neural network - error: Matrix dimensions must agree in matlab for building confusion matrix -

i know question has been asked before didn't answer out of them. , didn't mathworks.com i'm trying build confusion matrix ann. testsize number of testset files have.

output = sim(net,yt);%testing network     confusionmatrix(10,10);%building confusion matrix i=1:(testsize-2)*10     j=1:10         m(j) = abs(output(i,i)-1); end minimum = find(m == min(m)); confusionmatrix(floor((i-1)/(testsize-2)) + 1,minimum) = confusionmatrix(floor((i-1)/(testsize-2))+1); end 

and below whole code. it's speech recognition based on testsets , trainsets.

trainset=dir('trainset');  maxlength=0;   i=3:length(trainset) file=strcat('trainset\',trainset(i).name); sub_direction=dir(file);     j=3:length(sub_direction)          path=strcat(file,'\',sub_direction(j).name);         sample=wavread(path); % reading files         if(length(sample) > maxlength)             maxlength = length(sample);         end          end end  testset=dir('testset');  i=3:length(testset) file=strcat('testset\',testset(i).name); sub_direction=dir(file);     j=3:length(sub_direction)          path=strcat(file,'\',sub_direction(j).name);         sample=wavread(path);         if(length(sample) > maxlength)             maxlength = length(sample);         end     end  end   %equalizing trainset data  i=3:length(trainset) file=strcat('trainset\',trainset(i).name); sub_direction=dir(file); trainsize = length(sub_direction);     j=3:length(sub_direction)          path=strcat(file,'\',sub_direction(j).name);         trainsetmatrix(i-2,j-2,:) = zeros(1,maxlength);         sample=wavread(path); % reading files         k=1:length(sample)             trainsetmatrix(i-2,j-2,k) = sample(k);          end           end end   %equalizing testset data  i=3:length(testset) file=strcat('testset\',testset(i).name); sub_direction=dir(file); testsize = length(sub_direction);     j=3:length(sub_direction)          path=strcat(file,'\',sub_direction(j).name);          testsetmatrix(i-2,j-2,:) = zeros(1,maxlength);         sample=wavread(path); % reading files         k=1:length(sample)             testsetmatrix(i-2,j-2,k) = sample(k);          end          end end  i=3:length(trainset) file=strcat('trainset\',trainset(i).name); sub_direction=dir(file);     j=3:length(sub_direction)     framesize = 160;%framing: each frame equals 8khz*20msec = 160samples     frameoverlap = 80;%overlapping 50% of each frame     framedtrainsetmatrix(1,:) = trainsetmatrix(i-2,j-2,1:160);     k=1:floor(maxlength/80)-2         framedtrainsetmatrix(k+1,:)= trainsetmatrix(i-2,j-2,k*80:(k*80+framesize-1));     end     end end   i=3:length(trainset) file=strcat('trainset\',trainset(i).name); sub_direction=dir(file);     j=3:length(sub_direction)     window = hamming(framesize);     k=1:floor(maxlength/80)-1         windowedframe(k,:) = framedtrainsetmatrix(k,:).*window';         linearpredictivecoding(k,:) = lpc(windowedframe(k,:),12);         lpcresult(k,:) = linearpredictivecoding(k,2:13);     end     coefficient=12*(floor(maxlength/80)-1);     x((i-3)*(trainsize-2)+(j-2),:)=reshape(lpcresult,1,coefficient);      end end     i=3:length(testset) file=strcat('testset\',testset(i).name); sub_direction=dir(file);     j=3:length(sub_direction)     framesize = 160;%framing: each frame equals 8khz*20msec = 160samples     frameoverlap = 80;%overlapping 50% of each frame     framedtestsetmatrix(1,:) = testsetmatrix(i-2,j-2,1:160);     k=1:floor(maxlength/80)-2         framedtestsetmatrix(k+1,:)= testsetmatrix(i-2,j-2,k*80:(k*80+framesize-1));     end     end end   i=3:length(testset) file=strcat('testset\',testset(i).name); sub_direction=dir(file);     j=3:length(sub_direction)     window = hamming(framesize);     k=1:floor(maxlength/80)-1         windowedframe(k,:) = framedtestsetmatrix(k,:).*window';         linearpredictivecoding(k,:) = lpc(windowedframe(k,:),12);         lpcresult(k,:) = linearpredictivecoding(k,2:13);     end     coefficient=12*(floor(maxlength/80)-1);     y((i-3)*(testsize-2)+(j-2),:)=reshape(lpcresult,1,coefficient);      end end   xt = transpose(x); t(10,1900); hiddellayer=1158;  net=newff(xt,t,hiddellayer);%building network  net.divideparam.trainratio=0.2;  net.efficiency.memoryreduction=60; net=train(net,xt,t);%training network  yt = transpose(y); output = sim(net,yt);%testing network   %building confusion matrix confusionmatrix(10,10);    i=1:(testsize-2)*10     j=1:10         m(j) = abs(output(i,i)-1);     end minimum = find(m == min(m)); confusionmatrix(floor((i-1)/(testsize-2)) + 1,minimum) = confusionmatrix(floor((i-1)/(testsize-2))+1); end  i=1:10     j=1:10         confusionmatrix(i,j) = confusionmatrix(i,j)/(testsize-2);     end end 

the full error is:

??? error using ==> eq
matrix dimensions must agree.

error in ==> test @ 167
minimum = find(m == min(m));

please help.

see documentation min:

if matrix, min(a) row vector containing minimum value of each column.

your min(m) call returning vector, matlab can't use == (inputs , b == must same size unless 1 scalar), illustrated test case, produces same error:

a = rand(4); test = find(a == min(a)); 

if you're looking index of smallest value in entire matrix, use find(a == min(a(:)));

or, more simply: [minval idx] = min(a(:))