Implement the KNN algorithm yourself

import numpy as np
from math import sqrt
from collections import Counter

class KNNClassifier(object):
    """docstring for KNNClassifier"""
    def __init__(self, k):
        assert k>=1,"k must be valid"
        self.k = k
        self._X_train = None
        self._y_train = None

    def fit(self, X_train, y_train):
         ''' Train the KNN classifier according to the training dataset X_train and y_train ''' 
        self._X_train = X_train
        self._y_train = y_train
        return self

    def predict(self,X_predict):
        y_predict = [self._predict(x) for x in X_predict]
        return np.array(y_predict)

    def _predict(self,x):
        distances = [sqrt(np.sum((x_train-x)**2) for x_train in self._X_train)]

        nearest = np.argsort(distances)

        topK_y=[self._y_train[i] for i in nearest[:self.k]]
        votes = Counter(topK_y)

        return votes.most_common(1)[0][0]