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fcm.py
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#coding=utf8
__author__ = 'song'
import math
class FCM_normal:
def __init__(self, data, m=2, e=0.001):
"""
data-分类数据,[(特征,附加信息),(),()]
c-分类数,
m-加权指数,
e-误差
"""
self.data = data
self.m = m
self.e = e
return
def distance(self, d1, d2):
return float(abs(d1-d2))
def _U(self):
"""
计算划分矩阵
"""
self.u = {}
for i in range(2):
for j in range(len(self.data)):
self.u[i, j] = 0
for k in range(2):
tmp = self.distance(self.c[i], self.data[j][0])/self.distance(self.c[k], self.data[j][0])
self.u[i, j] += pow(tmp, (2.0/(self.m-1)))
pass
self.u[i, j] = 1.0 / self.u[i, j]
pass
pass
pass
def _C(self):
"""
计算分类中心
"""
for i in range(2):
tmp1 = 0
tmp2 = 0
for j in range(len(self.data)):
tmp1 += pow(self.u[i, j], self.m)*self.data[j][0]
pass
for j in range(len(self.data)):
tmp2 += pow(self.u[i, j], self.m)
pass
self.c[i] = float(tmp1)/float(tmp2)
pass
return
def start(self):
"""
初始化聚类中心,
"""
self.c = [0.5, 255.5]
self._U()
return
def J(self):
"""
"""
res = 0
for i in range(2):
for j in range(len(self.data)):
res += float(pow(self.u[i, j], self.m))*float(pow(self.distance(self.c[i], self.data[j][0]), 2))
pass
pass
return res
def Run(self):
"""
标准fcm算法
"""
self.counter = 1 #计数器
self.start()
while True:
print "c:", self.c
self.pre_J = self.J()
self._U()
self._C()
tmp_J = self.J()
if abs(tmp_J-self.pre_J) < self.e:
break
self.pre_J = tmp_J
self.counter += 1
return
#----------------------------------------------------------
class FCM_fast:
def __init__(self, data, m=2, e=0.001):
"""
data-分类数据,[(特征1,特征H(l),附加信息),(),()]
c-分类数,
m-加权指数,
e-误差
"""
self.data = data
self.m = m
self.e = e
return
def distance(self, d1, d2):
return float(abs(d1-d2))
def _U(self):
"""
计算划分矩阵
"""
self.u = {}
for i in range(2):
for j in range(len(self.data)):
self.u[i, j] = 0
dij = self.distance(self.c[i], self.data[j][0])
if dij == 0:
self.u[i, j] = 1
continue
for k in range(2):
tmp = dij/self.distance(self.c[k], self.data[j][0])
self.u[i, j] += pow(tmp, (2.0/(self.m-1)))
pass
self.u[i, j] = 1.0 / self.u[i, j]
pass
pass
pass
def _C(self):
"""
计算分类中心
"""
for i in range(2):
tmp1 = 0
tmp2 = 0
for j in range(len(self.data)):
tmp1 += pow(self.u[i, j], self.m)*self.data[j][0]*self.data[j][1]
pass
for j in range(len(self.data)):
tmp2 += pow(self.u[i, j], self.m)*self.data[j][1]
pass
self.c[i] = float(tmp1)/float(tmp2)
pass
return
def start(self):
"""
初始化聚类中心,
"""
self.c = [0.5, 255.5]
self._U()
return
def J(self):
"""
"""
res = 0
for i in range(2):
for j in range(len(self.data)):
tmp = float(pow(self.u[i, j], self.m))
tmp *= float(pow(self.distance(self.c[i], self.data[j][0]), 2))
tmp *= self.data[j][1]
res += tmp
pass
pass
return res
def Run(self):
"""
标准fcm算法
"""
self.counter = 1 #计数器
self.start()
while True:
print "c:", self.c
self.pre_J = self.J()
self._U()
self._C()
tmp_J = self.J()
if abs(tmp_J-self.pre_J) < self.e:
break
self.pre_J = tmp_J
self.counter += 1
return
#----------------------------------------------------------
class FCM_image:
def __init__(self, img, m=2, e=0.001, NB=9):
"""
data-分类数据,[(特征1,特征H(l),附加信息),(),()]
c-分类数,
m-加权指数,
e-误差
NB-滑动窗口
M,N 图像尺寸
"""
self.img =img
self.pix = img.load()
self.m = m
self.e = e
self.NB = NB
self.W, self.H = img.size
return
def similarity(self, x1, x2):
"""
x_ik,x_ij 灰度值
"""
return 1.0/(1.0+abs(x1-x2))
