用Python写knn算法

以下是一个简单的Python代码实现knn算法：

import numpy as np
from collections import Counter

class KNN:
    def __init__(self, k):
        self.k = k
    
    def fit(self, X, y):
        self.X_train = X
        self.y_train = y
    
    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return np.array(y_pred)
    
    def _predict(self, x):
        distances = [self._euclidean_distance(x, x_train) for x_train 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(1)
        return most_common[0][0]
    
    def _euclidean_distance(self, x1, x2):
        return np.sqrt(np.sum((x1 - x2) ** 2))

使用示例：

X_train = np.array([[1, 2], [2, 1], [3, 4], [5, 6], [6, 5]])
y_train = np.array([0, 0, 1, 1, 1])
X_test = np.array([[4, 5], [2, 3]])

knn = KNN(k=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)

print(y_pred)  # 输出 [1 1]