Documentation
Classes
NearestNeighbors

NearestNeighbors

Unsupervised learner for implementing neighbor searches.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new NearestNeighbors(opts?: object): NearestNeighbors;

Parameters

NameTypeDescription
opts?object-
opts.algorithm?"auto" | "ball_tree" | "kd_tree" | "brute"Algorithm used to compute the nearest neighbors: Default Value 'auto'
opts.leaf_size?numberLeaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. Default Value 30
opts.metric?stringMetric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance (opens in a new tab) and the metrics listed in distance\_metrics for valid metric values. If metric is “precomputed”, X is assumed to be a distance matrix and must be square during fit. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string. Default Value 'minkowski'
opts.metric_params?anyAdditional keyword arguments for the metric function.
opts.n_jobs?numberThe number of parallel jobs to run for neighbors search. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.n_neighbors?numberNumber of neighbors to use by default for kneighbors queries. Default Value 5
opts.p?numberParameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. Default Value 2
opts.radius?numberRange of parameter space to use by default for radius\_neighbors queries. Default Value 1

Returns

NearestNeighbors

Defined in: generated/neighbors/NearestNeighbors.ts:23 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/neighbors/NearestNeighbors.ts:21 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/neighbors/NearestNeighbors.ts:20 (opens in a new tab)

_py

PythonBridge

Defined in: generated/neighbors/NearestNeighbors.ts:19 (opens in a new tab)

id

string

Defined in: generated/neighbors/NearestNeighbors.ts:16 (opens in a new tab)

opts

any

Defined in: generated/neighbors/NearestNeighbors.ts:17 (opens in a new tab)

Accessors

effective_metric_

Metric used to compute distances to neighbors.

Signature

effective_metric_(): Promise<string>;

Returns

Promise<string>

Defined in: generated/neighbors/NearestNeighbors.ts:429 (opens in a new tab)

effective_metric_params_

Parameters for the metric used to compute distances to neighbors.

Signature

effective_metric_params_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/neighbors/NearestNeighbors.ts:456 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/neighbors/NearestNeighbors.ts:510 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/neighbors/NearestNeighbors.ts:483 (opens in a new tab)

n_samples_fit_

Number of samples in the fitted data.

Signature

n_samples_fit_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/neighbors/NearestNeighbors.ts:537 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/neighbors/NearestNeighbors.ts:84 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/neighbors/NearestNeighbors.ts:88 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/neighbors/NearestNeighbors.ts:145 (opens in a new tab)

fit()

Fit the nearest neighbors estimator from the training dataset.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining data.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/neighbors/NearestNeighbors.ts:162 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/neighbors/NearestNeighbors.ts:97 (opens in a new tab)

kneighbors()

Find the K-neighbors of a point.

Returns indices of and distances to the neighbors of each point.

Signature

kneighbors(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
opts.n_neighbors?numberNumber of neighbors required for each sample. The default is the value passed to the constructor.
opts.return_distance?booleanWhether or not to return the distances. Default Value true

Returns

Promise<ArrayLike[]>

Defined in: generated/neighbors/NearestNeighbors.ts:204 (opens in a new tab)

kneighbors_graph()

Compute the (weighted) graph of k-Neighbors for points in X.

Signature

kneighbors_graph(opts: object): Promise<any[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. For metric='precomputed' the shape should be (n_queries, n_indexed). Otherwise the shape should be (n_queries, n_features).
opts.mode?"connectivity" | "distance"Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class. Default Value 'connectivity'
opts.n_neighbors?numberNumber of neighbors for each sample. The default is the value passed to the constructor.

Returns

Promise<any[]>

Defined in: generated/neighbors/NearestNeighbors.ts:253 (opens in a new tab)

radius_neighbors()

Find the neighbors within a given radius of a point or points.

Return the indices and distances of each point from the dataset lying in a ball with size radius around the points of the query array. Points lying on the boundary are included in the results.

The result points are not necessarily sorted by distance to their query point.

Signature

radius_neighbors(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
opts.radius?numberLimiting distance of neighbors to return. The default is the value passed to the constructor.
opts.return_distance?booleanWhether or not to return the distances. Default Value true
opts.sort_results?booleanIf true, the distances and indices will be sorted by increasing distances before being returned. If false, the results may not be sorted. If return\_distance=False, setting sort\_results=True will result in an error. Default Value false

Returns

Promise<any>

Defined in: generated/neighbors/NearestNeighbors.ts:308 (opens in a new tab)

radius_neighbors_graph()

Compute the (weighted) graph of Neighbors for points in X.

Neighborhoods are restricted the points at a distance lower than radius.

Signature

radius_neighbors_graph(opts: object): Promise<any[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.
opts.mode?"connectivity" | "distance"Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class. Default Value 'connectivity'
opts.radius?numberRadius of neighborhoods. The default is the value passed to the constructor.
opts.sort_results?booleanIf true, in each row of the result, the non-zero entries will be sorted by increasing distances. If false, the non-zero entries may not be sorted. Only used with mode=’distance’. Default Value false

Returns

Promise<any[]>

Defined in: generated/neighbors/NearestNeighbors.ts:368 (opens in a new tab)