写这篇博客源于博友的提问：

1. 效果图

输入
100
5
3
7
得到结果
[2]
可视化效果图如下：待预测点红色x展示，

输入
88
2
1
9
得到结果：
[1]
可视化效果图如下：待预测点红色x展示，

2. 源码

# KNeighborsClassifier进行分类demo
# python KmeansClassify.py
# -*- coding: utf-8 -*-
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.neighbors import KNeighborsClassifier# 请在此添加代码 完成本关任务
#
# ********** Begin *********#
samples = eval(input())
state = eval(input())
feature1 = eval(input())
feature2 = eval(input())X, y = make_blobs(n_samples=samples, random_state=state)knn = KNeighborsClassifier()
knn.fit(X, y)X_sample = [feature1, feature2]
X_sample = np.array(X_sample).reshape(1, -1)
print(knn.predict(X_sample))
# ********** End **********## 绘图看一下
from matplotlib import pyplot as pltneighbors = knn.kneighbors(X_sample, return_distance=False)  # 求出邻居节点,默认求的是5个
plt.figure(figsize=(16, 10))
plt.scatter(X[:, 0], X[:, 1], s=100, cmap='cool')  # 样本
plt.scatter(X_sample[0][0], X_sample[0][1], marker="x",s=100, cmap='cool')  # 待预测的点for i in neighbors[0]:# 预测点与距离最近的 5 个样本的连线plt.plot([X[i][0], X_sample[0][0]], [X[i][1], X_sample[0][1]],'k--', linewidth=0.6);
plt.show()

参考

• https://blog.csdn.net/wzk4869/article/details/126744005
• https://blog.csdn.net/u011630575/article/details/79195450
• https://blog.csdn.net/qq_39309652/article/details/90382868