KNN_dev.py

import random
from scipy.spatial import distance


# def de la distance euclidienne entre deux points 
def euc(a,b): 
	return distance.euclidean(a,b)


#definition du classifier
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
import numpy as np

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()
my_classifier.fit(X_train, y_train)


predictions = my_classifier.predict(X_test)
print (predictions)


from sklearn.metrics import accuracy_score

print (accuracy_score(y_test, predictions))