 0.5; done
-
-watchexercises :
-	while true; do ! make -q exercises && make exercises; sleep 0.5; done
-
-watchsolutions :
-	while true; do ! make -q solutions && make solutions; sleep 0.5; done
-
-clean :
-	rm -f *~ *.aux *.log *.out
-
-cleanup : clean
-	rm -f $(SOLUTIONS) $(EXERCISES)

regression/exercises/gradientdescent-1.tex

@@ -1,60 +1,17 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
+\newcommand{\exercisetopic}{Resampling}
-\usepackage{natbib}
+\newcommand{\exercisenum}{9}
-\usepackage{xcolor}
+\newcommand{\exercisedate}{December 21th, 2020}
-\usepackage{graphicx}
+
-\usepackage[small]{caption}
+\input{../../exercisesheader}
-\usepackage{sidecap}
+
-\usepackage{pslatex}
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 10\stitle}}{{\bfseries\large Gradient descent}}{{\bfseries\large December 16th, 2019}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solution:}\par\noindent}
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\input{instructions}
+\input{../../exercisestitle}
 
 \begin{questions}
 
@@ -96,7 +53,7 @@
     the parameter values at the minimum of the cost function and a vector
     with the value of the cost function at each step of the algorithm.
     \begin{solution}
-      \lstinputlisting{../code/descent.m}
+      \lstinputlisting{descent.m}
     \end{solution}
 
     \part Plot the data and the straight line with the parameter
@@ -105,7 +62,7 @@
     \part Plot the development of the costs as a function of the
     iteration step.
     \begin{solution}
-      \lstinputlisting{../code/descentfit.m}
+      \lstinputlisting{descentfit.m}
     \end{solution}
 
     \part For checking the gradient descend method from (a) compare
@@ -116,7 +73,7 @@
     minimum gradient. What are good values such that the gradient
     descent gets closest to the true minimum of the cost function?
     \begin{solution}
-      \lstinputlisting{../code/checkdescent.m}
+      \lstinputlisting{checkdescent.m}
     \end{solution}
 
     \part Use the functions \code{polyfit()} and \code{lsqcurvefit()}
@@ -124,7 +81,7 @@
     line that fits the data. Compare the resulting fit parameters of
     those functions with the ones of your gradient descent algorithm.
     \begin{solution}
-      \lstinputlisting{../code/linefit.m}
+      \lstinputlisting{linefit.m}
     \end{solution}
 
   \end{parts}