我使用亲和传播算法获得了以下sklearn聚类结果。
import sklearn.clusterimport numpy as npsims = np.array([[0, 17, 10, 32, 32], [18, 0, 6, 20, 15], [10, 8, 0, 20, 21], [30, 16, 20, 0, 17], [30, 15, 21, 17, 0]])affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)affprop.fit(sims)cluster_centers_indices = affprop.cluster_centers_indices_labels = affprop.labels_#number of clustersn_clusters_ = len(cluster_centers_indices)现在我想绘制这些聚类结果。我是sklearn的新手。请建议我一个适合在Python中绘制聚类的方法。是否可以使用pandas数据框来完成这个任务？
编辑：
我直接使用了@MohammedKashif指出的sklearn中的代码，如下所示：
import sklearn.clusterimport numpy as npsims = np.array([[0, 17, 10, 32, 32], [18, 0, 6, 20, 15], [10, 8, 0, 20, 21], [30, 16, 20, 0, 17], [30, 15, 21, 17, 0]])affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)affprop.fit(sims)cluster_centers_indices = affprop.cluster_centers_indices_print(cluster_centers_indices)labels = affprop.labels_n_clusters_ = len(cluster_centers_indices)print(n_clusters_)import matplotlib.pyplot as pltfrom itertools import cycleplt.close('all')plt.figure(1)plt.clf()colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')for k, col in zip(range(n_clusters_), colors): class_members = labels == k cluster_center = sims[cluster_centers_indices[k]] plt.plot(sims[class_members, 0], sims[class_members, 1], col + '.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) for x in sims[class_members]: plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)plt.title('Estimated number of clusters: %d' % n_clusters_)plt.show()然而，我得到的输出有点奇怪，如下所示（第二个聚类点（绿色）位于蓝色线上。因此，我认为它不应该被单独聚类，应该也属于蓝色聚类）。请告诉我代码中是否有任何错误。
编辑2
如σηγ所指出的，我添加了以下代码：
se = SpectralEmbedding(n_components=2, affinity='precomputed')X = se.fit_transform(sims)print(X)然而，对于数组np.array([[0, 17, 10, 32, 32], [0, 17, 10, 32, 32], [0, 17, 10, 32, 33], [0, 17, 10, 32, 32], [0, 17, 10, 32, 32]])，它给我显示了3个点，如下图所示。这让我感到困惑，因为这5个数组代表一个点。
请帮助我。
回答：
根据前面的例子，我会尝试如下方法：
import sklearn.clusterfrom sklearn.manifold import SpectralEmbeddingimport numpy as npimport matplotlib.pyplot as pltfrom itertools import cyclesims = np.array([[0, 17, 10, 32, 32], [18, 0, 6, 20, 15], [10, 8, 0, 20, 21], [30, 16, 20, 0, 17], [30, 15, 21, 17, 0]])affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)affprop.fit(sims)cluster_centers_indices = affprop.cluster_centers_indices_print(cluster_centers_indices)labels = affprop.labels_n_clusters_ = len(cluster_centers_indices)print(n_clusters_)se = SpectralEmbedding(n_components=2, affinity='precomputed')X = se.fit_transform(sims)plt.close('all')plt.figure(1)plt.clf()colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')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], col + '.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) for x in X[class_members]: plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)plt.title('Estimated number of clusters: %d' % n_clusters_)plt.show() 
