已知数据点坐标和距离矩阵，请帮我用近邻传播算法进行聚类，然后将聚类结果可视化，请帮我生成具体的代码

这是一个基于Python的近邻传播算法聚类和可视化的示例代码：

# 导入需要的库
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# 生成数据并计算距离矩阵
X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
nbrs = NearestNeighbors(n_neighbors=10, algorithm='auto').fit(X)
distances, indices = nbrs.kneighbors(X)

# 运用近邻传播算法聚类
from sklearn.cluster import AffinityPropagation
# 自由参数，影响聚类效果
af = AffinityPropagation(preference=-50).fit(X)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_

n_clusters_ = len(cluster_centers_indices)

# 聚类结果可视化
colors = ['#377eb8', '#ff7f00', '#4daf4a', '#f781bf']
plt.figure(figsize=(12, 8))
for k, col in zip(range(n_clusters_), colors):
class_members = labels == k
cluster_center = X[cluster_centers_indices[k]]
plt.plot(X[class_members, 0], X[class_members, 1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=8)
plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14)
plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

# 结束