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authorEgor Tensin <Egor.Tensin@gmail.com>2016-04-18 18:28:39 +0300
committerEgor Tensin <Egor.Tensin@gmail.com>2016-04-18 18:28:39 +0300
commitcb2614105039d77c01b8c0e2bf2464bb8a2c1f76 (patch)
tree86e2a7eabfa796de4a904525d11268a204e18b43 /README.md
parentrefactoring + better plot captions (diff)
downloadsorting-algorithms-cb2614105039d77c01b8c0e2bf2464bb8a2c1f76.tar.gz
sorting-algorithms-cb2614105039d77c01b8c0e2bf2464bb8a2c1f76.zip
README update
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@@ -18,6 +18,8 @@ Currently the following algorithms are implemented:
* selection sort (`selection_sort.py`),
* calculating the median of a list (`median.py`).
+### Testing
+
You can test each of the algorithms above by passing a sequence of integer
numbers to the corresponding script:
@@ -36,9 +38,18 @@ 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 on this input.
+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.
+A few usage examples are listed below.
+
+ > test.py -l quicksort_random -i ascending -n 10
+ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+ > test.py --algorithm median_heaps --order random --length 100
+ 49.5
## Plots
@@ -57,20 +68,27 @@ plots to the `plots/` directory.
Both the hardware & the software that were used to produce the plots are listed
below.
- | |
--------------- | ---------------------------------------------------------------------------------------------------------- |
-**CPU** | [Intel Core i3-5005U](http://ark.intel.com/products/84695/Intel-Core-i3-5005U-Processor-3M-Cache-2_00-GHz) |
-**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 |
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`.
Consult the output of `plot.py -h` to learn how to use the script.
+A few usage examples are listed below.
+
+ > plot.py --algorithm merge_sort --min 0 --max 200 --order ascending -iterations 1000
+
+ > 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
@@ -79,10 +97,15 @@ 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:
- start /affinity 1 /realtime plot.py ...
+ > start /affinity 1 /realtime plot.py ...
## Licensing
This project, including all of the files and their contents, is licensed under
the terms of the MIT License.
-See [LICENSE.txt](LICENSE.txt) for details.
+See LICENSE.txt [[2]](#references) for details.
+
+## References
+
+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