-
Notifications
You must be signed in to change notification settings - Fork 0
/
LSSVM.py
387 lines (269 loc) · 12 KB
/
LSSVM.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
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 19 10:49:48 2018
@author: Dynasting
LSSVM module for indefinate kernels
"""
import numpy as np
import numpy
from numpy import linalg as LA
import Kernel
'''
_LSSVMtrain function without CV
kernel_dict = {'type':'RBF', 'gamma' : 1}
(alpha,b,K)=_LSSVMtrain(X,Y,kernel_dict,regulator)
Y np.array
'''
def _LSSVMtrain(X,Y,kernel_dict,regulator):
m = Y.shape[0]
# Kernel
if kernel_dict['type'] == 'RBF':
K = Kernel.RBF(m,kernel_dict['gamma'])
K.calculate(X)
elif kernel_dict['type'] == 'LINEAR':
K = Kernel.LINEAR(m)
K.calculate(X)
elif kernel_dict['type'] == 'POLY':
K = Kernel.POLY(m,kernel_dict['c'],kernel_dict['d'])
K.calculate(X)
elif kernel_dict['type'] == 'TANH':
K = Kernel.TANH(m,kernel_dict['c'],kernel_dict['d'])
K.calculate(X)
elif kernel_dict['type'] == 'TL1':
K = Kernel.TL1(m,kernel_dict['rho'])
K.calculate(X)
H = np.multiply(np.dot(np.matrix(Y).T,np.matrix(Y)),K.kernelMat)
M_BR = H + np.eye(m) / regulator
#Concatenate
L_L = np.concatenate((np.matrix(0),np.matrix(Y).T),axis = 0)
L_R = np.concatenate((np.matrix(Y),M_BR),axis = 0)
L = np.concatenate((L_L,L_R),axis = 1)
R = np.ones(m+1)
R[0] = 0
#solve
b_a = LA.solve(L,R)
b = b_a[0]
alpha = b_a[1:]
#return
return (alpha,b,K)
def _LSSVMpredict(Xtest,K,alpha,b,Y):
K.expand(Xtest)
A = np.multiply(alpha,Y)
#f = b + np.dot(K.testMat,alpha)
f = b + np.dot(K.testMat,A)
#f = b + np.dot(K.testMat,np.multiply(alpha,Y))
Y_predict = f
Y_predict[Y_predict >= 0] = 1
Y_predict[Y_predict < 0] = -1
return Y_predict
def _compare(Ytest,Y_predict):
#in np.array
Error = (Ytest.flatten() - Y_predict.flatten()) / 2
es = LA.norm(Error,1)
acc = 1 - es / Ytest.shape[0]
return acc
# arg List
def LSSVM_CV(X,Y,kernel_type,GList,arg1=None,arg2 = None):
#RBF
if kernel_type == 'RBF':
A = [0] * 10
B = [0] * 10
indices = numpy.random.permutation(X.shape[0]) # shape[0]表示第0轴的长度，通常是训练数据的数量
rand_data_x = X[indices]
rand_data_y = Y[indices] # data_y就是标记（label）
l = int(len(indices) / 10)
for i in range(9):
A[i] = rand_data_x[i*l:i*l+l]
B[i] = rand_data_y[i*l:i*l+l]
A[9] = rand_data_x[9*l:]
B[9] = rand_data_y[9*l:]
best_G = None
best_gamma = None
acc = 0
acc_best = 0
for G in GList:
for gammaVal in arg1:
avg_acc = 0
for i in range(10):
X_test = A[i]
Y_test = B[i]
X_train = numpy.concatenate([A[(i+1)%10],A[(i+2)%10],A[(i+3)%10],A[(i+4)%10],A[(i+5)%10],A[(i+6)%10],A[(i+7)%10],A[(i+8)%10],A[(i+9)%10]], axis=0)
Y_train = numpy.concatenate([B[(i+1)%10],B[(i+2)%10],B[(i+3)%10],B[(i+4)%10],B[(i+5)%10],B[(i+6)%10],B[(i+7)%10],B[(i+8)%10],B[(i+9)%10]], axis=0)
kernel_dict = {'type':'RBF', 'gamma' : gammaVal}
