def stepfcm(data,U,cluster_n,m):
mf = U**m
center = ClusterAidedComputing.centercompute(data,mf)
dist = ClusterAidedComputing.distfcm(data,center)
U = ClusterAidedComputing.tmp( dist**(-2/(m-1)) )
obj_fcn = np.sum( (dist**2)*U**m )
return U,center,obj_fcn
def stepmec(data,U,cluster_n,gamma):
center = ClusterAidedComputing.centercompute(data,U)
dist = ClusterAidedComputing.distfcm(data,center)
dist_exp = ClusterAidedComputing.MatrixElementPower(dist,gamma) #矩阵逐个元素平方取负数再除以gamma
U = ClusterAidedComputing.tmp( dist_exp )
obj_fcn = np.sum( (dist**2)*U ) + gamma * np.sum( U * ClusterAidedComputing.MatrixElementLog(U) )
return U,center,obj_fcn
def stepesfcm(data,U,cluster_n,F,lamda):
center = ClusterAidedComputing.centercompute(data,U)
dist = ClusterAidedComputing.distfcm(data,center)
dist_exp = ClusterAidedComputing.MatrixElementPower(dist,lamda)
U = ClusterAidedComputing.tmp( dist_exp )
U = F+ U * (( np.ones((1,F.shape[1]))- np.sum(F,axis=0) ).T * np.ones((1,cluster_n))).T
obj_fcn = abs( np.sum( (dist**2)*U ) + 1/lamda * np.sum( (U-F)*ClusterAidedComputing.MatrixElementLog(U-F) ) )#相比论文，加了个绝对值
return U,center,obj_fcn
def stepsfcm(data,U,cluster_n,m,F,alpha):
mf = U**m
center = ClusterAidedComputing.centercompute(data,mf)
dist = ClusterAidedComputing.distfcm(data,center)
U_fcm = ClusterAidedComputing.tmp( dist**(-2/(m-1)) )
U_2 = (alpha/(alpha+1)) * U_fcm * np.matmul( np.ones((cluster_n,1)) ,np.sum(F,0).reshape(1,data.shape[0]))
U = U_fcm + (alpha/(1+alpha)) * F - U_2
obj_fcn = np.sum( (dist**2)*U**m ) + alpha * np.sum( (dist**2)*(U-F)**m )
return U,center,obj_fcn
def stepkfcm(data,U,cluster_n,sigma,m):
U = U**m
center = ClusterAidedComputing.centercompute(data,U)
dist = ClusterAidedComputing.distfcm(data,center)
dist_K = ClusterAidedComputing.GaussKernel(dist,sigma)
dist_exp = ClusterAidedComputing.MatrixElementPower((1-dist),-1/(m-1))
U = ClusterAidedComputing.tmp( dist_exp )
obj_fcn = np.sum( (2- 2*dist_K)*U**m )
return U,center,obj_fcn
def stepsmuc(data,U,cluster_n,F,lamda):
center = ClusterAidedComputing.centercompute(data,U)
dist = np.zeros(U.shape)
# 求马氏距离
# for i in range(cluster_n):
# for j in range(data.shape[0]):
# dist[i][j] = ClusterAidedComputing.mahalanobis(data[j,:],center[i,:])
dist = ClusterAidedComputing.distfcm(data,center)
dist_exp = ClusterAidedComputing.MatrixElementPower(dist,lamda)
U = ClusterAidedComputing.tmp( dist_exp )
U = F+ U * (( np.ones((1,F.shape[1]))- np.sum(F,axis=0) ).T * np.ones((1,cluster_n))).T
obj_fcn = abs( np.sum( (dist**2)*U ) + 1/lamda * np.sum( (U-F)*ClusterAidedComputing.MatrixElementLog(U-F) ) )#相比论文，加了个绝对值
return U,center,obj_fcn
def esfcm(data,cluster_n,label,max_iter = 1000,e = 0.00001,lamda=1,printOn = 1):
data = np.array(data)
obj_fcn = np.zeros(max_iter)
# 随机初始化聚类中心(并根据初始聚类中心生成初始隶属度矩阵)
U,center = ClusterAidedComputing.initcenter(data,cluster_n)
# 根据类别标签信息生产先验隶属度矩阵
F = ClusterAidedComputing.PriorMembership(label,U)
# 主循环
for i in range(max_iter):
U,center,obj_fcn[i] = ClusteringIteration.stepesfcm(data,U,cluster_n,F,lamda)
if printOn == 1:
print("eSFCM第",i,"次迭代的目标函数值为:",obj_fcn[i])
if i > 0:
if abs(obj_fcn[i] - obj_fcn[i-1]) < e :
break
return U,center,obj_fcn
def fcm(data,cluster_n,m = 2,max_iter = 1000,e = 0.00001,printOn = 1):
data = np.array(data)
obj_fcn = np.zeros(max_iter)
# 随机初始化聚类中心(并根据初始聚类中心生成初始隶属度矩阵)
U,center = ClusterAidedComputing.initcenter(data,cluster_n)
# 主循环
for i in range(max_iter):
U,center,obj_fcn[i] = ClusteringIteration.stepfcm(data,U,cluster_n,m)
if printOn == 1:
print("FCM第",i,"次迭代的目标函数值为:",obj_fcn[i])
if i > 0:
if abs(obj_fcn[i] - obj_fcn[i-1]) < e :
break
return U,center,obj_fcn