├── PSO_RBF_SVM.py ├── README.md └── rbf_data /PSO_RBF_SVM.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sat Dec 1 17:00:23 2018 4 | 5 | @author: lj 6 | """ 7 | 8 | import numpy as np 9 | from sklearn import svm 10 | from sklearn import cross_validation 11 | import random 12 | import matplotlib.pyplot as plt 13 | 14 | ## 1.加载数据 15 | def load_data(data_file): 16 | '''导入训练数据 17 | input: data_file(string):训练数据所在文件 18 | output: data(mat):训练样本的特征 19 | label(mat):训练样本的标签 20 | ''' 21 | data = [] 22 | label = [] 23 | f = open(data_file) 24 | for line in f.readlines(): 25 | lines = line.strip().split(' ') 26 | 27 | # 提取得出label 28 | label.append(float(lines[0])) 29 | # 提取出特征，并将其放入到矩阵中 30 | index = 0 31 | tmp = [] 32 | for i in range(1, len(lines)): 33 | li = lines[i].strip().split(":") 34 | if int(li[0]) - 1 == index: 35 | tmp.append(float(li[1])) 36 | else: 37 | while(int(li[0]) - 1 > index): 38 | tmp.append(0) 39 | index += 1 40 | tmp.append(float(li[1])) 41 | index += 1 42 | while len(tmp) < 13: 43 | tmp.append(0) 44 | data.append(tmp) 45 | f.close() 46 | return np.array(data), np.array(label).T 47 | 48 | 49 | 50 | ## 2. PSO优化算法 51 | class PSO(object): 52 | def __init__(self,particle_num,particle_dim,iter_num,c1,c2,w,max_value,min_value): 53 | '''参数初始化 54 | particle_num(int):粒子群的粒子数量 55 | particle_dim(int):粒子维度，对应待寻优参数的个数 56 | iter_num(int):最大迭代次数 57 | c1(float):局部学习因子，表示粒子移动到该粒子历史最优位置(pbest)的加速项的权重 58 | c2(float):全局学习因子，表示粒子移动到所有粒子最优位置(gbest)的加速项的权重 59 | w(float):惯性因子，表示粒子之前运动方向在本次方向上的惯性 60 | max_value(float):参数的最大值 61 | min_value(float):参数的最小值 62 | ''' 63 | self.particle_num = particle_num 64 | self.particle_dim = particle_dim 65 | self.iter_num = iter_num 66 | self.c1 = c1 ##通常设为2.0 67 | self.c2 = c2 ##通常设为2.0 68 | self.w = w 69 | self.max_value = max_value 70 | self.min_value = min_value 71 | 72 | 73 | ### 2.1 粒子群初始化 74 | def swarm_origin(self): 75 | '''粒子群初始化 76 | input:self(object):PSO类 77 | output:particle_loc(list):粒子群位置列表 78 | particle_dir(list):粒子群方向列表 79 | ''' 80 | particle_loc = [] 81 | particle_dir = [] 82 | for i in range(self.particle_num): 83 | tmp1 = [] 84 | tmp2 = [] 85 | for j in range(self.particle_dim): 86 | a = random.random() 87 | b = random.random() 88 | tmp1.append(a * (self.max_value - self.min_value) + self.min_value) 89 | tmp2.append(b) 90 | particle_loc.append(tmp1) 91 | particle_dir.append(tmp2) 92 | 93 | return particle_loc,particle_dir 94 | 95 | ## 2.2 计算适应度函数数值列表;初始化pbest_parameters和gbest_parameter 96 | def fitness(self,particle_loc): 97 | '''计算适应度函数值 98 | input:self(object):PSO类 99 | particle_loc(list):粒子群位置列表 100 | output:fitness_value(list):适应度函数值列表 101 | ''' 102 | fitness_value = [] 103 | ### 1.适应度函数为RBF_SVM的3_fold交叉校验平均值 104 | for i in range(self.particle_num): 105 | rbf_svm = svm.SVC(kernel = 'rbf', C = particle_loc[i][0], gamma = particle_loc[i][1]) 106 | cv_scores = cross_validation.cross_val_score(rbf_svm,trainX,trainY,cv =3,scoring = 'accuracy') 107 | fitness_value.append(cv_scores.mean()) 108 | ### 2. 当前粒子群最优适应度函数值和对应的参数 109 | current_fitness = 0.0 110 | current_parameter = [] 111 | for i in range(self.particle_num): 112 | if current_fitness < fitness_value[i]: 113 | current_fitness = fitness_value[i] 114 | current_parameter = particle_loc[i] 115 | 116 | return fitness_value,current_fitness,current_parameter 117 | 118 | 119 | ## 2.3 粒子位置更新 120 | def updata(self,particle_loc,particle_dir,gbest_parameter,pbest_parameters): 121 | '''粒子群位置更新 122 | input:self(object):PSO类 123 | particle_loc(list):粒子群位置列表 124 | particle_dir(list):粒子群方向列表 125 | gbest_parameter(list):全局最优参数 126 | pbest_parameters(list):每个粒子的历史最优值 127 | output:particle_loc(list):新的粒子群位置列表 128 | particle_dir(list):新的粒子群方向列表 129 | ''' 130 | ## 1.