libsvm cross validation with precomputed kernel in matlab

Question

I am trying to do a 5 fold cross validation with libsvm (matlab) using a precomputed kernel, but, I get the following error message : Undefined function 'ge' for input arguments of type 'struct'. this is because the Libsvm return a structure instead of a value in cross validation, How can I solve this problem, this is my code:

load('iris.dat')
data=iris(:,1:4);
class=iris(:,5);

% normalize the data
range=repmat((max(data)-min(data)),size(data,1),1);
data=(data-repmat(min(data),size(data,1),1))./range;

% train  
tr_data=[data(1:5,:);data(52:56,:);data(101:105,:)];
tr_lbl=[ones(5,1);2*ones(5,1);3*ones(5,1)];


% kernel computation
sigma=.8
rbfKernel = @(X,Y,sigma) exp((-pdist2(X,Y,'euclidean').^2)./(2*sigma^2));
Ktr=[(1:15)',rbfKernel(tr_data,tr_data,sigma)];
kts=[ (1:150)',rbfKernel(data,tr_data,sigma)];

% svmptrain
bestcv = 0;
for log2c = -1:3
    cmd = ['Ktr -t 4 -v 5 -c ', num2str(2^log2c)];  
    cv = svmtrain2(tr_lbl,tr_data, cmd);
    if (cv >= bestcv)
      bestcv = cv; 
      bestc = 2^log2c;
    end 
end


cmd=['-s 0 -c ', num2str(bestc), 'Ktr -t 4']
model=svmtrain2(tr_lbl,tr_data,cmd)

% svm predict
labels=svmpredict(class,data,model,kts)

first of all, you shouldn't define a variable named 'class'. Then, can you specify the line that producing the error? — NKN
I renamed the variable 'class' but this did not solve the problem, the error message in line 29 ( if (cv >= bestcv)). — Saied niazmardy

NKN NKN · Accepted Answer · 2014-03-21T15:11:28

The function svmtrain2 you are using is not part of standard MATLAB and also the output of the function is not a structure. But if you insist to use that, you can calculate an score for data using the other existing function:

[f,K] = svmeval(X_eval,varargin)

that evaluates the trained svm using the outputs from svmtrain2. But I prefer to use first the standard functions embedded in MATLAB. In standard MATLAB library there is:

SVMStruct = svmtrain(Training,Group)

that returns a structure, SVMStruct, containing information about the trained support vector machine (SVM) classifier. or

SVMModel = fitcsvm(X,Y)

that returns a support vector machine classifier SVMModel, trained by predictors X and class labels Y for one- or two-class classification. and then you can get some score for each prediction using:

[label,Score] = predict(SVMModel,X)

that returns class likelihood measures, i.e., either scores or posterior probabilities.

libsvm cross validation with precomputed kernel in matlab

2 Answers