<a id="readme-top"></a>
<!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="https://github.com/Kynemallv/python_mipt_dafe/blob/main/homeworks/sem2_hw1/solidipy_framework/">
<img src="https://github.com/Kynemallv/python_mipt_dafe/blob/main/homeworks/sem2_hw1/solidipy_framework/assets/images/logo.png?raw=true" alt="Logo" width="80" height="80">
</a>
<h3 align="center">Solidipy-MIPT</h3>
<p align="center">
Make your ML solid!
<br />
<a href="https://github.com/Kynemallv/python_mipt_dafe/tree/main/homeworks/sem2_hw1/solidipy_framework/examples">Examples</a>
·
<a href="https://github.com/Kynemallv/python_mipt_dafe/issues/new?labels=bug&template=bug-report---.md">Report Bug</a>
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</p>
</div>
<!-- TABLE OF CONTENTS -->
<details>
<summary>Table of Contents</summary>
<ol>
<li>
<a href="#about-the-project">About The Project</a>
<ul>
<li><a href="#built-with">Built With</a></li>
</ul>
</li>
<li>
<a href="#getting-started">Getting Started</a>
<ul>
<li><a href="#prerequisites">Prerequisites</a></li>
<li><a href="#installation">Installation</a></li>
</ul>
</li>
<li><a href="#usage">Usage</a></li>
<li><a href="#contributing">Contributing</a></li>
<li><a href="#license">License</a></li>
<li><a href="#contact">Contact</a></li>
</ol>
</details>
<!-- ABOUT THE PROJECT -->
<a id="about-the-project"></a>
## About The Project
*Solidipy-MIPT* is a Python library designed to provide a solid foundation for machine learning tasks. It includes various machine learning algorithms such as Weighted k-nearest neighbors (WKNN) and regressions, along with evaluation metrics to assess model performance.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<a id="built-with"></a>
### Built With
Major frameworks/libraries used to bootstrap solidipy-mipt.
* [NumPy](https://numpy.org/)
* [Matplotlib](https://matplotlib.org/)
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- GETTING STARTED -->
<a id="getting-started"></a>
## Getting Started
To get a local copy up and running follow these simple example steps.
<a id="prerequisites"></a>
### Prerequisites
Before installing *Solidipy-MIPT* make sure you have last version of Python3 and pip.
<a id="installation"></a>
### Installation
You can install solidipy-mipt using pip:
```bash
pip install solidipy-mipt
```
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<!-- USAGE EXAMPLES -->
<a id="usage"></a>
## Usage
#### Simple Weighted KNN example
```python
import numpy as np
from solidipy_mipt import accuracy
from solidipy_mipt.algorithms import WKNN
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y = np.array([0, 1, 0, 1])
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_ratio=0.6, shuffle=True
)
wknn = WKNN()
wknn.fit(X_train, y_train)
prediction = wknn.predict(X_test)
print(accuracy(prediction, y_test))
```
_For more examples, please refer to the [solidipy_mipt examples](https://github.com/Kynemallv/python_mipt_dafe/tree/main/homeworks/sem2_hw1/solidipy_framework/examples)_
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<!-- CONTRIBUTING -->
<a id="contributing"></a>
## 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 -->
<a id="license"></a>
## License
Distributed under the MIT License. See `LICENSE.txt` for more information.
