-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathfuzzy_firefly_fcm.py
298 lines (287 loc) · 10.2 KB
/
fuzzy_firefly_fcm.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
import sys
import copy
import math
import numpy as np
from PIL import Image
from collections import namedtuple
from operator import attrgetter
import random
import matplotlib.pyplot as plt
firefly = namedtuple("firefly", "error intensity")
def ffa(numfireflies):
numclusters=int(input("Enter no. of clusters: "))
maxepochs=50
ming=0.0
maxg=255
threshold=3
l=0.5
b0=0.0
g=1.0
a=0.2
s=np.zeros(threshold)
bst_gs=np.zeros((threshold,numclusters))
displayinterval=maxepochs/10
besterror=sys.maxsize
swarm=[]
gs=np.empty((0))
for i in range(numfireflies):
tmp = np.empty((0))
for j in range(numclusters):
g=(maxg-ming)*random.uniform(0,1)+ming
tmp=np.append(tmp,g)
swarm.append(firefly(0.0,0.0))
if i==0:
gs=tmp
else:
gs=np.vstack([gs,tmp])
err=ffa_clustering(gs,numclusters,numfireflies)
print(gs)
print(err)
for i in range(numfireflies):
swarm[i]=swarm[i]._replace(error=err[i])
swarm[i]=swarm[i]._replace(intensity=1/(swarm[i].error+1))
if swarm[i].error<besterror:
besterror=swarm[i].error
bestposition=np.copy(gs[i])
epoch=0
while epoch<maxepochs:
if epoch%displayinterval==0 and epoch<maxepochs:
print("epoch = ",epoch," error = ",besterror)
key1 = 0
key2 = 0
key3 = 0
for i in range(1, numfireflies):
key1 = swarm[i].error
key2 = swarm[i].intensity
key3 = np.copy(gs[i])
j = i - 1
while j >= 0 and swarm[j].error > key1:
swarm[j + 1] = swarm[j + 1]._replace(error=swarm[j].error)
swarm[j + 1] = swarm[j + 1]._replace(intensity=swarm[j].intensity)
gs[j + 1][:] = gs[j][:]
j = j - 1
swarm[j + 1] = swarm[j + 1]._replace(error=key1)
swarm[j + 1] = swarm[j + 1]._replace(intensity=key2)
gs[j + 1][:] = key3[:]
for i in range(numfireflies):
for j in range(1,numclusters):
key1=gs[i][j]
k=j-1
while k>=0 and gs[i][k]>key1:
gs[i][k+1]=gs[i][k]
k=k-1
gs[i][k+1]=key1
print(gs)
if swarm[0].error<besterror:
besterror=swarm[0].error
bestposition=gs[0]
b=swarm[threshold+1].intensity/l
for i in range(numfireflies):
key1 = swarm[i].error
key2 = swarm[i].intensity
key3 = np.copy(gs[i])
if i<threshold:
s[i]=1/((swarm[i].intensity-swarm[threshold+1].intensity)/b)
bst_gs[i]=gs[i]
else:
for j in range(threshold,numfireflies):
#r=np.sqrt(((gs[i]-gs[j])**2))
#beta=b0*math.exp(-g*np.sum(r)*np.sum(r))
beta=0.01
for k in range(numclusters):
gs[i][k]=gs[i][k]+(beta*(gs[j][k]-gs[i][k]))+(np.sum(s*(bst_gs[:,k]-gs[i][k])*beta))
gs[i][k]=gs[i][k]*(random.uniform(0,1)-0.5)
for k in range(numclusters):
if gs[i][k]<ming or gs[i][k]>maxg:
gs[i][k]=(maxg-ming)*random.uniform(0,1)+ming
err = ffa_clustering(gs,numclusters,numfireflies)
swarm[i] = swarm[i]._replace(error=err[i])
swarm[i] = swarm[i]._replace(intensity=1 / (swarm[i].error + 1))
if key1<swarm[i].error:
swarm[i] = swarm[i]._replace(error=key1)
swarm[i] = swarm[i]._replace(intensity=key2)
gs[i][:] = key3[:]
for i in range(threshold):
key1 = swarm[i].error
key2 = swarm[i].intensity
key3 = np.copy(gs[i])
for k in range(numclusters):
gs[i][k] = gs[i][k] + random.uniform(0, 1)
err = ffa_clustering(gs, numclusters, numfireflies)
swarm[i] = swarm[i]._replace(error=err[i])
swarm[i] = swarm[i]._replace(intensity=1 / (swarm[i].error + 1))
if key1 < swarm[i].error:
swarm[i] = swarm[i]._replace(error=key1)
swarm[i] = swarm[i]._replace(intensity=key2)
gs[i][:] = key3[:]
for i in range(numfireflies):
print(swarm[i].error)
epoch=epoch+1
print(epoch)
key1 = 0
key2 = 0
key3 = 0
for i in range(1, numfireflies):
key1 = swarm[i].error
key2 = swarm[i].intensity
key3 = gs[i]
j = i - 1
while j >= 0 and swarm[j].error > key1:
swarm[j + 1] = swarm[j + 1]._replace(error=swarm[j].error)
swarm[j + 1] = swarm[j + 1]._replace(intensity=swarm[j].intensity)
