knn example

gistfile1.txt

[INFO] m_num_classes: 4 (+0 to +3) num_train: 92
[DEBUG] entering KNN::apply(DenseFeatures at 0x96a3a70)
[INFO] 92 test examples
[DEBUG] leaving KNN::apply(DenseFeatures at 0x96a3a70)
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
[WARN] In file /home/trac/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()
[WARN] In file /home/trac/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=[0,1,3,4,5,6,9,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,28,29,30,31,32,33,34,36,37,38,39,40,41,43,44,46,47,48,49,50,51,52,53,54,56,57,59,60,61,62,63,65,67,69,71,72,73,74,75,76,77,78,79,80,81,83,84,86,87,89,90,91]
[DEBUG] starting training
[INFO] m_num_classes: 4 (+0 to +3) num_train: 74
[INFO] doing without cache.
[DEBUG] finished training
[DEBUG] test set 0:
test indices=[12,8,7,11,2,27,42,35,45,66,55,58,68,64,82,88,85,70]
[DEBUG] starting evaluation
[DEBUG] 0x96a3a70
[DEBUG] entering KNN::apply(DenseFeatures at 0x96a3a70)
[INFO] 18 test examples
[DEBUG] leaving KNN::apply(DenseFeatures at 0x96a3a70)
[DEBUG] finished evaluation
[DEBUG] correct=16, total=18, rejected=0
[DEBUG] result on fold 0 is 0.888889
[DEBUG] training set 1:
training indices=[1,2,3,5,6,7,8,9,10,11,12,13,15,16,19,20,21,22,23,24,25,27,28,29,30,31,32,33,35,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,55,56,58,61,62,63,64,65,66,68,69,70,72,73,74,75,76,78,79,80,81,82,83,85,86,87,88,91]
[DEBUG] starting training
[INFO] m_num_classes: 4 (+0 to +3) num_train: 73
[INFO] doing without cache.
[DEBUG] finished training
[DEBUG] test set 1:
test indices=[17,14,18,4,0,26,34,36,44,54,57,60,59,67,89,71,77,84,90]
[DEBUG] starting evaluation
[DEBUG] 0x96a3a70
[DEBUG] entering KNN::apply(DenseFeatures at 0x96a3a70)
[INFO] 19 test examples
[DEBUG] leaving KNN::apply(DenseFeatures at 0x96a3a70)
[DEBUG] finished evaluation
[DEBUG] correct=17, total=19, rejected=0
[DEBUG] result on fold 1 is 0.894737
[DEBUG] training set 2:
training indices=[0,2,3,4,5,6,7,8,9,11,12,14,16,17,18,19,21,22,23,24,26,27,28,32,33,34,35,36,37,38,39,40,42,43,44,45,48,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,75,77,79,80,81,82,83,84,85,86,87,88,89,90]
[DEBUG] starting training
[INFO] m_num_classes: 4 (+0 to +3) num_train: 73
[INFO] doing without cache.
[DEBUG] finished training
[DEBUG] test set 2:
test indices=[1,15,20,10,13,29,41,31,25,30,46,50,47,49,91,78,74,73,76]
[DEBUG] starting evaluation
[DEBUG] 0x96a3a70
[DEBUG] entering KNN::apply(DenseFeatures at 0x96a3a70)
[INFO] 19 test examples
[DEBUG] leaving KNN::apply(DenseFeatures at 0x96a3a70)
[DEBUG] finished evaluation
[DEBUG] correct=17, total=19, rejected=0
[DEBUG] result on fold 2 is 0.894737
[DEBUG] training set 3:
training indices=[0,1,2,3,4,7,8,10,11,12,13,14,15,16,17,18,19,20,22,23,25,26,27,28,29,30,31,33,34,35,36,37,39,41,42,44,45,46,47,48,49,50,54,55,56,57,58,59,60,62,64,65,66,67,68,69,70,71,73,74,75,76,77,78,79,81,82,83,84,85,88,89,90,91]
[DEBUG] starting training
[INFO] m_num_classes: 4 (+0 to +3) num_train: 74
[INFO] doing without cache.
[DEBUG] finished training
[DEBUG] test set 3:
test indices=[5,9,6,21,43,24,38,32,40,52,51,53,61,63,87,72,86,80]
[DEBUG] starting evaluation
[DEBUG] 0x96a3a70
[DEBUG] entering KNN::apply(DenseFeatures at 0x96a3a70)
[INFO] 18 test examples
[DEBUG] leaving KNN::apply(DenseFeatures at 0x96a3a70)
[DEBUG] finished evaluation
[DEBUG] correct=16, total=18, rejected=0
[DEBUG] result on fold 3 is 0.888889
[DEBUG] training set 4:
training indices=[0,1,2,4,5,6,7,8,9,10,11,12,13,14,15,17,18,20,21,24,25,26,27,29,30,31,32,34,35,36,38,40,41,42,43,44,45,46,47,49,50,51,52,53,54,55,57,58,59,60,61,63,64,66,67,68,70,71,72,73,74,76,77,78,80,82,84,85,86,87,88,89,90,91]
[DEBUG] starting training
[INFO] m_num_classes: 4 (+0 to +3) num_train: 74
[INFO] doing without cache.
[DEBUG] finished training
[DEBUG] test set 4:
test indices=[16,19,22,3,37,39,28,33,23,65,48,62,56,83,79,81,69,75]
[DEBUG] starting evaluation
[DEBUG] 0x96a3a70
[DEBUG] entering KNN::apply(DenseFeatures at 0x96a3a70)
[INFO] 18 test examples
[DEBUG] leaving KNN::apply(DenseFeatures at 0x96a3a70)
[DEBUG] finished evaluation
[DEBUG] correct=15, total=18, rejected=0
[DEBUG] result on fold 4 is 0.833333
[DEBUG] done unlocked evaluation
[DEBUG] leaving CrossValidation::evaluate_one_run()
[DEBUG] result of cross-validation run 0 is 0.880117
[DEBUG] leaving CrossValidation::evaluate()
0.880117
[DEBUG] Destroying List 0x96d6b40