Too many input arguments.
    3 visualizaciones (últimos 30 días)
  
       Mostrar comentarios más antiguos
    
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 cneuburg@fau.edu
%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 d=1:numClasses
        if(predict(models{d},TestSet(t,: ))) 
            matched = true;
            break;
        end
    end
    if matched
      result(t,1) = u(d);
    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);
HOG2
load featurs_T
load featurs_S
load Group_Train
load Group_Test
result1= multisvm(TrainingSet,Group_Train1,TestSet,Group_Test1);
testresult = result1;
Error using multisvm
Too many input arguments.
Error in HOG2 (line 30)
result1= multisvm(TrainingSet,Group_Train1,TestSet,Group_Test1);
3 comentarios
Respuestas (0)
Ver también
Categorías
				Más información sobre Classification en Help Center y File Exchange.
			
	Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!