在MATLAB中，索引不能产生多个结果_Matlab_Indexing_Confusion Matrix

在MATLAB中，索引不能产生多个结果

matlab indexing

在MATLAB中，索引不能产生多个结果,matlab,indexing,confusion-matrix,Matlab,Indexing,Confusion Matrix,我试图在Matlab中建立混淆矩阵，但我得到了这个错误 Indexing cannot yield multiple results. Error in Untitled3 (line 184) [Max, argmax1]= max(simoutelem); 我想在这个矩阵及其索引中找到最大值。我知道max可以用来找到最大元素的值和索引。这是我的密码。矩阵=零（102316）我还做了测试 max(simoutelem(:)); 在我提到的链接中，有一个我正在尝试实现的示例

我试图在Matlab中建立混淆矩阵，但我得到了这个错误

    Indexing cannot yield multiple results.

Error in Untitled3 (line 184)
 [Max, argmax1]= max(simoutelem);

我想在这个矩阵及其索引中找到最大值。我知道

max

可以用来找到最大元素的值和索引。这是我的密码。矩阵=零（102316）

我还做了测试

max(simoutelem(:));

在我提到的链接中，有一个我正在尝试实现的示例：创建一个矩阵a并计算每列中的最大元素，以及它们出现的a的行索引

A=[19-2；84-5]

[M，I]=max（A）

我=

我的全部代码：

    TrainSet=cell(200);
TestSet=cell(30);
%zeros(n,m);
%......................................................................................
%Reading train dataset
directory=dir('C:\Users\Rihanna\Desktop\TrainSet');
for i=3:length(directory)    
    folderstring=strcat('C:\Users\Rihanna\Desktop\TrainSet\',directory(i).name);
    directory1=dir(folderstring);    
    for j=3:length(directory1)     
        TrainSet{i-2}{j-2}=audioread(strcat(folderstring,'\',directory1(j).name));
    end
end
%--------------------------------------------------------------------------------
%Reading test dataset
directory2=dir('C:\Users\Rihanna\Desktop\TestSet');
for i=3:length(directory2)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory2(i).name);
    directory3=dir(folderstring);
    for j=3:length(directory3)     
        TestSet{i-2}{j-2}=audioread(strcat(folderstring,'\',directory3(j).name));
    end
end
%.......................................................................................
% make files equal in size using zero padding
%find longest
max=0;
TrainSize=0;
TestSize=0;

for i=3: length(directory) 
    for j=3:length(directory1)  
         if(size(TrainSet{i-2}{j-2},1) > TrainSize)             
             TrainSize=size(TrainSet{i-2}{j-2},1);
         end   
    end  
end
%----------------------------------------------------------------------------------------------
for i=3:length(directory2)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory2(i).name);
    directory3=dir(folderstring);    
    for j=3:length(directory3)     
        if(size(TestSet{i-2}{j-2},1) > TestSize)             
             TestSize=size(TestSet{i-2}{j-2},1);
  %tempsize=size(TestSet{i-2}{j-2},1);
  % TestSize=max(tempsize,TestSize);
        end   
        end  
end

if(TestSize>TrainSize)
    max=TestSize;
else
    max=TrainSize;
end

%zero padding-----------------------------------

for i=3: length(directory2) 
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory2(i).name);
    directory3=dir(folderstring);
    for j=3:length(directory3)           
        m=zeros(1,max-size(TestSet{i-2}{j-2},1));        
        t=[TestSet{i-2}{j-2}',m];
        TestSet{i-2}{j-2}=t';

    end
end

for i=3: length(directory) 
    for j=3:length(directory1)   
        m=zeros(1,max-size(TrainSet{i-2}{j-2},1));
       t=[TrainSet{i-2}{j-2}',m];
        TrainSet{i-2}{j-2}=t';

    end
end

%----------------------------------------------------------------------------------------
%Implementation of mlp

