semvec/pypi-packages huggingface dataset:
walkmygraph 0.0.3
WalkMyGraph (WMG) is a specification by example software development using Knowledge Graph Traversals and Named Parameterized Queries
Requires Python: >=3.10

WalkMyGraph (WMG) is a specification by example software development using Knowledge Graph Traversals and Named Parameterized Queries
Created by BITPlan
Copyright(c) 2026 BITPlan GmbH, Willich, Germany https://www.bitplan.com

| | |
| :--- | :--- |
| **PyPi** | [![PyPI Status](https://img.shields.io/pypi/v/walkmygraph.svg)](https://pypi.python.org/pypi/walkmygraph/) [![License](https://img.shields.io/github/license/BITPlan/WalkMyGraph.svg)](https://www.apache.org/licenses/LICENSE-2.0) [![pypi](https://img.shields.io/pypi/pyversions/walkmygraph)](https://pypi.org/project/walkmygraph/) [![format](https://img.shields.io/pypi/format/walkmygraph)](https://pypi.org/project/walkmygraph/) [![downloads](https://img.shields.io/pypi/dd/walkmygraph)](https://pypi.org/project/walkmygraph/) |
| **GitHub** | [![Github Actions Build](https://github.com/BITPlan/WalkMyGraph/actions/workflows/build.yml/badge.svg)](https://github.com/BITPlan/WalkMyGraph/actions/workflows/build.yml) [![Release](https://img.shields.io/github/v/release/BITPlan/WalkMyGraph)](https://github.com/BITPlan/WalkMyGraph/releases) [![Contributors](https://img.shields.io/github/contributors/BITPlan/WalkMyGraph)](https://github.com/BITPlan/WalkMyGraph/graphs/contributors) [![Last Commit](https://img.shields.io/github/last-commit/BITPlan/WalkMyGraph)](https://github.com/BITPlan/WalkMyGraph/commits/) [![GitHub issues](https://img.shields.io/github/issues/BITPlan/WalkMyGraph.svg)](https://github.com/BITPlan/WalkMyGraph/issues) [![GitHub closed issues](https://img.shields.io/github/issues-closed/BITPlan/WalkMyGraph.svg)](https://github.com/BITPlan/WalkMyGraph/issues/?q=is%3Aissue+is%3Aclosed) |
| **Code** | [![style-black](https://img.shields.io/badge/%20style-black-000000.svg)](https://github.com/psf/black) [![imports-isort](https://img.shields.io/badge/%20imports-isort-%231674b1)](https://pycqa.github.io/isort/) |
| **Docs** | [![API Docs](https://img.shields.io/badge/API-Documentation-blue)](https://BITPlan.github.io/WalkMyGraph/) [![formatter-docformatter](https://img.shields.io/badge/%20formatter-docformatter-fedcba.svg)](https://github.com/PyCQA/docformatter) [![style-google](https://img.shields.io/badge/%20style-google-3666d6.svg)](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings) |

# Prototypes
- https://github.com/WolfgangFahl/DualViewKnowledgeGraphExplorer

# Papers
- https://github.com/WolfgangFahl/DualViewKnowledgeGraphExplorerPaper

# Docs
[Wiki](https://wiki.bitplan.com/index.php/WalkMyGraph)
agentengine-sdk-python 0.2.0
Kingsoft Cloud Agent Engine SDK (alias for ksadk)
Requires Python: >=3.10
Keywords: agent, langchain, langgraph, adk, kingsoft cloud, ai, llm

# agentengine-sdk-python

**Kingsoft Cloud Agent Engine SDK** - 别名包

这是 [ksadk](https://pypi.org/project/ksadk/) 的别名包，安装后会自动安装 ksadk。

## 安装

```bash
# 任选一种方式安装
pip install agentengine-sdk-python
# 或
pip install ksadk
```

## 使用

```bash
agentengine --help
```

更多文档请参考 [ksadk](https://github.com/kingsoftcloud/ksadk-python)。
hyhound 1.1.1
Hyperbolic Householder transformations for Up- and Downdating Cholesky factorizations.
Requires Python: >=3.9
Keywords: linear-algebra, cholesky, update, downdate, matrix

.. image:: https://img.shields.io/badge/arXiv-Preprint-b31b1b
   :target: https://arxiv.org/abs/2503.15372v1
   :alt: arXiv Preprint

.. image:: https://github.com/kul-optec/hyhound/actions/workflows/linux.yml/badge.svg
   :target: https://github.com/kul-optec/hyhound/actions/workflows/linux.yml
   :alt: CI: Linux

.. image:: https://img.shields.io/pypi/dm/hyhound?label=PyPI&logo=python
   :target: https://pypi.org/project/hyhound
   :alt: PyPI Downloads


hyhound
=======

**Hy**\perbolic **Ho**\useholder transformations for **U**\p- ‘**n**’ **D**\owndating Cholesky factorizations.


Purpose
-------

Given a Cholesky factor :math:`L` of a dense matrix :math:`H`, the
``hyhound::update_cholesky`` function computes the Cholesky factor
:math:`\tilde L` of the matrix

.. math::

   \tilde H = \tilde L \tilde L^\top = H + A \Sigma A^\top,

where :math:`H,\tilde H\in\mathbb{R}^{n\times n}` with :math:`H \succ 0`
and :math:`\tilde H \succ 0`, :math:`A \in \mathbb{R}^{n\times m}`,
:math:`\Sigma \in \mathbb{R}^{m\times m}` diagonal,
and :math:`L, \tilde L\in\mathbb{R}^{n\times n}` lower triangular.

