Bad results when testing libsvm in matlab

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bek
bek 2013-3-23
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??

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回答（1 个）

pang jianfei
pang jianfei 2013-3-23
编辑：pang jianfei 2013-3-23
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.

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