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%%  清空环境变量
clear
clc
warning off

%%  导入数据
bianliang1 = xlsread('实际名称.xlsx');

%%  参数设置
leibieshumu = 2;              
Tag = 0;                    
Mshumu  = size(bianliang1, 1);          

%%  划分特征与标签
if (Tag == 1)

    leibieshumu = length(unique(bianliang1(:, end)));  
    tezheng = bianliang1(: , 1 : end - 1)';          
    T_train = bianliang1(: , end)';                 
else

    tezheng = bianliang1';                           % 输入特征
end

%%  数据归一化
[pca1, ps_input] = mapminmax(tezheng, 0, 1);

%%  建立网络
wangluo = newsom(pca1, leibieshumu);

%%  设置训练参数
wangluo.trainParam.epochs = 3000;   
wangluo.trainParam.goal = 1e-5;     
wangluo.trainParam.lr = 0.01;       

%%  训练网络
wangluo = train(wangluo, pca1);

%%  仿真预测
yuce = sim(wangluo, pca1);

%%  反归一化
jvle = vec2ind(yuce);

%%  降维应用主成分分析
pca1 = pca1';
[~, pc_train] =  pca(pca1);

%%  根据类别的空间预设置
kongjian1 = []; kongjian2 = []; 

%%  聚类类别
for i = 1: Mshumu

    % 聚类1
    if jvle(i) == 1
        kongjian1 = [kongjian1; pc_train(i, 1 : 2)];
    end
    
    % 聚类2
    if jvle(i) == 2
        kongjian2 = [kongjian2; pc_train(i, 1 : 2)];
    end
    
   


    
end

%%  绘制聚类类别散点图
figure
plot(kongjian1(:, 1), kongjian1(:, 2), '*', 'LineWidth', 1)
hold on
plot(kongjian2(:, 1), kongjian2(:, 2), '*', 'LineWidth', 1)
hold on




%%  空间预设置
kongjian1 = []; kongjian2 = []; 

%%  存在真实类别
if Tag == 1
    for i = 1: Mshumu

        % 类别1
        if T_train(i) == 1
            kongjian1 = [kongjian1; pc_train(i, 1 : 2)];
        end

        % 类别2
        if T_train(i) == 2
            kongjian2 = [kongjian2; pc_train(i, 1 : 2)];
        end
    
       
    end

%%  绘制真实类别散点图
    plot(kongjian1(:, 1), kongjian1(:, 2), 'o', 'LineWidth', 1)
    hold on
    plot(kongjian2(:, 1), kongjian2(:, 2), 'o', 'LineWidth', 1)
    hold on
    
    
end


legend('聚类类别A', '聚类类别B')

xlabel('降维后第一维度')
ylabel('降维后第二维度')
keshihua = {'聚类可视化'};
title(keshihua)
grid on