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December 21, 2013 14:47
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| [INFO] m_num_classes: 4 (+0 to +3) num_train: 92 | |
| [DEBUG] entering KNN::apply(DenseFeatures at 0x15ec860) | |
| [INFO] 92 test examples | |
| [DEBUG] leaving KNN::apply(DenseFeatures at 0x15ec860) | |
| i=0, class=0.000000, | |
| i=1, class=0.000000, | |
| i=2, class=0.000000, | |
| i=3, class=0.000000, | |
| i=4, class=0.000000, | |
| i=5, class=0.000000, | |
| i=6, class=0.000000, | |
| i=7, class=0.000000, | |
| i=8, class=0.000000, | |
| i=9, class=0.000000, | |
| i=10, class=0.000000, | |
| i=11, class=0.000000, | |
| i=12, class=0.000000, | |
| i=13, class=0.000000, | |
| i=14, class=0.000000, | |
| i=15, class=0.000000, | |
| i=16, class=0.000000, | |
| i=17, class=0.000000, | |
| i=18, class=0.000000, | |
| i=19, class=0.000000, | |
| i=20, class=0.000000, | |
| i=21, class=0.000000, | |
| i=22, class=0.000000, | |
| i=23, class=1.000000, | |
| i=24, class=1.000000, | |
| i=25, class=1.000000, | |
| i=26, class=1.000000, | |
| i=27, class=1.000000, | |
| i=28, class=1.000000, | |
| i=29, class=1.000000, | |
| i=30, class=1.000000, | |
| i=31, class=1.000000, | |
| i=32, class=1.000000, | |
| i=33, class=1.000000, | |
| i=34, class=1.000000, | |
| i=35, class=1.000000, | |
| i=36, class=1.000000, | |
| i=37, class=1.000000, | |
| i=38, class=1.000000, | |
| i=39, class=1.000000, | |
| i=40, class=1.000000, | |
| i=41, class=1.000000, | |
| i=42, class=1.000000, | |
| i=43, class=1.000000, | |
| i=44, class=1.000000, | |
| i=45, class=1.000000, | |
| i=46, class=2.000000, | |
| i=47, class=2.000000, | |
| i=48, class=2.000000, | |
| i=49, class=2.000000, | |
| i=50, class=2.000000, | |
| i=51, class=3.000000, | |
| i=52, class=1.000000, | |
| i=53, class=2.000000, | |
| i=54, class=1.000000, | |
| i=55, class=1.000000, | |
| i=56, class=2.000000, | |
| i=57, class=2.000000, | |
| i=58, class=2.000000, | |
| i=59, class=2.000000, | |
| i=60, class=2.000000, | |
| i=61, class=2.000000, | |
| i=62, class=2.000000, | |
| i=63, class=2.000000, | |
| i=64, class=2.000000, | |
| i=65, class=2.000000, | |
| i=66, class=2.000000, | |
| i=67, class=2.000000, | |
| i=68, class=2.000000, | |
| i=69, class=3.000000, | |
| i=70, class=3.000000, | |
| i=71, class=3.000000, | |
| i=72, class=3.000000, | |
| i=73, class=3.000000, | |
| i=74, class=1.000000, | |
| i=75, class=3.000000, | |
| i=76, class=3.000000, | |
| i=77, class=3.000000, | |
| i=78, class=3.000000, | |
| i=79, class=2.000000, | |
| i=80, class=3.000000, | |
| i=81, class=3.000000, | |
| i=82, class=3.000000, | |
| i=83, class=3.000000, | |
| i=84, class=3.000000, | |
| i=85, class=1.000000, | |
| i=86, class=3.000000, | |
| i=87, class=3.000000, | |
| i=88, class=3.000000, | |
| i=89, class=3.000000, | |
| i=90, class=3.000000, | |
| i=91, class=1.000000, | |
| [DEBUG] correct=84, total=92, rejected=0 | |
| training accuracy: 0.913043 | |
| [1;34m[WARN][0m In file /home/trac/shogun-work/shogun/src/shogun/evaluation/CrossValidation.cpp line 188: Confidence interval for Cross-Validation only possible when number of runs is >1, ignoring. | |
| [DEBUG] entering CrossValidation::evaluate() | |
| [1;34m[WARN][0m In file /home/trac/shogun-work/shogun/src/shogun/evaluation/CrossValidation.cpp line 107: KNN does not support locking. Autolocking is skipped. Set autolock flag to false to get rid of warning. | |
| [DEBUG] starting 1 runs of cross-validation | |
| [DEBUG] entering cross-validation run 0 | |
| [DEBUG] entering CrossValidation::evaluate_one_run() | |
| [DEBUG] building index sets for 5-fold cross-validation | |
| [DEBUG] starting unlocked evaluation | |
| [DEBUG] training set 0: | |
| training indices=[0,1,2,3,4,6,7,9,10,11,13,14,15,17,18,19,20,22,23,24,25,28,29,30,32,33,34,35,36,37,38,39,40,41,43,44,45,47,48,49,50,51,52,54,56,57,58,59,60,61,62,64,65,66,67,69,70,72,73,74,76,77,79,80,81,83,84,85,87,88,89,90,91] | |
| [DEBUG] starting training | |
| [INFO] m_num_classes: 4 (+0 to +3) num_train: 73 | |
| [INFO] doing without cache. | |
| [DEBUG] finished training | |
| [DEBUG] test set 0: | |
