Metadata-Version: 2.4
Name: radioactiveshrimp
Version: 0.1.13
Requires-Dist: altair>=5.5.0
Requires-Dist: matplotlib>=3.10.6
Requires-Dist: numpy>=2.3.3
Requires-Dist: onnx>=1.19.1
Requires-Dist: onnxruntime>=1.23.2
Requires-Dist: pandas>=2.3.3
Requires-Dist: polars>=1.33.1
Requires-Dist: scikit-learn>=1.7.2
Requires-Dist: scipy>=1.16.2
Requires-Dist: seaborn>=0.13.2
Requires-Dist: statsmodels>=0.14.5
Requires-Dist: torch>=2.8.0
Requires-Dist: twine>=6.1.0
Summary: Add your description here
Author-email: radioactiveshrimp <radioactiveshrimpmail@gmail.com>
Requires-Python: >=3.12
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# To install package with pip:
pip install radioactiveshrimp. 

# To install package with git:
ensure uv is installed on system: curl -LsSf https://astral.sh/uv/install.sh | sh
clone the repository
cd radioactiveshrimp
uv sync
source .venv/bin/activate

# Examples: 
## Binary Classification
### To run binary_classification example: 
cd scripts
python3 binaryclassification_impl.py
### To view result:
binaryclassification_impl.py will save the loss history plot to the current directory as a file named crossentropyloss_YYYYMMDDhhmmss.pdf
(where YYYYMMDDhhmmss is the time stamp of file creation).
The fit() function returns the four weight matricies and the loss history so these can be stored as variables and/or printed. 
