machine learning - SVM-Light displays corrupted precision/recall results -

i run svm-light classifier recall/precision row outputs seem corrupted:

reading model...ok. (20 support vectors read) classifying test examples..100..200..done runtime (without io) in cpu-seconds: 0.00 accuracy on test set: 95.50% (191 correct, 9 incorrect, 200 total) precision/recall on test set: 0.00%/0.00% 

what should configure valid precision , recall?

for example, if classifier predicting "-1" -- negative class; test dataset, however, contains 191 "-1" , 9 "+1" golden labels, 191 of them correctly classified , 9 of them incorrect.

true positives : 0        (tp) true negatives : 191      (tn) false negatives: 9        (fn) false positives: 0        (fp) thus:                tp             0 precision = -----------  = --------- = undefined              tp + fp         0 + 0                 tp             0 recall    = -----------  = --------- = 0              tp + fn        0 + 9 

from formula above, know long tp zero, precision/recall either 0 or undefined.

to debug, should output (for each test example) golden label , predicted label know issue is.