Python聚类分析手写版（拒sklearn和scipy、numpy）

for 系统聚类分析（python手写代码，matrix有疑问的请见前一条博客）

from matrix import copy from matrix import cal from matrix import matrix as m import xlrd import matplotlib.pyplot as plt import openpyxl import warnings def read_data(path): wk=xlrd.open_workbook(path) sh=wk.sheets()[0] row=sh.nrows
    col=sh.ncols
    mat=m.zero(row-1,col-1) for each in range(1,row): for i in range(1,col): mat[each-1][i-1]=sh.row_values(each)[i] namelist=sh.col_values(0,start_rowx=1) col_name=sh.row_values(0,start_colx=1) return mat,namelist,col_name def stand_trans(mat): row=len(mat) col=len(mat[0]) average=cal([sum([mat[each][i] for each in range(row)])/row for i in range(col)]) error=cal([(sum([(mat[each][i]-average[i])**2 for each in range(row)])/(row-1))**0.5 for i in range(col)]) mata=cal([[(mat[each][i]-average[i])/error[i] for i in range(col)] for each in range(row)]) return mata def ming_distance(mat,q=2): target=m.zero(len(mat),len(mat)) for each in range(len(mat)): for i in range(each): data1=mat[each] data2=mat[i] if q!='inf': add_distance=sum([abs(data1[y]-data2[y])**q for y in range(len(data1))])**(1/q) else: add_distance=max([abs(data1[x]-data2[x]) for x in range(len(data1))]) target[each][i]=add_distance
            target[i][each]=add_distance return target def ma_distance(mat): row=len(mat) col=len(mat[0]) if row<=col: raise TypeError('矩阵的指标个数必须小于观测量') average=cal([[sum([mat[each][i] for each in range(row)])/row for i in range(col)]]*row) mat1=mat-average
    coefficient=(mat1.t*mat1)/(row-1) target=coefficient.ni
    result=m.zero(row,row) for each in range(row): for i in range(each): x=cal([mat[each]]) y=cal([mat[i]]) final=((x-y)*target*(x-y).t)**0.5 result[each][i]=final
            result[i][each]=final return result def correlation(mat):#变量相关聚类 stand=stand_trans(mat) corr=(stand.t*stand)/(len(mat)-1) for each in range(len(corr)): for i in range(each+1,len(corr)): corr[each][i]=corr[i][each] corr[each][each]=1 return m.one(len(corr),len(corr))-corr def cos(mat):#变量夹角余弦聚类 target=m.zero(len(mat),len(mat)) mata=mat.t for each in range(len(mata)): for i in range(each): x=mata[each] y=mata[i] multiple=sum([x[j]*y[j] for j in range(len(x))]) add=(sum([x[j]**2 for j in range(len(x))])*sum([y[j]**2 for j in range(len(x))]))**0.5 target[each][i]=multiple/add
            target[i][each]=multiple/add return m.one(len(target),len(target))-target def distance(mat,method='euler',p=None): if method=='euler': return ming_distance(mat) elif method=='absolute': return ming_distance(mat,q=1) elif method=='ming': if not p: raise TypeError('p值未输入') else: return ming_distance(mat,q=p) elif method=='mahalanobis': return ma_distance(mat) elif method=='chebyshev': return ming_distance(mat,q='inf') elif method=='correlation': return correlation(mat) elif method=='cos': return cos(mat) else: raise TypeError('无效命令') def group_dist(group1,group2,method='average',dist_mat=None,base_mat=None): if method=='nearest': if not dist_mat: raise TypeError('未输入距离矩阵') dist=[dist_mat[each][i] for each in group1 for i in group2] return min(dist) elif method=='farthest': if not dist_mat: raise TypeError('未输入距离矩阵') dist=[dist_mat[each][i] for each in group1 for i in group2] return max(dist) elif method=='average': if not dist_mat: raise TypeError('未输入距离矩阵') dist=sum([dist_mat[each][i] for each in group1 for i in group2]) dist/=(len(group1)*len(group2)) return dist elif method=='centroid': if not base_mat: raise TypeError('原始矩阵未输入') else: