ServerRun 39208
Creatorchuertas
Programsvmlight-linear
DatasetOt Products
Task typeBinaryClassification
Created2y120d ago
Done! Flag_green
3m58s
71M
MulticlassClassification
3m8s
0.317
30s
0.310
15s

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...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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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...............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (31286 iterations)
Optimization finished (1333 misclassified, maxdiff=0.00085).
Runtime in cpu-seconds: 21.40
Number of SV: 2759 (including 2629 at upper bound)
L1 loss: loss=2693.42111
Norm of weight vector: |w|=0.06731
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=322.46369
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.02
XiAlpha-estimate of the error: error<=6.36% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.66% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.64% (rho=1.00,depth=0)
Number of kernel evaluations: 2162102
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [24s]

===== 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...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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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(12556 iterations)
Optimization finished (6678 misclassified, maxdiff=0.00091).
Runtime in cpu-seconds: 28.98
Number of SV: 16606 (including 16521 at upper bound)
L1 loss: loss=16053.21110
Norm of weight vector: |w|=1.16110
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=18341.33556
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.01
XiAlpha-estimate of the error: error<=38.34% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>26.35% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>26.33% (rho=1.00,depth=0)
Number of kernel evaluations: 1173493
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [33s]

===== One versus all: training label y=3 versus the rest =====
=== START _one-vs-all-learner3: ./run learn ../data3
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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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(34773 iterations)
Optimization finished (5532 misclassified, maxdiff=0.00096).
Runtime in cpu-seconds: 42.14
Number of SV: 11315 (including 11163 at upper bound)
L1 loss: loss=11227.86191
Norm of weight vector: |w|=0.16559
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=199.38286
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=26.10% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.00% (rho=1.00,depth=0)
Number of kernel evaluations: 2379555
Writing model file...done
=== END _one-vs-all-learner3: ./run learn ../data3 --- OK [46s]

===== One versus all: training label y=4 versus the rest =====
=== START _one-vs-all-learner4: ./run learn ../data4
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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (4180 iterations)
Optimization finished (1889 misclassified, maxdiff=0.00100).
Runtime in cpu-seconds: 15.47
Number of SV: 4157 (including 3633 at upper bound)
L1 loss: loss=3777.10841
Norm of weight vector: |w|=0.00213
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=1.05679
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.01
XiAlpha-estimate of the error: error<=9.30% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.00% (rho=1.00,depth=0)
Number of kernel evaluations: 675466
Writing model file...done
=== END _one-vs-all-learner4: ./run learn ../data4 --- OK [18s]

===== One versus all: training label y=5 versus the rest =====
=== START _one-vs-all-learner5: ./run learn ../data5
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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (573 iterations)
Optimization finished (211 misclassified, maxdiff=0.00097).
Runtime in cpu-seconds: 2.36
Number of SV: 790 (including 753 at upper bound)
L1 loss: loss=579.62056
Norm of weight vector: |w|=0.71233
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=776.84082
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.01
XiAlpha-estimate of the error: error<=1.82% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>79.14% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>78.55% (rho=1.00,depth=0)
Number of kernel evaluations: 466980
Writing model file...done
=== END _one-vs-all-learner5: ./run learn ../data5 --- OK [5s]

===== One versus all: training label y=6 versus the rest =====
=== START _one-vs-all-learner6: ./run learn ../data6
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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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(7360 iterations)
Optimization finished (1476 misclassified, maxdiff=0.00098).
Runtime in cpu-seconds: 11.64
Number of SV: 4184 (including 4092 at upper bound)
L1 loss: loss=3913.89558
Norm of weight vector: |w|=0.77200
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=22136.13041
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.01
XiAlpha-estimate of the error: error<=9.66% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>79.02% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>78.79% (rho=1.00,depth=0)
Number of kernel evaluations: 850447
Writing model file...done
=== END _one-vs-all-learner6: ./run learn ../data6 --- OK [15s]

===== One versus all: training label y=7 versus the rest =====
=== START _one-vs-all-learner7: ./run learn ../data7
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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (10458 iterations)
Optimization finished (1502 misclassified, maxdiff=0.00100).
Runtime in cpu-seconds: 10.93
Number of SV: 3362 (including 3266 at upper bound)
L1 loss: loss=3274.44329
Norm of weight vector: |w|=0.31000
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=6818.81194
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.01
XiAlpha-estimate of the error: error<=7.76% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>16.08% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>15.75% (rho=1.00,depth=0)
Number of kernel evaluations: 1018371
Writing model file...done
=== END _one-vs-all-learner7: ./run learn ../data7 --- OK [14s]