(alpha,b,K)=_LSSVMtrain(X_train,Y_train,kernel_dict,G)
Y_predict = _LSSVMpredict(X_test,K,alpha,b,Y_train)
acc = _compare(Y_test,Y_predict)
avg_acc = avg_acc +acc/10
if avg_acc > acc_best:
acc_best = avg_acc
best_gamma = gammaVal
best_G =G
#最后一遍train
kernel_dict = {'type':'RBF', 'gamma' : best_gamma}
(alpha,b,K)=_LSSVMtrain(X,Y,kernel_dict,best_G)
return (alpha,b,K)
#Linear
if kernel_type == 'LINEAR':
A = [0] * 10
B = [0] * 10
indices = numpy.random.permutation(X.shape[0]) # shape[0]表示第0轴的长度，通常是训练数据的数量
rand_data_x = X[indices]
rand_data_y = Y[indices] # data_y就是标记（label）
l = int(len(indices) / 10)
for i in range(9):
A[i] = rand_data_x[i*l:i*l+l]
B[i] = rand_data_y[i*l:i*l+l]
A[9] = rand_data_x[9*l:]
B[9] = rand_data_y[9*l:]
best_G = None
acc = 0
acc_best = 0
for G in GList:
avg_acc = 0
for i in range(10):
X_test = A[i]
Y_test = B[i]
X_train = numpy.concatenate([A[(i+1)%10],A[(i+2)%10],A[(i+3)%10],A[(i+4)%10],A[(i+5)%10],A[(i+6)%10],A[(i+7)%10],A[(i+8)%10],A[(i+9)%10]], axis=0)
Y_train = numpy.concatenate([B[(i+1)%10],B[(i+2)%10],B[(i+3)%10],B[(i+4)%10],B[(i+5)%10],B[(i+6)%10],B[(i+7)%10],B[(i+8)%10],B[(i+9)%10]], axis=0)
kernel_dict = {'type':'LINEAR'}
(alpha,b,K)=_LSSVMtrain(X_train,Y_train,kernel_dict,G)
Y_predict = _LSSVMpredict(X_test,K,alpha,b,Y_train)
acc = _compare(Y_test,Y_predict)
avg_acc = avg_acc +acc/10
if avg_acc > acc_best:
acc_best = avg_acc
best_G =G
#最后一遍train
kernel_dict = {'type':'LINEAR'}
(alpha,b,K)=_LSSVMtrain(X,Y,kernel_dict,best_G)
return (alpha,b,K)
#POLY
if kernel_type == 'POLY':
A = [0] * 10
B = [0] * 10
indices = numpy.random.permutation(X.shape[0]) # shape[0]表示第0轴的长度，通常是训练数据的数量
rand_data_x = X[indices]
rand_data_y = Y[indices] # data_y就是标记（label）
l = int(len(indices) / 10)
for i in range(9):
A[i] = rand_data_x[i*l:i*l+l]
B[i] = rand_data_y[i*l:i*l+l]
A[9] = rand_data_x[9*l:]
B[9] = rand_data_y[9*l:]
best_G = None
best_c = None
best_d= None
acc = 0
acc_best = 0
for G in GList:
for c in arg1:
for d in arg2:
avg_acc = 0
for i in range(10):
X_test = A[i]
Y_test = B[i]
X_train = numpy.concatenate([A[(i+1)%10],A[(i+2)%10],A[(i+3)%10],A[(i+4)%10],A[(i+5)%10],A[(i+6)%10],A[(i+7)%10],A[(i+8)%10],A[(i+9)%10]], axis=0)
Y_train = numpy.concatenate([B[(i+1)%10],B[(i+2)%10],B[(i+3)%10],B[(i+4)%10],B[(i+5)%10],B[(i+6)%10],B[(i+7)%10],B[(i+8)%10],B[(i+9)%10]], axis=0)
kernel_dict = {'type':'POLY', 'c' : c, 'd' : d}
(alpha,b,K)=_LSSVMtrain(X_train,Y_train,kernel_dict,G)
Y_predict = _LSSVMpredict(X_test,K,alpha,b,Y_train)
acc = _compare(Y_test,Y_predict)
avg_acc = avg_acc +acc/10
if avg_acc > acc_best:
acc_best = avg_acc
best_c = c
best_d = d
best_G =G
#最后一遍train
kernel_dict = {'type':'POLY', 'c' : best_c, 'd' : best_d}
(alpha,b,K)=_LSSVMtrain(X,Y,kernel_dict,best_G)
return (alpha,b,K)
#TANH
if kernel_type == 'TANH':
