kNN.py

import numpy as np
from collections import Counter

def eu_dis(x1, x2):
    '''
    function to find euclidean distance
    '''
    distance = np.sqrt(np.sum(x1-x2)**2)
    return distance

class kNN:
    def __init__(self, k=3):
        self.k = k
        
    def fit(self, X, y):
        self.X_train = X
        self.y_train = y
        
    def predict(self, X):
        predictions = [self._predict(x) for x in X]
        return predictions
    
    def _predict(self, x):
        distances = [eu_dis(x, x_tr) for x_tr in self.X_train]
        
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]
        
        most_common, _ = Counter(k_nearest_labels).most_common()[0]
        return int(most_common)