frlearn.neighbours.neighbour_search.KDTree

class frlearn.neighbours.neighbour_search.KDTree(*, metric: str = 'manhattan', leaf_size: int = 30, n_jobs: int = 1)[source]

Nearest neighbour search with a KD-tree.

Parameters
metricstr, default=’manhattan’

The metric through which distances are defined.

leaf_sizeint, default=30

The leaf size to be used for the KD-tree.

n_jobsint, default=1

The number of parallel jobs to run for neighbour search. -1 means using all processors.

__init__(self, *, metric: 'str' = 'manhattan', leaf_size: 'int' = 30, n_jobs: 'int' = 1)[source]

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

class Index(search: frlearn.neighbours.neighbour_search.KDTree, X)[source]
query(self, X, k: 'int')[source]

Identify the k nearest neighbours for each of the instances in X.

Parameters
Xarray shape=(n_instances, n_features, )

Query instances.

kint

Number of neighbours to return

Returns
Iarray shape=(n_instances, k, )

Indices of the k nearest neighbours among the construction instances for each query instance.

Darray shape=(n_instances, k, )

Distances to the k nearest neighbours among the construction instances for each query instance.

construct(self, X) → 'Index'

Construct the index based on the data X.

Parameters
Xarray shape=(n_instances, n_features, )

Construction instances.

Returns
IIndex

Constructed index