PSO_SVM分类测试及分类结果可视化

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训练集wine：

Snipaste_2024-05-06_19-49-26.png

Snipaste_2024-05-06_19-50-54.png

训练集的可视化：

Snipaste_2024-05-06_19-56-54.png

PSO-SVM运行代码：

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%%
clc
clear all
load wine.mat;
data1 = wine(1:115,:);
data2 = wine(116:225,:);
%选择两维进行分类可视化
p = 2;
q = p+1;
%合并样本数据
data0=[data1;data2];
train_data=data0(:,p:q);
train_label=data0(:,1);
%将选定训练集进行归一化
[train_data,pstrain] = mapminmax(train_data');
pstrain.ymin = 0;
pstrain.ymax = 1;
[train_data,pstrain] = mapminmax(train_data,pstrain);
train_data =train_data';


%原始样本分布图灰度均值与灰度方差
figure('NumberTitle', 'on', 'Name','灰度均值与灰度方差');
hold on;
grid on;
plot(data1(:,p),data1(:,q),'*'),
plot(data2(:,p),data2(:,q),'+'),
title('训练样本数据');
%% 参数初始化
%粒子群算法中的两个参数
c1 = 1.6; % c1 belongs to [0,2]
c2 = 1.5; % c2 belongs to [0,2]
maxgen=200;   % 进化次数 
sizepop=30;   % 种群规模
popcmax=10^(2);
popcmin=10^(-1);
popgmax=10^(3);
popgmin=10^(-2);
k = 0.6; % k belongs to [0.1,1.0];
Vcmax = k*popcmax;
Vcmin = -Vcmax ;
Vgmax = k*popgmax;
Vgmin = -Vgmax ;
% SVM参数初始化,随机的将数据分为3个部分
v = 3;
%% 产生初始粒子和速度
for i=1:sizepop
    % 随机产生种群
    pop(i,1) = (popcmax-popcmin)*rand+popcmin;    % 初始种群
    pop(i,2) = (popgmax-popgmin)*rand+popgmin;
    V(i,1)=Vcmax*rands(1);  % 初始化速度
    V(i,2)=Vgmax*rands(1);
    % 计算初始适应度
    cmd = ['-v ',num2str(v),' -c ',num2str( pop(i,1) ),' -g ',num2str( pop(i,2) )];
    fitness(i) = libsvmtrain(train_label,train_data, 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
tic
%% 迭代寻优
for i=1:maxgen
   
    for j=1:sizepop
       
        %速度更新
        wV = 0.9; % wV best belongs to [0.8,1.2]
        V(j,:) = wV*V(j,:) + c1*rand*(local_x(j,:) - pop(j,:)) + 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
        wP = 0.6;
        pop(j,:)=pop(j,:)+wP*V(j,:);
        if pop(j,1) > popcmax
            pop(j,1) = popcmax;
        end
        if pop(j,1) < popcmin
            pop(j,1) = popcmin;
        end
        if pop(j,2) > popgmax
            pop(j,2) = popgmax;
        end
        if pop(j,2) < popgmin
            pop(j,2) = 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) = (popgmax-popgmin)*rand+popgmin;
            end           
        end
       
        %适应度值
        cmd = ['-v ',num2str(v),' -c ',num2str( pop(j,1) ),' -g ',num2str( pop(j,2) )];
        fitness(j) = libsvmtrain(train_label,train_data, cmd);
        fitness(j) = -fitness(j);
    end
   
    %个体最优更新
    if fitness(j) < local_fitness(j)
        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
   
    fit_gen(i)=global_fitness;   
       
end
toc
%% 结果分析
figure,
plot(-fit_gen,'LineWidth',2);
title(['适应度曲线','(参数c1=',num2str(c1),',c2=',num2str(c2),',终止代数=',num2str(maxgen),')'],'FontSize',13);
xlabel('进化代数');ylabel('适应度');
bestc = global_x(1)
bestg = global_x(2)
bestCVaccuarcy = -fit_gen(maxgen);
cmd = ['-c ',num2str( bestc ),' -g ',num2str( bestg )];

分类结果可视化：

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model=libsvmtrain(train_label,train_data,cmd); %径向基函数
test_label=train_label;
test_data=train_data;
[predict_label,accuracy,dec_values] = svmpredict(test_label,test_data, model);

%%
demension1 = 1;
demension2 = 2;
minX = min(test_data(:, demension1));
maxX = max(test_data(:, demension1));
minY = min(test_data(:, demension2));
maxY = max(test_data(:, demension2));

gridX = (maxX - minX) ./ 100;
gridY = (maxY - minY) ./ 100;

minX = minX - 10 * gridX;
maxX = maxX + 10 * gridX;
minY = minY - 10 * gridY;
maxY = maxY + 10 * gridY;

[denseX, denseY] = meshgrid(minX:gridX:maxX, minY:gridY:maxY);

%%

model.Parameters(1) = 3; 
[m,n]=size(denseX);
dense_data=[reshape(denseX,m*n, 1), reshape(denseY,m*n,1)];
dense_label = ones(m*n,1);
%密集点分类
model.Parameters(1) = 3;
[lab] =svmpredict(dense_label,dense_data, model);
dense_pre_lab = reshape(lab, m,n);

%%
%画分类后的点及SV
figure('NumberTitle', 'on', 'Name','分类结果可视化');
hold on;
grid on;
m=length(predict_label);
for i=1:m
    if (predict_label(i)==1)
    a= plot(test_data(i, 1), test_data(i, 2), 'r+');    
    end
    if (predict_label(i)==2)
      b= plot(test_data(i, 1), test_data(i, 2), 'k*');  
    end
end
c= plot( model.SVs(:,1),model.SVs(:,2),'o' );
legend([a,b,c],'class1','class2','Support Vectors');

[C,h] = contour(denseX, denseY, dense_pre_lab,-1:1);
clabel(C,h,'Color','r');
xlabel('灰度均值','FontSize',12);
ylabel('灰度方差','FontSize',12);
title('The visualization of classification','FontSize',12);

输出适应度曲线及分类结果可视化：

Snipaste_2024-05-06_20-23-54.png

Snipaste_2024-05-06_19-52-09.png