%newff( p,t ,[10 5], {'tansig' 'logsig'},'traingd','','mse',{},{},''); 
%net = init(net); 
%[trained_net, stats] = train(net, p, t);
%coeff = 0.2;
%

%frame=20ms-160sample
%y = buffer(window, 1, floor(64 * 0.5));
wnum=floor(max/80)-1;
w=hamming(160);
%for k=1: wnum
 %   if k==1
  %      sig=TestSet{i-2}{j-2};
   %     final(1,:)=(sig,1:160);
    %end
%end
 %--------------------------------------framing   
% finalLpc[10][190];

%...............................................Test...............
for i=3:length(directory2)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory(i).name);
    directory3=dir(folderstring);    
    for j=3:length(directory3)     
          %cats=[];
          lpcTest=[];        
        for k=1 :wnum
           sig1=TestSet{i-2}{j-2};
           if k==1              
        framTest=sig1(1:160);
           else
            framTest=sig1((k-1)*80+1:((k+1)*80)); 
           end
           winTest=framTest.* w;  
           cats1=lpc(winTest,12);
       catss1(k,:)=cats1(2:13); 

        end        
        features1=12*(floor(max/80)-1);
        %features=2316
        n=length(directory3);  
      anninput1((i-3)*(length(directory3)-2 ) +(j-2),:) =reshape(catss1,1,2316);
    end
end
%.............
anninput1=anninput1';
%...............................................Train..................
for i=3:length(directory)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TrainSet\',directory(i).name);
    directory1=dir(folderstring);    
    for j=3:length(directory1)     
          %cats=[];
          lpcTrain=[];        
        for k=1 :wnum
           sig=TrainSet{i-2}{j-2};
           if k==1              
        framTrain=sig(1:160);
           else
            framTrain=sig((k-1)*80+1:((k+1)*80)); 
            %((k-1).*80)+1 :(k+1).*80
           end
           winTrain=framTrain.* w;  
           cats=lpc(winTrain,12);
       catss(k,:)=cats(2:13); 

   % lpcTrain =[lpcTrain,l];
        end        
        features=12*(floor(max/80)-1);
        %features=2316
        n=length(directory1);  
          anninput((i-3)*(length(directory1)-2 ) +(j-2),:) =reshape(catss,1,2316);
 %input(i*(190)+(ii),:)=reshape(l_effective,1,2316);
   % finalLpc{i-2}{j-2}=lpcTrain;
   %1900*2316
    end
end
%...............................ANN...............................................................
input=anninput';
output=[zeros(10,190) ones(10,190) repmat(2,10,190) repmat(3,10,190) repmat(4,10,190) repmat(5,10,190) repmat(6,10,190) repmat(7,10,190) repmat(8,10,190) repmat(9,10,190) ];
net=newff(input,output,1158);
net=init(net);
%Learning rate
net.trainParam.lr=0.2;
net.trainParam.showWindow=1;
net.trainParam.min_grad=0;
net.trainParam.mem_reduc=50;
net.efficiency.memoryReduction = 60;
net= trainrp(net, input,output);

%plot_xor(net);


%...............................................

Foutput=sim(net, anninput1); % Returns one output.
%figure, plotconfusion(Foutput, sim(net,input));


matris=zeros(10,2316);

 for i=1:300
    simoutelem=Foutput(:,i);
    [Max, argmax1]= max(simoutelem);
    matris(argmax1,i)=1;
 end

 confusion=zeros(10,10);
    for i=1:10
        for j=1:26
        confusion(i,1)=((matris(i,j)+confusion(i,1)))/26; 
        end
    end
for i=1:10
     for k=2:10
        for j=(k-1)*26+1:(k-1)*26+30
        confusion(i,k)=((matris(i,j)+confusion(i,k)))/30; 
        end
     end
end

