"""K-Nearest neighbors code
"""

import numpy as np
import matplotlib.pyplot as plt
import warnings
from matplotlib import style
from collections import Counter

style.use('fivethirtyeight')

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


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

result = k_nearest_neighbors(dataset, new_features, k=3)
print(result)

[[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=200, color=result)
plt.show()

"""K-Nearest neighbors code
"""

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


def k_nearest_neighbors(data, predict, k=3):
    if len(data) >= k:
        warnings.warn('K set to value less than total voting groups ')
    distances = []
    for groups in data:
        for features in data[groups]:
            euclidean_distance = np.linalg.norm(np.array(features) - np.array(predict))
            distances.append([euclidean_distance, groups])
    votes = [idx[1] for idx in sorted(distances)[:k]]
    vote_result = Counter(votes).most_common(1)[0][0]
    return vote_result

# read data, replace missing values, drop useless id column
df = pd.read_csv('breast-cancer-wisconsin.data')
df.replace('?', -99999, inplace=True)
df.drop(['id'], 1, inplace=True)

# Convert to list making sure values are float (some data seems to be strings)
full_data = df.astype(float).values.tolist()

random.shuffle(full_data)

# Split 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 info in test_set[group]:
        vote = k_nearest_neighbors(train_set, info, 5)
        if group == vote:
            correct += 1
        total += 1

print('Accuracy: ', correct/total)

add test on the same data used in sci-kit learn k-nearest