Metadata-Version: 2.1
Name: matrixkit
Version: 0.1.2
Summary: Synthetic Matrix Generation for Machine Learning and Scientific Computing
Home-page: https://github.com/AnnaValentinaHirsch/matrixkit
Author: ['Toni Johann Schulze Dieckhoff', 'Anna-Valentina Hirsch']
Author-email: a-valentina.hirsch@hotmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy~=1.26.4
Requires-Dist: scipy~=1.13.1
Requires-Dist: pillow~=10.3.0
Requires-Dist: matplotlib~=3.5.2
Requires-Dist: seaborn~=0.13.2

# MatrixKit: Synthetic Matrix Generation for Machine Learning and Scientific Computing

## Link to the initial project repository

[GitHub repo "opencampus-preconditioner-ai-project"](https://github.com/24io/opencampus-preconditioner-ai-project)

## Overview
MatrixKit is a sophisticated Python library designed for generating synthetic matrix data,
primarily focused on machine learning applications. 
It was created as part of a machine learning project at OpenCampus Kiel, 
where my project partner and I faced the challenge of finding labelled real-world matrices to train our models. 
MatrixKit offers powerful tools for creating custom matrices that simulate real-world data structures and patterns.

Additionally, the library contains a variety of functions to create and apply block jacobi preconditioners. 

## Features

* **Flexible Matrix Generation**: Create matrices of various sizes and shapes with customizable properties.
* **Realistic Noise Simulation**: Add controlled background noise to matrices.
* **Complex Block Structures**: Generate matrices with intricate block patterns using truncated normal distributions.
* **Fine-Tuned Control**: Adjust parameters like matrix dimensions, noise levels, block sizes, and densities.
* **Comprehensive Metadata**: Maintain detailed information about generated matrices, including block positions and user-defined parameters.
* **Versatile Applications**: Suitable for machine learning, data analysis, scientific computing, and more.

## Installation
Install MatrixKit easily using pip:

```pip install matrixkit```

