Status: Done!
Total Time
Max Memory Usage
Domain
BinaryClassification
Learn time
Train error
Predict train time
Test error
Predict test time
Log file
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
=== START program2: ./run learn ../program1/data
Using hyperparameter c = 0.1
Reading training examples... (445 examples) done
Training set properties: 410 features, 2 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=288.2510, QPEps=80804.3456)
Iter 3: *(NumConst=3, SV=0, CEps=6963.3364, QPEps=99.4531)
Iter 4: *(NumConst=4, SV=3, CEps=100.0000, QPEps=93707789.8136)
Iter 5: *(NumConst=5, SV=4, CEps=79905191.7033, QPEps=28399793.9601)
Iter 6: *(NumConst=6, SV=5, CEps=8850991.7300, QPEps=21691146.7344)
Iter 7: *(NumConst=7, SV=5, CEps=427.5037, QPEps=3821067.2340)
Iter 8: *(NumConst=8, SV=5, CEps=5990773.0152, QPEps=3805362.7673)
Iter 9: *(NumConst=9, SV=5, CEps=5985189.9667, QPEps=3789723.5128)
Iter 10: *(NumConst=10, SV=5, CEps=5979629.6857, QPEps=3774149.3522)
Iter 11: *(NumConst=11, SV=5, CEps=5974092.1304, QPEps=3758639.9233)
Iter 12: *(NumConst=12, SV=5, CEps=5968577.1487, QPEps=3743194.9636)
Iter 13: *(NumConst=13, SV=5, CEps=5963084.6598, QPEps=3727814.2162)
Iter 14: *(NumConst=14, SV=5, CEps=5957614.6028, QPEps=3712497.4834)
Iter 15: *(NumConst=15, SV=5, CEps=5952166.9361, QPEps=3697244.3702)
Iter 16: *(NumConst=16, SV=5, CEps=5946741.3962, QPEps=3682054.6432)
Iter 17: *(NumConst=17, SV=5, CEps=5941337.9991, QPEps=3666928.0220)
Iter 18: *(NumConst=18, SV=5, CEps=5935956.5534, QPEps=3651864.3601)
Iter 19: *(NumConst=19, SV=5, CEps=5930597.1821, QPEps=3636863.1879)
Iter 20: *(NumConst=20, SV=5, CEps=5925259.5140, QPEps=3621924.5332)
Iter 21: *(NumConst=21, SV=5, CEps=5919943.7796, QPEps=3607047.8137)
Iter 22: *(NumConst=22, SV=5, CEps=5914649.5066, QPEps=3592233.0597)
Iter 23: *(NumConst=23, SV=5, CEps=5909376.9815, QPEps=3577479.8444)
Iter 24: *(NumConst=24, SV=5, CEps=5904125.8653, QPEps=3562787.9382)
Iter 25: *(NumConst=25, SV=5, CEps=5898896.1881, QPEps=3548157.1017)
Iter 26: *(NumConst=26, SV=5, CEps=5893687.7820, QPEps=3533587.0975)
Iter 27: *(NumConst=27, SV=5, CEps=5888500.6214, QPEps=3519077.6899)
Iter 28: *(NumConst=28, SV=5, CEps=5883334.6443, QPEps=3504628.5494)
Iter 29: *(NumConst=29, SV=5, CEps=5878189.6580, QPEps=3490239.5083)
Iter 30: *(NumConst=30, SV=5, CEps=5873065.6708, QPEps=3475910.3065)
Iter 31: *(NumConst=31, SV=5, CEps=5867962.5749, QPEps=3461640.4870)
Iter 32: *(NumConst=32, SV=5, CEps=5862880.1180, QPEps=3447430.0911)
Iter 33: *(NumConst=33, SV=5, CEps=5857818.4511, QPEps=3433278.8043)
Iter 34: *(NumConst=34, SV=5, CEps=5852777.4447, QPEps=3419186.3000)
Iter 35: *(NumConst=35, SV=5, CEps=5847756.9343, QPEps=3405152.3774)
Iter 36: *(NumConst=36, SV=5, CEps=5842756.8692, QPEps=3391176.7582)
Iter 37: *(NumConst=37, SV=5, CEps=5837777.1060, QPEps=3377259.1908)
Iter 38: *(NumConst=38, SV=5, CEps=5832817.6341, QPEps=3363399.5675)
Iter 39: *(NumConst=39, SV=5, CEps=5827878.4361, QPEps=3349597.4812)
Iter 40: *(NumConst=40, SV=5, CEps=5822959.2694, QPEps=3335852.7956)
Iter 41: *(NumConst=41, SV=5, CEps=5818060.1583, QPEps=3322165.2180)
Iter 42: *(NumConst=42, SV=5, CEps=5813180.9774, QPEps=3308534.5921)
Iter 43: *(NumConst=43, SV=5, CEps=5808321.7060, QPEps=3294960.4453)
Iter 44: *(NumConst=44, SV=5, CEps=5803481.9809, QPEps=3281442.9181)
Iter 45: *(NumConst=45, SV=5, CEps=5798662.2776, QPEps=3267981.4044)
Iter 46: *(NumConst=46, SV=5, CEps=5793861.9094, QPEps=3254575.8946)
Iter 47: *(NumConst=47, SV=5, CEps=5789081.1456, QPEps=3241226.1994)
Iter 48: *(NumConst=48, SV=5, CEps=5784320.0022, QPEps=3227931.9092)
Iter 49: *(NumConst=49, SV=5, CEps=5779578.0868, QPEps=3214692.7858)
Iter 50: *Worker killed run: Current time usage 5h0m exceeds allowed 5h0m
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) binary-to-multi : Allows multiclass classification to be run on binary classification datasets (trivial reduction).
(multiclassLearner:Program[MulticlassClassification]) svmlight_multiclass : SVMlight for multiclass classification (http://svmlight.joachims.org/svm_multiclass.html)
(dataset:Dataset) stefansdata :
(stripper:Program[Strip]) binary-utils : Validates and inspects a dataset in BinaryClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
exitCode: 0
success: true
Comments:
Post comment:
Must be logged in to post comments.