三维空间中n个最近邻域的knn实现

import numpy as np
from collections import Counter

def euclidean_distance(point1, point2):
    return np.sqrt(np.sum((point1 - point2) ** 2))

class KNNClassifier:
    def __init__(self, k=3):
        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._predict(x) for x in X_test]
        return np.array(predictions)
    
    def _predict(self, x):
        distances = [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]

# 示例数据
X_train = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y_train = np.array([0, 1, 0])
X_test = np.array([[2, 3, 4], [5, 6, 7]])

# 创建并训练模型
knn = KNNClassifier(k=2)
knn.fit(X_train, y_train)

# 预测
predictions = knn.predict(X_test)
print(predictions)  # 输出预测类别