matchrow1=[base_mat[each] for each in group1] matchrow2=[base_mat[each] for each in group2] row1=len(matchrow1) row2=len(matchrow2) col=len(matchrow1[0]) result1=[sum([matchrow1[each][i] for each in range(row1)])/row1 for i in range(col)] result2=[sum([matchrow2[each][i] for each in range(row2)])/row2 for i in range(col)] result=sum([(result2[each]-result1[each])**2 for each in range(col)])**0.5 return result elif method=='square':#数模书上的离差平方和ward法 if not base_mat: raise TypeError('原始矩阵未输入') else: matchrow1=[base_mat[each] for each in group1] matchrow2=[base_mat[each] for each in group2] matchrow=matchrow1+matchrow2
            row1=len(matchrow1) row2=len(matchrow2) col=len(matchrow1[0]) average1=cal([[sum([matchrow1[each][i] for each in range(row1)])/row1 for i in range(col)]]) average2=cal([[sum([matchrow2[each][i] for each in range(row2)])/row2 for i in range(col)]]) average=cal([[sum([matchrow[each][i] for each in range(row1+row2)])/(row1+row2) for i in range(col)]]) D1=sum([(cal([matchrow1[each]])-average1)*(cal([matchrow1[each]])-average1).t for each in range(row1)]) D2=sum([(cal([matchrow2[each]])-average2)*(cal([matchrow2[each]])-average2).t for each in range(row2)]) D12=sum([(cal([matchrow[each]])-average)*(cal([matchrow[each]])-average).t for each in range(row1+row2)]) return D12-D1-D2 elif method=='ward':#matlab的ward法 if not base_mat: raise TypeError('原始矩阵未输入') else: init=group_dist(group1,group2,method='centroid',dist_mat=dist_mat,base_mat=base_mat) len1=len(group1) len2=len(group2) return ((2*len1*len2)/(len1+len2))**0.5*init else: raise TypeError('无效命令') def linkage(base_mat1,simple_method='euler',group_method='average',ming_p=None): if (simple_method=='cos' or simple_method=='correlation') and (group_method=='centroid' or group_method=='ward'): warnings.warn('重心法或离差平方和法对于R型聚类不太适用,请谨慎使用') base_mat1=cal(base_mat1) dist_mat1=distance(base_mat1,method=simple_method,p=ming_p) row=len(dist_mat1) change_mat=copy.deepcopy(dist_mat1) accompany=list(range(row)) adddict={} result_mat=cal([]) accumulate={} accumulate[0]=[[each] for each in range(row)] margin={} i=1 while True: maxnum=max(accompany) if len(change_mat)==1: break else: accumulate[i]=copy.deepcopy(accumulate[i-1]) next1=maxnum+1 compare=change_mat[1][0] min_key=(1,0) for each in range(len(change_mat)): for j in range(each): if change_mat[each][j]<compare: min_key=(each,j) compare=change_mat[each][j] else: pass minvalue=compare
            real_key=accompany[min_key[0]],accompany[min_key[1]] result_mat.append([real_key[0],real_key[1],minvalue]) if real_key[0] not in adddict and real_key[1] not in adddict: adddict[maxnum+1]=[real_key[0],real_key[1]] accumulate[i].remove([real_key[0]]) accumulate[i].remove([real_key[1]]) accumulate[i].append([real_key[0],real_key[1]]) margin[real_key]=minvalue elif real_key[0] not in adddict: adddict[maxnum+1]=adddict[real_key[1]]+[real_key[0]] accumulate[i].remove(adddict[real_key[1]]) accumulate[i].remove([real_key[0]]) accumulate[i].append(adddict[real_key[1]]+[real_key[0]]) margin[(adddict[real_key[1]][-1],real_key[0])]=minvalue elif real_key[1] not in adddict: adddict[maxnum+1]=adddict[real_key[0]]+[real_key[1]] accumulate[i].remove(adddict[real_key[0]]) accumulate[i].remove([real_key[1]]) accumulate[i].append(adddict[real_key[0]]+[real_key[1]]) margin[(adddict[real_key[0]][-1],real_key[1])]=minvalue else: adddict[maxnum+1]=adddict[real_key[0]]+adddict[real_key[1]] accumulate[i].remove(adddict[real_key[0]]) accumulate[i].remove(adddict[real_key[1]]) accumulate[i].append(adddict[real_key[0]]+adddict[real_key[1]]) margin[(adddict[real_key[0]][-1],adddict[real_key[1]][0])]=minvalue
