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,并用图片清晰的显示聚类结果，每一个聚类用一个大的圆包围起来

以下是一个Python实现的近邻传播聚类代码。使用了sklearn库中的NearestNeighbors进行近邻计算，使用matplotlib库进行结果可视化：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors
from itertools import cycle

# 定义参数
X = np.array([(2,4), (2,3), (3,4), (3,3), (4,3), (4,2), (5,2), (5,1), (6,1), (7,1)])
D = np.array([
    [0, 1, 1, 2, 2, 3, 3, 4, 5, 6],
    [1, 0, 2, 1, 3, 2, 4, 3, 4, 5],
    [1, 2, 0, 1, 1, 2, 3, 3, 4, 5],
    [2, 1, 1, 0, 2, 1, 2, 4, 3, 4],
    [2, 3, 1, 2, 0, 1, 1, 3, 2, 3],
    [3, 2, 2, 1, 1, 0, 1, 2, 1, 2],
    [3, 4, 3, 2, 1, 1, 0, 1, 1, 2],
    [4, 3, 3, 4, 3, 2, 1, 0, 1, 1],
    [5, 4, 4, 3, 2, 1, 1, 1, 0, 1],
    [6, 5, 5, 4, 3, 2, 2, 1, 1, 0]
])
alpha = 0.9
eps = 1e-6

# 近邻传播聚类
nbrs = NearestNeighbors(n_neighbors=len(X)-1, algorithm='ball_tree').fit(X)
distances, indices = nbrs.kneighbors(X)
A = np.zeros((len(X), len(X)))
for i in range(len(X)):
    for j in range(len(X)):
        if j in indices[i]:
            A[i][j] = D[i][j]
        else:
            A[i][j] = np.inf
for i in range(len(X)):
    A[i][i] = 0
S = np.zeros((len(X), len(X)))
R = np.zeros(len(X))
while True:
    R_old = np.copy(R)
    for i in range(len(X)):
        A_i = np.maximum(A[i] - A[i][indices[i]][-1], 0)
        R[i] = alpha * np.sum(S[i][indices[i]] * A_i) + (1 - alpha) * R_old[i]
        for j in indices[i]:
            S[i][j] = (1 - eps) * S[i][j] + eps * (R[i] + R[j] - 2 * A[i][j])
    diff = R - R_old
    if np.all(np.abs(diff) < 1e-5):
        break
clusters = {}
for i in range(len(X)):
    argmax = np.argmax(S[i])
    if argmax not in clusters:
        clusters[argmax] = [i]
    else:
        clusters[argmax].append(i)

# 结果可视化
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for cluster, color in zip(clusters.values(), colors):
    cluster_points = [X[i] for i in cluster]
    circle = plt.Circle((np.mean([p[0] for p in cluster_points]), np.mean([p[1] for p in cluster_points])), 
                        max([np.linalg.norm(p-np.mean(cluster_points)) for p in cluster_points]), 
                        color=color, fill=False)
    plt.gca().add_artist(circle)
    plt.scatter([p[0] for p in cluster_points], [p[1] for p in cluster_points], color=color)
plt.xlim(np.min(X[:,0])-1, np.max(X[:,0])+1)
plt.ylim(np.min(X[:,1])-1, np.max(X[:,1])+1)
plt.show()

在上面的代码中，首先定义了数据点的坐标矩阵X和距离矩阵D，然后使用NearestNeighbors计算所有点之间的距离和近邻关系。接着根据公式计算相似度矩阵S和传播矩阵R。最后，将所有点按照相似度矩阵的结果进行聚类（相似度最高的点属于同一类），并使用matplotlib库进行结果可视化。

在结果可视化的部分，我们使用了matplotlib库中的scatter和Circle函数。scatter函数用来可视化散点图，Circle函数用来画圆。对于每一个聚类，我们首先计算出其中所有点的平均坐标和最远点与平均坐标的距离，然后使用Circle函数画出圆来表示聚类范围。最后使用scatter函数将所有点可视化出来，不同的聚类用不同的颜色表示。最终的结果如下所示：