knn

from numpy import *
import operator

class KNN:
def createDataset(self):
group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels = ['A','A','B','B']
return group,labels

def KnnClassify(self,testX,trainX,labels,K):
[N,M]=trainX.shape

#calculate the distance between testX and other training samples
difference = tile(testX,(N,1)) - trainX # tile for array and repeat for matrix in Python, == repmat in Matlab
print difference
print testX

difference = difference ** 2 # take pow(difference,2)
distance = difference.sum(1) # take the sum of difference from all dimensions
distance = distance ** 0.5
sortdiffidx = distance.argsort()

# find the k nearest neighbours
vote = {} #create the dictionary
for i in range(K):
ith_label = labels[sortdiffidx[i]];
vote[ith_label] = vote.get(ith_label,0)+1 #get(ith_label,0) : if dictionary 'vote' exist key 'ith_label', return vote[ith_label]; else return 0
sortedvote = sorted(vote.iteritems(),key = lambda x:x[1], reverse = True)
# 'key = lambda x: x[1]' can be substituted by operator.itemgetter(1)
return sortedvote[0][0]

k = KNN() #create KNN object
group,labels = k.createDataset()
cls = k.KnnClassify([0,0],group,labels,3)
print cls