            accompany.pop(min_key[0]) accompany.pop(min_key[1]) accompany.append(maxnum+1) change_mat.pop(min_key[0]) change_mat.pop(min_key[1]) for each in change_mat: each.pop(min_key[0]) each.pop(min_key[1]) each.append(0) addlist=[] for each in accompany: if each not in adddict: addlist.append(group_dist([each],adddict[maxnum+1],method=group_method,dist_mat=dist_mat1,base_mat=base_mat1)) else: addlist.append(group_dist(adddict[each],adddict[maxnum+1],method=group_method,dist_mat=dist_mat1,base_mat=base_mat1)) change_mat.append(addlist) change_mat=m.symmetric(change_mat) i+=1 return result_mat,accumulate,adddict,margin def cluster(result_tuple,max_clust,namelist=None): result=result_tuple[1] extract=result[len(result)-max_clust] totalsort={} for each in range(len(extract)): if len(extract[each])==1: print('第%s类:'%(each+1),end='') if namelist: print(namelist[extract[each][0]]) totalsort[each+1]=namelist[extract[each][0]] else: print(extract[each][0]) totalsort[each+1]=str(extract[each][0]) else: print('第%s类:'%(each+1),end='') if namelist: name=[namelist[i] for i in extract[each]] totalstr='、'.join(name) print(totalstr) totalsort[each+1]=name else: transform=[str(i) for i in extract[each]] totalstr='、'.join(transform) print(totalstr) totalsort[each+1]=transform return totalsort def write_excel(path,extract,namelist1): wk=openpyxl.load_workbook(path) sh=wk.create_sheet('系统聚类结果') sh.cell(1,1).value='序列' sh.cell(1,2).value='分类结果' totalrow=2 for each in extract: if type(extract[each])!=list: sh.cell(totalrow,1).value=extract[each] sh.cell(totalrow,2).value=each
            totalrow+=1 else: for i in extract[each]: sh.cell(totalrow,1).value=i
                sh.cell(totalrow,2).value=each
                totalrow+=1 wk.save(path) def plot(result_tuple,namelist1): margin=result_tuple[3] sequence=list(result_tuple[2].values())[-1] y_distance=[margin[(sequence[each],sequence[each+1])] for each in range(len(sequence)-1)] name_seq=[namelist1[each] for each in sequence] real_seq=[] addition=[] for each in range(len(name_seq)): real_seq.append(name_seq[each]) if each!=len(name_seq)-1: real_seq.append(str(each)) addition.append(str(each)) real_y=[] for each in range(len(real_seq)): try: h=int(real_seq[each]) real_y.append(y_distance[int((each-1)/2)]) except: real_y.append(0) plt.rcParams['font.sans-serif']=['STSONG'] plt.barh(real_seq,real_y,height=2) ax=plt.gca() ax.set_yticks(name_seq) ax.set_yticks(addition,minor=True) plt.grid(True) plt.ylabel('聚类类别') plt.xlabel('聚类距离') plt.show() path=input('请输入路径:') datamat,namelist,col_name=read_data(path) stand=input('请选择是否标准化') choice_type=input('请选择是Q型还是R型聚类') if stand: datamat=stand_trans(datamat) if choice_type=='Q': link=linkage(datamat) max_clust=int(input('请输入最大聚类数:')) clust=cluster(link,max_clust,namelist=namelist) plot(link,namelist) judge=input('请输入是否写入excel:') if judge: write_excel(path,clust,namelist) else: pass else: link=linkage(datamat,simple_method='correlation') max_clust=int(input('请输入最大聚类数:')) clust=cluster(link,max_clust,namelist=col_name) plot(link,col_name) 

利用前期国家统计局爬得的31个省份250余个经济指标（人均）进行系统聚类（欧式距离，组间平均连接），结果如下

图是柱形图，跟树状图类似，每一条柱形的含义即在该距离下柱形底部的两个省份聚成一类，当然这两个“省份”并不是说一定是单独的两个省份，可能这两个省份之前已经和其他省份聚类而变成了两个“省份群”（具体情况可以通过画一条y=0的线，然后通过x轴平移得到不同聚类距离下的聚类情况），仍有不懂请百度“怎么看聚类图（或SPSS冰状图）“或在评论区回复

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