semvec/pypi-packages huggingface dataset:
omega-machine-learning 0.1.0
A collection of generated machine learning algorithms
Requires Python: >=3.6

# Omega: A Library of AI Invented Machine Learning Algorithms

![Omega Logo](omega_logo.png)

Omega is an open-source Python library containing novel machine learning algorithms generated through automated algorithmic synthesis techniques. These algorithms push the boundaries of traditional machine learning approaches and offer unique capabilities for tackling complex data analysis tasks.

## Key Features

- **Novel Algorithms**: Includes machine learning algorithms not found in other libraries, created through evolutionary computation and neural architecture search.
- **Simple Scikit-Learn Style Interface**: Follows scikit-learn API conventions for easy integration into existing ML pipelines.

## Installation

```bash
pip install omega-ml
```

## Quick Start

```python
from omega import HybridKNNClassifier

# Load your dataset
X, y = load_dataset()

# Initialize and train the model
model = HybridKNNClassifier()
model.fit(X, y)

# Make predictions
predictions = model.predict(X_test)
```

## Featured Algorithms

- **HybridKNNClassifier**: An advanced k-nearest neighbors algorithm incorporating multi-level abstraction.
- **MultiLevelAbstractionKNN**: KNN variant with enhanced feature space transformation.
- **EntropyGuidedKNN**: KNN algorithm guided by information-theoretic principles.
- **BiasVarianceOptimizedKNNEnsemble**: Ensemble method balancing bias and variance trade-offs.

## Performance

Omega's algorithms have demonstrated superior performance on a variety of benchmark datasets:

| Classifier | Wine | Breast Cancer | Digits | Diabetes | Covertype | Abalone |
|------------|------|---------------|--------|----------|-----------|---------|
| KNeighborsClassifier | 0.750 | 0.667 | 0.667 | 0.667 | 0.667 | 0.611 |
| HybridKNNClassifier | 0.972 | 0.861 | 0.944 | 1.000 | 0.972 | 0.972 |
| MultiLevelAbstractionKNN | 0.972 | 0.944 | 0.972 | 0.972 | 0.944 | 0.944 |

## Citation

If you use Omega in your research, please cite:

```
@article{nixon2024omega,
  title={Automating the Generation of Novel Machine Learning Algorithms},
  author={Nixon, Jeremy},
  journal={arXiv preprint arXiv:2404.00001},
  year={2024}
}
```

## License

Omega is released under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contact

For questions and feedback:
- Issue Tracker: https://github.com/omniscience-research/omega/issues
- Email: jeremy@omniscience.tech

Let's push the boundaries of machine learning together with Omega!
constallation-mmr 1.1.0
An OOP-heavy wrapper for the Mining Rig Rentals API
Requires Python: >=3.9

[![PyPi](https://img.shields.io/badge/PyPi-1.1.0-yellow?labelColor=blue&style=flat&logo=pypi&logoColor=yellow&link=https://pypi.org/project/constallation_mmr)](https://pypi.org/project/constallation_mmr)
![Python](https://img.shields.io/badge/Python-3.8-blue?labelColor=yellow&style=flat&logo=python)
![PyPI - Monthly](https://img.shields.io/pypi/dm/constallation_mmr)
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# Constallation-MMR
Constallation-MMR is a OOP oriented API wrapper part of the greater constallation networking library.

#### Installation
##### Installing With Pip
```shell
pip install constallation_mmr
```
##### Installing With Poetry
```shell
poetry add constallation_mmr
```

## ChangeLogs
- #### 1.0.0
  - added Rig class
  - added method `fetch_rigs`
- #### 1.0.1
  - added method `fetch_rig`
- #### 1.0.2
  - Added method inside class `Rig` for deleting the rig
  - Updated `Rig` class to support credential passthrough
- #### 1.0.3
  - Rewrote `Rig` class properties to handle more verbose output
- #### 1.0.4
  - Allowed `Rig` class ids to also be strings.
startout 0.5.1
Draft version of the StartOut CLI'
Requires Python: <4.0,>=3.8

# Golden Paths

[![Coverage Status](https://coveralls.io/repos/github/Start-Out/golden-paths/badge.svg?branch=dev)](https://coveralls.io/github/Start-Out/golden-paths?branch=dev)

## StartOut-Supported Paths (official)

### Web

- [postgresql-express-react](https://github.com/Start-Out/path-postgresql-express-react)
hudu-api-ctek 0.0.48
A few functions for the Hudu API
Requires Python: >=3.8

Scripts rely on .env file with the required variables defined


Available functions:

- get_assets_by_layout_id(api_baseurl, api_key, asset_layout_id)
- create_magic_dash(api_baseurl, api_key, dash_name, dash_message, dash_shade, dash_content, company_name):
hquant 0.1.0
High quality quantization for Pillow images.
Requires Python: >=3.8
Keywords: image, dither, dithering, quantization, palette

# hquant

High quality quantization for [Pillow](https://python-pillow.org/) images.

Pillow uses Euclidean distance for color matching during quantization, which yields poor results
when using RGB due to the nonlinearity of color perception. YCbCr is a better fit for Euclidean
distance, but Pillow does not natively support quantization of such images.

This module improves the quantization by converting the images to YCbCr, and then tricking
Pillow into thinking it's an RGB image so quantization works.

## Samples

The following samples dither the original image to the classic 16 colors supported in all terminals,
using RGB (as Pillow quantize method would normally use), CIELAB and YCbCr.

| Original                                                                                              | RGB                                                                                                   | CIELAB                                                                                                   | YCbCr                                                                                                     |
| ----------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
| ![Original Lena](https://github.com/socram8888/hquant/blob/master/samples/lena_orig.png?raw=true)     | ![Lena using RGB](https://github.com/socram8888/hquant/blob/master/samples/lena_rgb.png?raw=true)     | ![Lena using CIELAB](https://github.com/socram8888/hquant/blob/master/samples/lena_lab.png?raw=true)     | ![Lena using YCbCr](https://github.com/socram8888/hquant/blob/master/samples/lena_ycbcr.png?raw=true)     |
| ![Original Kobold](https://github.com/socram8888/hquant/blob/master/samples/kobold_orig.png?raw=true) | ![Kobold using RGB](https://github.com/socram8888/hquant/blob/master/samples/kobold_rgb.png?raw=true) | ![Kobold using CIELAB](https://github.com/socram8888/hquant/blob/master/samples/kobold_lab.png?raw=true) | ![Kobold using YCbCr](https://github.com/socram8888/hquant/blob/master/samples/kobold_ycbcr.png?raw=true) |

## Usage example

```python
from PIL import Image
import hquant

terminal_palette = bytes([
	# Primary 3-bit (8 colors). Unique representation!
	0x00, 0x00, 0x00,
	0x80, 0x00, 0x00,
	0x00, 0x80, 0x00,
	0x80, 0x80, 0x00,
	0x00, 0x00, 0x80,
	0x80, 0x00, 0x80,
	0x00, 0x80, 0x80,
	0xc0, 0xc0, 0xc0,

	# Equivalent "bright" versions of original 8 colors.
	0x80, 0x80, 0x80,
	0xff, 0x00, 0x00,
	0x00, 0xff, 0x00,
	0xff, 0xff, 0x00,
	0x00, 0x00, 0xff,
	0xff, 0x00, 0xff,
	0x00, 0xff, 0xff,
	0xff, 0xff, 0xff,
])

original = Image.open('lena.png')
dithered = hquant.quantize(original, terminal_palette)
dithered.save('dithered.png')
```