Metadata-Version: 2.1
Name: oclrfc
Version: 0.1.1
Summary: OpenCL-based Random Forest Classifier
Home-page: https://github.com/haesleinhupef/oclrfc
Author: haesleinhuepf
Author-email: robert.haase@tu-dresden.de
License: UNKNOWN
Description: # oclrfc
        
        [cle](https://github.com/clEsperanto/pyclesperanto_prototype) meets [sklearn](https://scikit-learn.org/stable/)
        
        To see OpenCL-based Random Forest Classifiers in action, check out the 
        [demo-notebook](https://nbviewer.jupyter.org/github/haesleinhuepf/oclrfc/blob/master/demo/demo.ipynb).
        For optimal performance and classification quality, it is recommended to 
        [generate feature stacks](https://nbviewer.jupyter.org/github/haesleinhuepf/oclrfc/blob/master/demo/feature_stacks.ipynb)
        that fit well to the the image data you would like to process.
        
        ## Installation
        
        You can install `napari-oclrfc` via [pip]:
        
            git clone https://github.com/haesleinhuepf/oclrfc
            cd oclrfc
            pip install -e .
        
        ## Contributing
        
        Contributions are very welcome. Tests can be run with [tox], please ensure
        the coverage at least stays the same before you submit a pull request.
        
        ## License
        
        Distributed under the terms of the BSD-3 license,
        "oclrfc" is free and open source software
        
        ## Issues
        
        If you encounter any problems, please [open a thread on image.sc](https://image.sc) along with a detailed description and tag [@haesleinhuepf](https://github.com/haesleinhuepf).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.6
Description-Content-Type: text/markdown
