distance_o_nor.m

用matlab实现的几种模式识别的方法

function distance_o_nor(filename,train_num,chara_num)
Temp_Data=xlsread(filename);
Cork_Data=Temp_Data(:,2:(1+chara_num));

Template=zeros(3,chara_num);

Temp=Cork_Data(1:train_num,:);
Template(1,:)=mean(Temp);
Temp=Cork_Data(51:(train_num+50),:);
Template(2,:)=mean(Temp);
Temp=Cork_Data(101:(train_num+100),:);
Template(3,:)=mean(Temp);

ojl_Num=0;
Sum1=zeros(3,1);

test_num=(150-3*train_num);
for k=1:test_num
    if k<=test_num/3
        Sample=Cork_Data((train_num+k),:);
        for ii=1:3
            Sum1(ii,1)=sqrtm(sum((Sample-Template(ii,:)).^2));
        end
        [Result,I1]=min(Sum1);
        if I1==1
            ojl_Num=ojl_Num+1;
        end
    else if k>test_num/3 && k<=test_num*2/3
            Sample=Cork_Data((2*train_num+k),:);
            for ii=1:3
                Sum1(ii,1)=sqrtm(sum((Sample-Template(ii,:)).^2));
            end
            [Result,I1]=min(Sum1);
            if I1==2
                ojl_Num=ojl_Num+1;
            end
        else if k>test_num*2/3
                Sample=Cork_Data((3*train_num+k),:);
                for ii=1:3
                    Sum1(ii,1)=sqrtm(sum((Sample-Template(ii,:)).^2));
                end
                [Result,I1]=min(Sum1);
                if I1==3
                    ojl_Num=ojl_Num+1;
               end
            end
        end
    end
end

disp('欧几里德识别率是：');
ojl_Num/test_num
end