classifier.py

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 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 = 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]

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.neighbors import KNeighborsClassifier
my_classifier = ScrappyKNN()   #KNeighborsClassifier()

my_classifier.fit(x_train, y_train)

prediction = my_classifier.predict(x_test)

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