dislib.neighbors.NearestNeighbors

class dislib.neighbors.base.NearestNeighbors(n_neighbors=5)

Bases: object

Unsupervised learner for implementing neighbor searches.

Parameters:n_neighbors (int, optional (default=5)) – Number of neighbors to use by default for kneighbors queries.

Examples

>>> from dislib.neighbors import NearestNeighbors
>>> from dislib.data import load_data
>>> x = np.random.random((100, 5))
>>> data = load_data(x, subset_size=25)
>>> knn = NearestNeighbors(n_neighbors=10)
>>> knn.fit(data)
>>> distances, indices = knn.kneighbors(data)

fit(dataset)

Fit the model using dataset as training data.

Parameters:dataset (Dataset) – Training data.

kneighbors(dataset, n_neighbors=None, return_distance=True)

Finds the K nearest neighbors of the samples in dataset. Returns indices and distances to the neighbors of each sample.

Parameters:

• dataset (Dataset) – The query samples.
• n_neighbors (int, optional (default=None)) – Number of neighbors to get. If None, the value passed in the constructor is employed.

Returns:

• dist (array) – Array representing the lengths to points, only present if return_distance=True.
• ind (array) – Indices of the nearest samples in the fitted data.