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# -*- coding: utf-8 -*-
import numpy as np
from sklearn.cluster import KMeans #導(dǎo)入Kmeans算法包
from sklearn.metrics import silhouette_score #計(jì)算輪廓系數(shù)
import matplotlib.pyplot as plt #畫(huà)圖工具

plt.subplot(3,2,1)

x1=np.array([1,2,3,1,5,6,5,5,6,7,8,9,7,9])
x2=np.array([1,3,2,2,8,6,7,6,7,1,2,1,1,3])
x=np.array(zip(x1,x2)).reshape(len(x1),2)#原始數(shù)據(jù)

plt.xlim([0,10])
plt.ylim([0,10])
plt.title('Instances')
plt.scatter(x1,x2)
#在1號(hào)子圖做出原始數(shù)據(jù)點(diǎn)陣的分布

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))
    #繪制輪廓系數(shù)與不同類簇?cái)?shù)量的直觀顯示圖

plt.figure()

#繪制輪廓系數(shù)與不同類簇?cái)?shù)量的直觀顯示圖
plt.plot(clusters,sc_scores,'*-')
plt.xlabel('Numbers of clusters')
plt.ylabel('Silhouette Coefficient score')
plt.show()