semvec/pypi-packages huggingface dataset:
unikan 0.2.5
A Python library based on pytorch for universally building KAN-type networks
Requires Python: >=3.6

# Uni-KAN

<p align="center"><b>English</b> / <a href="https://github.com/chikkkit/uni-kan/blob/main/README_zh.md">简体中文</a></p>

This is a Python library based on PyTorch for universally building KAN-type networks. Universal KAN-type networks is named Uni-KAN.

## Installation

```bash
pip install unikan
```

## Features

- Framework for building Universal KAN type network (uni-kan)
- SKAN (Single-Parameterized KAN) implementation 
- Contain pre defined basis functions in SKAN
- Pytorch compatible (GPU acceleration, etc.)

## Quick Start

### Universal KAN Example

```python
from unikan import UniversalKAN
import unikan.basis as basis

# Define node configurations for each layer
function_lists = [
    [
        {'function': basis.lshifted_softplus, 'param_num': 1, 
         'node_type': 'add', 'node_num': 90, 'use_bias': True},
        {'function': basis.lshifted_softplus, 'param_num': 1, 
         'node_type': 'mul', 'node_num': 10, 'use_bias': True}
    ],
    [
        {'function': basis.lshifted_softplus, 'param_num': 1, 
         'node_type': 'add', 'node_num': 10, 'use_bias': True}
    ]
]

# Create universal KAN network
net = UniversalKAN([784, 100, 10],  # layer sizes
                   function_lists=function_lists,  # node configs
                   device=device)  # device selection
```

### SKAN Example

```python
import torch
from unikan import SKAN_pure
import unikan.basis as basis

# Create SKAN network
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = SKAN_pure([784, 100, 10],  # layer sizes: input 784, hidden 100, output 10
                basis_function=basis.lshifted_softplus,  # basis function
                device=device)  # device selection
```

## License

MIT License

## Citation

If you use this project in your research, please cite:

```bibtex
@misc{chen2024lssskanefficientkolmogorovarnoldnetworks,
      title={LSS-SKAN: Efficient Kolmogorov-Arnold Networks based on Single-Parameterized Function}, 
      author={Zhijie Chen and Xinglin Zhang},
      year={2024},
      eprint={2410.14951},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2410.14951}, 
}
```

## Contact

- Email: zhijiechencs@gmail.com
geotensor 0.0.1
The GeoTensor format
Requires Python: <4.0,>=3.8

# geotensor

[![Release](https://img.shields.io/github/v/release/csaybar/geotensor)](https://img.shields.io/github/v/release/csaybar/geotensor)
[![Build status](https://img.shields.io/github/actions/workflow/status/csaybar/geotensor/main.yml?branch=main)](https://github.com/csaybar/geotensor/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/csaybar/geotensor/branch/main/graph/badge.svg)](https://codecov.io/gh/csaybar/geotensor)
[![Commit activity](https://img.shields.io/github/commit-activity/m/csaybar/geotensor)](https://img.shields.io/github/commit-activity/m/csaybar/geotensor)
[![License](https://img.shields.io/github/license/csaybar/geotensor)](https://img.shields.io/github/license/csaybar/geotensor)

The GeoTensor format

- **Github repository**: <https://github.com/csaybar/geotensor/>
- **Documentation** <https://csaybar.github.io/geotensor/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:csaybar/geotensor.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPI or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [PyPI](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/csaybar/geotensor/settings/secrets/actions/new).
- Create a [new release](https://github.com/csaybar/geotensor/releases/new) on Github.
- Create a new tag in the form `*.*.*`.
- For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
simple-star-patterns 0.2.3
A simple library for printing star patterns
Requires Python: >=3.6

# Simple Star Patterns

Simple Star Patterns is a Python library for printing various star (or any character) patterns. It's designed to be easy to use and understand, making it perfect for beginners learning about loops and pattern printing in Python.

## Installation

You can install the Simple Star Patterns library using pip:

```
pip install simple-star-patterns
```

## Usage

Here's a quick example of how to use the Simple Star Patterns library:

```python
from simple_star_patterns import print_pattern

# Print a triangle
print_pattern('triangle', 4)

# Print an inverted triangle with '+' symbol
print_pattern('inverted_triangle', 5, '+')

# Print a pyramid with '#' symbol
print_pattern('pyramid', 3, '#')
```

This will output:

```
*
**
***
****

+++++
++++
+++
++
+

  #
 ###
#####
```

### Available Patterns

1. `triangle`: A right-angled triangle
2. `inverted_triangle`: An inverted right-angled triangle
3. `pyramid`: A symmetrical pyramid

### Function Parameters

- `pattern`: The type of pattern to print ('triangle', 'inverted_triangle', or 'pyramid')
- `size`: The number of rows in the pattern (must be a positive integer)
- `symbol`: The character to use for the pattern (default is '*')