import numpy as np
from math import sqrt
import matplotlib.pyplot as plt
from matplotlib import style
import warnings
from collections import Counter
style.use('fivethirtyeight')
import pandas as pd
import random

dataset =  {'k':[[1,2],[2,3],[3,1]],'r':[[6,5],[7,7],[8,6]]}
new = [4,3]

def knn(data,predict,k=3):

    if(len(data)>k):
        warnings.warn('K is set to value less than voting groups')
    distances = []

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


    votes = [i[1] for i in sorted(distances)[:k]]
    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()
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])

correct =0
total =0

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

print('Accuracy: ',correct/total)