k_nearest_neighbours_ml.py

import numpy as np
from math import sqrt
import matplotlib.pyplot as plt
import warnings
from matplotlib import style
from collections import Counter

style.use('fivethirtyeight')

dataset = {'k': [[1,2], [2,3], [3,1]], 'r': [[6,5], [7,7], [8,5]]}
new_features = [5,7]

def k_nearset_neighbours(data, predict, k=3):
	if len(data) >= k:
		warnings.warn('K is set to a value less than the total voting groups.')

	distances = []
	for group in data:
		for features in data[group]:
			euclidean_distance = np.linalg.norm(np.array(features)-np.array(predict))
			distances.append([euclidean_distance, group])

	votes = [i[1] for i in sorted(distances)[:k]]
	vote_result = Counter(votes).most_common(1)[0][0]

	return vote_result

result = k_nearset_neighbours(dataset, new_features, k=3)
print result

for i in dataset:
	for ii in dataset[i]:
		plt.scatter(ii[0], ii[1], s=100, color=i)

plt.scatter(new_features[0], new_features[1], color=result)
plt.show()