这个错误告诉我您在代码的某个地方定义了一个变量

max

索引不能产生多个结果

为什么?？因为否则

[Max，argmax1]=Max（simoutelem）不会被视为“索引”的情况
在命令行上轻松证明：
[a b] = max([1 2 3 4 5]) % works
max = 1:100;
[a b] = max([1 2 3 4 5]) % gives your error
clear max
[a b] = max([1 2 3 4 5]) % works again

那么，您是否有多个最大值，并希望找到所有这些值？在这种情况下，使用find（v==max（v））我想找到这个矩阵及其索引中的最大值。好的，max
有什么问题吗？用这个表示值：max（max（A））
用这个表示索引find（A==max（max（A））
我添加了一些详细信息。是的，我添加了！我将添加我的完整代码。但是我定义的最大值在这个循环的局部范围内。是的，它似乎不在局部范围内！所以我只需要重命名max？永远不要将变量命名为像max
这样会带来麻烦的基本函数。for循环不是一个单独的函数，因此这里没有“局部范围”。（不相信我？请将哪个max
放在simoutelem=Foutput（：，i）；
之后，然后再尝试调用max作为函数）。谢谢。。。我对MATLAB是新手。在其他语言（如C）中，不允许这样做，但MATLAB不会给出错误信息。
 8     9    -2

 2     1     1

    TrainSet=cell(200);
TestSet=cell(30);
%zeros(n,m);
%......................................................................................
%Reading train dataset
directory=dir('C:\Users\Rihanna\Desktop\TrainSet');
for i=3:length(directory)    
    folderstring=strcat('C:\Users\Rihanna\Desktop\TrainSet\',directory(i).name);
    directory1=dir(folderstring);    
    for j=3:length(directory1)     
        TrainSet{i-2}{j-2}=audioread(strcat(folderstring,'\',directory1(j).name));
    end
end
%--------------------------------------------------------------------------------
%Reading test dataset
directory2=dir('C:\Users\Rihanna\Desktop\TestSet');
for i=3:length(directory2)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory2(i).name);
    directory3=dir(folderstring);
    for j=3:length(directory3)     
        TestSet{i-2}{j-2}=audioread(strcat(folderstring,'\',directory3(j).name));
    end
end
%.......................................................................................
% make files equal in size using zero padding
%find longest
max=0;
TrainSize=0;
TestSize=0;

for i=3: length(directory) 
    for j=3:length(directory1)  
         if(size(TrainSet{i-2}{j-2},1) > TrainSize)             
             TrainSize=size(TrainSet{i-2}{j-2},1);
         end   
    end  
end
%----------------------------------------------------------------------------------------------
for i=3:length(directory2)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory2(i).name);
    directory3=dir(folderstring);    
    for j=3:length(directory3)     
        if(size(TestSet{i-2}{j-2},1) > TestSize)             
             TestSize=size(TestSet{i-2}{j-2},1);
  %tempsize=size(TestSet{i-2}{j-2},1);
  % TestSize=max(tempsize,TestSize);
        end   
        end  
end

if(TestSize>TrainSize)
    max=TestSize;
else
    max=TrainSize;
end

%zero padding-----------------------------------

for i=3: length(directory2) 
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory2(i).name);
    directory3=dir(folderstring);
    for j=3:length(directory3)           
        m=zeros(1,max-size(TestSet{i-2}{j-2},1));        
        t=[TestSet{i-2}{j-2}',m];
        TestSet{i-2}{j-2}=t';

    end
end

for i=3: length(directory) 
    for j=3:length(directory1)   
        m=zeros(1,max-size(TrainSet{i-2}{j-2},1));
       t=[TrainSet{i-2}{j-2}',m];
        TrainSet{i-2}{j-2}=t';

    end
end

%----------------------------------------------------------------------------------------
%Implementation of mlp

%newff( p,t ,[10 5], {'tansig' 'logsig'},'traingd','','mse',{},{},''); 
%net = init(net); 
%[trained_net, stats] = train(net, p, t);
%coeff = 0.2;
%

