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| -rw-r--r-- | README.md | 53 |
1 files changed, 25 insertions, 28 deletions
@@ -1,8 +1,9 @@ # Sorting algorithms Gettting the hang out of (sorting) algorithms. -Hosted on [GitHub Pages](https://pages.github.com) at -https://egor-tensin.github.io/sorting-algorithms/. +Hosted on [GitHub Pages] at https://egor-tensin.github.io/sorting-algorithms/. + +[GitHub Pages]: https://pages.github.com/ ## Algorithms @@ -38,11 +39,9 @@ middle, the last or a random element of the sequence. [1, 2, 3, 4, 5] [1, 2, 3, 4, 5] -#### test.py - -You can use the generic script `test.py` to quickly generate an input list of -some kind and display the result of executing an algorithm for this input. -Consult the output of `test.py -h` to learn how to use the script. +You can use `test.py` to quickly generate an input list of some kind and +display the result of executing one of the implemented algorithms. +Consult the output of `test.py --help` to learn how to use the script. A few usage examples are listed below. > test.py -l quicksort_random -i ascending -n 10 @@ -64,25 +63,26 @@ The results of course are highly dependent on the hardware used, while the graph's look depends on the software used for rendering. I've made the measurements for each of the implemented algorithms and put the -plots to the `plots/` directory. -Both the hardware & the software that were used to produce the plots are listed -below. +plots to the "plots/" directory. +Both the hardware & the software versions that were used to produce the plots +are listed below. + +| Component | Version | +| ------------ | --------------------- | +| CPU | [Intel Core i3-5005U] | +| OS | Windows 8.1 | +| Python | 3.5.1 | +| [matplotlib] | 1.5.1 | -Component | Version | ----------- | -------------------------------------- | -CPU | Intel Core i3-5005U [[1]](#references) | -OS | Windows 8.1 | -Python | 3.5.1 | -matplotlib | 1.5.1 | +[Intel Core i3-5005U]: http://ark.intel.com/products/84695/Intel-Core-i3-5005U-Processor-3M-Cache-2_00-GHz +[matplotlib]: http://matplotlib.org/ Each of the implemented algorithms was provided with three input sequences: * a list of *n* consecutive numbers sorted in ascending order, * ... in descending order, * ... in random order. -### plot.py - -You can generate the plots using `plot.py`. +You can generate similar plots using `plot.py`. Consult the output of `plot.py -h` to learn how to use the script. A few usage examples are listed below. @@ -91,21 +91,18 @@ A few usage examples are listed below. > plot.py -l median_sort_first -a 0 -b 200 -i random -r 100 -o median_sort_first.png If you're having problems using the script (like having excessive noise in the -measurement results), try minimizing background activity of the OS and the +measurement results), try minimizing background activity of the OS and running applications. For example, on Windows 8.1 I managed to produce some very nice plots by -booting into Safe Mode and running the scripts with a higher priority while -also setting their CPU affinity: +booting into Safe Mode and running the script with a higher priority while +also setting its CPU affinity: > start /affinity 1 /realtime plot.py ... -## Licensing +## License This project, including all of the files and their contents, is licensed under the terms of the MIT License. -See LICENSE.txt [[2]](#references) for details. - -## References +See [LICENSE.txt] for details. -1. http://ark.intel.com/products/84695/Intel-Core-i3-5005U-Processor-3M-Cache-2_00-GHz -2. https://github.com/egor-tensin/sorting-algorithms/blob/master/LICENSE.txt +[LICENSE.txt]: LICENSE.txt |