I need to generate K nearest neighbours given a datapoint. I read up the sklearn.neighbours module of sklearn but it generates neighbours between two sets of data. What I want is probably a list of 100 datapoints closest to the datapoint passed.
Any KNN algorithm shall anyways be finding these K datapoints under the hood. Is there any way these K points could be returned as output?
Here is my sample notebook.
from sklearn.neighbors import NearestNeighbors
This can give you the index of the k nearest neighbors in your dataset. use kneighbors, first value is the distance and second value is the index of the neighbors. From documentation:
>>> 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]]))