skl2neonsvm
Library for converting scikit-learn SVM-based classification and regression models to neonsvm YAML format for optimized inference on ARM devices using C++ library neonsvm.
Installation
From PyPi
pip install skl2neonsvm
From repository
pip install git+https://github.com/R3dKar/sklearn-neonsvm
Usage
# Training model
from sklearn.datasets import make_classification
from sklearn.svm import SVC
X, y = make_classification(random_state=42)
model = SVC().fit(X, y)
# Converting and exporting
from skl2neonsvm import to_neonsvm
data = to_neonsvm(model)
with open("model.yaml", "w") as f:
f.write(data)
Supported models
All models of submodule sklearn.svm are supported.
Supported classification models:
SVCNuSVCLinearSVCOneClassSVM
Supported regression models:
SVRNuSVRLinearSVR
1 maintainer
Metadata
1 maintainer
Release files for skl2neonsvm 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| skl2neonsvm-0.1.0.tar.gz | 5.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| skl2neonsvm-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 11.7 kB
Release files / skl2neonsvm-0.1.0.tar.gz
| Download URL | skl2neonsvm-0.1.0.tar.gz |
|---|---|
| Size | 5.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / skl2neonsvm-0.1.0-py3-none-any.whl
| Download URL | skl2neonsvm-0.1.0-py3-none-any.whl |
|---|---|
| Size | 6.5 kB |
| Tags | Python 3 |
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uv/0.11.6 {"installer":{"name":"uv","version":"0.11.6","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":null,"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
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