def distance(p,point):
import numpy as np
value = np.sqrt(sum(np.power((p-point),2)))
return(value)
def find_neighbors(p,list_of_points, k = 3):
import numpy as np
distances = np.zeros(list_of_points.shape[0])
for i in range(list_of_points.shape[0]):
distances[i]= distance(p,list_of_points[i])
ind = np.argsort(distances)
return(ind[0:k])
def majority_votes(votes):
import random
vote_result = {}
for key in votes:
if key in vote_result:
vote_result[key] += 1
else:
vote_result[key] = 1
final_list = []
for (number, vote) in vote_result.items():
if vote == max(vote_result.values()):
final_list.append(number)
Winner = random.choice(final_list)
return(Winner)
def KNN_method(p , list_of_points , outcomes , k = 3):
ind = find_neighbors(p , list_of_points , k)
Final = majority_votes(outcomes[ind])
return(Final)