用python实现KNN算法

KNN算法是一种常用的分类算法，下面是用Python实现KNN算法的示例代码：

import numpy as np
import math

# 计算欧几里得距离
def euclidean_distance(x1, x2):
    distance = 0
    for i in range(len(x1)):
        distance += (x1[i] - x2[i]) ** 2
    return math.sqrt(distance)

# KNN算法
def knn(X_train, y_train, X_test, k):
    predictions = []
    for i in range(len(X_test)):
        distances = []
        for j in range(len(X_train)):
            distance = euclidean_distance(X_test[i], X_train[j])
            distances.append((distance, y_train[j]))
        distances.sort()
        neighbors = distances[:k]
        classes = {}
        for neighbor in neighbors:
            if neighbor[1] in classes:
                classes[neighbor[1]] += 1
            else:
                classes[neighbor[1]] = 1
        predictions.append(max(classes, key=classes.get))
    return predictions

其中，euclidean_distance函数用于计算欧几里得距离，knn函数实现了KNN算法，X_train和y_train分别表示训练集的特征和标签，X_test表示测试集的特征，k表示最近邻的个数。返回predictions列表，包含每个测试样本的预测结果。