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sonnaMOHUC.py
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sonnaMOHUC.py
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import copy
import math
import random
import time
import numpy as np
import operator
from sklearn.model_selection import KFold
from matplotlib import pyplot as plt
from csv import reader
global MAX # 用于初始化隶属度矩阵U
MAX = 10000.0
global Epsilon # 结束条件
Epsilon = 0.0000001
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
continue
dataset.append(row)
return dataset
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
def str_column_to_int(dataset):
for i in range(len(dataset)):
if dataset[i][60]=='R':
dataset[i][60]=1
else:
dataset[i][60]=2
filename = 'sonar.all-data.csv'
dataset = load_csv(filename)
for i in range(0, len(dataset[0])-1):
str_column_to_float(dataset, i)
str_column_to_int(dataset)
dataset=np.array(dataset)
#利用random库中的函数将原始数据的顺序进行随机重排,在order中记录原始数据重排的顺序
def randomize_data(data):
order = list(range(0, len(data)))
random.shuffle(order)
new_data = [[] for i in range(0, len(data))]
for index in range(0, len(order)):
new_data[index] = data[order[index]]
return new_data, order
#利用上一个函数保存的顺序信息对数据进行还原
def de_randomise_data(data, order):
new_data = [[] for i in range(0, len(data))]
for index in range(len(order)):
new_data[order[index]] = data[index]
return new_data
def print_matrix(list):
for i in range(0, len(list)):
print(list[i])
#初始化U矩阵,使每个样本满足归一化条件
def initialize_U(data, cluster_number):
global MAX
U = []
for i in range(0, len(data)):
current = []
rand_sum = 0.0
for j in range(0, cluster_number):
dummy = random.randint(1, int(MAX))
current.append(dummy)
rand_sum += dummy
for j in range(0, cluster_number):
current[j] = current[j] / rand_sum
U.append(current)
return U
#计算特征空间中两个样本点的欧氏距离。
def distance(point, center):
if len(point) != len(center):
return -1
dummy = 0.0
for i in range(0, len(point)):
dummy += abs(point[i] - center[i]) ** 2
return math.sqrt(dummy)
#给定的最小阈值来判断算法是否停止。
#如果某次迭代中隶属度矩阵每个元素的前后变化都小于阈值的话,停止更新隶属度矩阵并退出算法
def end_conditon(U, U_old):
global Epsilon
for i in range(0, len(U)):
for j in range(0, len(U[0])):
if abs(U[i][j] - U_old[i][j]) > Epsilon:
return False
return True
#对于每个样本将隶属度最大的那个类设置为要把它分的类别,
#即把隶属度设置为1,把其他的隶属度设置为0
def normalise_U(U):
for i in range(0, len(U)):
maximum = max(U[i])
for j in range(0, len(U[0])):
if U[i][j] != maximum:
U[i][j] = 0
else:
U[i][j] = 1
return U
# m的最佳取值范围为[1.5,2.5]
def fuzzy(data, cluster_number, m):
# 初始化隶属度矩阵U
U = initialize_U(data, cluster_number)
print_matrix(U)
# 循环更新U
while (True):
# 创建它的副本,以检查结束条件
U_old = copy.deepcopy(U)
# 计算聚类中心
C = []
for j in range(0, cluster_number):
current_cluster_center = []
for i in range(0, len(data[0])):
dummy_sum_num = 0.0
dummy_sum_dum = 0.0
for k in range(0, len(data)):
# 分子
dummy_sum_num += (U[k][j] ** m) * data[k][i]
# 分母
dummy_sum_dum += (U[k][j] ** m)
# 第i列的聚类中心
current_cluster_center.append(dummy_sum_num / dummy_sum_dum)
# 第j簇的所有聚类中心
C.append(current_cluster_center)
# 创建一个距离向量, 用于计算U矩阵。
distance_matrix = []
for i in range(0, len(data)):
current = []
for j in range(0, cluster_number):
current.append(distance(data[i], C[j]))
distance_matrix.append(current)
# 更新U
for j in range(0, cluster_number):
for i in range(0, len(data)):
dummy = 0.0
for k in range(0, cluster_number):
dummy += (distance_matrix[i][j] / distance_matrix[i][k]) ** (2 / (m - 1))# 分母
U[i][j] = 1 / dummy
if end_conditon(U, U_old):
print("结束聚类")
break
U = normalise_U(U) #去模糊化 U
return U
def checker_sonar(final_location):
right=0.0
checker1=[0,0]
for i in range(0,97):
for j in range(0,len(final_location[0])):
if final_location[i][j]==1:
checker1[j]+=1
right+=max(checker1)
#print(checker1)
checker2=[0,0]
for i in range(0,111):
for j in range(0,len(final_location[0])):
if final_location[i + 97][j] == 1:
checker2[j] += 1 # checker分别统计每一类分类正确的个数
right+=max(checker2)
#print(checker2)
print('分类正确的个数是:', right)
answer = right / 208 * 100
c=answer
print("准确率:" + str(answer) + "%")
return c
if __name__ == '__main__':
# 加载数据
data = dataset
# 随机化数据
data, order = randomize_data(data)
# 调用模糊C均值函数
a=[]
for ii in np.arange(2,2.1,0.1):
final_location = fuzzy(data, 2, ii)
# 还原数据
final_location = de_randomise_data(final_location, order)
aa=checker_sonar(final_location)
a.append(aa)
#print_matrix(final_location)
# 准确度分析
print(a)
plt.figure(1)
plt.xlabel('b的取值')
plt.ylabel('准确率')
plt.title('b取值不同时聚类的准确率')
plt.plot(np.arange(2,2.1,0.1),a)
plt.show()