ServerRun 28576
Creatordusigh
Programsvmlight_regression
Datasetpan
Task typeRegression
Created4y25d ago
Done! Flag_green
2s
28M
Regression
2s
0.159
1s
0.098
1s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..OK. (5000 examples read)
Setting default regularization parameter C=0.6451
Optimizing.......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (1960 iterations)
Optimization finished (maxdiff=0.00093).
Runtime in cpu-seconds: 0.77
Number of SV: 3888 (including 3888 at upper bound)
L1 loss: loss=1175.41409
Norm of weight vector: |w|=0.00000
Norm of longest example vector: |x|=1.24504
Number of kernel evaluations: 189389
Writing model file...done
=== END program1: ./run learn ../dataset2/train --- OK [2s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Reading model...OK. (3888 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 0.00% (0 correct, 5000 incorrect, 5000 total)
Precision/recall on test set: -nan%/0.00%
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Reading model...OK. (3888 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 5000 incorrect, 5000 total)
Precision/recall on test set: -nan%/0.00%
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	0m6.556s
user	0m3.772s
sys	0m0.384s

Run specification Arrow_right
Results Arrow_right


Comments:


Must be logged in to post comments.