This project was created for personal use mostly while studying for an exam (starting in the month of June in 2015) of a previous course that I followed called Algorithms and Data Structures I decided to make it publicly available to use and modify, so that people with difficulties in understanding and applying these topics can take benefit from it.

I discourage every beginner from copying shamelessly the source code, but instead you should definitely give a chance to your brain and sense of challenge first! At the end, you will definetely feel a better and more serious programmer! If you really do not have any ideas on how to do something, try to read the comments next to each function and/or class (or even the code itself) that you are interested in. They are there for areason!

Any suggestions to improve the code, or the design of an algorithm or data structure, or corrections are of course welcome, and therefore I encourage you to fork this repository and eventually send a pull request. See below for more info.

In this repository, you will find data structures, such as binary-search trees or graphs, and algorithms that often work on (those) data structures. You will also find some algorithms related to some particular design paradigm, for example algorithms related to the greedy or dynamic programming design paradigms.

• This is a work in progress, don't expect to find here all the data structures and algorithms you're searching. Consider to contribute to the quality and size of the project.

• Again, mistakes are possible, even if decent tests are starting to being done. You can find them under the folder tests. So, as the license says, this project is provided "as is", etc.

• No optimisation has been done to any algorithm or data structure. The purpose of the implementations is just for exposition of the concepts!

• My intent is to continue to contribute to this repository in my free time, and new data structures and algorithms will therefore be added.

You can support this project by either reporting a bug or suggesting an improvement after reading the source code, the documentation and/or the doc strings, or you can do it by fixing the problem or introducing new algorithms and data structures by writing the code by yourself.

To accomplish the first one, I suggest you to simply open an issue on Github. To accomplish the second one, read the next section.

• This description and commands are for unix-like systems, i.e. Linux and Mac OS X. If you need help to do it on Windows, just send me an e-mail eventually. Note that the process should be identical, only the commands and eventually tools could be slightly different.

• I'm currently using Python 3.5 and also pip3.5 to develop, but the module is currently being tested against Python 3.3, 3.4 and 3.5 on Travis CI. Use other versions of Python and pip at your own responsibility!

First things first, you should fork the ands repository from my Github's account to your own. After that you should download your forked repository to your local machine. Once that's done, follow the following steps.

1. Open a terminal, and enter inside the ands folder. For example, if ands is in your desktop and your current working directory is your desktop, type:

    cd ands
   

2. You're going to install the ands module in editable mode in a virtual environment. If you don't know what's a virtual environment, it's time to know it. Here are a few useful resources:

   • https://docs.python-guide.org/en/latest/dev/virtualenvs/

   • https://stackoverflow.com/questions/5844869/comprehensive-beginners-virtualenv-tutorial

   The essential reason for developing in a virtual environment is that you probably just want to develop or simply modify the code, i.e. you probably don't want to use this module in other projects yet, because it's in early stage.

   So, the first thing to do is to install the virtualenv module, which you can do as follows

    pip3.5 install virtualenv
   

   Then, to create a virtual environment named venv (you can name it as you want), type:

    virtualenv venv
   

   Finally just type the following command to install ands on the virtual environment venv:

    pip3.5 install -e .
   

3. Once you finish developing, you need to

   1. commit your changes to your local repository,

   2. push your local changes (as per the last commit) to your remote (forked) repository, and then

   3. do a pull request

   If you don't know what this means, check online, because these things are very useful for any serious programmer who considers itself to know Git and Github. You can do all of these steps without writing any command on the terminal...

   Before that though, you should write tests to test what you've done. I would like that every algorithm and data structure is tested (which is not the case right now), in order to reduce the number of bugs as much as possible. Read the next section to know more about testing.

   Furthermore, before committing your changes, I recommended you to run the script ./automate.sh, which is responsible for doing the following tasks:

   1. Format all the source code using the tool autopep8

   2. Create a virtual environment to test the module

   3. Run the tests in the virtual environment

   4. Create the new documentation

   5. Clean up "junk" files

If you want to create tests (I strongly recommend you to write or modify them whenever respectively you write something new or modify something), you need to write them within the folder tests, and their names' conventions (i.e. scripts' and functions' names) should follow the same name conventions of the already existent tests.

From inside either tests/ds or tests/algorithms, you can do

python -m unittest discover . -v

or, after installing the package coveralls

coverage run -m unittest discover . -v

This last one should also report you the amount of code covered by the tests, after your run the command

coverage report

I created this script to automate the tasks of creating the virtual environment, installing dependencies, running the tests and reporting its results. If you want to run the script by running all tests (which takes some time), from inside the main folder, execute the following command on the terminal

./automate.sh

On the other hand, if you want to run a specific single-class test, e.g. test_DSForests.py, execute

./automate.sh -st ds test_DSForests.py

For each module I always try not to forget to specify the specific references that I used to implement the particular concept exposed in that module.

Apart from those, the following are the references which I always keep an eye on.

• Introduction to Algorithms (3rd ed.), book by Cormen, Leiserson, Rivest, Stein

• Slides provided by the professor of the course Algorithms and Data Structures, i.e. Antonio Carzaniga

• Slides provided by the professor of the course Algorithms and Data Structures 2, Evanthia Papadopoulou.

• Algorithms, 4th Edition, online book by Robert Sedgewick and Kevin Wayne

• Wikipedia

• Stack Overflow

• MIT 6.006 Introduction to Algorithms, Fall 2011

• Interactive Python, website by Brad Miller and David Ranum

• Algorithms: Design and Analysis, Part 1, Coursera's course taught by Tim Roughgarden

• Algorithms: Design and Analysis, Part 2, Coursera's course taught by Tim Roughgarden

• The Archive of Interesting Code by Keith Schwarz

• https://www.sanfoundry.com/

• Notes on Data Structures and Programming Techniques

There many useful resources around the web to help you (and me) understand how certain algorithms or data structures work.

One curated list that I found useful which points to a bunch of other resources is the following:

• https://github.com/tayllan/awesome-algorithms

Feel free to contribute to that list by adding other links to useful and interesting material.