k最近邻算法引用

# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans

sns.set()
X, y_true = make_blobs(n_samples=300, centers=4,
                       cluster_std=0.60, random_state=0)
# plt.scatter(X[:, 0], X[:, 1], s=50)
kmeans = KMeans(n_clusters=4)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)  # 返回颜色信息列表
# print('y_kmeans:\n', y_kmeans)
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
centers = kmeans.cluster_centers_
print('centers:\n', centers)
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)

plt.show()