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()
