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K-Nearest Neighbour Classifier

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K-Nearest Neighbour Classifier

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The Nearest Neighbour Classifier is one of the most straightforward classifier in the arsenal of machine learning techniques. It performs the classification by identifying the nearest neighbours to a query pattern and using those neighbors to determine the label of the query. The idea behind the algorithm is simple: Assign the query pattern to the class which occurs the most in the k nearest neighbors. In this post we'll use the function knn_search(...) that we have seen in the last post to implement a K-Nearest Neighbour Classifier. The implementation of the classifier is as follows:
from numpy import random,argsort,argmax,bincount,int_,array,vstack,round
from pylab import scatter,show

def knn_classifier(x, D, labels, K):
 """ Classify the vector x
     D - data matrix (each row is a pattern).
     labels - class of each pattern.
     K - number of neighbour to use.
     Returns the class label and the neighbors indexes.
 neig_idx = knn_search(x,D,K)
 counts = bincount(labels[neig_idx]) # voting
 return argmax(counts),neig_idx
Let's test the classifier on some random data:
 # generating a random dataset with random labels
data = random.rand(2,150) # random points
labels = int_(round(random.rand(150)*1)) # random labels 0 or 1
x = random.rand(2,1) # random test point

# label assignment using k=5
result,neig_idx = knn_classifier(x,data,labels,5)
print 'Label assignment:', result

# plotting the data and the input pattern
# class 1, red points, class 0 blue points
scatter(data[0,:],data[1,:], c=labels,alpha=0.8)
# highlighting the neighbours
The script will show the following graph:

The query vector is represented with a green point and we can see that the 3 out of 5 nearest neighbors are red points (label 1) while the remaining 2 are blue (label 2).
The result of the classification will be printed on the console:

Label assignment: 1

As we expected, the green point have been assigned to the class with red markers.

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