https://github.com/jeremiegince/learning_svm

Support vector machines (SVM) tutorial with classical and quantum kernels.
https://github.com/jeremiegince/learning_svm

kernel quantum-computing svm tutorial

Last synced: about 1 month ago
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Support vector machines (SVM) tutorial with classical and quantum kernels.

• Host: GitHub
• URL: https://github.com/jeremiegince/learning_svm
• Owner: JeremieGince
• License: apache-2.0
• Created: 2023-09-08T00:35:14.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-25T16:30:28.000Z (about 1 year ago)
• Last Synced: 2024-05-01T14:31:52.367Z (8 months ago)
• Topics: kernel, quantum-computing, svm, tutorial
• Language: Python
• Homepage:
• Size: 1.71 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Support vector machines (SVM)

## Introduction

Support vector machines (SVM) is a supervised machine learning algorithm which can be used for both classification or 

regression challenges. However,  it is mostly used in classification problems.

This repository contains the implementation of SVM algorithm from scratch in python and also using sklearn library.

## Table of contents

* [Introduction](#introduction)

* [Jupyter notebook](https://github.com/JeremieGince/Learning_SVM/blob/main/notebook.ipynb)

* [Google Colab](https://colab.research.google.com/github/JeremieGince/Learning_SVM/blob/main/notebook.ipynb)

* [Scripts](#Scripts)

* [Requirements](#requirements)

* [Slides](Prez_SVM.pdf)

## Scripts

* In the [main.py](main.py) file, you can find the training, testing and comparison of the SVMs 

using the sklearn library, from scratch and with a classical and quantum kernel.

* In the [kernels.py](kernels.py) file, you can find the implementation of the classical and quantum kernels.

* In the [scratch.py](scratch.py) file, you can find the implementation of the SVM algorithm from scratch.

* In the [visualization.py](visualization.py) file, you can find the implementation of the visualization of the

decision boundary of the SVMs.

## Requirements

* Python 3.8 or higher

* Other requirements are in the [requirements.txt](requirements.txt) file. You can install them using the following 

command:

```bash

pip install -r requirements.txt

```

## Note

This repository is currently under construction. More details will be added soon.