输出结果

设计思路

核心代码

metrics.adjusted_rand_score(y_test, y_pred)

plt.xlim([0, 10])

plt.ylim([0, 10])

plt.title('Instances')

plt.scatter(x1, x2)

colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'b']

markers = ['o', 's', 'D', 'v', '^', 'p', '*', '+']

clusters = [2, 3, 4, 5, 8]

subplot_counter = 1

sc_scores = []

for t in clusters:

   subplot_counter += 1

   plt.subplot(3, 2, subplot_counter)

   kmeans_model = KMeans(n_clusters=t).fit(X)

   for i, l in enumerate(kmeans_model.labels_):

       plt.plot(x1[i], x2[i], color=colors[l], marker=markers[l], ls='None')

   plt.xlim([0, 10])

   plt.ylim([0, 10])

   sc_score = silhouette_score(X, kmeans_model.labels_, metric='euclidean')

   sc_scores.append(sc_score)

   plt.title('K = %s, silhouette coefficient= %0.03f' %(t, sc_score))

   

plt.figure()

plt.plot(clusters, sc_scores, '*-')

plt.xlabel('Number of Clusters')

plt.ylabel('Silhouette Coefficient Score')

plt.title('Handwritten digits Datasets：the relationship curve between Silhouette Coefficient and the number of different clusters')

plt.show()