计算新的量子群方向和粒子群位置 131 | for i in range(self.particle_num): 132 | a1 = [x * self.w for x in particle_dir[i]] 133 | a2 = [y * self.c1 * random.random() for y in list(np.array(pbest_parameters[i]) - np.array(particle_loc[i]))] 134 | a3 = [z * self.c2 * random.random() for z in list(np.array(gbest_parameter) - np.array(particle_dir[i]))] 135 | particle_dir[i] = list(np.array(a1) + np.array(a2) + np.array(a3)) 136 | # particle_dir[i] = self.w * particle_dir[i] + self.c1 * random.random() * (pbest_parameters[i] - particle_loc[i]) + self.c2 * random.random() * (gbest_parameter - particle_dir[i]) 137 | particle_loc[i] = list(np.array(particle_loc[i]) + np.array(particle_dir[i])) 138 | 139 | ## 2.将更新后的量子位置参数固定在[min_value,max_value]内 140 | ### 2.1 每个参数的取值列表 141 | parameter_list = [] 142 | for i in range(self.particle_dim): 143 | tmp1 = [] 144 | for j in range(self.particle_num): 145 | tmp1.append(particle_loc[j][i]) 146 | parameter_list.append(tmp1) 147 | ### 2.2 每个参数取值的最大值、最小值、平均值 148 | value = [] 149 | for i in range(self.particle_dim): 150 | tmp2 = [] 151 | tmp2.append(max(parameter_list[i])) 152 | tmp2.append(min(parameter_list[i])) 153 | value.append(tmp2) 154 | 155 | for i in range(self.particle_num): 156 | for j in range(self.particle_dim): 157 | particle_loc[i][j] = (particle_loc[i][j] - value[j][1])/(value[j][0] - value[j][1]) * (self.max_value - self.min_value) + self.min_value 158 | 159 | return particle_loc,particle_dir 160 | 161 | ## 2.4 画出适应度函数值变化图 162 | def plot(self,results): 163 | '''画图 164 | ''' 165 | X = [] 166 | Y = [] 167 | for i in range(self.iter_num): 168 | X.append(i + 1) 169 | Y.append(results[i]) 170 | plt.plot(X,Y) 171 | plt.xlabel('Number of iteration',size = 15) 172 | plt.ylabel('Value of CV',size = 15) 173 | plt.title('PSO_RBF_SVM parameter optimization') 174 | plt.show() 175 | 176 | ## 2.5 主函数 177 | def main(self): 178 | '''主函数 179 | ''' 180 | results = [] 181 | best_fitness = 0.0 182 | ## 1、粒子群初始化 183 | particle_loc,particle_dir = self.swarm_origin() 184 | ## 2、初始化gbest_parameter、pbest_parameters、fitness_value列表 185 | ### 2.1 gbest_parameter 186 | gbest_parameter = [] 187 | for i in range(self.particle_dim): 188 | gbest_parameter.append(0.0) 189 | ### 2.2 pbest_parameters 190 | pbest_parameters = [] 191 | for i in range(self.particle_num): 192 | tmp1 = [] 193 | for j in range(self.particle_dim): 194 | tmp1.append(0.0) 195 | pbest_parameters.append(tmp1) 196 | ### 2.3 fitness_value 197 | fitness_value = [] 198 | for i in range(self.particle_num): 199 | fitness_value.append(0.0) 200 | 201 | ## 3.迭代 202 | for i in range(self.iter_num): 203 | ### 3.1 计算当前适应度函数值列表 204 | current_fitness_value,current_best_fitness,current_best_parameter = self.fitness(particle_loc) 205 | ### 3.2 求当前的gbest_parameter、pbest_parameters和best_fitness 206 | for j in range(self.particle_num): 207 | if current_fitness_value[j] > fitness_value[j]: 208 | pbest_parameters[j] = particle_loc[j] 209 | if current_best_fitness > best_fitness: 210 | best_fitness = current_best_fitness 211 | gbest_parameter = current_best_parameter 212 | 213 | print('iteration is :',i+1,';Best parameters:',gbest_parameter,';Best fitness',best_fitness) 214 | results.append(best_fitness) 215 | ### 3.3 更新fitness_value 216 | fitness_value = current_fitness_value 217 | ### 3.4 更新粒子群 218 | particle_loc,particle_dir = self.updata(particle_loc,particle_dir,gbest_parameter,pbest_parameters) 219 | ## 4.结果展示 220 | results.sort() 221 | self.plot(results) 222 | print('Final parameters are :',gbest_parameter) 223 | 224 | 225 | if __name__ == '__main__': 226 | print('----------------1.Load Data-------------------') 227 | trainX,trainY = load_data('rbf_data') 228 | print('----------------2.Parameter Seting------------') 229 | particle_num = 100 230 | particle_dim = 2 231 | iter_num = 50 232 | c1 = 2 233 | c2 = 2 234 | w = 0.8 235 | max_value = 15 236 | min_value = 0.001 237 | print('----------------3.PSO_RBF_SVM-----------------') 238 | pso = PSO(particle_num,particle_dim,iter_num,c1,c2,w,max_value,min_value) 239 | pso.main() 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 | 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