├── GA_RBF_SVM.py ├── README.md └── rbf_data /GA_RBF_SVM.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Nov 29 13:03:50 2018 4 | 5 | @author: lj 6 | """ 7 | import numpy as np 8 | from sklearn import svm 9 | from sklearn import cross_validation 10 | import random 11 | import math 12 | import matplotlib.pyplot as plt 13 | 14 | def load_data(data_file): 15 | '''导入训练数据 16 | input: data_file(string):训练数据所在文件 17 | output: data(mat):训练样本的特征 18 | label(mat):训练样本的标签 19 | ''' 20 | data = [] 21 | label = [] 22 | f = open(data_file) 23 | for line in f.readlines(): 24 | lines = line.strip().split(' ') 25 | 26 | # 提取得出label 27 | label.append(float(lines[0])) 28 | # 提取出特征，并将其放入到矩阵中 29 | index = 0 30 | tmp = [] 31 | for i in range(1, len(lines)): 32 | li = lines[i].strip().split(":") 33 | if int(li[0]) - 1 == index: 34 | tmp.append(float(li[1])) 35 | else: 36 | while(int(li[0]) - 1 > index): 37 | tmp.append(0) 38 | index += 1 39 | tmp.append(float(li[1])) 40 | index += 1 41 | while len(tmp) < 13: 42 | tmp.append(0) 43 | data.append(tmp) 44 | f.close() 45 | return np.array(data), np.array(label).T 46 | 47 | ## 2. GA优化算法 48 | class GA(object): 49 | ###2.1 初始化 50 | def __init__(self,population_size,chromosome_num,chromosome_length,max_value,iter_num,pc,pm): 51 | '''初始化参数 52 | input:population_size(int):种群数 53 | chromosome_num(int):染色体数，对应需要寻优的参数个数 54 | chromosome_length:染色体的基因长度 55 | max_value(float):作用于二进制基因转化为染色体十进制数值 56 | iter_num(int):迭代次数 57 | pc(float):交叉概率阈值(0 0): 130 | tmp1 = fitness[l] 131 | else: 132 | tmp1 = 0.0 133 | fitness_value.append(tmp1) 134 | return fitness_value 135 | 136 | ###2.3 选择操作 137 | def sum_value(self,fitness_value): 138 | '''适应度求和 139 | input:self(object):定义的类参数 140 | fitness_value(list):每组染色体对应的适应度函数值 141 | output:total(float):适应度函数值之和 142 | ''' 143 | total = 0.0 144 | for i in range(len(fitness_value)): 145 | total += fitness_value[i] 146 | return total 147 | 148 | def cumsum(self,fitness1): 149 | '''计算适应度函数值累加列表 150 | input:self(object):定义的类参数 151 | fitness1(list):适应度函数值列表 152 | output:适应度函数值累加列表 153 | ''' 154 | ##计算适应度函数值累加列表 155 | for i in range(len(fitness1)-1,-1,-1): # range(start,stop,[step]) # 倒计数 156 | total = 0.0 157 | j=0 158 | while(j<=i): 159 | total += fitness1[j] 160 | j += 1 161 | fitness1[i] = total 162 | 163 | def selection(self,population,fitness_value): 164 | '''选择操作 165 | input:self(object):定义的类参数 166 | population(list):当前种群 167 | fitness_value(list):每一组染色体对应的适应度函数值 168 | ''' 169 | new_fitness = [] ## 用于存储适应度函归一化数值 170 | total_fitness = self.sum_value(fitness_value) ## 适应度函数值之和 171 | for i in range(len(fitness_value)): 172 | new_fitness.append(fitness_value[i] / total_fitness) 173 | 174 | self.cumsum(new_fitness) 175 | 176 | ms = [] ##用于存档随机数 177 | pop_len=len(population[0]) ##种群数 178 | 179 | for i in range(pop_len): 180 | ms.append(random.randint(0,1)) 181 | ms.sort() ## 随机数从小到大排列 182 | 183 | ##存储每个染色体的取值指针 184 | fitin = 0 185 | newin = 0 186 | 187 | new_population = population 188 | 189 | ## 轮盘赌方式选择染色体 190 | while newin < pop_len & fitin < pop_len: 191 | if(ms[newin] < new_fitness[fitin]): 192 | for j in range(len(population)): 193 | new_population[j][newin]=population[j][fitin] 194 | newin += 1 195 | else: 196 | fitin += 1 197 | 198 | population = new_population 199 | 200 | ### 2.4 交叉操作 201 | def crossover(self,population): 202 | '''交叉操作 203 | input:self(object):定义的类参数 204 | population(list):当前种群 205 | ''' 206 | pop_len = len(population[0]) 207 | 208 | for i in range(len(population)): 209 | for j in range(pop_len - 1): 210 | if (random.random() < self.pc): 211 | cpoint = random.randint(0,len(population[i][j])) ## 随机选择基因中的交叉点 212 | ###实现相邻的染色体基因取值的交叉 213 | tmp1 = [] 214 | tmp2 = [] 215 | #将tmp1作为暂存器，暂时存放第i个染色体第j个取值中的前0到cpoint个基因， 216 | #然后再把第i个染色体第j+1个取值中的后面的基因，补充到tem1后面 217 | tmp1.extend(population[i][j][0:cpoint]) 218 | tmp1.extend(population[i][j+1][cpoint:len(population[i][j])]) 219 | #将tmp2作为暂存器，暂时存放第i个染色体第j+1个取值中的前0到cpoint个基因， 220 | #然后再把第i个染色体第j个取值中的后面的基因，补充到tem2后面 221 | tmp2.extend(population[i][j+1][0:cpoint]) 222 | tmp2.extend(population[i][j][cpoint:len(population[i][j])]) 223 | #将交叉后的染色体取值放入新的种群中 224 | population[i][j] = tmp1 225 | population[i][j+1] = tmp2 226 | ### 2.5 变异操作 227 | def mutation(self,population): 228 | '''变异操作 229 | input:self(object):定义的类参数 230 | population(list):当前种群 231 | ''' 232 | pop_len = len(population[0]) #种群数 233 | Gene_len = len(population[0][0]) #基因长度 234 | for i in range(len(population)): 235 | for j in range(pop_len): 236 | if (random.random() < self.pm): 237 | mpoint = random.randint(0,Gene_len - 1) ##基因变异位点 238 | ##将第mpoint个基因点随机变异，变为0或者1 239 | if (population[i][j][mpoint] == 1): 240 | population[i][j][mpoint] = 0 241 | else: 242 | population[i][j][mpoint] = 1 243 | 244 | ### 2.6 找出当前种群中最好的适应度和对应的参数值 245 | def best(self,population_decimalism,fitness_value): 246 | '''找出最好的适应度和对应的参数值 247 | input:self(object):定义的类参数 248 | population(list):当前种群 249 | fitness_value:当前适应度函数值列表 250 | output:[bestparameters,bestfitness]:最优参数和最优适应度函数值 251 | ''' 252 | pop_len = len(population_decimalism[0]) 253 | bestparameters = [] ##用于存储当前种群最优适应度函数值对应的参数 254 | bestfitness = 0.0 ##用于存储当前种群最优适应度函数值 255 | 256 | for i in range(0,pop_len): 257 | tmp = [] 258 | if (fitness_value[i] > bestfitness): 259 | bestfitness = fitness_value[i] 260 | for j in range(len(population_decimalism)): 261 | tmp.append(abs(population_decimalism[j][i] * self.max_value / (math.pow(2,self.choromosome_length) - 10))) 262 | bestparameters = tmp 263 | 264 | return bestparameters,bestfitness 265 | 266 | ### 2.7 画出适应度函数值变化图 267 | def plot(self,results): 268 | '''画图 269 | ''' 270 | X = [] 271 | Y = [] 272 | for i in range(self.iter_num): 273 | X.append(i + 1) 274 | Y.append(results[i]) 275 | plt.plot(X,Y) 276 | plt.xlabel('Number of iteration',size = 15) 277 | plt.ylabel('Value of CV',size = 15) 278 | plt.title('GA_RBF_SVM parameter optimization') 279 | plt.show() 280 | 281 | ### 2.8 主函数 282 | def main(self): 283 | results = [] 284 | parameters = [] 285 | best_fitness = 0.0 286 | best_parameters = [] 287 | ## 初始化种群 288 | population = self.species_origin() 289 | ## 迭代参数寻优 290 | for i in range(self.iter_num): 291 | ##计算适应函数数值列表 292 | fitness_value = self.fitness(population) 293 | ## 计算当前种群每个染色体的10进制取值 294 | population_decimalism = self.translation(population) 295 | ## 寻找当前种群最好的参数值和最优适应度函数值 296 | current_parameters, current_fitness = self.best(population_decimalism,fitness_value) 297 | ## 与之前的最优适应度函数值比较，如果更优秀则替换最优适应度函数值和对应的参数 298 | if current_fitness > best_fitness: 299 | best_fitness = current_fitness 300 | best_parameters = current_parameters 301 | print('iteration is :',i,';Best parameters:',best_parameters,';Best fitness',best_fitness) 302 | results.append(best_fitness) 303 | parameters.append(best_parameters) 304 | 305 | ## 种群更新 306 | ## 选择 307 | self.selection(population,fitness_value) 308 | ## 交叉 309 | self.crossover(population) 310 | ## 变异 311 | self.mutation(population) 312 | results.sort() 313 | self.plot(results) 314 | print('Final parameters are :',parameters[-1]) 315 | 316 | if __name__ == '__main__': 317 | print('----------------1.Load Data-------------------') 318 | trainX,trainY = load_data('rbf_data') 319 | print('----------------2.Parameter Seting------------') 320 | population_size=200 321 | chromosome_num = 2 322 | max_value=10 323 | chromosome_length=20 324 | iter_num = 100 325 | pc=0.6 326 | pm=0.01 327 | print('----------------3.GA_RBF_SVM-----------------') 328 | ga = GA(population_size,chromosome_num,chromosome_length,max_value,iter_num,pc,pm) 329 | ga.main() 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 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 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