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

# define function
def K_nearest_neighbours(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]:
            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]]
    #print(Counter(votes).most_common(1))
    vote_result = Counter(votes).most_common(1)[0][0]
    confidence = Counter(votes).most_common(1)[0][1] / k
    #print(vote_result, confidence)
    return vote_result, confidence
accuracies = []

for i in range(25):

    # import data
    df = pd.read_csv('breast-cancer-wisconsin.data')
    # there are gaps in the data denoted by '?' - these need to be converted to -99999 so the algorythm treats it as an outlier
    df.replace('?',-99999,inplace=True)
    # drop any useless data - in this case the ID
    df.drop('id',1,inplace=True)
    #print(df)
    # convert everything in the list to a number
    full_data = df.astype(float).values.tolist()
    #print(full_data[:5]) # print first 5 rows
    random.shuffle(full_data) # no need to define variable again i.e. full_data = 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))] # slicing the full data set by the test_size
    test_data = full_data[-int(test_size*len(full_data)):] # last 20%

    for i in train_data:
        train_set[i[-1]].append(i[:-1]) # -1 gives the last column
    for i in test_data:
        test_set[i[-1]].append(i[:-1]) # -1 gives the last column

    correct = 0
    total = 0

    for group in test_set:
        for data in test_set[group]:
            vote, confidence  = K_nearest_neighbours(train_set,data, k=5)
            if group == vote:
                correct +=1
        #    else:
        #        print(confidence)
            total +=1

    #print('Accuracy', correct/total)
    accuracies.append(correct / total)

print((sum(accuracies)/len(accuracies)*100))