GWO-LSSVM 灰狼算法优化最小二乘支持向量机的 Matlab 实现 数据分类预测
最编程
2024-04-14 15:33:36
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%% 清空环境变量
warning off % 关闭报警信息
close all % 关闭开启的图窗
clear % 清空变量
clc % 清空命令行
%% 导入数据
res = xlsread('数据集.xlsx');
%% 划分训练集和测试集%
P_train = res(1: 250, 1: 12)';
T_train = res(1: 250, 13)';
M = size(P_train, 2);
P_test = res(251: end, 1: 12)';
T_test = res(251: end, 13)';
N = size(P_test, 2);
%% 数据归一化
[p_train, ps_input] = mapminmax(P_train,0,1);
p_test = mapminmax('apply',P_test,ps_input);
t_train = T_train;
t_test = T_test;
%% LS参数设置
type = 'c'; % 模型类型 分类
kernel_type = 'RBF_kernel'; % 线性核函数
codefct = 'code_OneVsOne'; % 一对一编码分类
fun = @getObjValue; % 目标函数
dim = 2; % 优化参数个数
ub = [300, 300]; % 优化参数目标上限
lb = [1, 1]; % 优化参数目标下限
pop = 8; % 数量
Max_iteration = 20; % 最大迭代次数
c = Best_pos(1);
g = Best_pos(2);
%% 编码
[t_train,codebook,old_codebook] = code(t_train,codefct);
%% 建立模型
model = initlssvm(p_train,t_train,type,c,g,kernel_type,codefct); %SSA
%% 训练模型
model = trainlssvm(model);
%% 测试模型
t_sim1 = simlssvm(model,p_train);
t_sim2 = simlssvm(model,p_test);
T_sim1 = T_sim1(index_1);
T_sim2 = T_sim2(index_2);
%% 性能评价
error1 = sum((T_sim1' == T_train))/M * 100 ;
error2 = sum((T_sim2' == T_test))/N * 100 ;
%% 优化曲线
figure
plot(curve, 'linewidth',1.5);
title('GWO-LSSVM')
xlabel('The number of iterations')
ylabel('Fitness')
grid on;
%% 绘图
figure
plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1)
legend('真实值', 'GWO-LSSVM预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'训练集预测结果对比'; ['准确率=' num2str(error1) '%']};
title(string)
xlim([1, M])
grid
figure
plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1)
legend('真实值', 'GWO-LSSVM预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'测试集预测结果对比'; ['准确率=' num2str(error2) '%']};
title(string)
xlim([1, N])
grid
%% 混淆矩阵
figure
cm = confusionchart(T_train, T_sim1);
cm.Title = 'Confusion Matrix for Train Data';
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';
figure
cm = confusionchart(T_test, T_sim2);
cm.Title = 'Confusion Matrix for Test Data';
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';