KNN and NC Tester

 

    The goal of this project is to evaluate the performance of two machine learning algorithms, K-Nearest Neighbors and Nearest Centroid.  

 

Table of Contents

 

   
1.       About The Project    
   
2.       Getting Started      
          
   • Prerequisites
        
      
   
3. Usage
   
4. Contributing
   
5. License
   
6. Contact
   
7. Acknowledgments
 

About The Project

In this program, different sizes of datasets of images and texts provide a good understanding of the performance of the algorithms under different conditions, and the indicators analyzed are accuracy, training time per sample, and inference time per sample.

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running, follow these simple example steps.

Prerequisites

If you don't have the OpenCV package installed, run

• pip     pip install opencv-python  

Usage

To use this program, you should simply clone the repository with     git clone https://github.com/Eduardo-Ferraz/TRAB2-POO-UFES   Then open the folder TRAB2-POO-UFES with VS Code and open the Run and Debug section. There, you should find 10 options for analyses to run, 5 for each algorithm and with different types and sizes of datasets. By running the desired test, the program will create a report.txt file in the TRAB2-POO-UFES folder. There, you can see the results of the different parameters analyzed in the evaluation. Every time you run the test, a new report.txt file is created, overwriting the old one if it still has the same name.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Eduardo Ferraz: https://www.linkedin.com/in/eduardo-ferraz1/

Igor Baiocco: https://www.linkedin.com/in/igor-baiocco/

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Acknowledgments

Special thanks to our professor Filipe Mutz (https://github.com/filipemtz) for the guidance during this journey, always exploring new frontiers of the capabilities of their mentored

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