test10.m

模式识别中的十大基本算法

function result=test10()
clear all
clc
load 'D:/iris.mat'

n=1000;
acc_ratio=zeros(1,n);
acc=zeros(1,n);
itera=zeros(1,n);

for i=1:1:n
    m=25;
    p1 = randperm(50)+50;
    train_index1 = p1(1,1:m);
    tain_data1 = Iris(train_index1,:);
    predict_data1=Iris(setdiff(p1,train_index1),:);

    p3 = randperm(50)+100;
    tain_index3=p3(1,1:m);
    tain_data3 = Iris(tain_index3,:);
    predict_data3=Iris(setdiff(p3,tain_index3),:);

    initw=[1 linspace(0,0,size(tain_data1,2))];  %投影向量的初始值
    totallen=size(tain_data1,1)+size(tain_data3,1);
    b=ones(totallen,1)*10;
    
    [w,itera(i)]=LMS1(tain_data1,tain_data3,initw,b,1,0.1);
    
    acc(i)=cacul_acc(tain_data1,tain_data3,w);
    acc_ratio(i)=cacul_acc(predict_data1,predict_data3,w);
end

format long
result.exp_times=n;
result.train_samples=m;
result.test_samples=50-m;
result.iterations=mean(itera);
result.train_acc=mean(acc);
result.acc_mean=mean(acc_ratio);
result.acc_var=std(acc_ratio,1,2);

function [CoefVector,itera]=LMS1(Data1,Data2,initw,b,step,level)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% sequential Preception algorithm 
%%% increment step length change 
%%% add margin b
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[RowData1,ColumnData1]=size(Data1);
[RowData2,ColumnData2]=size(Data2);
ExpandData1=cat(2,(linspace(1,1,RowData1))',Data1);
ExpandData2=cat(2,(linspace(1,1,RowData2))',Data2);
TotalData=cat(1,ExpandData1,-ExpandData2);
[Row,Column]=size(TotalData);

CoefVector=initw;
itera=0;Guard=1;k=0;indall=1:1:Row;

while (sum(Guard)&(itera<1000))
    Guard=0;itera=itera+1;k=itera;
    gradall=repmat((b(indall,:)-TotalData(indall,:)*CoefVector'),1,Column).*(TotalData(indall,:));
    index=find(sum(abs((step/k).*gradall),2)>level);
    
    if(~isempty(index))
        tempg=gradall(index,:);
        grad=sum(tempg,1);
        CoefVector=CoefVector+(step/k).*(grad./norm(grad,1));
        Guard=1;
    end  
end
ss=0;

function [CoefVector,itera]=LMS2(Data1,Data2,initw,b,step,level)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% sequential Preception algorithm 
%%% increment step length change 
%%% add margin b
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[RowData1,ColumnData1]=size(Data1);
[RowData2,ColumnData2]=size(Data2);
ExpandData1=cat(2,(linspace(1,1,RowData1))',Data1);
ExpandData2=cat(2,(linspace(1,1,RowData2))',Data2);
TotalData=cat(1,ExpandData1,-ExpandData2);
[Row,Column]=size(TotalData);

CoefVector=initw;
itera=0;Guard=1;

while (sum(Guard)&(itera<1000))
    Guard=0;  
    for k=1:1:Row
        itera=itera+1;
        grad=(b(k,:)-TotalData(k,:)*CoefVector').*(TotalData(k,:));
        CoefVector=CoefVector+(step/k).*(grad./norm(grad,1)+0.001);
        if(sum(abs((step).*grad),2)>level) 
            Guard=1; 
        end
        
    end  
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function accuracy_ratio=cacul_acc(posdata,negdata,w)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
%%  caculate classifier error
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[RowData1,ColumnData1]=size(posdata);
[RowData2,ColumnData2]=size(negdata);
ExpandData1=cat(2,(linspace(1,1,RowData1))',posdata);
ExpandData2=cat(2,(linspace(1,1,RowData2))',negdata);
TotalData=cat(1,ExpandData1,-ExpandData2);
[Row,Column]=size(TotalData);

i=1:1:Row;
dotarray=sum(repmat(w,Row,1).*TotalData(i,:),2);
accuracy_num=length(find(dotarray>0));
accuracy_ratio=accuracy_num/length(dotarray);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%