if i=j:except the leading diagonal j+1 continue sk.append(t[i][j]-T[i]j]) av+=t(i](j]-T[i](j] j+=1 i+=1 i=0 av/=6#mean value whilei<6: d+=(sk[i]-av)*(sk[i]-av) +=1 d=math.sqrt(d/5)#variance d=abs(3*av/d) ifdk4.032: return 1 return 0 defans(r):determine whether"r"fit for the condition =0 k=[0,01,[0,01,[0,0] whilei<3: j=0 while j<3: cc=float(pow(cii,r)) if cc==0: T0]=0 j+=1 continue k[i][0]=1.0 k[j][o]=t[i]]*P[i]*A[iVec/k[i][o] k[i][1]=t[i]]*P[i]*A[jVec/k[j][o] k][1]=t]*P[门*Aycc/k[[1] t=0 while abs(k[tt-k[[(t+1)%2])/k[[t]D>0.05 Or abs(k][t]-k][(t+1)%2]/k][tt)>0.05: tt=(tt+1)%2 k[[(tt+1)%2]=t[[]*P*A]/cc/k][t] k][(t+1)%2]=t0]*P*A]/cc/k[[(t+1)%2] T[0]=P[*A]*cc/k[[t/k][t] j+=1 i+=1 iftjianyan()-0: return 0if i==j: # except the leading diagonal j+=1 continue sk.append(t[i][j]-T[i][j]) av+=t[i][j]-T[i][j] j+=1 i+=1 i=0 av/=6 # mean value whilei<6: d+=(sk[i]-av)*(sk[i]-av) i+=1 d=math.sqrt(d/5) # variance d=abs(3*av/d) if d<4.032: return 1 return 0 defans(r): # determine whether “r” fit for the condition i=0 k=[[0,0],[0,0],[0,0]] whilei<3: j=0 while j<3: cc=float(pow(c[i][j],r)) if cc==0: T[i][j]=0 j+=1 continue k[i][0]=1.0 k[j][0]=t[i][j]*P[i]*A[j]/cc/k[i][0] k[i][1]=t[i][j]*P[i]*A[j]/cc/k[j][0] k[j][1]=t[i][j]*P[i]*A[j]/cc/k[i][1] tt=0 while abs((k[i][tt]-k[i][(tt+1)%2])/k[i][tt])>0.05 or abs((k[j][tt]-k[j][(tt+1)%2])/k[j][tt])>0.05: tt=(tt+1)%2 k[i][(tt+1)%2]=t[i][j]*P[i]*A[j]/cc/k[j][tt] k[j][(tt+1)%2]=t[i][j]*P[i]*A[j]/cc/k[i][(tt+1)%2] T[i][j]=P[i]*A[j]*cc/k[i][tt]/k[j][tt] j+=1 i+=1 iftjianyan()==0: return 0