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ALGORITHMS AND DATA STRUCTURES

Table of Contents

Overview

This repository contains solutions to exercises from Data Structures and Algorithms course at AGH university.

Features

  • prepared set of test graphs that can be easily imported or copy pasted -> graphs.py
  • tool for rendering visual representations of graphs -> renderGraph.py

Project Structure

└── algorithms_and_data_structures.git/
    ├── README.md
    ├── data_structures
    ├── exams
    │   ├── 2020-21
    │   ├── 2021-22
    │   ├── 2022-23
    │   ├── 2023-24
    │   └── mock
    ├── exercises
    │   ├── lab1
    │   ├── lab10
    │   ├── lab2
    │   ├── lab3
    │   ├── lab4
    │   ├── lab5
    │   ├── lab6
    │   ├── lab7
    │   ├── lab8
    │   ├── lab9
    │   └── usefullFunctions.py
    ├── graph_algorithms
    │   ├── MST
    │   ├── basic
    │   ├── flows
    │   ├── graphs
    │   └── path_finding
    ├── offline
    │   ├── offline1
    │   ├── offline2
    │   ├── offline3
    │   ├── offline4
    │   ├── offline5
    │   ├── offline6
    │   ├── offline7
    │   ├── offline8
    │   └── offline9
    ├── old_offlines
    │   └── 2021-22
    ├── progress_tests
    │   ├── 2016-17
    │   ├── 2019-20
    │   ├── 2020-21
    │   ├── 2021-22
    │   ├── 2022-23
    │   └── 2023-24
    ├── sorting
    └── typical_algorithms

Getting Started

Prerequisites

This project requires the following dependencies:

  • Programming Language: Python

Installation

Build algorithms_and_data_structures.git from the source and intsall dependencies:

  1. Clone the repository:

    ❯ git clone https://github.com/WSm-77/algorithms_and_data_structures.git
  2. Navigate to the project directory:

    cd algorithms_and_data_structures

Usage

To run any algorithm simply navigate to directory containing algorithm_to_run.py file and run it with:

python3 algorithm_to_run.py

Contributing

Contributing Guidelines
  1. Fork the Repository: Start by forking the project repository to your github account.
  2. Clone Locally: Clone the forked repository to your local machine using a git client.
    git clone https://github.com/WSm-77/algorithms_and_data_structures.git
  3. Create a New Branch: Always work on a new branch, giving it a descriptive name.
    git checkout -b new-feature-x
  4. Make Your Changes: Develop and test your changes locally.
  5. Commit Your Changes: Commit with a clear message describing your updates.
    git commit -m 'Implemented new feature x.'
  6. Push to github: Push the changes to your forked repository.
    git push origin new-feature-x
  7. Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.
  8. Review: Once your PR is reviewed and approved, it will be merged into the main branch. Congratulations on your contribution!

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