dislib.neighbors.NearestNeighbors¶
- class dislib.neighbors.base.NearestNeighbors(n_neighbors=5)[source]¶
Bases:
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)
- fit(x)[source]¶
Fit the model using training data.
- Parameters
x (ds-array, shape=(n_samples, n_features)) – Training data.
- Returns
self
- Return type
- 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.