%frame=20ms-160sample
%y = buffer(window, 1, floor(64 * 0.5));
wnum=floor(max/80)-1;
w=hamming(160);
%for k=1: wnum
 %   if k==1
  %      sig=TestSet{i-2}{j-2};
   %     final(1,:)=(sig,1:160);
    %end
%end
 %--------------------------------------framing   
% finalLpc[10][190];

%...............................................Test...............
for i=3:length(directory2)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TestSet\',directory(i).name);
    directory3=dir(folderstring);    
    for j=3:length(directory3)     
          %cats=[];
          lpcTest=[];        
        for k=1 :wnum
           sig1=TestSet{i-2}{j-2};
           if k==1              
        framTest=sig1(1:160);
           else
            framTest=sig1((k-1)*80+1:((k+1)*80)); 
           end
           winTest=framTest.* w;  
           cats1=lpc(winTest,12);
       catss1(k,:)=cats1(2:13); 

        end        
        features1=12*(floor(max/80)-1);
        %features=2316
        n=length(directory3);  
      anninput1((i-3)*(length(directory3)-2 ) +(j-2),:) =reshape(catss1,1,2316);
    end
end
%.............
anninput1=anninput1';
%...............................................Train..................
for i=3:length(directory)
    folderstring=strcat('C:\Users\Rihanna\Desktop\TrainSet\',directory(i).name);
    directory1=dir(folderstring);    
    for j=3:length(directory1)     
          %cats=[];
          lpcTrain=[];        
        for k=1 :wnum
           sig=TrainSet{i-2}{j-2};
           if k==1              
        framTrain=sig(1:160);
           else
            framTrain=sig((k-1)*80+1:((k+1)*80)); 
            %((k-1).*80)+1 :(k+1).*80
           end
           winTrain=framTrain.* w;  
           cats=lpc(winTrain,12);
       catss(k,:)=cats(2:13); 

   % lpcTrain =[lpcTrain,l];
        end        
        features=12*(floor(max/80)-1);
        %features=2316
        n=length(directory1);  
          anninput((i-3)*(length(directory1)-2 ) +(j-2),:) =reshape(catss,1,2316);
 %input(i*(190)+(ii),:)=reshape(l_effective,1,2316);
   % finalLpc{i-2}{j-2}=lpcTrain;
   %1900*2316
    end
end
%...............................ANN...............................................................
input=anninput';
output=[zeros(10,190) ones(10,190) repmat(2,10,190) repmat(3,10,190) repmat(4,10,190) repmat(5,10,190) repmat(6,10,190) repmat(7,10,190) repmat(8,10,190) repmat(9,10,190) ];
net=newff(input,output,1158);
net=init(net);
%Learning rate
net.trainParam.lr=0.2;
net.trainParam.showWindow=1;
net.trainParam.min_grad=0;
net.trainParam.mem_reduc=50;
net.efficiency.memoryReduction = 60;
net= trainrp(net, input,output);

%plot_xor(net);


%...............................................

Foutput=sim(net, anninput1); % Returns one output.
%figure, plotconfusion(Foutput, sim(net,input));


matris=zeros(10,2316);

 for i=1:300
    simoutelem=Foutput(:,i);
    [Max, argmax1]= max(simoutelem);
    matris(argmax1,i)=1;
 end

 confusion=zeros(10,10);
    for i=1:10
        for j=1:26
        confusion(i,1)=((matris(i,j)+confusion(i,1)))/26; 
        end
    end
for i=1:10
     for k=2:10
        for j=(k-1)*26+1:(k-1)*26+30
        confusion(i,k)=((matris(i,j)+confusion(i,k)))/30; 
        end
     end
end

[a b] = max([1 2 3 4 5]) % works
max = 1:100;
[a b] = max([1 2 3 4 5]) % gives your error
clear max
[a b] = max([1 2 3 4 5]) % works again