report(evaluation): Introductary text before figure done

1 files changed, 49 insertions, 50 deletions

diff --git a/report/report.tex b/report/report.tex
index 5b252ba..d412a4e 100644
--- a/report/report.tex
+++ b/report/report.tex
@@ -227,66 +227,65 @@ the inner loop. If j at some point hits 0, the element A[i] is the current small
 and should be inserted there. This process continues until R15 which holds the index of "i" is greater than the number of coordinates.
 
 \section{Evaluation}
-\begin{itemize}
-    \item Test insertion-sort
-    \item Test quick-sort
-    \item Benchmark insertion-sort vs. quick-sort random data
-    \item Benchmark insertion-sort vs. quick-sort sorted data
-    \item Benchmark insertion-sort vs. quick-sort unsorted data
-\end{itemize}
 
 To evaluate whether out program works, we created three \textit{shell scripts},
 one for generating test data (\textit{generate\_test\_data.sh}), one for testing
 the validity of the output of the program (\textit{test.sh}) and one for testing
 the execution time of the program (\textit{benchmark.sh}).
 
-The script that generates test files, generates files of size $n$ for all $n\in
-\{10000\cdot i \mid 0 <= i <= 10\}$ (a size of 10, means the file consists of
-10 coordinates). For each $n$ we also create three different kinds of test files. Now we can use our \textit{test.sh} script, which runs the
-program with each of the generated test files and compares the output with the
-output from the \textit{builtin} function \textit{sort}.
+The script that generates test files, generates files of size 0, 5000, 10000,
+50000, 100000, 500000 and 1000000. (a size of 10, means the file consists of 10
+coordinates). For each $n$ we also create three different kinds of test files.
+One where all of the data is random, one where the data is already sorted, and
+one where it is reversely sorted. This way we can see both the average case, and
+worst case of any algorithm we decide to use. Now we can use our
+\textit{test.sh} script, which runs the program with each of the generated test
+files for all sorting algorithms of the program, and compares the output with
+the output from the \textit{builtin} function \textit{sort}.
 
 Given all of the tests pass, we can then run \textit{benchmark.sh}, which times
-how long it takes for out program to run with each test file, and saves the
-result in a \textit{csv} file.
+how long it takes for our program to run with each test file for each algorithm,
+and saves the result in a \textit{csv} file. The results of this \textit{csv}
+file is shown 
 
 \begin{figure}[H]
-    \centering
-    \begin{tikzpicture}
-
-      \begin{axis}[
-          xlabel={size},
-          ylabel={seconds},
-          ymode=log,
-          width=\textwidth,
-          legend pos=north west,
-          scatter/classes={
-            isort_random={mark=*,red},
-            isort_reverse={mark=*,green},
-            isort_sorted={mark=*,blue},
-            qsort_random={mark=*,cyan},
-            qsort_reverse={mark=*,magenta},
-            qsort_sorted={mark=*,yellow}
-          }
-        ]
-
-        \addplot[
-          scatter, only marks,
-          scatter src=explicit symbolic
-        ]
-        table [x=size, y=real, meta=type, col sep=comma]
-        {../test/benchmark_results.csv};
-        \legend{isort\_random, isort\_reverse, isort\_sorted, qsort\_random, qsort\_reverse, qsort\_sorted}
-
-        % \addplot[domain=100:100000, samples=10, thick, densely dotted, black]{x/250000};
-        %   \addlegendentry{$\mathcal O(\text{size})$}
-        %
-        % \addplot[domain=100:100000, samples=10, thick, dashed, black]{(x*x)/4000000000};
-        %   \addlegendentry{$\mathcal O(\text{size}^2)$}
-
-      \end{axis}
-    \end{tikzpicture}
-    \caption{}\label{fig:}
+%     \centering
+%     \begin{tikzpicture}
+%
+%       \begin{axis}[
+%           xlabel={size},
+%           ylabel={seconds},
+%           ymode=log,
+%           width=\textwidth,
+%           legend pos=north west,
+%           scatter/classes={
+%             isort_random={mark=*,red},
+%             isort_reverse={mark=*,green},
+%             isort_sorted={mark=*,blue},
+%             qsort_random={mark=*,cyan},
+%             qsort_reverse={mark=*,magenta},
+%             qsort_sorted={mark=*,yellow}
+%           }
+%         ]
+%
+%         \addplot[
+%           scatter, only marks,
+%           scatter src=explicit symbolic
+%         ]
+%         table [x=size, y=real, meta=type, col sep=comma]
+%         {../test/benchmark_results.csv};
+%         \legend{isort\_random, isort\_reverse, isort\_sorted, qsort\_random, qsort\_reverse, qsort\_sorted}
+%
+%         % \addplot[domain=100:100000, samples=10, thick, densely dotted, black]{x/250000};
+%         %   \addlegendentry{$\mathcal O(\text{size})$}
+%         %
+%         % \addplot[domain=100:100000, samples=10, thick, dashed, black]{(x*x)/4000000000};
+%         %   \addlegendentry{$\mathcal O(\text{size}^2)$}
+%
+%       \end{axis}
+%     \end{tikzpicture}
+    \caption{Benchmarking data from Insertionsort and Quicksort for both random,
+    sorted and reverse sorted data.}\label{fig:benchmark}
 \end{figure}