www.gusucode.com > MATLAB+神经网络30个案例分析》程序和数据 > 源程序/案例13 SVM神经网络中的参数优化---提升分类器性能/chapter13_PSO.m
%% SVM神经网络中的参数优化---如何更好的提升分类器的性能
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%% 清空环境变量
function chapter13_PSO
close all;
clear;
clc;
format compact;
%% 数据提取
% 载入测试数据wine,其中包含的数据为classnumber = 3,wine:178*13的矩阵,wine_labes:178*1的列向量
load chapter13_wine.mat;
% 画出测试数据的box可视化图
figure;
boxplot(wine,'orientation','horizontal','labels',categories);
title('wine数据的box可视化图','FontSize',12);
xlabel('属性值','FontSize',12);
grid on;
% 画出测试数据的分维可视化图
figure
subplot(3,5,1);
hold on
for run = 1:178
plot(run,wine_labels(run),'*');
end
xlabel('样本','FontSize',10);
ylabel('类别标签','FontSize',10);
title('class','FontSize',10);
for run = 2:14
subplot(3,5,run);
hold on;
str = ['attrib ',num2str(run-1)];
for i = 1:178
plot(i,wine(i,run-1),'*');
end
xlabel('样本','FontSize',10);
ylabel('属性值','FontSize',10);
title(str,'FontSize',10);
end
% 选定训练集和测试集
% 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集
train_wine = [wine(1:30,:);wine(60:95,:);wine(131:153,:)];
% 相应的训练集的标签也要分离出来
train_wine_labels = [wine_labels(1:30);wine_labels(60:95);wine_labels(131:153)];
% 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集
test_wine = [wine(31:59,:);wine(96:130,:);wine(154:178,:)];
% 相应的测试集的标签也要分离出来
test_wine_labels = [wine_labels(31:59);wine_labels(96:130);wine_labels(154:178)];
%% 数据预处理
% 数据预处理,将训练集和测试集归一化到[0,1]区间
[mtrain,ntrain] = size(train_wine);
[mtest,ntest] = size(test_wine);
dataset = [train_wine;test_wine];
% mapminmax为MATLAB自带的归一化函数
[dataset_scale,ps] = mapminmax(dataset',0,1);
dataset_scale = dataset_scale';
train_wine = dataset_scale(1:mtrain,:);
test_wine = dataset_scale( (mtrain+1):(mtrain+mtest),: );
%% 选择最佳的SVM参数c&g
[bestacc,bestc,bestg] = psoSVMcgForClass(train_wine_labels,train_wine);
% 打印选择结果
disp('打印选择结果');
str = sprintf( 'Best Cross Validation Accuracy = %g%% Best c = %g Best g = %g',bestacc,bestc,bestg);
disp(str);
%% 利用最佳的参数进行SVM网络训练
cmd = ['-c ',num2str(bestc),' -g ',num2str(bestg)];
model = svmtrain(train_wine_labels,train_wine,cmd);
%% SVM网络预测
[predict_label,accuracy] = svmpredict(test_wine_labels,test_wine,model);
% 打印测试集分类准确率
total = length(test_wine_labels);
right = sum(predict_label == test_wine_labels);
disp('打印测试集分类准确率');
str = sprintf( 'Accuracy = %g%% (%d/%d)',accuracy(1),right,total);
disp(str);
%% 结果分析
% 测试集的实际分类和预测分类图
% 通过图可以看出只有三个测试样本是被错分的
figure;
hold on;
plot(test_wine_labels,'o');
plot(predict_label,'r*');
xlabel('测试集样本','FontSize',12);
ylabel('类别标签','FontSize',12);
legend('实际测试集分类','预测测试集分类');
title('测试集的实际分类和预测分类图','FontSize',12);
grid on;
snapnow;
%% 子函数 psoSVMcgForClass.m
function [bestCVaccuarcy,bestc,bestg,pso_option] = psoSVMcgForClass(train_label,train,pso_option)
% psoSVMcgForClass
%
% by faruto
%Email:patrick.lee@foxmail.com QQ:516667408 http://blog.sina.com.cn/faruto BNU
%last modified 2010.01.17
%Super Moderator @ www.ilovematlab.cn
% 若转载请注明：
% faruto and liyang , LIBSVM-farutoUltimateVersion
% a toolbox with implements for support vector machines based on libsvm, 2009.
% Software available at http://www.ilovematlab.cn
%
% Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for
% support vector machines, 2001. Software available at
% http://www.csie.ntu.edu.tw/~cjlin/libsvm
% 参数初始化
if nargin == 2
pso_option = struct('c1',1.5,'c2',1.7,'maxgen',200,'sizepop',20, ...
'k',0.6,'wV',1,'wP',1,'v',5, ...
'popcmax',10^2,'popcmin',10^(-1),'popgmax',10^3,'popgmin',10^(-2));
end
% c1:初始为1.5,pso参数局部搜索能力
% c2:初始为1.7,pso参数全局搜索能力
% maxgen:初始为200,最大进化数量
% sizepop:初始为20,种群最大数量
% k:初始为0.6(k belongs to [0.1,1.0]),速率和x的关系(V = kX)
% wV:初始为1(wV best belongs to [0.8,1.2]),速率更新公式中速度前面的弹性系数
% wP:初始为1,种群更新公式中速度前面的弹性系数
% v:初始为3,SVM Cross Validation参数
% popcmax:初始为100,SVM 参数c的变化的最大值.
