写一段python代码，生成点云数据集，用kmeans聚类，GMM聚类，DBSCAN聚类和mean shift聚类分别对生成的数据进行聚类，并衡量他们的评价指标

生成点云数据集，可以使用Python的NumPy库和Scikit-learn库。以下是一个简单的示例代码：

import numpy as np
from sklearn.datasets import make_blobs

# 生成点云数据集
X, y = make_blobs(n_samples=1000, centers=4, random_state=42)

# 展示数据分布
import matplotlib.pyplot as plt
plt.scatter(X[:,0], X[:,1], c=y)
plt.show()

接下来，使用Scikit-learn库中的KMeans、GaussianMixture、DBSCAN和MeanShift类进行聚类，并计算评价指标。以下是一个示例代码：

from sklearn.cluster import KMeans, DBSCAN, MeanShift
from sklearn.mixture import GaussianMixture
from sklearn.metrics import silhouette_score, adjusted_rand_score

# 定义聚类器
kmeans = KMeans(n_clusters=4, random_state=42)
gmm = GaussianMixture(n_components=4, random_state=42)
dbscan = DBSCAN(eps=0.5, min_samples=5)
ms = MeanShift()

# 进行聚类
kmeans_labels = kmeans.fit_predict(X)
gmm_labels = gmm.fit_predict(X)
dbscan_labels = dbscan.fit_predict(X)
ms_labels = ms.fit_predict(X)

# 计算评价指标
kmeans_silhouette = silhouette_score(X, kmeans_labels)
kmeans_ari = adjusted_rand_score(y, kmeans_labels)
gmm_silhouette = silhouette_score(X, gmm_labels)
gmm_ari = adjusted_rand_score(y, gmm_labels)
dbscan_silhouette = silhouette_score(X, dbscan_labels)
dbscan_ari = adjusted_rand_score(y, dbscan_labels)
ms_silhouette = silhouette_score(X, ms_labels)
ms_ari = adjusted_rand_score(y, ms_labels)

# 输出评价指标
print("KMeans: silhouette score = {:.2f}, ARI = {:.2f}".format(kmeans_silhouette, kmeans_ari))
print("GMM: silhouette score = {:.2f}, ARI = {:.2f}".format(gmm_silhouette, gmm_ari))
print("DBSCAN: silhouette score = {:.2f}, ARI = {:.2f}".format(dbscan_silhouette, dbscan_ari))
print("MeanShift: silhouette score = {:.2f}, ARI = {:.2f}".format(ms_silhouette, ms_ari))

这里使用了轮廓系数和ARI作为评价指标，轮廓系数用于衡量聚类结果的紧密度和分离度，ARI用于衡量聚类结果与真实标签的一致性。你可以根据实际情况选择其他的评价指标。