from scipy.spatial import distance


def euc(a,b):
	return distance.euclidean(a,b)
class ScrappyKNN():
	def fit(self,X_train,y_train):
		self.X_train=X_train
		self.y_train=y_train

	def closest(self,row):
		best_dist=euc(row,self.X_train[0])
		best_index=0
		for i in range(1,len(self.X_train)):
			dist = euc(row, self.X_train[i])
			if dist < best_dist:
				best_dist=dist
				best_index=i
		return self.y_train[best_index]			

	def predict(self,X_test):
		predictions=[]
		for row in X_test:
			label=self.closest(row)	
			predictions.append(label)
		return predictions
		

from sklearn import datasets
iris=datasets.load_iris()

x=iris.data
y=iris.target

from sklearn.cross_validation import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.5)


#from sklearn import tree
my_classifier=ScrappyKNN()
my_classifier.fit(x_train,y_train)
predictions=my_classifier.predict(x_test)

from sklearn.metrics import accuracy_score
print accuracy_score(y_test, predictions)