## Python, , k_nearest_neighbours.py

``````from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

X = np.array(data["data"])
y = np.array(data["target"])
classes = data["target_names"]

X_train, X_test, y_train, y_test = train_test_split(X, y)

def euclidean_distance(a, b):
"""
Gives the euclidean distance between two points
>>> euclidean_distance([0, 0], [3, 4])
5.0
>>> euclidean_distance([1, 2, 3], [1, 8, 11])
10.0
"""
return np.linalg.norm(np.array(a) - np.array(b))

def classifier(train_data, train_target, classes, point, k=5):
"""
Classifies the point using the KNN algorithm
k closest points are found (ranked in ascending order of euclidean distance)
Params:
:train_data: Set of points that are classified into two or more classes
:train_target: List of classes in the order of train_data points
:classes: Labels of the classes
:point: The data point that needs to be classified

>>> X_train = [[0, 0], [1, 0], [0, 1], [0.5, 0.5], [3, 3], [2, 3], [3, 2]]
>>> y_train = [0, 0, 0, 0, 1, 1, 1]
>>> classes = ['A','B']; point = [1.2,1.2]
>>> classifier(X_train, y_train, classes,point)
'A'
"""
data = zip(train_data, train_target)
# List of distances of all points from the point to be classified
distances = []
for data_point in data:
distance = euclidean_distance(data_point[0], point)
distances.append((distance, data_point[1]))
# Choosing 'k' points with the least distances.
votes = [i[1] for i in sorted(distances)[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified