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

style.use('fivethirtyeight')


def k_nearest_neighbors(data, predict, k=3):
    if len(data) >= k:
        warnings.warn('K is set to a 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]]
    vote_result = Counter(votes).most_common(1)[0][0]
    confidence = Counter(votes).most_common(1)[0][1] / k
    return vote_result, confidence


# dataset = {'k': [[1, 2], [2, 3], [3, 1]], 'r': [[6, 5], [7, 7], [8, 6]]}
# new_features = [5, 7]
#
# [[plt.scatter(ii[0], ii[1], s=100, color=i) for ii in dataset[i]] for i in dataset]
# plt.scatter(new_features[0], new_features[1], s=100)
# result = k_nearest_neighbors(dataset, new_features)
# plt.scatter(new_features[0], new_features[1], s=100, color=result)
# plt.show()
df = pd.read_csv('breast-cancer-wisconsin.data.txt')
df.replace('?', -99999, inplace=True)
df.drop(['id'], 1, inplace=True)
full_data = df.astype(float).values.tolist()

random.shuffle(full_data)
test_size = 0.8
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, confidence = k_nearest_neighbors(train_set, data, k=5)
        print(vote, confidence)
        if group == vote:
            correct += 1
        total += 1
print('Accuracy:', correct / total)
