iri.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_train):
    predictions=[]
    for row in X_test:
      labels=self.closest(row)
      predictions.append(labels)
    return predictions
  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))