from math import sqrt 
import numpy as np
from collections import Counter
import warnings
import pandas as pd 
import random

def k_nearest_neighbors(data, predict, k=3) :
	if len(data) >= k:
		warnings.warn('K is set to value less than total voting groups!')
	distances = []
	for group in data :
		for features in data[group]:
			eculidean_distance = np.linalg.norm(np.array(features)- np.array(predict))
			distances.append([eculidean_distance, group])

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

df = pd.read_csv("breast-cancer-wisconsin.data")
df.replace("?", -99999, inplace = True)
df.drop(['id'], 1, inplace = True)
full_data = df.astype(float).values.tolist()
# print(full_data[:10])
# print(30*"-")
random.shuffle(full_data)

test_size = 0.2
train_set = {2:[], 4:[]}
test_set = {2:[], 4:[]}
train_data = full_data[:-int(test_size* len(full_data))]
test_data = full_data[-int(test_size* len(full_data)):]


for i in train_data:
	train_set[i[-1]].append(i[:-1])

for i in test_data:
	test_set[i[-1]].append(i[:-1])

# print(full_data[:10])

correct = 0 
total = 0

for group in test_set:
	for data in test_set[group]:
		vote = k_nearest_neighbors(train_set, data, k =5)
		if group == vote:
			correct += 1
		total += 1

print("accuracy", correct/total)