% popcmin:初始为0.1,SVM 参数c的变化的最小值.
% popgmax:初始为1000,SVM 参数g的变化的最大值.
% popgmin:初始为0.01,SVM 参数c的变化的最小值.
Vcmax = pso_option.k*pso_option.popcmax;
Vcmin = -Vcmax ;
Vgmax = pso_option.k*pso_option.popgmax;
Vgmin = -Vgmax ;
eps = 10^(-3);
% 产生初始粒子和速度
for i=1:pso_option.sizepop
% 随机产生种群和速度
pop(i,1) = (pso_option.popcmax-pso_option.popcmin)*rand+pso_option.popcmin;
pop(i,2) = (pso_option.popgmax-pso_option.popgmin)*rand+pso_option.popgmin;
V(i,1)=Vcmax*rands(1,1);
V(i,2)=Vgmax*rands(1,1);
% 计算初始适应度
cmd = ['-v ',num2str(pso_option.v),' -c ',num2str( pop(i,1) ),' -g ',num2str( pop(i,2) )];
fitness(i) = svmtrain(train_label, train, cmd);
fitness(i) = -fitness(i);
end
% 找极值和极值点
[global_fitness bestindex]=min(fitness); % 全局极值
local_fitness=fitness; % 个体极值初始化
global_x=pop(bestindex,:); % 全局极值点
local_x=pop; % 个体极值点初始化
% 每一代种群的平均适应度
avgfitness_gen = zeros(1,pso_option.maxgen);
% 迭代寻优
for i=1:pso_option.maxgen
for j=1:pso_option.sizepop
%速度更新
V(j,:) = pso_option.wV*V(j,:) + pso_option.c1*rand*(local_x(j,:) - pop(j,:)) + pso_option.c2*rand*(global_x - pop(j,:));
if V(j,1) > Vcmax
V(j,1) = Vcmax;
end
if V(j,1) < Vcmin
V(j,1) = Vcmin;
end
if V(j,2) > Vgmax
V(j,2) = Vgmax;
end
if V(j,2) < Vgmin
V(j,2) = Vgmin;
end
%种群更新
pop(j,:)=pop(j,:) + pso_option.wP*V(j,:);
if pop(j,1) > pso_option.popcmax
pop(j,1) = pso_option.popcmax;
end
if pop(j,1) < pso_option.popcmin
pop(j,1) = pso_option.popcmin;
end
if pop(j,2) > pso_option.popgmax
pop(j,2) = pso_option.popgmax;
end
if pop(j,2) < pso_option.popgmin
pop(j,2) = pso_option.popgmin;
end
% 自适应粒子变异
if rand>0.5
k=ceil(2*rand);
if k == 1
pop(j,k) = (20-1)*rand+1;
end
if k == 2
pop(j,k) = (pso_option.popgmax-pso_option.popgmin)*rand + pso_option.popgmin;
end
end
%适应度值
cmd = ['-v ',num2str(pso_option.v),' -c ',num2str( pop(j,1) ),' -g ',num2str( pop(j,2) )];
fitness(j) = svmtrain(train_label, train, cmd);
fitness(j) = -fitness(j);
cmd_temp = ['-c ',num2str( pop(j,1) ),' -g ',num2str( pop(j,2) )];
model = svmtrain(train_label, train, cmd_temp);
if fitness(j) >= -65
continue;
end
%个体最优更新
if fitness(j) < local_fitness(j)
local_x(j,:) = pop(j,:);
local_fitness(j) = fitness(j);
end
if abs( fitness(j)-local_fitness(j) )<=eps && pop(j,1) < local_x(j,1)
local_x(j,:) = pop(j,:);
local_fitness(j) = fitness(j);
end
%群体最优更新
if fitness(j) < global_fitness
global_x = pop(j,:);
global_fitness = fitness(j);
end
if abs( fitness(j)-global_fitness )<=eps && pop(j,1) < global_x(1)
global_x = pop(j,:);
global_fitness = fitness(j);
end
end
fit_gen(i) = global_fitness;
avgfitness_gen(i) = sum(fitness)/pso_option.sizepop;
end
% 结果分析
figure;
hold on;
plot(-fit_gen,'r*-','LineWidth',1.5);
plot(-avgfitness_gen,'o-','LineWidth',1.5);
legend('最佳适应度','平均适应度',3);
xlabel('进化代数','FontSize',12);
ylabel('适应度','FontSize',12);
grid on;
bestc = global_x(1);
bestg = global_x(2);
bestCVaccuarcy = -fit_gen(pso_option.maxgen);
line1 = '适应度曲线Accuracy[PSOmethod]';
line2 = ['(参数c1=',num2str(pso_option.c1), ...
',c2=',num2str(pso_option.c2),',终止代数=', ...
num2str(pso_option.maxgen),',种群数量pop=', ...
num2str(pso_option.sizepop),')'];
line3 = ['Best c=',num2str(bestc),' g=',num2str(bestg), ...
' CVAccuracy=',num2str(bestCVaccuarcy),'%'];
title({line1;line2;line3},'FontSize',12);
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