Computing :math:`\tilde L` in this way is done in
:math:`mn^2 + \mathcal{O}(n^2 + mn)` operations rather than the
:math:`\tfrac16 n^3 + \tfrac12 mn^2 + \mathcal{O}(n^2 + mn)` operations
required for the explicit evaluation and factorization of :math:`\tilde H`.
When :math:`m \ll n`, this results in a considerable speedup over full
factorization, enabling efficient low-rank updates of Cholesky
factorizations, for use in e.g. iterative algorithms for numerical
optimization.

Additionally, hyhound includes efficient routines for updating
factorizations of the Riccati recursion for optimal control problems.


Preprint
--------

The paper describing the algorithms in this repository can be found on arXiv:  
`https://arxiv.org/abs/2503.15372v1 <https://arxiv.org/abs/2503.15372v1>`_

.. code-block:: bibtex

   @misc{pas_blocked_2025,
      title = {Blocked {Cholesky} factorization updates of the {Riccati} recursion using hyperbolic {Householder} transformations},
      url = {http://arxiv.org/abs/2503.15372},
      doi = {10.48550/arXiv.2503.15372},
      publisher = {arXiv},
      author = {Pas, Pieter and Patrinos, Panagiotis},
      month = mar,
      year = {2025},
      note = {Accepted for publication in the Proceedings of CDC 2025}
   }
ikali 2.0.1
iKali, runs a command when typing ikali into the terminal.
Requires Python: >=3.6

# iKALI v2.0.1

iKali is a terrible program fuelled by hate and death. this program barely works correctly, 
and cannot run on windows for reasons i seriously do not know or understand. 
Please note that this crap is wholly untested, I am new to programming, and especially using linux. 

# Update v2.0.0 - 
This update adds settings that allows for customisation of the program, reimplements a typewriter effect that was originally planned. 

# Update v2.0.1 - 
Removed previous updates, as they provide no insight to the program and bulk space out. 
Fixed a catastrophic error that results in text repetition on smaller screens. 

## Installation
```bash
sudo apt install pipx 
pipx install ikali
pipx ensurepath
```
**After running `pipx ensurepath`, open a new terminal, then run `ikali`**

## Update
```bash
pipx upgrade ikali
```

## Uninstall
```bash
pipx uninstall ikali
```

## Usage
```bash

ikali
turkish-tokenizer 1.0.4
A high-performance Turkish tokenizer based on linguistic rules and BPE, written in Rust.
Requires Python: >=3.8

# Turkish Tokenizer

**PyPI version** | **Python 3.8+** | **License: MIT**

`turkish-tokenizer`, Türkçe doğal dil işleme görevleri için geliştirilmiş, dilbilim kurallarına (ünlü uyumu, ünsüz yumuşaması vb.) sadık kalan, yüksek performanslı ve hibrid (Rule-Based + BPE) bir tokenizer kütüphanesidir.

Rust ile yeniden yazılarak Python versiyonuna kıyasla **~100x performans artışı** sağlanmıştır.

## Özellikler

- **Hibrid Yapı**: Kök bulma ve ek analizi ile BPE (Byte Pair Encoding) yöntemlerini birleştirir.
- **Dilbilimsel Doğruluk**:
  - Ünlü uyumu (Vowel Harmony)
  - Ünsüz yumuşaması (Consonant Softening)
  - Ünsüz düşmesi ve daralması
- **Yüksek Performans**: Rust tabanlı motor ile milisaniyeler içinde büyük metinleri tokenize eder.
- **Tam Uyumluluk**: Orijinal Python implementasyonu ile %99.8 oranında çıktı benzerliği.

## Kurulum

PyPI üzerinden en güncel versiyonu indirebilirsiniz:

```bash
pip install turkish-tokenizer
```

## Hızlı Başlangıç

### Tokenizasyon (Encode)

Metinleri modelin anlayabileceği ID listelerine dönüştürmek için:

```python
from turkish_tokenizer import TurkishTokenizer

# Tokenizer'ı başlat
tokenizer = TurkishTokenizer()

text = "Bugün hava çok güzel."
tokens = tokenizer.encode(text)

print(f"Text: {text}")
print(f"Token IDs: {tokens}")
# Çıktı: [2, 0, 1234, 5678, ...]
```

### Geri Dönüştürme (Decode)

ID listelerini tekrar okunabilir metne dönüştürmek için. Decoder, kelimelerin kök ve eklerini birleştirirken ses olaylarını otomatik olarak uygular.

```python
decoded_text = tokenizer.decode(tokens)
print(f"Decoded: {decoded_text}")
# Çıktı: Bugün hava çok güzel.
```

## Gelişmiş Kullanım

Tokenizer, cümleleri parçalara ayırırken (tokenization) ve birleştirirken (decoding) bağlama duyarlı işlemler yapar.

```python
# Örnek: Yumuşama ve Ünlü Uyumu
ids = tokenizer.encode("Kitabı bana ver")
# "Kitap" + "ı" -> "Kitabı" (p -> b yumuşaması)

decoded = tokenizer.decode(ids)
assert decoded == " Kitabı bana ver"
```

## Performans

Bu kütüphane Rust dili (`pyo3` ve `maturin`) kullanılarak geliştirilmiştir. Büyük veri setlerinde (örneğin Cosmos Corpus) saniyede yüz binlerce kelimeyi işleyebilir.

## Lisans

Bu proje MIT lisansı ile lisanslanmıştır.