HNSW

class hubness.neighbors.HNSW(n_candidates: int = 5, metric: str = 'euclidean', method: str = 'hnsw', post_processing: int = 2, n_jobs: int = 1, verbose: int = 0)

Bases: hubness.neighbors.approximate_neighbors.ApproximateNearestNeighbor

Attributes Summary

valid_metrics

Methods Summary

fit(X[, y])

Setup the HNSW index.

kneighbors(X[, n_candidates, return_distance])

Attributes Documentation

valid_metrics = ['euclidean', 'l2', 'minkowski', 'cosine', 'cosinesimil']

Methods Documentation

fit(X, y=None) → hubness.neighbors.hnsw.HNSW

Setup the HNSW index.

kneighbors(X: numpy.ndarray, n_candidates: Optional[int] = None, return_distance: bool = True)