myknn.py

from scipy.spatial import distance

def eucli(a,b):
	return distance.euclidean(a,b);

class myknn():
	#fit method of classifier
	def fit(self,x_train,y_train):
		self.x_train=x_train
		self.y_train=y_train
	#predict method of classifier
	def predict(self,x_test):
		predictions=[]
		for row in x_test:
			labels=self.closest(row)
			predictions.append(labels)
		return predictions
	#closest distance
	def closest(self,row):
		best_dist=eucli(row,self.x_train[0])
		best_index=0
		for i in range(1,len(self.x_train)):
			dist=eucli(row,self.x_train[i])
			if dist < best_dist:
				best_dist=dist
				best_index=i
		return self.y_train[best_index]

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris

iris=load_iris()

features=iris.data
labels=iris.target

from sklearn.cross_validation import train_test_split
x_train,x_test,y_train,y_test=train_test_split(features,labels,test_size=.3)

clf=myknn()
clf.fit(x_train,y_train)

p=clf.predict(x_test)

from sklearn.metrics import accuracy_score
print("Accuracy=",accuracy_score(y_test,p))