gs[j + 1] = gs[j]
j = j - 1
swarm[j + 1] = swarm[j + 1]._replace(error=key1)
swarm[j + 1] = swarm[j + 1]._replace(intensity=key2)
gs[j + 1] = key3
for i in range(numfireflies):
for j in range(1,numclusters):
key1 = gs[i][j]
k = j - 1
while k >= 0 and gs[i][k] > key1:
gs[i][k + 1] = gs[i][k]
k = k - 1
gs[i][k + 1] = key1
print(gs)
if swarm[0].error < besterror:
besterror = swarm[0].error
bestposition = gs[0]
clustering(bestposition,numclusters)
return
def clustering(centroids,num_centroids):
num_data=height*width
cond=0
e=0.01
loop=0
m=2
while cond==0:
loop=loop+1
v = np.zeros(num_centroids)
tmp=np.zeros((num_centroids,num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i]=tmp[i]+((distki/distkj)**(2/(m-1)))
tmp[i]=1/tmp[i]
for i in range(num_centroids):
num=np.sum((tmp[i]**m)*pixels)
den=np.sum(tmp[i]**m)
v[i]=num/den
if np.average(abs(centroids-v))<e or loop==150:
print("Final centroids are : ",v)
cond=cond+1
else:
print(v)
centroids=np.copy(v)
db(v, num_centroids)
d(v, num_centroids)
print(loop)
draw_img(v, num_centroids)
return
def db(centroids,num_centroids):
num_data = height * width
m=2
max_c=np.zeros(num_centroids)
tmp = np.zeros((num_centroids,num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i] = tmp[i] + ((distki / distkj) ** (2 / (m - 1)))
tmp[i] = 1 / tmp[i]
for i in range(num_centroids):
for j in range(num_centroids):
if i!=j:
dist=abs(centroids[i]-centroids[j])
i_spr = np.sum((tmp[i]**m)*((pixels-centroids[i])**2))/np.sum(tmp[i]**m)
j_spr = np.sum((tmp[j]**m)*((pixels-centroids[j])**2))/np.sum(tmp[j]**m)
final=(i_spr+j_spr)/dist
if final>max_c[i]:
max_c[i]=final
db_val=np.sum(max_c)/num_centroids
print("DB index value is : ",db_val)
return
def d(centroids,num_centroids):
num_data=height*width
m=2
final=0
tmp = np.zeros((num_centroids, num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i] = tmp[i] + ((distki / distkj) ** (2 / (m - 1)))
tmp[i] = 1 / tmp[i]
for i in range(num_centroids):
i_spr = np.sum((tmp[i] ** m) * ((pixels - centroids[i]) ** 2)) / np.sum(tmp[i] ** m)
if i_spr>final:
final=i_spr
i_spr=final
d_val=sys.maxsize
for i in range(num_centroids):
for j in range(num_centroids):
if i!=j:
dist=abs(centroids[i]-centroids[j])
final=dist/i_spr
if final<d_val:
d_val=final
print("Dunn index value is : ",d_val)
return
def draw_img(centroids,num_centroids):
num_data = height * width
m = 2
tmp = np.zeros((num_centroids, num_data))
for i in range(num_centroids):
distki = abs(pixels - centroids[i])
for j in range(num_centroids):
distkj = abs(pixels - centroids[j])
tmp[i] = tmp[i] + ((distki / distkj) ** (2 / (m - 1)))
tmp[i] = 1 / tmp[i]
img=np.zeros((num_centroids,height,width))
for k in range(num_centroids):
z=0
for i in range(height):
for j in range(width):
img[k][i][j]=tmp[k][z]
z=z+1
fig = plt.figure()
for i in range(num_centroids):
ax = fig.add_subplot(2,2,i+1)
ax.imshow(img[i])
plt.show()
return
def ffa_clustering(centroids,numclusters,num_fireflies):
num_data=height*width
m=2
mem=np.zeros((num_fireflies,numclusters,num_data))
for i in range(num_fireflies):
tmp1=np.zeros(num_data)
for k in range(numclusters):
distki=abs(pixels-centroids[i][k])
tmp2=np.zeros(num_data)
for j in range(numclusters):
distkj=abs(pixels-centroids[i][j])
tmp2=tmp2+((distki/distkj)**(2/(m-1)))
tmp2=1/tmp2
if k==0:
tmp1=tmp2
else:
tmp1=np.vstack([tmp1,tmp2])
mem[i]=tmp1
err=[]
for i in range(num_fireflies):
add=0
for j in range(numclusters):
dist=(pixels-centroids[i][j])**2
tmp=(mem[i][j]**m)*dist
add=add+np.sum(tmp)
err.append(add)
return err
im=Image.open("C:/Users/User/Desktop/brain.tif")
pixels = list(im.getdata())
width,height=im.size
#for i in range(len(pixels)):
# pixels[i]=int(round(sum(pixels[i]) / float(len(pixels[i]))))
pixels=np.array(pixels)
ffa(15)