semvec/pypi-packages huggingface dataset:
ccHBGF 0.2.0
ccHBGF - consensus clustering using Hybrid Bipartite Graph Formulation (HBGF)
License: MIT
Requires Python: >=3.10

# ccHBGF: Graph-based Consensus Clustering

<p align="center">
  <img src="./docs/html/_images/workflow.svg" alt="Overview of Consensus Clustering Workflow"/>
</p>

A python-based consensus clustering function utilising Hybrid Bipartite Graph Formulation (HBGF). 

The `ccHBGF.find_consensus` function performs consensus clustering by following these steps:
1. Definition of a bipartite graph adjaceny matrix `A`
2. Decomposition of `A` into a spectral embedding `UVt`
3. K-means clustering of `UVt` into a consensus solution

## Installation

```bash
pip install ccHBGF
pip install 'ccHBGF[tutorial]' # When running example notebooks
```

## Hybrid Bipartite Graph Formulation (HBGF)

<p align="center">
  <img src="./docs/html/_images/graph.png" alt="Overview of Consensus Clustering Workflow"/>
</p>

HBGF is a graph-based consensus ensemble clustering technique. This method constructs a bipartite graph with two types of vertices: observations and clusters from different clusteirng solutions. An edge exists only between an observation vertex and a cluster vertex, indicating the object's membership in that cluster. The graph is then partitioned using spectral partitioning to derive consensus labels for all observations.

## Example Usage

```python
from ccHBGF import find_consensus, config

config.LOG_LEVEL = 2 # Info level (0=silent, 1=warnings)

consensus_labels = find_consensus(solutions_matrix, init='orthogonal', tol=0.1, random_state=0)

```
Where the `solutions_matrix` is of shape (m,n):
- m = the number of observations
- n = the number of different clustering solutions.

Please refer to `notebooks/` for more detailed examples.

## References

[1] Hu, Tianming, et al. "A comparison of three graph partitioning based methods for consensus clustering." Rough Sets and Knowledge Technology: First International Conference, RSKT 2006, Chongquing, China, July 24-26, 2006. Proceedings 1. Springer Berlin Heidelberg, 2006.

[2] Fern, Xiaoli Zhang, and Carla E. Brodley. "Solving cluster ensemble problems by bipartite graph partitioning." Proceedings of the twenty-first international conference on Machine learning. 2004.

[3] Ng, Andrew, Michael Jordan, and Yair Weiss. "On spectral clustering: Analysis and an algorithm." Advances in neural information processing systems 14 (2001).
geometry-library 0.1.0
A simple Python library for 2D geometry.
License: MIT
Requires Python: >=3.7


# Geometry Library

A simple Python library for 2D geometry, including core concepts like points, lines, circles, rectangles, and polygons, with basic operations such as calculating distance, area, and perimeter.

## Features

-   **Point**: Represents a point in 2D space with x and y coordinates.
-   **Line**: Represents a line defined by two points.
-   **Circle**: Represents a circle defined by a center point and a radius.
-   **Rectangle**: Represents a rectangle defined by its top-left corner, width, and height.
-   **Polygon**: Represents a polygon defined by a list of points.
-   Basic operations:
    -   Distance between points
    -   Length of a line
    -   Area and perimeter of circles, rectangles, and polygons

## Installation

Assuming you have the library code in a directory named `geometry_library_package` with the correct structure (including `setup.py`), you can install it locally using pip:
notepdf 1.1.0
Simple tool to create Notepages from a pdf slidedeck.
License: MIT
Requires Python: >=3.13
Keywords: notes, pdf, slide, Remarkable, tablet

# NotePDF

Script to make lined notepages from a PDF slidedeck. For use with notetaking devices such as the Remarkable tablet. 

This project is not connected to Remarkable AS. 

## Installation

The project can be installed directly from PyPI. Variations on 
```sh
pip install notepdf
```
can be used.

## Usage

The script is used
```sh
notepdf <input_file.pdf>
```


## Development

Uses `uv` to syncronise packages and run commands:
```sh
uv sync
uv add <package>
```
types-singleton-decorator 0.1.0
Type stubs for singleton_decorator
License: MIT
Requires Python: >=3.8
Keywords: decorator, singleton, stubs, typing

# types-singleton-decorator

Type stubs for the `singleton_decorator` package to provide proper type hints and fix Pylance type checking issues.

## Problem Solved

When using the `singleton_decorator` decorator, Pylance shows the error:
```
Expected class but received "_SingletonWrapper" PylancereportGeneralTypeIssues
```

This package provides proper type stubs to resolve this issue and ensure your IDE understands that decorated classes remain classes.

## Installation

Install using pip:
```bash
pip install types-singleton-decorator
```

Or using uv:
```bash
uv add types-singleton-decorator
```

## Usage

Once installed, the type stubs will automatically be picked up by your type checker (Pylance, mypy, etc.). No additional configuration is needed.

Your singleton decorators will now have proper type support:

```python
from singleton_decorator import singleton

@singleton
class MyClass:
    def __init__(self, value: str):
        self.value = value
    
    def get_value(self) -> str:
        return self.value

# This will now have proper type hints
instance = MyClass("hello")  # Type: MyClass, not _SingletonWrapper
print(instance.get_value())  # Pylance understands this method exists
```

## What's Included

This package provides type stubs for:
- `@singleton` decorator
- `SingletonMeta` metaclass (if used)
- Proper preservation of class types and methods

## Requirements

- Python >= 3.8
- The original `singleton_decorator` package 

## Contributing

Issues and pull requests are welcome at the [GitHub repository](https://github.com/rohitshubham/types-singleton-decorator).

## License

MIT License