 | Run 1630 |
| Creator | pliang |
| Program | svmlight-rbf |
| Dataset | multiclass-sample |
| Task type | BinaryClassification |
| Created | 1y120d ago |
| Download | Login required! |
Done! 
| 1s | |
| 0B | |
| MulticlassClassification | |
| 0.250 | |
| 0.500 | |
Log file
===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset3/train ===== One versus all: training label y=1 versus the rest ===== === START _one-vs-all-learner1: ./run learn ../data1 Scanning examples...done Reading examples into memory...OK. (4 examples read) Setting default regularization parameter C=0.6226 Optimizing.done. (2 iterations) Optimization finished (1 misclassified, maxdiff=0.00000). Runtime in cpu-seconds: 0.00 Number of SV: 3 (including 1 at upper bound) L1 loss: loss=1.07048 Norm of weight vector: |w|=0.76072 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=2.00075 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=25.00% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>nan% (rho=1.00,depth=0) Number of kernel evaluations: 69 Writing model file...done === END _one-vs-all-learner1: ./run learn ../data1 --- OK [0s] ===== One versus all: training label y=2 versus the rest ===== === START _one-vs-all-learner2: ./run learn ../data2 Scanning examples...done Reading examples into memory...OK. (4 examples read) Setting default regularization parameter C=0.6226 Optimizing.done. (2 iterations) Optimization finished (1 misclassified, maxdiff=0.00000). Runtime in cpu-seconds: 0.00 Number of SV: 3 (including 1 at upper bound) L1 loss: loss=1.07048 Norm of weight vector: |w|=0.76072 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=2.00075 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=25.00% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>100.00% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>75.00% (rho=1.00,depth=0) Number of kernel evaluations: 69 Writing model file...done === END _one-vs-all-learner2: ./run learn ../data2 --- OK [0s] === END program1: ./run learn ../dataset3/train --- OK [0s] ===== MAIN: predict/evaluate on train data ===== === START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in === END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [0s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out === START _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 Reading model...OK. (3 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 0.00% (0 correct, 4 incorrect, 4 total) Precision/recall on test set: nan%/0.00% === END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [0s] === START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 Reading model...OK. (3 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 100.00% (4 correct, 0 incorrect, 4 total) Precision/recall on test set: 100.00%/100.00% === END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [0s] 4 examples === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out === END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [0s] ===== MAIN: predict/evaluate on test data ===== === START program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in === END program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in --- OK [0s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out === START _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 Reading model...OK. (3 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 0.00% (0 correct, 2 incorrect, 2 total) Precision/recall on test set: nan%/0.00% === END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [0s] === START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 Reading model...OK. (3 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 100.00% (2 correct, 0 incorrect, 2 total) Precision/recall on test set: 100.00%/100.00% === END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [0s] 2 examples === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out === END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s] real 0m0.483s user 0m0.256s sys 0m0.104s
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