"""
K_Near Neighbours Classifier
Testing on Simple Data
Let's say K = 3, so then we're looking for the two closest neighboring points.
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
from collections import Counter
import warnings

style.use('fivethirtyeight')

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

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]

    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],color=result)
plt.show()
