sklearn.neighbors.NearestNeighbors¶
- class sklearn.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', **kwargs)¶
Unsupervised learner for implementing neighbor searches.
Parameters: n_neighbors : int, optional (default = 5)
Number of neighbors to use by default for k_neighbors queries.
radius : float, optional (default = 1.0)
Range of parameter space to use by default for :meth`radius_neighbors` queries.
algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
Algorithm used to compute the nearest neighbors:
- ‘ball_tree’ will use BallTree
- ‘kd_tree’ will use KDtree
- ‘brute’ will use a brute-force search.
- ‘auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit method.
Note: fitting on sparse input will override the setting of this parameter, using brute force.
leaf_size : int, optional (default = 30)
Leaf 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.
p: integer, optional (default = 2) :
Parameter 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.
See also
KNeighborsClassifier, RadiusNeighborsClassifier, KNeighborsRegressor, RadiusNeighborsRegressor, BallTree
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of algorithm and leaf_size.
http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
Examples
>>> from sklearn.neighbors import NearestNeighbors >>> samples = [[0, 0, 2], [1, 0, 0], [0, 0, 1]]
>>> neigh = NearestNeighbors(2, 0.4) >>> neigh.fit(samples) NearestNeighbors(...)
>>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False) ... array([[2, 0]]...)
>>> neigh.radius_neighbors([0, 0, 1.3], 0.4, return_distance=False) array([[2]])
Methods
fit(X[, y]) Fit the model using X as training data get_params([deep]) Get parameters for this estimator. kneighbors(X[, n_neighbors, return_distance]) Finds the K-neighbors of a point. kneighbors_graph(X[, n_neighbors, mode]) Computes the (weighted) graph of k-Neighbors for points in X radius_neighbors(X[, radius, return_distance]) Finds the neighbors within a given radius of a point or points. radius_neighbors_graph(X[, radius, mode]) Computes the (weighted) graph of Neighbors for points in X set_params(**params) Set the parameters of this estimator. - __init__(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', **kwargs)¶
- fit(X, y=None)¶
Fit the model using X as training data
Parameters: X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape = [n_samples, n_features]
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- kneighbors(X, n_neighbors=None, return_distance=True)¶
Finds the K-neighbors of a point.
Returns distance
Parameters: X : array-like, last dimension same as that of fit data
The new point.
n_neighbors : int
Number of neighbors to get (default is the value passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns: dist : array
Array representing the lengths to point, only present if return_distance=True
ind : array
Indices of the nearest points in the population matrix.
Examples
In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1,1,1]
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=1) >>> neigh.fit(samples) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> print(neigh.kneighbors([1., 1., 1.])) (array([[ 0.5]]), array([[2]]...))
As you can see, it returns [[0.5]], and [[2]], which means that the element is at distance 0.5 and is the third element of samples (indexes start at 0). You can also query for multiple points:
>>> X = [[0., 1., 0.], [1., 0., 1.]] >>> neigh.kneighbors(X, return_distance=False) array([[1], [2]]...)
- kneighbors_graph(X, n_neighbors=None, mode='connectivity')¶
Computes the (weighted) graph of k-Neighbors for points in X
Parameters: X : array-like, shape = [n_samples, n_features]
Sample data
n_neighbors : int
Number of neighbors for each sample. (default is value passed to the constructor).
mode : {‘connectivity’, ‘distance’}, optional
Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are Euclidean distance between points.
Returns: A : sparse matrix in CSR format, shape = [n_samples, n_samples_fit]
n_samples_fit is the number of samples in the fitted data A[i, j] is assigned the weight of edge that connects i to j.
Examples
>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=2) >>> neigh.fit(X) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.kneighbors_graph(X) >>> A.toarray() array([[ 1., 0., 1.], [ 0., 1., 1.], [ 1., 0., 1.]])
- radius_neighbors(X, radius=None, return_distance=True)¶
Finds the neighbors within a given radius of a point or points.
Returns indices of and distances to the neighbors of each point.
Parameters: X : array-like, last dimension same as that of fit data
The new point or points
radius : float
Limiting distance of neighbors to return. (default is the value passed to the constructor).
return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns: dist : array
Array representing the euclidean distances to each point, only present if return_distance=True.
ind : array
Indices of the nearest points in the population matrix.
Notes
Because the number of neighbors of each point is not necessarily equal, the results for multiple query points cannot be fit in a standard data array. For efficiency, radius_neighbors returns arrays of objects, where each object is a 1D array of indices or distances.
Examples
In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1,1,1]
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.6) >>> neigh.fit(samples) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> print(neigh.radius_neighbors([1., 1., 1.])) (array([[ 1.5, 0.5]]...), array([[1, 2]]...)
The first array returned contains the distances to all points which are closer than 1.6, while the second array returned contains their indices. In general, multiple points can be queried at the same time.
- radius_neighbors_graph(X, radius=None, mode='connectivity')¶
Computes the (weighted) graph of Neighbors for points in X
Neighborhoods are restricted the points at a distance lower than radius.
Parameters: X : array-like, shape = [n_samples, n_features]
Sample data
radius : float
Radius of neighborhoods. (default is the value passed to the constructor).
mode : {‘connectivity’, ‘distance’}, optional
Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are Euclidean distance between points.
Returns: A : sparse matrix in CSR format, shape = [n_samples, n_samples]
A[i, j] is assigned the weight of edge that connects i to j.
See also
Examples
>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.5) >>> neigh.fit(X) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.radius_neighbors_graph(X) >>> A.toarray() array([[ 1., 0., 1.], [ 0., 1., 0.], [ 1., 0., 1.]])
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :