Metadata-Version: 1.1
Name: lshknn
Version: 0.1.2
Summary: k nearest neighbor (KNN) graphs via Pearson correlation distance and local sensitive hashing (LSH).
Home-page: https://github.com/iosonofabio/lshknn
Author: Fabio Zanini
Author-email: fabio.zanini@stanford.edu
License: MIT
Description-Content-Type: UNKNOWN
Description: 
        k nearest neighbor (KNN) graphs via Pearson correlation distance and local sensitive hashing (LSH).
        
        - **Development**: https://github.com/iosonofabio/lshknn
        - **Authors**: Fabio Zanini and Paolo Carnevali
        - **License**: MIT
        - **Copyright**: Fabio Zanini and Chan Zuckerberg Initiative
        
        .. code-block:: python
        
            import numpy as np
            import lshknn
        
            # Make mock data
            # 2 features (rows), 4 samples (columns)
            data = np.array(
                    [[1, 0, 1, 0],
                     [0, 1, 0, 1]],
                    dtype=np.float64)
        
            # Instantiate class
            c = lshknn.Lshknn(
                    data=data,
                    k=1,
                    threshold=0.2,
                    m=10,
                    slice_length=4)
        
            # Call subroutine
            knn, similarity, n_neighbors = c()
        
            # Check result
            assert (knn == [[2], [3], [0], [1]]).all()
        
              
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