A = [0] * 10
B = [0] * 10
indices = numpy.random.permutation(X.shape[0]) # shape[0]表示第0轴的长度，通常是训练数据的数量
rand_data_x = X[indices]
rand_data_y = Y[indices] # data_y就是标记（label）
l = int(len(indices) / 10)
for i in range(9):
A[i] = rand_data_x[i*l:i*l+l]
B[i] = rand_data_y[i*l:i*l+l]
A[9] = rand_data_x[9*l:]
B[9] = rand_data_y[9*l:]
best_G = None
best_c = None
best_d= None
acc = 0
acc_best = 0
for G in GList:
for c in arg1:
for d in arg2:
avg_acc = 0
for i in range(10):
X_test = A[i]
Y_test = B[i]
X_train = numpy.concatenate([A[(i+1)%10],A[(i+2)%10],A[(i+3)%10],A[(i+4)%10],A[(i+5)%10],A[(i+6)%10],A[(i+7)%10],A[(i+8)%10],A[(i+9)%10]], axis=0)
Y_train = numpy.concatenate([B[(i+1)%10],B[(i+2)%10],B[(i+3)%10],B[(i+4)%10],B[(i+5)%10],B[(i+6)%10],B[(i+7)%10],B[(i+8)%10],B[(i+9)%10]], axis=0)
kernel_dict = {'type':'TANH', 'c' : c, 'd' : d}
(alpha,b,K)=_LSSVMtrain(X_train,Y_train,kernel_dict,G)
Y_predict = _LSSVMpredict(X_test,K,alpha,b,Y_train)
acc = _compare(Y_test,Y_predict)
avg_acc = avg_acc +acc/10
if avg_acc > acc_best:
acc_best = avg_acc
best_c = c
best_d = d
best_G =G
#最后一遍train
kernel_dict = {'type':'TANH', 'c' : best_c, 'd' : best_d}
(alpha,b,K)=_LSSVMtrain(X,Y,kernel_dict,best_G)
return (alpha,b,K)
#TL1
if kernel_type == 'TL1':
A = [0] * 10
B = [0] * 10
indices = numpy.random.permutation(X.shape[0]) # shape[0]表示第0轴的长度，通常是训练数据的数量
rand_data_x = X[indices]
rand_data_y = Y[indices] # data_y就是标记（label）
l = int(len(indices) / 10)
for i in range(9):
A[i] = rand_data_x[i*l:i*l+l]
B[i] = rand_data_y[i*l:i*l+l]
A[9] = rand_data_x[9*l:]
B[9] = rand_data_y[9*l:]
best_G = None
best_rho = None
acc = 0
acc_best = 0
for G in GList:
for rho in arg1:
avg_acc = 0
for i in range(10):
X_test = A[i]
Y_test = B[i]
X_train = numpy.concatenate([A[(i+1)%10],A[(i+2)%10],A[(i+3)%10],A[(i+4)%10],A[(i+5)%10],A[(i+6)%10],A[(i+7)%10],A[(i+8)%10],A[(i+9)%10]], axis=0)
Y_train = numpy.concatenate([B[(i+1)%10],B[(i+2)%10],B[(i+3)%10],B[(i+4)%10],B[(i+5)%10],B[(i+6)%10],B[(i+7)%10],B[(i+8)%10],B[(i+9)%10]], axis=0)
kernel_dict = {'type':'TL1', 'rho' : rho}
(alpha,b,K)=_LSSVMtrain(X_train,Y_train,kernel_dict,G)
Y_predict = _LSSVMpredict(X_test,K,alpha,b,Y_train)
acc = _compare(Y_test,Y_predict)
avg_acc = avg_acc +acc/10
if avg_acc > acc_best:
acc_best = avg_acc
best_rho = rho
best_G =G
#最后一遍train
kernel_dict = {'type':'TL1', 'rho' : best_rho}
(alpha,b,K)=_LSSVMtrain(X,Y,kernel_dict,best_G)
return (alpha,b,K)
# Test Code for _LSSVMtrain
if __name__ == '__main__':
X = np.random.rand(5,8) - 0.5
Y = np.array([1,-1,-1,-1,-1])
kernel_dict = {'type':'RBF', 'gamma' : 1}
(alpha,b,K)=_LSSVMtrain(X,Y,kernel_dict,1)
Xtest = np.random.rand(3,8) - 0.5
Ytest = np.random.rand(3)
Y_predict = _LSSVMpredict(Xtest,K,alpha,b)
LSSVM_CV(X,Y,'RBF',[0.1,1,10],[0.1,1,10],arg2 = None)