K-近邻算法的设计与实现python代码

以下是K-近邻算法的Python代码实现：

import numpy as np
from collections import Counter

class KNN:
    def __init__(self, k):
        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):
        y_pred = []
        for x in X_test:
            distances = [np.sqrt(np.sum((x - x_train)**2)) for x_train in self.X_train]
            nearest_indexes = np.argsort(distances)[:self.k]
            nearest_labels = [self.y_train[i] for i in nearest_indexes]
            most_common_label = Counter(nearest_labels).most_common(1)[0][0]
            y_pred.append(most_common_label)
        return y_pred

其中，`KNN`类包括以下方法：

- `__init__(self, k)`：初始化方法，传入K值。
- `fit(self, X_train, y_train)`：训练方法，传入训练集特征和标签。
- `predict(self, X_test)`：预测方法，传入测试集特征，返回预测结果。