semvec/pypi-packages huggingface dataset:
beta-solver 0.0.2
A transparent, pure-math implementation of Multiple Linear Regression using the Normal Equation.
Requires Python: >=3.7

# beta_solver

![Python](https://img.shields.io/badge/python-3.7+-blue.svg)
![License](https://img.shields.io/badge/license-MIT-green.svg)
![Status](https://img.shields.io/badge/status-active%20learning-orange.svg)

**beta_solver** is a personal project where I implement Machine Learning algorithms from scratch to understand the mathematics behind the "black box."

I am currently practicing core algorithms, starting with **Linear Regression** using the Normal Equation. As I learn and practice more algorithms, I will update this library to include them.

## 🎯 Project Goal
The goal of this project is not to replace libraries like `scikit-learn`, but to:
1.  **Demystify the math** behind ML algorithms.
2.  **Implement pure Python/Numpy solutions** without high-level wrappers.
3.  **Document my learning journey** in code.

---

# 📐 Current Algorithm
## Linear Regression

Right now, the library solves for the coefficient vector $\beta$ using Linear Algebra:

$$\beta = (X^T X)^{-1} (X^T Y)$$

Where:
* $X$: The input feature matrix.
* $Y$: The target vector.
* $\beta$: The resulting coefficients (Intercept + Slopes).

---
## Logistic Regression
Logistic Regression is implemented using the Gradient Descent optimization technique to minimize the Log Loss function.
The model predicts probabilities using the Sigmoid function:
$$P(Y=1|X) = \sigma(X\beta) = \frac{1}{1 + e^{-X\beta}}$$
Where:
* $X$: The input feature matrix.
* $Y$: The target vector (0 or 1).
* $\beta$: The coefficient vector (Intercept + Slopes).

---

## 🚀 Installation

You can install `beta_solver` using pip:

```bash
pip install beta_solver
```

## 🤝 Contributing
This is a learning repository. If you see a way to optimize the math or make the code cleaner, feel free to open a Pull Request!

## 📄 License
Distributed under the MIT License. See LICENSE for more information.

## 👤 Author
**Abhish Bondre**
GitHub: [abhishbondre](https://github.com/abhishbondre)
opvious 0.24.0
Opvious Python SDK
Requires Python: <4,>=3.12

# Opvious Python SDK  [![CI](https://github.com/opvious/sdk.py/actions/workflows/ci.yml/badge.svg)](https://github.com/opvious/sdk.py/actions/workflows/ci.yml) [![Pypi badge](https://badge.fury.io/py/opvious.svg)](https://pypi.python.org/pypi/opvious/)

<div align="center">
  <p>
    Define and deploy optimization models in minutes with <a href="https://www.opvious.io">Opvious</a>.
  </p>
  <a href="https://www.opvious.io/notebooks/retro/notebooks/?path=guides/welcome.ipynb"><img src="https://www.opvious.io/opvious-steps.png" style="height: 600px;"/></a>
    <p>
    <a href="https://www.opvious.io/notebooks/retro/notebooks/?path=guides/welcome.ipynb">Try it out!</a>
  </p>
</div>


## Highlights

+ Declarative modeling API with __extensive static checks and automatic LaTeX generation__
+ Remote solves with __real-time progress notifications__
+ __Flexible data import/export__ via `pandas`
+ __Advanced multi-objective support__: weighted sums, epsilon constraints, ...
+ __Smart debugging capabilities__: semantic constraint relaxations, annotated LP formatting, ...


## Documentation

+ [Getting started guide](https://www.opvious.io/notebooks/retro/notebooks/?path=guides/welcome.ipynb)
+ [SDK API reference](https://opvious.readthedocs.io)
+ [Interactive optimization notebooks](https://github.com/opvious/notebooks)


## Licensing

The SDK is licensed under Apache 2.0. The Opvious platform is available as a self-hosted service, free for small projects. See our [plans page](https://www.opvious.io/plans) for more information.
pyroll-karman-force-torque 3.0.0a1
Plugin for PyRoll providing the power and labour model solving the von-Karman equation.
Requires Python: <4.0,>=3.9

# PyRolL Karman power and labour

PyRolL Plugin for calculation of power and labour solving von-Karman ODE for a equivalent flat pass.

For the docs, see [here](docs/docs.pdf).

This project is licensed under the [BSD-3-Clause license](LICENSE).

The package is available via [PyPi](https://pypi.org/project/pyroll-karman-power-and-labour/) and can be installed with

    pip install pyroll-karman-power-and-labour