function [bestCVaccuarcy,bestc,bestg,pso_option] = psoSVMcgForClass(train_label,train,pso_option)
% psoSVMcgForClass
% 参数初始化
if nargin == 2
pso_option = struct('c1',3,'c2',3,'maxgen',100,'sizepop',20, ...
'k',0.6,'wV',1,'wP',1,'v',5, ...
'popcmax',10^2,'popcmin',10^(-1),'popgmax',10,'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g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) )];
ObjV(i,1) = svmtrain(train_label, train, cmd);
fitness = ranking(- ObjV);
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*-');
plot(-avgfitness_gen,'o-');
legend('最佳适应度','平均适应度');
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);
