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k - Nearest Neighbor Search

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k - Nearest Neighbor Search

· Web Dev Zone ·
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Deploying code to production can be filled with uncertainty. Reduce the risks, and deploy earlier and more often. Download this free guide to learn more. Brought to you in partnership with Rollbar.

A k-nearest neighbor search identifies the top k nearest neighbors to a query. The problem is: given a dataset D of vectors in a d-dimensional space and a query point x in the same space, find the closest point in D to x. The following function performs a k-nearest neighbor search using the euclidean distance:
from numpy import random,argsort,sqrt
from pylab import plot,show

def knn_search(x, D, K):
 """ find K nearest neighbours of data among D """
 ndata = D.shape[1]
 K = K if K < ndata else ndata
 # euclidean distances from the other points
 sqd = sqrt(((D - x[:,:ndata])**2).sum(axis=0))
 idx = argsort(sqd) # sorting
 # return the indexes of K nearest neighbours
 return idx[:K]
The function computes the euclidean distance between every point of D and x then returns the indexes of the points for which the distance is smaller.
Now, we will test this function on a random bidimensional dataset:
# knn_search test
data = random.rand(2,200) # random dataset
x = random.rand(2,1) # query point

# performing the search
neig_idx = knn_search(x,data,10)

# plotting the data and the input point
plot(data[0,:],data[1,:],'ob',x[0,0],x[1,0],'or')
# highlighting the neighbours
plot(data[0,neig_idx],data[1,neig_idx],'o',
  markerfacecolor='None',markersize=15,markeredgewidth=1)
show()
The result is as follows:



















The red point is the query vector and the blue ones represent the data. The blue points surrounded by a black circle are the nearest neighbors.

Deploying code to production can be filled with uncertainty. Reduce the risks, and deploy earlier and more often. Download this free guide to learn more. Brought to you in partnership with Rollbar.

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