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<!-- CONTACT -->
<a id="contact"></a>
## Contact
Matvei Gorskii - [t.me/Kynemallv](https://twitter.com/your_username) - matveygor41@gmail.com
Project Link: [https://github.com/your_username/repo_name](https://github.com/your_username/repo_name)
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"description": "<a id=\"readme-top\"></a>\r\n\r\n\r\n\r\n<!-- PROJECT LOGO -->\r\n<br />\r\n<div align=\"center\">\r\n <a href=\"https://github.com/Kynemallv/python_mipt_dafe/blob/main/homeworks/sem2_hw1/solidipy_framework/\">\r\n <img src=\"https://github.com/Kynemallv/python_mipt_dafe/blob/main/homeworks/sem2_hw1/solidipy_framework/assets/images/logo.png?raw=true\" alt=\"Logo\" width=\"80\" height=\"80\">\r\n </a>\r\n\r\n <h3 align=\"center\">Solidipy-MIPT</h3>\r\n\r\n <p align=\"center\">\r\n Make your ML solid!\r\n <br />\r\n <a href=\"https://github.com/Kynemallv/python_mipt_dafe/tree/main/homeworks/sem2_hw1/solidipy_framework/examples\">Examples</a>\r\n \u00b7\r\n <a href=\"https://github.com/Kynemallv/python_mipt_dafe/issues/new?labels=bug&template=bug-report---.md\">Report Bug</a>\r\n \u00b7\r\n <a href=\"https://github.com/Kynemallv/python_mipt_dafe/issues/new?labels=enhancement&template=feature-request---.md\">Request Feature</a>\r\n </p>\r\n</div>\r\n\r\n\r\n<!-- TABLE OF CONTENTS -->\r\n<details>\r\n <summary>Table of Contents</summary>\r\n <ol>\r\n <li>\r\n <a href=\"#about-the-project\">About The Project</a>\r\n <ul>\r\n <li><a href=\"#built-with\">Built With</a></li>\r\n </ul>\r\n </li>\r\n <li>\r\n <a href=\"#getting-started\">Getting Started</a>\r\n <ul>\r\n <li><a href=\"#prerequisites\">Prerequisites</a></li>\r\n <li><a href=\"#installation\">Installation</a></li>\r\n </ul>\r\n </li>\r\n <li><a href=\"#usage\">Usage</a></li>\r\n <li><a href=\"#contributing\">Contributing</a></li>\r\n <li><a href=\"#license\">License</a></li>\r\n <li><a href=\"#contact\">Contact</a></li>\r\n </ol>\r\n</details>\r\n\r\n\r\n\r\n<!-- ABOUT THE PROJECT -->\r\n<a id=\"about-the-project\"></a>\r\n\r\n## About The Project\r\n\r\n*Solidipy-MIPT* is a Python library designed to provide a solid foundation for machine learning tasks. It includes various machine learning algorithms such as Weighted k-nearest neighbors (WKNN) and regressions, along with evaluation metrics to assess model performance.\r\n\r\n\r\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\r\n\r\n\r\n<a id=\"built-with\"></a>\r\n\r\n### Built With\r\n\r\nMajor frameworks/libraries used to bootstrap solidipy-mipt.\r\n\r\n* [NumPy](https://numpy.org/)\r\n* [Matplotlib](https://matplotlib.org/)\r\n\r\n\r\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\r\n\r\n\r\n\r\n<!-- GETTING STARTED -->\r\n<a id=\"getting-started\"></a>\r\n\r\n## Getting Started\r\n\r\nTo get a local copy up and running follow these simple example steps.\r\n\r\n<a id=\"prerequisites\"></a>\r\n\r\n### Prerequisites\r\n\r\nBefore installing *Solidipy-MIPT* make sure you have last version of Python3 and pip.\r\n\r\n<a id=\"installation\"></a>\r\n\r\n### Installation\r\n\r\nYou can install solidipy-mipt using pip:\r\n\r\n```bash\r\npip install solidipy-mipt\r\n```\r\n\r\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\r\n\r\n\r\n\r\n<!-- USAGE EXAMPLES -->\r\n<a id=\"usage\"></a>\r\n\r\n## Usage\r\n\r\n#### Simple Weighted KNN example\r\n```python\r\nimport numpy as np\r\nfrom solidipy_mipt import accuracy\r\nfrom solidipy_mipt.algorithms import WKNN\r\n\r\nX = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])\r\ny = np.array([0, 1, 0, 1])\r\nX_train, X_test, y_train, y_test = train_test_split(\r\n X, y, train_ratio=0.6, shuffle=True\r\n)\r\n\r\nwknn = WKNN()\r\nwknn.fit(X_train, y_train)\r\nprediction = wknn.predict(X_test)\r\n\r\nprint(accuracy(prediction, y_test))\r\n```\r\n\r\n_For more examples, please refer to the [solidipy_mipt examples](https://github.com/Kynemallv/python_mipt_dafe/tree/main/homeworks/sem2_hw1/solidipy_framework/examples)_\r\n\r\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\r\n\r\n\r\n\r\n<!-- CONTRIBUTING -->\r\n<a id=\"contributing\"></a>\r\n\r\n## Contributing\r\n\r\nContributions are what make the open source community such an amazing place to learn, inspire, and create. 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