肿瘤基因图谱信息提取和分类方法研究(7)

%biaohao=[114 259 533 635 859 994 1354 1360 1700 1896];%加权RAS

biaohao=[114 259 533 635 859 994 1354 1896];%加权RAS较优，正确19个

for i=1:length(biaohao) for j=1:1909

if (j==biaohao(i)) P1=data(j,1:14); P2=data(j,23:48); P(i,1:40)=[P1 P2]; break; end end end

%T1=[1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2];

T1=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ]; % 将下标矩阵变为单值矢量组作为网络的目标输出 T=ind2vec(T1);

% 设计概率神经网络 sp=0.1; %扩展常数 net=newpnn(P,T,sp);

% 对网络进行仿真，并绘出分类结果 Y=sim(net,P); Y1=vec2ind(Y);

% 对一组新的数据进行分类 for i=1:length(biaohao) for j=1:1909

if (j==biaohao(i)) P3=data(j,15:22); P4=data(j,49:62); PP(i,1:22)=[P3 P4]; break; end end end

Y=sim(net,PP); Y3=vec2ind(Y); correct=0; for i=1:22

if (Y3(i)==1)&(i<=8)

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correct=correct+1; elseif (Y3(i)==2)&(i>8) correct=correct+1; end end

%%%%%%%%%%%%%%%%%%%%%% a1=0.35565; a2=0.414226; a3=0.230126; % a1=0.4843; % a2=0.35897; % a3=0.15673;

for i=1:22

if (Y3(i)==1) X3(i)=0; elseif (Y3(i)==2) X3(i)=1; end end

% X2=[0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ]; % X3=[0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ]; Score=a1*X1+a2*X2+a3*X3; for i=1:22

if (Score(i)<=a1) Result(i)=0;

elseif (Score(i)>=(a2+a3)) Result(i)=1; end end

correct=0; for i=1:22

if (Result(i)==0)&(i<=8) correct=correct+1; elseif (Result(i)==1)&(i>8) correct=correct+1; end end

correct_ratio=correct/22;

fprintf('正确个数：%g\\n\\n',correct); fprintf('正确率： %g\\n\\n',correct_ratio);

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