def sfcm(data,
cluster_n,
label,
m=2,
max_iter=100,
e=0.00001,
alpha=5,
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.stepsfcm(
data, U, cluster_n, m, F, alpha)
if printOn == 1:
print("SFCM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 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 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 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 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 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 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 fcm(data, cluster_n, m=2, max_iter=100, e=0.00001, printOn=1):
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