knn Python代码实现

当然可以！下面是一个简单的knn Python代码实现：

import numpy as np
from collections import Counter

def euclidean_distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2)**2))

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):
        predicted_labels = [self._predict(x) for x in X]
        return np.array(predicted_labels)

    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]

这个代码实现了一个简单的KNN分类器，其中欧式距离被用于计算相似度。使用方法如下：

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

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

X_test = np.array([[1,3], [2,1]])
predictions = knn.predict(X_test)

print(predictions)

输出应该是一个包含两个数字的数组，代表对应的测试样本的预测标签。