| test indices=[12,5,21,8,16,42,31,27,26,68,55,53,46,63,71,86,82,75,78] | |
| [DEBUG] starting evaluation | |
| [DEBUG] 0x15ec860 | |
| [DEBUG] entering KNN::apply(DenseFeatures at 0x15ec860) | |
| [INFO] 19 test examples | |
| [DEBUG] leaving KNN::apply(DenseFeatures at 0x15ec860) | |
| [DEBUG] finished evaluation | |
| [DEBUG] correct=18, total=19, rejected=0 | |
| [DEBUG] result on fold 0 is 0.947368 | |
| [DEBUG] training set 1: | |
| training indices=[1,2,3,4,5,6,7,8,9,12,13,14,15,16,17,18,19,21,22,23,26,27,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,52,53,54,55,56,57,60,62,63,64,65,66,67,68,69,70,71,72,73,74,75,77,78,79,80,81,82,83,84,85,86,87,91] | |
| [DEBUG] starting training | |
| [INFO] m_num_classes: 4 (+0 to +3) num_train: 74 | |
| [INFO] doing without cache. | |
| [DEBUG] finished training | |
| [DEBUG] test set 1: | |
| test indices=[20,0,11,10,24,25,28,32,45,58,61,50,51,59,76,89,90,88] | |
| [DEBUG] starting evaluation | |
| [DEBUG] 0x15ec860 | |
| [DEBUG] entering KNN::apply(DenseFeatures at 0x15ec860) | |
| [INFO] 18 test examples | |
| [DEBUG] leaving KNN::apply(DenseFeatures at 0x15ec860) | |
| [DEBUG] finished evaluation | |
| [DEBUG] correct=17, total=18, rejected=0 | |
| [DEBUG] result on fold 1 is 0.944444 | |
| [DEBUG] training set 2: | |
| training indices=[0,2,4,5,6,7,8,10,11,12,14,15,16,17,18,20,21,22,24,25,26,27,28,29,30,31,32,33,34,35,37,38,39,40,42,44,45,46,48,49,50,51,53,54,55,57,58,59,61,63,64,65,66,67,68,69,70,71,72,73,75,76,78,79,81,82,84,85,86,87,88,89,90,91] | |
| [DEBUG] starting training | |
| [INFO] m_num_classes: 4 (+0 to +3) num_train: 74 | |
| [INFO] doing without cache. | |
| [DEBUG] finished training | |
| [DEBUG] test set 2: | |
| test indices=[9,13,19,1,3,36,23,43,41,62,56,52,60,47,83,77,74,80] | |
| [DEBUG] starting evaluation | |
| [DEBUG] 0x15ec860 | |
| [DEBUG] entering KNN::apply(DenseFeatures at 0x15ec860) | |
| [INFO] 18 test examples | |
| [DEBUG] leaving KNN::apply(DenseFeatures at 0x15ec860) | |
| [DEBUG] finished evaluation | |
| [DEBUG] correct=15, total=18, rejected=0 | |
| [DEBUG] result on fold 2 is 0.833333 | |
| [DEBUG] training set 3: | |
| training indices=[0,1,3,4,5,7,8,9,10,11,12,13,15,16,17,19,20,21,23,24,25,26,27,28,29,30,31,32,34,35,36,37,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,58,59,60,61,62,63,64,68,69,71,72,73,74,75,76,77,78,80,81,82,83,85,86,88,89,90] | |
| [DEBUG] starting training | |
| [INFO] m_num_classes: 4 (+0 to +3) num_train: 73 | |
| [INFO] doing without cache. | |
| [DEBUG] finished training | |
| [DEBUG] test set 3: | |
| test indices=[22,14,18,2,6,38,33,40,44,39,67,65,66,57,70,79,84,91,87] | |
| [DEBUG] starting evaluation | |
| [DEBUG] 0x15ec860 | |
| [DEBUG] entering KNN::apply(DenseFeatures at 0x15ec860) | |
| [INFO] 19 test examples | |
| [DEBUG] leaving KNN::apply(DenseFeatures at 0x15ec860) | |
| [DEBUG] finished evaluation | |
| [DEBUG] correct=17, total=19, rejected=0 | |
| [DEBUG] result on fold 3 is 0.894737 | |
| [DEBUG] training set 4: | |
| training indices=[0,1,2,3,5,6,8,9,10,11,12,13,14,16,18,19,20,21,22,23,24,25,26,27,28,31,32,33,36,38,39,40,41,42,43,44,45,46,47,50,51,52,53,55,56,57,58,59,60,61,62,63,65,66,67,68,70,71,74,75,76,77,78,79,80,82,83,84,86,87,88,89,90,91] | |
| [DEBUG] starting training | |
| [INFO] m_num_classes: 4 (+0 to +3) num_train: 74 | |
| [INFO] doing without cache. | |
| [DEBUG] finished training | |
| [DEBUG] test set 4: | |
| test indices=[17,7,4,15,37,34,30,35,29,49,64,48,54,81,69,85,73,72] | |
| [DEBUG] starting evaluation | |
| [DEBUG] 0x15ec860 | |
| [DEBUG] entering KNN::apply(DenseFeatures at 0x15ec860) | |
| [INFO] 18 test examples | |
| [DEBUG] leaving KNN::apply(DenseFeatures at 0x15ec860) | |
| [DEBUG] finished evaluation | |
| [DEBUG] correct=16, total=18, rejected=0 | |
| [DEBUG] result on fold 4 is 0.888889 | |
| [DEBUG] done unlocked evaluation | |
| [DEBUG] leaving CrossValidation::evaluate_one_run() | |
| [DEBUG] result of cross-validation run 0 is 0.901754 | |
| [DEBUG] leaving CrossValidation::evaluate() | |
| 0.901754 | |
| [DEBUG] Destroying List 0x160c2c0 |
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