LSH¶
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class
hubness.neighbors.
LSH
(n_candidates: int = 5, radius: float = 1.0, metric: str = 'euclidean', num_probes: int = 50, n_jobs: int = 1, verbose: int = 0)¶ Bases:
hubness.neighbors.approximate_neighbors.ApproximateNearestNeighbor
Attributes Summary
Methods Summary
fit
(X[, y])Setup the LSH index from training data.
kneighbors
([X, n_candidates, return_distance])radius_neighbors
([X, radius, return_distance])TODO add docstring
Attributes Documentation
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valid_metrics
= ['euclidean', 'l2', 'minkowski', 'cosine', 'neg_inner', 'NegativeInnerProduct']¶
Methods Documentation
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fit
(X: numpy.ndarray, y: Optional[numpy.ndarray] = None) → hubness.neighbors.lsh.LSH¶ Setup the LSH index from training data.
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kneighbors
(X: Optional[numpy.ndarray] = None, n_candidates: Optional[int] = None, return_distance: bool = True)¶
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radius_neighbors
(X: Optional[numpy.ndarray] = None, radius: Optional[float] = None, return_distance: bool = True)¶ TODO add docstring
Notes
From the falconn docs: radius can be negative, and for the distance function ‘negative_inner_product’ it actually makes sense.
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