粒子群算法PSO优化支持向量机回归算法SVR，python写，自带数据集

import csv import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from datetime import datetime from sklearn.metrics import explained_variance_score from sklearn.svm import SVR from sklearn.metrics import mean_squared_error from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.metrics import explained_variance_score from sklearn import metrics from sklearn.metrics import mean_absolute_error # 平方绝对误差 import random import pandas as pd class PSO: def __init__(self, parameters): """ particle swarm optimization parameter: a list type, like [NGEN, pop_size, var_num_min, var_num_max] """ # 初始化 self.NGEN = parameters[0] # 迭代的代数 self.pop_size = parameters[1] # 种群大小 self.var_num = len(parameters[2]) # 变量个数 self.bound = [] # 变量的约束范围 self.bound.append(parameters[2]) self.bound.append(parameters[3]) self.pop_x = np.zeros((self.pop_size, self.var_num)) # 所有粒子的位置 self.pop_v = np.zeros((self.pop_size, self.var_num)) # 所有粒子的速度 self.p_best = np.zeros((self.pop_size, self.var_num)) # 每个粒子最优的位置 self.g_best = np.zeros((1, self.var_num)) # 全局最优的位置 # 初始化第0代初始全局最优解 temp = -1 for i in range(self.pop_size): for j in range(self.var_num): self.pop_x[i][j] = random.uniform(self.bound[0][j], self.bound[1][j]) self.pop_v[i][j] = random.uniform(0, 1) self.p_best[i] = self.pop_x[i] # 储存最优的个体 fit = self.fitness(self.p_best[i]) if fit > temp: self.g_best = self.p_best[i] temp = fit def fitness(self, ind_var): data = pd.read_excel('C:/Users/孙海涛/Desktop/x.xlsx', sheet_name='Sheet1') # 读取数据 target = pd.read_excel('C:/Users/孙海涛/Desktop/y.xlsx', sheet_name='Sheet1') # 读取数据 x_train, x_test, y_train, y_test = train_test_split(data, target, random_state=22, test_size=0.25) std_x = StandardScaler() x_train = std_x.fit_transform(x_train) x_test = std_x.transform(x_test) std_y = StandardScaler() y_train = std_y.fit_transform(y_train) y_test = std_y.transform(y_test) y_test = std_y.inverse_transform(y_test) """ 个体适应值计算 """ x1 = ind_var[0] x2 = ind_var[1] x3 = ind_var[2] if x1 == 0: x1 = 0.001 if x2 == 0: x2 = 0.001 if x3 == 0: x3 = 0.001 model_svr = SVR(C=x1, epsilon=x2, gamma=x3) model_svr.fit(x_train, y_train) predict_results = std_y.inverse_transform(model_svr.predict(x_test)) # 预测结果 print("R2 = ", metrics.r2_score(y_test, predict_results)) # R2 return metrics.r2_score(y_test, predict_results) def update_operator(self, pop_size): """ 更新算子：更新下一时刻的位置和速度 """ c1 = 2 # 学习因子，一般为2 c2 = 2 w = 0.4 # 自身权重因子 for i in range(pop_size): # 更新速度 self.pop_v[i] = w * self.pop_v[i] + c1 * random.uniform(0, 1) * ( self.p_best[i] - self.pop_x[i]) + c2 * random.uniform(0, 1) * (self.g_best - self.pop_x[i]) # 更新位置 self.pop_x[i] = self.pop_x[i] + self.pop_v[i] # 越界保护 for j in range(self.var_num): if self.pop_x[i][j] < self.bound[0][j]: self.pop_x[i][j] = self.bound[0][j] if self.pop_x[i][j] > self.bound[1][j]: self.pop_x[i][j] = self.bound[1][j] # 更新p_best和g_best if self.fitness(self.pop_x[i]) > self.fitness(self.p_best[i]): self.p_best[i] = self.pop_x[i] if self.fitness(self.pop_x[i]) > self.fitness(self.g_best): self.g_best = self.pop_x[i] def main(self): popobj = [] self.ng_best = np.zeros((1, self.var_num))[0] for gen in range(self.NGEN): self.update_operator(self.pop_size) popobj.append(self.fitness(self.g_best)) print('############ Generation {} ############'.format(str(gen + 1))) if self.fitness(self.g_best) > self.fitness(self.ng_best): self.ng_best = self.g_best.copy() print('最好的位置：{}'.format(self.ng_best)) print('最大的函数值：{}'.format(self.fitness(self.ng_best))) print("---- End of (successful) Searching ----") # plt.figure() # fig = plt.gcf() # fig.set_size_inches(18.5, 10.5) # plt.title("Figure1") # plt.xlabel("iterators", size=14) # plt.ylabel("fitness", size=14) # t = [t for t in range(self.NGEN)] # plt.plot(t, popobj, color='b', linewidth=2) # plt.show() plt.figure() plt.title("PSO-SVM") plt.xlabel("GENS", size=14) plt.ylabel("R2", size=14) t = [t for t in range(self.NGEN)] plt.plot(t, popobj, 'b', linewidth=2) plt.show() # R2 = 0.999597635768267 # 最好的位置：[7.09454400e+01 1.00000000e-02 4.68724001e-01] # R2 = 0.999597635768267 # 最大的函数值：0.999597635768267 if __name__ == '__main__': NGEN = 100 popsize = 20 low = [0,0.01,0] up = [200,0.01,50] parameters = [NGEN, popsize, low, up] pso = PSO(parameters) pso.main()