dislib.neighbors.NearestNeighbors¶
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class
dislib.neighbors.base.
NearestNeighbors
(n_neighbors=5)[source]¶ Bases:
sklearn.base.BaseEstimator
Unsupervised learner for implementing neighbor searches.
Parameters: n_neighbors (int, optional (default=5)) – Number of neighbors to use by default for kneighbors queries. Examples
>>> import dislib as ds >>> from dislib.neighbors import NearestNeighbors >>> >>> >>> if __name__ == '__main__': >>> data = ds.random_array((100, 5), block_size=(25, 5)) >>> knn = NearestNeighbors(n_neighbors=10) >>> knn.fit(data) >>> distances, indices = knn.kneighbors(data)
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fit
(x)[source]¶ Fit the model using training data.
Parameters: x (ds-array, shape=(n_samples, n_features)) – Training data. Returns: self Return type: NearestNeighbors
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kneighbors
(x, n_neighbors=None, return_distance=True)[source]¶ Finds the K nearest neighbors of the input samples. Returns indices and distances to the neighbors of each sample.
Parameters: - x (ds-array, shape=(n_samples, n_features)) – The query samples.
- n_neighbors (int, optional (default=None)) – Number of neighbors to get. If None, the value passed in the constructor is employed.
- return_distance (boolean, optional (default=True)) – Whether to return distances.
Returns: - dist (ds-array, shape=(n_samples, n_neighbors)) – Array representing the lengths to points, only present if return_distance=True.
- ind (ds-array, shape=(n_samples, n_neighbors)) – Indices of the nearest samples in the fitted data.
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