代码全解析，可另见博文：https://iphysresearch.github.io/posts/S_Dbw.html
此文的 motivation 来自于近期接的某无监督 k-means 聚类项目，并计划是用基于 K-means 算法的 k-prototypes 聚类算法来打发了事。为了对聚类结果给出合理靠谱的评估评价，最终决定主要参考 S_Dbw 评估指标，并且打算写作此文，非原理性的解析 S_Dbw，原因有二：
- 在2001年，有一篇引用率挺高(300+)的 paper 1谈到说，
S_Dbw聚类评价指标对于各种噪声，不同密度的数据集等等干扰项来调参的鲁棒性最强，直接完爆其他所有评价指标~ S_Dbw算法在 sciki-learn 中至今还没有被添加到 api 中 2，相比， R 语言里却有现成且很好的 api 可以调用，如clv和NbClust3。关于S_Dbw算法现成的 Python 代码版本，在网络上也难以寻觅，唯一的参考是 fanfanda 的 版本。不过，此代码应该是有问题的，它聚类中心的定义是有误的。
综上，自己决定在 fanfanda 的代码基础上修正代码，并且贴出此代码算法的详细解析（详情见博文）。
博文注：符号完全参考论文原文，且已尽可能的说明算法的内涵和代码实现原理，更详细信息请参阅原论文。
S_Dbw算法的原论文地址：Clustering Validity Assessment: Finding the optimal partitioning of a data set
固定住模拟数据的中心点，变化散布程度：
变化模拟数据的中心点，固定每类的散布程度：
上面两个图的代码如下：（亦可见 Plot.ipynb）
import numpy as np
import S_Dbw as sdbw
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
from sklearn.metrics.pairwise import pairwise_distances_argmin
np.random.seed(0)
S_Dbw_result = []
batch_size = 45
centers = [[1, 1], [-1, -1], [1, -1]]
cluster_std=[0.7,0.3,1.2]
n_clusters = len(centers)
X1, _ = make_blobs(n_samples=3000, centers=centers, cluster_std=cluster_std[0])
X2, _ = make_blobs(n_samples=3000, centers=centers, cluster_std=cluster_std[1])
X3, _ = make_blobs(n_samples=3000, centers=centers, cluster_std=cluster_std[2])
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(9, 3))
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.08, top=0.9)
colors = ['#4EACC5', '#FF9C34', '#4E9A06']
for item, X in enumerate(list([X1, X2, X3])):
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)
k_means.fit(X)
k_means_cluster_centers = k_means.cluster_centers_
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers)
KS = sdbw.S_Dbw(X, k_means_labels, k_means_cluster_centers)
S_Dbw_result.append(KS.S_Dbw_result())
ax = fig.add_subplot(1,3,item+1)
for k, col in zip(range(n_clusters), colors):
my_members = k_means_labels == k
cluster_center = k_means_cluster_centers[k]
ax.plot(X[my_members, 0], X[my_members, 1], 'w',
markerfacecolor=col, marker='.')
ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=6)
ax.set_title('S_Dbw: %.3f' %(S_Dbw_result[item]))
ax.set_ylim((-4,4))
ax.set_xlim((-4,4))
plt.text(-3.5, 1.8, 'cluster_std: %f' %(cluster_std[item]))
plt.savefig('./pic1.png', dpi=150)np.random.seed(0)
S_Dbw_result = []
batch_size = 45
centers = [[[1, 1], [-1, -1], [1, -1]],
[[0.8, 0.8], [-0.8, -0.8], [0.8, -0.8]],
[[1.2, 1.2], [-1.2, -1.2], [1.2, -1.2]]]
n_clusters = len(centers)
X1, _ = make_blobs(n_samples=3000, centers=centers[0], cluster_std=0.7)
X2, _ = make_blobs(n_samples=3000, centers=centers[1], cluster_std=0.7)
X3, _ = make_blobs(n_samples=3000, centers=centers[2], cluster_std=0.7)
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 3))
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.2, top=0.9)
colors = ['#4EACC5', '#FF9C34', '#4E9A06']
for item, X in enumerate(list([X1, X2, X3])):
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)
k_means.fit(X)
k_means_cluster_centers = k_means.cluster_centers_
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers)
KS = sdbw.S_Dbw(X, k_means_labels, k_means_cluster_centers)
S_Dbw_result.append(KS.S_Dbw_result())
ax = fig.add_subplot(1,3,item+1)
for k, col in zip(range(n_clusters), colors):
my_members = k_means_labels == k
cluster_center = k_means_cluster_centers[k]
ax.plot(X[my_members, 0], X[my_members, 1], 'w',
markerfacecolor=col, marker='.')
ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=6)
ax.set_title('S_Dbw: %.3f ' %(S_Dbw_result[item]))
# ax.set_xticks(())
# ax.set_yticks(())
ax.set_ylim((-4,4))
ax.set_xlim((-4,4))
ax.set_xlabel('centers: \n%s' %(centers[item]))
plt.savefig('./pic2.png', dpi=150)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent