knn.py

from scipy.spatial import distance
def eu(a, b):
	return distance.euclidean(a, b)

class KNN:
	def fit(self, x_train, y_train):
		self.x_train = x_train
		self.y_train = y_train
	
	def predict(self, x_test):
		predictions = []
		for row in x_test:
			label = self.closest(row)
			predictions.append(label)
		return predictions

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

from sklearn.datasets import load_iris
#from sklearn.neighbors import KNeighborsClassifier 
iris = load_iris()
x = iris.data
y = iris.target

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)

clf = KNN()
#clf2= KNeighborsClassifier()
clf.fit(x_train, y_train)
#clf2.fit(x_train,y_train)
prediction = clf.predict(x_test)
#pred=clf2.predict(x_test)
from sklearn.metrics import accuracy_score as acs
print("Accuracy by custom kNN is: "+str(acs(prediction, y_test)*100))
#print(acs(pred, y_test))