def get_window_pix(self, x, y):
"""
获取第k个数据的窗口象素
"""
r = (self.NB-1)/2 #半径
#滑动窗口包含的像素点
window_pix_list = []
for i in range(self.NB):
for j in range(self.NB):
_x, _y = x-r+i, y-r+j
try:
tmp = self.pix[_x, _y]
window_pix_list.append((_x, _y))
except Exception,e:
#print "window:",(x, y),(_x, _y),e,self.img.size,r,i,j
continue
window_pix_list.remove((x, y))
return window_pix_list
def uk(self, x, y):
"""
计算滑动窗口中心象素点x,y和其余个点平均相似度
"""
res = 0
#滑动窗口包含的像素点
window_pix_list = self.get_window_pix(x, y)
for _x, _y in window_pix_list:
res += self.similarity(self.pix[x, y], self.pix[_x, _y])
try:
res /= len(window_pix_list)
except:
print x, y
exit()
return res
def Uik_(self, i, x, y):
"""
第i类
第k个数据
"""
return self.uik[i, x, y]*self.Uk[x, y]
def hik(self, i, x, y):
"""
空间邻域函数
k = x, y
"""
res = 0
r = (self.NB-1)/2 #半径
#pos_start = x-r, y-r
#滑动窗口包含的像素点
window_pix_list = self.get_window_pix(x, y)
for x, y in window_pix_list:
res += self.uik_[i, x, y]
return res
def distance(self, d1, d2):
return float(abs(d1-d2))
def _U_fast(self):
"""
计算划分矩阵
"""
self.ul = {}
for i in range(2):
for j in range(len(self.data)):
self.ul[i, j] = 0
for k in range(2):
tmp = self.distance(self.c[i], self.data[j][0])/self.distance(self.c[k], self.data[j][0])
self.ul[i, j] += pow(tmp, (2.0/(self.m-1)))
pass
self.ul[i, j] = 1.0 / self.ul[i, j]
pass
pass
pass
def _U(self):
"""
计算划分矩阵
"""
self._U_fast()
self.uik = {}
for i in range(len(self.data)):
for x, y in self.gray_hist_pix[i]:
self.uik[0, x, y] = self.ul[0, i]
self.uik[1, x, y] = self.ul[1, i]
self.uik_ = {}
for i in range(2):
for x in range(self.W):
for y in range(self.H):
self.uik_[i, x, y] = self.Uik_(i, x, y)
self.Hik = {}
for i in range(2):
for x in range(self.W):
for y in range(self.H):
self.Hik[i, x, y] = self.hik(i, x, y)
self.u = {}
for i in range(2):
print "_U", i
for x in range(self.W):
for y in range(self.H):
self.u[i, x, y] = self.uik_[i, x, y]*self.Hik[i, x, y]
self.u[i, x, y] = self.uik_[i, x, y]
tmp = 0
for j in range(2):
tmp += self.uik_[j, x, y]*self.Hik[j, x, y]
#tmp += self.uik_[j, x, y]
#print "tmp:", tmp, (x, y)
pass
self.u[i, x, y] = self.u[i, x, y]/tmp
def _C(self):
"""
计算分类中心
"""
for i in range(2):
tmp1 = 0
tmp2 = 0
for x in range(self.W):
for y in range(self.H):
tmp = pow(self.u[i, x, y], self.m)
tmp1 += tmp*self.pix[x, y]
tmp2 += tmp
self.c[i] = float(tmp1)/float(tmp2)
return
def sim(self):
"""
计算像素窗口相似度
"""
print "计算像素的邻域相似度"
self.Uk = {}
for x in range(self.W):
for y in range(self.H):
self.Uk[x, y] = self.uk(x, y)
def start(self):
"""
初始化聚类中心,使用快速聚类
"""
gray_hist = [0]*256
self.gray_hist_pix = [[] for i in range(256)]
for x in range(self.W):
for y in range(self.H):
gray_hist[self.pix[x, y]] += 1
self.gray_hist_pix[self.pix[x, y]].append((x, y))
self.data = []
for l in range(len(gray_hist)):
self.data.append((l, gray_hist[l]))
ff = FCM_fast(self.data)
ff.Run()
self.c = ff.c
self.sim()
self._U()
return
def J(self):
"""
"""
res = 0
for i in range(2):
for x in range(self.W):
for y in range(self.H):
tmp = float(pow(self.u[i, x, y], self.m))
tmp *= float(pow(self.distance(self.c[i], self.pix[x, y]), 2))
res += tmp
return res
def Run(self):
"""
标准fcm算法
"""
self.counter = 1 #计数器
self.start()
while True:
print "c:", self.c
self.pre_J = self.J()
self._U()
self._C()
tmp_J = self.J()
if abs(tmp_J-self.pre_J) < self.e:
break
self.pre_J = tmp_J
self.counter += 1
return