Why Accuracy is zero

function [result] = multisvm(TrainingSet,Group_Train1,TestSet,Group_Test1)

%Models a given training set with a corresponding group vector and 
%classifies a given test set using an SVM classifier according to a 
%one vs. all relation. 
%
%This code was written by Cody Neuburger [e-mail]
%Florida Atlantic University, Florida USA...



%This code was adapted and cleaned from Anand Mishra's multisvm function
%found at http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine/
%GroupTrain=GroupTrain';
u=unique(Group_Train1);
numClasses=length(u);
%TestSet=TestSet';
%TrainingSet=TrainingSet';

result = categorical.empty();
%build models

models = cell(numClasses,1);
for k=1:numClasses
    %Vectorized statement that binarizes Group
    %where 1 is the current class and 0 is all other classes
    G1vAll=(Group_Train1==u(k));
    %models{k} = fitcsvm(TrainingSet,G1vAll);
    models{k} = fitcsvm(TrainingSet,G1vAll,'KernelFunction','polynomial','polynomialorder',3,'Solver','ISDA','Verbose',0,'Standardize',true);
    if ~models{k}.ConvergenceInfo.Converged
        fprintf('Training did not converge for class "%s"\n', string(u(k)));
    end
end

%classify test cases
for t=1:size(TestSet,1)
    matched = false;
    for M=1:numClasses
        if(predict(models{M},TestSet(t,: ))) 
            matched = true;
            break;
        end
    end
    if matched
      result(t,1) = u(M);
    else
      result(t,1) = 'No Match';
    end
%--------------------------------

end

Accuracy = mean(Group_Test1==result) * 100;
fprintf('Accuracy = %.2f\n', Accuracy);
fprintf('error rate = %.2f\n ', mean(result ~= Group_Test1 ) * 100);

end
 

Accuracy = 0.00

error rate = 100.00

[sun rise]

Now use a few pictures to try but the error is in this part

why M= 2?

%classify test cases
for t=1:size(TestSet,1)
    matched = false;
    for M = 1:numClasses
        if(predict(models{M},TestSet(t,: ))) 
            matched = true;
            break;
        end
    end
    if matched
      result(t,1) = u(M);
    else
      result(t,1) = 'No Match';
    end
    

[sun rise]

This is the code and dataset

[sun rise]

The result matrix is ​​not equal to the Group_test matrix. Hence the accuracy is zero. Because the counter k reaches 2 only and goes out to the outer loop and does not complete the count to 5. After entering the outer loop again, the count starts from 1 again .. I want the count to continue to 5 and put the value in the result matrix

[Walter Roberson]

Temporary work around: change

    for M = 1:numClasses
        if(predict(models{M},TestSet(t,: ))) 
            matched = true;
            break;
        end
    end

to

    for M = numClasses:-1:1
        if(predict(models{M},TestSet(t,: ))) 
            matched = true;
            break;
        end
    end

Answer 1

[Walter Roberson]

Your data does not fit your model at all

I showed in https://www.mathworks.com/matlabcentral/answers/894727-why-accuracy-is-zero#comment_1699059 that you can get up to 19% with those exact same training parameters, for the database that you were using then.

I also showed that with that database, it was practically impossible for you to run that code on your system. I suggested some performance improvements, but you do not appear to have used any of them.

Did you get a much bigger computer to run all of this on? Or did you reduce the image size a lot ?

[sun rise]

https://www.mathworks.com/matlabcentral/answers/1465734-why-accuracy-is-zero#comment_1780831