聚类算法 sklearn k_means (返回一维数据的最优聚类)

from sklearn.cluster import KMeans
import numpy
import collections
import pandas
from sklearn import metrics

def k_means(pp1,clus):
    
    
    pv=list(pp1)
    if len(set(pv))>clus:
        gf=numpy.array([pv]).T
        estimator = KMeans(n_clusters=clus)#构造聚类器
        
        estimator.fit(gf)#聚类
        label_pred = estimator.labels_ #获取聚类标签
        
        #print(label_pred)
        aa=collections.Counter(label_pred)
        
        print('aa=',aa)
        v=pandas.Series(aa)
        gg=list(v)
        index_max=gg.index(max(gg))
        
        print('index_max=',index_max)
        
        centroids = estimator.cluster_centers_ #获取聚类中心
        
        print('centroids=',centroids)
        #inertia = estimator.inertia_ # 获取聚类准则的总和
        center=centroids[index_max][0]
        return ((center))
    else:
        return (pp1.mean())
    
    
def k_means_label(a):
    
    
    def km_index(k):
  
        pv=list(a)
        
        gf=numpy.array([pv]).T
            
        #from sklearn.cluster import KMeans
        y_pred = KMeans(n_clusters=k, random_state=9).fit_predict(gf)
        
        index=metrics.silhouette_score(gf, y_pred, metric='euclidean')
        
        print('index',index)
        
        return index
    cs=list(range(2,6))
    
    df=list(map(km_index,cs))
    
    df1=pandas.Series(df,index=cs)
    df2=df1.sort_values(ascending=False)
    
    df3=list(df2.index)[0]
    
    return df3


a=numpy.random.randint(0,1000,10)

cc=k_means_label(a)

b=k_means(a,cc)

print('b=',b)

index 0.804055967401
index 0.805649685362
index 0.65899543985
index 0.517110170591
aa= Counter({0: 5, 1: 3, 2: 2})
index_max= 0
centroids= [[ 160.8]
 [ 610. ]
 [ 824.5]]
b= 160.8