skhubness.neighbors.LSH

class skhubness.neighbors.LSH(n_candidates: int = 5, radius: float = 1.0, metric: str = 'euclidean', num_probes: int = 50, n_jobs: int = 1, verbose: int = 0)[source]
__init__(self, n_candidates: 'int' = 5, radius: 'float' = 1.0, metric: 'str' = 'euclidean', num_probes: 'int' = 50, n_jobs: 'int' = 1, verbose: 'int' = 0)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, n_candidates, radius, metric, …)

Initialize self.

fit(self, X, y)

Setup the LSH index from training data.

kneighbors(self, X, n_candidates, …)

radius_neighbors(self, X, radius, …)

TODO add docstring

Attributes

valid_metrics

fit(self, X: 'np.ndarray', y: 'np.ndarray' = None) → 'LSH'[source]

Setup the LSH index from training data.

radius_neighbors(self, X: 'np.ndarray' = None, radius: 'float' = None, return_distance: 'bool' = True)[source]

TODO add docstring

Notes

From the falconn docs: radius can be negative, and for the distance function ‘negative_inner_product’ it actually makes sense.