Bad results when testing libsvm in matlab

can someone help me to solve this? I want to test whether this classification is already good or not. So, I try with data testing=data training. it will give 100% (acc) if the classification is good.

data= [170           66           ;
160            50           ;
170            63           ;
173            61           ;
168            58           ;
184            88           ;
189            94           ;
185            88           ]

labels=[-1;-1;-1;-1;-1;1;1;1];

numInst = size(data,1);
numLabels = max(labels);

   testVal = [1 2 3 4 5 6 7 8];
    trainLabel = labels(testVal,:);
    trainData = data(testVal,:);
    testData=data(testVal,:);
    testLabel=labels(testVal,:);
   numTrain = 8; numTest =8

%# train one-against-all models
model = cell(numLabels,1);
for k=1:numLabels
    model{k} = svmtrain(double(trainLabel==k), trainData, '-c 1 -t 2 -g 0.2 -b 1');
end

%# get probability estimates of test instances using each model
prob = zeros(numTest,numLabels);
for k=1:numLabels
    [~,~,p] = svmpredict(double(testLabel==k), testData, model{k}, '-b 1');
    prob(:,k) = p(:,model{k}.Label==1);    %# probability of class==k
end

%# predict the class with the highest probability
[~,pred] = max(prob,[],2);
acc = sum(pred == testLabel) ./ numel(testLabel)    %# accuracy
C = confusionmat(testLabel, pred)                   %# confusion matrix

and this is the results

optimization finished, #iter = 16  
nu = 0.645259 obj = -2.799682, 
rho = -0.437644 nSV = 8, nBSV = 1 Total nSV = 8 
Accuracy = 100% (8/8) (classification)

    acc =

      0.3750

C =

       0     5
       0     3

I dont know why there's two accuracy, and its different. the first one is 100% and the second one is 0.375. is my code false? it should be 100% not 37.5%. Can u help me to correct this code??

Answer 1

[pang jianfei]

The Accuracy = 100% (8/8) (classification) is from the svm toolbox ,which is automatic when you use the funttion of svmpredict.It is CRR which assume the right labels given by double(testLabel==k) .Well,acc is the CRR that you calculated.