FCM模糊c均值聚类

参考学习：https://blog.csdn.net/zwqhehe/article/details/75174918
https://www.cnblogs.com/sddai/p/6259553.html
https://blog.csdn.net/lyxleft/article/details/88964494
相关代码：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 27 10:51:45 2019
@author: youxinlin
"""
import copy
import math
import random
import time
 
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
 
def import_data_format_iris(file):
    """
    file这里是输入文件的路径，如iris.txt.
    格式化数据，前四列为data，最后一列为类标号（有0，1，2三类）
    如果是你自己的data，就不需要执行此段函数了。
    """
    data = []
    cluster_location =[]  
    with open(str(file), 'r') as f:
        for line in f:
            current = line.strip().split(",")  #对每一行以逗号为分割，返回一个list
            current_dummy = []
            for j in range(0, len(current)-1):
                current_dummy.append(float(current[j]))  #current_dummy存放data
 
    #下面注这段话提供了一个范例：若类标号不是0，1，2之类数字时该怎么给数据集
            j += 1 
            if  current[j] == "Iris-setosa
":
                cluster_location.append(0)
            elif current[j] == "Iris-versicolor
":
                cluster_location.append(1)
            else:
                cluster_location.append(2)
            data.append(current_dummy)
    print("加载数据完毕")
    return data
#	return data , cluster_location
 
def randomize_data(data):
	"""
	该功能将数据随机化，并保持随机化顺序的记录
	"""
	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
 
def de_randomise_data(data, order):
	"""
	此函数将返回数据的原始顺序，将randomise_data()返回的order列表作为参数
	"""
	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):
	""" 
	以可重复的方式打印矩阵
	"""
	for i in range(0, len(list)):
		print (list[i])
 
def initialize_U(data, cluster_number):
	"""
	这个函数是隶属度矩阵U的每行加起来都为1. 此处需要一个全局变量MAX.
	"""
	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
 
def distance(point, center):
	"""
	该函数计算2点之间的距离（作为列表）。我们指欧几里德距离。闵可夫斯基距离
	"""
	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)
 
def end_conditon(U, U_old):
    """
	结束条件。当U矩阵随着连续迭代停止变化时，触发结束
	"""
    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
 
def normalise_U(U):
	"""
	在聚类结束时使U模糊化。每个样本的隶属度最大的为1，其余为0
	"""
	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):
	"""
	这是主函数，它将计算所需的聚类中心，并返回最终的归一化隶属矩阵U.
    参数是：簇数(cluster_number)和隶属度的因子(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):
    				# 分母
					dummy += (distance_matrix[i][j ] / distance_matrix[i][k]) ** (2/(m-1))
				U[i][j] = 1 / dummy
 
		if end_conditon(U, U_old):
			print ("结束聚类")
			break
	print ("标准化 U")
	U = normalise_U(U)
	return U
 
def checker_iris(final_location):
    """
    和真实的聚类结果进行校验比对
    """
    right = 0.0
    for k in range(0, 3):
        checker =[0,0,0]
        for i in range(0, 50):
            for j in range(0, len(final_location[0])):
                if final_location[i + (50*k)][j] == 1:  #i+(50*k)表示 j表示第j类
                    checker[j] += 1  #checker分别统计每一类分类正确的个数    
        right += max(checker) #累加分类正确的个数
    print ('分类正确的个数是:',right)
    answer =  right / 150 * 100
    return "准确率：" + str(answer) +  "%"
 
if __name__ == '__main__':
	
	# 加载数据
	data = import_data_format_iris("iris.txt")
	# print_matrix(data)
 
	# 随机化数据
	data , order = randomize_data(data)
	# print_matrix(data)
 
	start = time.time()
	# 现在我们有一个名为data的列表，它只是数字
	# 我们还有另一个名为cluster_location的列表，它给出了正确的聚类结果位置
	# 调用模糊C均值函数
	final_location = fuzzy(data , 3 , 2)
 
	# 还原数据
	final_location = de_randomise_data(final_location, order)
#	print_matrix(final_location)
 
	# 准确度分析
	print (checker_iris(final_location))
	print ("用时：{0}".format(time.time() - start))