This is an attempt to build a cohesive module of algorithms in Python.
The purpose of this repo is to be a learning tool for myself and others.
I used psuedo code from various sources and I have listed them as references in the source code of each algorithm.
Algorithms implemented so far:
- Disjoint Set
- Single Linked List
- Undirected Graph
- Bogo Sort
- Bubble Sort
- Cocktail Sort
- Comb Sort
- Heap Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- In Place Quick Sort
- Selection Sort
- Shell Sort
- Gnome Sort
- Binary Search
- Depth First Search (Recursive)
- Knuth/Fisher-Yates Shuffle
- Extended GCD
- Standard Normal Probability Density Function
- Cumulative Density Function (Approximation; 16 digit precision for 300 iter.)
- Sieve of Eratosthenes
- Mersenne Twister
If you want to use the algorithms directly, simply
$ pip install algorithms
If you want to examine the algorithms source, then you should clone this repo.
Once installed you can simply do the following in your program:
from algorithms.sorting import bubble_sort my_list = bubble_sort.sort(my_list)
All prequisites for the algorithms are listed in the source code for each algorithm.
Nose is used as the main test runner and all Unit Tests can be run by:
$ python algorithms/run_tests.py
If there is an algorithm or data structure that you do not see, but would like to add please feel free to do a pull request. I only ask two things:
- For each algorithm and data structure you implement please have corresponding unit tests to prove correctness.
- Please make sure that your module follows similar style guidelines that are laid out in the other modules.
I want to personally thank everybody that has contributed so far and your names will be added to AUTHORS.rst.
Copyright (c) 2012 by Nic Young and contributors. See AUTHORS.rst for more details
Some rights reserved.
Redistribution and use in source and binary forms of the software as well as documentation, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- The names of the contributors may not be used to endorse or promote products derived from this software without specific prior written permission.
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