A Naïve implementation of the K-Nearest neighbor's algorithm

neighbors.py

from scipy.spatial.distance import euclidean
from collections import Counter


class KNeighborsClassifier:

    def __init__(self, k=5):
        self.k = k

    def fit(self, X_train, y_train):
        self.X_train = X_train
        self.y_train = y_train

    def predict(self, X_test):
        predictions = [self.nearest(feature) for feature in X_test]
        return predictions

    def nearest(self, feature):
        distances = [(euclidean(feature,f), self.y_train[i]) for i,f in enumerate(self.X_train)]
        labels = [l[1] for l in sorted(distances)[:self.k]]
        return Counter(labels).most_common(1)[0][0]