def dist(a, b):
"""
输入：向量A, 向量B
输出：两个向量的欧式距离
"""
a_new = copy.deepcopy(a)
b_new = copy.deepcopy(b)
return LCS.get_lcs_distance(a_new, b_new)
def dist(a, b):
"""
输入：向量A, 向量B
输出：两个向量的欧式距离
"""
a_new = copy.deepcopy(a)
b_new = copy.deepcopy(b)
if operator.eq(a_new,b_new) == True:
return 0
return LCS.get_lcs_distance(a_new,b_new)
def CalculateSilhouetteCoefficient(result):
"""
计算聚类结果的轮廓系数
:param result: 聚类结果
:return: 轮廓系数
"""
S = 0
num_global = 0
if len(result) == 1:
return -1
for i in range(len(result)):
if len(result[i]) == 1:
S = S + 0
num_global = num_global + 1
else:
for j in range(len(result[i])):
a = 0
b_list = []
num_global = num_global + 1
for k in range(len(result[i])):
if j == k:
continue
a = a + LCS.get_lcs_distance(result[i][j],result[i][k])
a = a/(len(result[i])-1)
for i_tmp in range(len(result)):
if i == i_tmp:
continue
for j_tmp in range(len(result[i_tmp])):
b_list.append(LCS.get_lcs_distance(result[i][j],result[i_tmp][j_tmp]))
if len(b_list) == 0:
return 0
b = min(b_list)
if max(a,b) == 0:
break
S = S + (b - a)/max(a,b)
S = S/num_global
return S
