已知数据点坐标，以及数据点距离矩阵，请帮我生成一段近邻传播聚类代码,并用图片显示聚类结果

对于近邻传播聚类算法，我们需要先做以下准备工作：

1.导入必要的库和数据

import numpy as np
from sklearn.cluster import AffinityPropagation
import matplotlib.pyplot as plt

# 数据点坐标
data_points = np.array([
    [2.3, 3.2],
    [1.8, 2.3],
    [3.3, 4.4],
    [0.7, 1.2],
    [1.8, 1.7],
    [2.9, 3.8],
    [2.7, 2.7],
    [3.8, 3.3],
    [0.9, 1.8],
    [1.1, 1.1]
])

# 数据点距离矩阵
dist_mat = np.array([
    [0.0, 0.7, 0.8, 1.2, 1.1, 0.6, 0.5, 0.4, 1.4, 1.4],
    [0.7, 0.0, 1.3, 1.4, 1.4, 1.1, 0.9, 1.1, 1.3, 1.3],
    [0.8, 1.3, 0.0, 1.5, 1.4, 0.5, 1.1, 0.8, 1.7, 1.6],
    [1.2, 1.4, 1.5, 0.0, 0.3, 1.9, 1.9, 1.8, 0.5, 0.8],
    [1.1, 1.4, 1.4, 0.3, 0.0, 1.7, 1.7, 1.6, 0.7, 0.9],
    [0.6, 1.1, 0.5, 1.9, 1.7, 0.0, 0.8, 0.7, 2.1, 2.0],
    [0.5, 0.9, 1.1, 1.9, 1.7, 0.8, 0.0, 0.9, 1.7, 1.6],
    [0.4, 1.1, 0.8, 1.8, 1.6, 0.7, 0.9, 0.0, 1.8, 1.7],
    [1.4, 1.3, 1.7, 0.5, 0.7, 2.1, 1.7, 1.8, 0.0, 0.3],
    [1.4, 1.3, 1.6, 0.8, 0.9, 2.0, 1.6, 1.7, 0.3, 0.0]
])

2.使用AffinityPropagation算法进行聚类

af = AffinityPropagation(affinity='precomputed')
labels = af.fit_predict(dist_mat)

3.可视化聚类结果

cluster_centers_indices = af.cluster_centers_indices_
n_clusters_ = len(cluster_centers_indices)

colors = plt.cm.Spectral(np.linspace(0, 1, n_clusters_))
for k, col in zip(range(n_clusters_), colors):
    class_members = labels == k
    cluster_center = data_points[cluster_centers_indices[k]]
    plt.plot(data_points[class_members, 0], data_points[class_members, 1], 'w', markerfacecolor=col, marker='.')
    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markersize=10)

plt.title('Affinity Propagation Clustering')
plt.show()

完整代码如下：

import numpy as np
from sklearn.cluster import AffinityPropagation
import matplotlib.pyplot as plt

# 数据点坐标
data_points = np.array([
    [2.3, 3.2],
    [1.8, 2.3],
    [3.3, 4.4],
    [0.7, 1.2],
    [1.8, 1.7],
    [2.9, 3.8],
    [2.7, 2.7],
    [3.8, 3.3],
    [0.9, 1.8],
    [1.1, 1.1]
])

# 数据点距离矩阵
dist_mat = np.array([
    [0.0, 0.7, 0.8, 1.2, 1.1, 0.6, 0.5, 0.4, 1.4, 1.4],
    [0.7, 0.0, 1.3, 1.4, 1.4, 1.1, 0.9, 1.1, 1.3, 1.3],
    [0.8, 1.3, 0.0, 1.5, 1.4, 0.5, 1.1, 0.8, 1.7, 1.6],
    [1.2, 1.4, 1.5, 0.0, 0.3, 1.9, 1.9, 1.8, 0.5, 0.8],
    [1.1, 1.4, 1.4, 0.3, 0.0, 1.7, 1.7, 1.6, 0.7, 0.9],
    [0.6, 1.1, 0.5, 1.9, 1.7, 0.0, 0.8, 0.7, 2.1, 2.0],
    [0.5, 0.9, 1.1, 1.9, 1.7, 0.8, 0.0, 0.9, 1.7, 1.6],
    [0.4, 1.1, 0.8, 1.8, 1.6, 0.7, 0.9, 0.0, 1.8, 1.7],
    [1.4, 1.3, 1.7, 0.5, 0.7, 2.1, 1.7, 1.8, 0.0, 0.3],
    [1.4, 1.3, 1.6, 0.8, 0.9, 2.0, 1.6, 1.7, 0.3, 0.0]
])

# 使用AffinityPropagation算法进行聚类
af = AffinityPropagation(affinity='precomputed')
labels = af.fit_predict(dist_mat)

# 可视化聚类结果
cluster_centers_indices = af.cluster_centers_indices_
n_clusters_ = len(cluster_centers_indices)

colors = plt.cm.Spectral(np.linspace(0, 1, n_clusters_))
for k, col in zip(range(n_clusters_), colors):
    class_members = labels == k
    cluster_center = data_points[cluster_centers_indices[k]]
    plt.plot(data_points[class_members, 0], data_points[class_members, 1], 'w', markerfacecolor=col, marker='.')
    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markersize=10)

plt.title('Affinity Propagation Clustering')
plt.show()

运行以上代码后，会生成一个可视化的聚类结果，如下图所示：