svmclassify - not enough input arguments

Dear All I am trying to train and classify svm algorithm using k=10 fold cross-validation. I have input matrices for train and test data which are provided globally. I build the model suing svmtrain. But when I want to test it using svmclassify function, it return an error "not enough input arguments".

The question is although I have called the svmcalssify function at it mentioned in the documents of the matlab what causes this error ?

the dataset ordered in way that each row of the train_data matrix contains one sample, and columns are variables.

I would really appreciate your help on this issue.

Thanks in advance

Ramin

function test_result = train_svm_classifier()

global train_data ;%= 300*312[] ;
global train_labels; % =300*1 [];

global test_data;% = 194*312[];
global test_labels;% = 194*1*[];

global SVMmodel;
groups = train_labels;

k=10;

cvFolds = crossvalind('Kfold', groups, k);   %# get indices of 10-fold CV
cp = classperf(groups);                      %# init performance tracker

for i = 1:k                                  %# for each fold

      testIdx = (cvFolds == i);  %# get indices of test instances

      trainIdx = ~testIdx;  %# get indices training instances    

      SVMmodelCross = svmtrain(train_data(trainIdx,:),...
          train_labels(trainIdx),'autoscale',false,'kernel_function'...
      ,'rbf','RBF_Sigma',1e-3,'BoxConstraint',1e3);

    pred = svmclassify(SVMmodelCross,train_data(testIdx,1),'Showplot',false);<--- here is the error

    cp = classperf(cp, pred, testIdx);

end

cp.CorrectRate

cp.CountingMatrix

SVMmodel = svmtrain(train_data,train_labels,...
  'autoscale',false,'kernel_function'...
 ,'rbf','RBF_Sigma',1/size(train_data,2),'ShowPlot', false);

test_pred = svmclassify(SVMmodel, test_data, 'ShowPlot', false);

test_cp = classperf(test_labels); 

test_cp = classperf(test_cp, test_pred);

test_cp.CorrectRate
test_cp.CountingMatrix
test_result = test_cp;
end