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I would like to calculate K-nearest neighbour in python. what library should i use?
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First, import the KNeighborsClassifier module and create KNN classifier object by passing argument number of neighbors in KNeighborsClassifier() function. Then, fit your model on the train set using fit() and perform prediction on the test set using predict().
The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value. KNN performs well with multi-label classes, but you must be aware of the outliers.
Disadvantages of KNN Algorithm:Always needs to determine the value of K which may be complex some time. The computation cost is high because of calculating the distance between the data points for all the training samples.
scipy.spatial.cKDTree is fast and solid. For an example of using it for NN interpolation, see (ahem) inverse-distance-weighted-idw-interpolation-with-python on SO.
(If you could say e.g. "I have 1M points in 3d, and want k=5 nearest neighbors of 1k new points",
you might get better answers or code examples.
What do you want to do with the neighbors once you've found them ?)
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It is natively in scipy if you're looking to do a kd-tree approach: http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html#scipy.spatial.KDTree
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