===== One versus all: training label y=8 versus the rest =====
=== START _one-vs-all-learner8: ./run learn ../data8
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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (7890 iterations)
Optimization finished (1528 misclassified, maxdiff=0.00091).
Runtime in cpu-seconds: 11.13
Number of SV: 4214 (including 4115 at upper bound)
L1 loss: loss=3908.86407
Norm of weight vector: |w|=0.82149
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=25064.92771
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.01
XiAlpha-estimate of the error: error<=9.72% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>64.80% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>64.60% (rho=1.00,depth=0)
Number of kernel evaluations: 879687
Writing model file...done
=== END _one-vs-all-learner8: ./run learn ../data8 --- OK [14s]

===== One versus all: training label y=9 versus the rest =====
=== START _one-vs-all-learner9: ./run learn ../data9
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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..OK. (43315 examples read)
Setting default regularization parameter C=0.0027
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (5567 iterations)
Optimization finished (1312 misclassified, maxdiff=0.00098).
Runtime in cpu-seconds: 11.40
Number of SV: 3488 (including 3407 at upper bound)
L1 loss: loss=3274.71943
Norm of weight vector: |w|=0.67270
Norm of longest example vector: |x|=353.56612
Estimated VCdim of classifier: VCdim<=9275.44024
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=8.05% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>50.34% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>50.00% (rho=1.00,depth=0)
Number of kernel evaluations: 749744
Writing model file...done
=== END _one-vs-all-learner9: ./run learn ../data9 --- OK [14s]

=== END program1: ./run learn ../dataset3/train --- OK [188s]

===== 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 [1s]
=== 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. (2759 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.06% (25 correct, 43290 incorrect, 43315 total)
Precision/recall on test set: 100.00%/0.06%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [3s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (16606 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.09
Accuracy on test set: 29.17% (12634 correct, 30681 incorrect, 43315 total)
Precision/recall on test set: 100.00%/29.17%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [3s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3
Reading model...OK. (11315 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.08
Accuracy on test set: 0.53% (228 correct, 43087 incorrect, 43315 total)
Precision/recall on test set: 100.00%/0.53%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [3s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4
Reading model...OK. (4157 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 0.00% (0 correct, 43315 incorrect, 43315 total)
Precision/recall on test set: -nan%/0.00%
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4 --- OK [3s]
=== START _one-vs-all-learner5: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y5
Reading model...OK. (790 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.05
Accuracy on test set: 4.08% (1767 correct, 41548 incorrect, 43315 total)
Precision/recall on test set: 100.00%/4.08%
=== END _one-vs-all-learner5: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y5 --- OK [3s]
=== START _one-vs-all-learner6: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y6
Reading model...OK. (4184 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.04
Accuracy on test set: 21.11% (9142 correct, 34173 incorrect, 43315 total)
Precision/recall on test set: 100.00%/21.11%
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y6 --- OK [3s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7
Reading model...OK. (3362 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 1.63% (707 correct, 42608 incorrect, 43315 total)
Precision/recall on test set: 100.00%/1.63%
=== END _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7 --- OK [3s]
=== START _one-vs-all-learner8: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y8
Reading model...OK. (4214 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.07
Accuracy on test set: 12.15% (5263 correct, 38052 incorrect, 43315 total)
Precision/recall on test set: 100.00%/12.15%
=== END _one-vs-all-learner8: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y8 --- OK [3s]
=== START _one-vs-all-learner9: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y9
Reading model...OK. (3488 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..done
Runtime (without IO) in cpu-seconds: 0.03
Accuracy on test set: 6.50% (2816 correct, 40499 incorrect, 43315 total)
Precision/recall on test set: 100.00%/6.50%
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y9 --- OK [3s]
43315 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [30s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [3s]

===== 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. (2759 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 0.08% (15 correct, 18548 incorrect, 18563 total)
Precision/recall on test set: 100.00%/0.08%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [1s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (16606 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.04
Accuracy on test set: 29.18% (5417 correct, 13146 incorrect, 18563 total)
Precision/recall on test set: 100.00%/29.18%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [2s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3
Reading model...OK. (11315 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.45% (84 correct, 18479 incorrect, 18563 total)
Precision/recall on test set: 100.00%/0.45%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [2s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4
Reading model...OK. (4157 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 0.00% (0 correct, 18563 incorrect, 18563 total)
Precision/recall on test set: -nan%/0.00%
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4 --- OK [2s]
=== START _one-vs-all-learner5: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y5
Reading model...OK. (790 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 4.53% (841 correct, 17722 incorrect, 18563 total)
Precision/recall on test set: 100.00%/4.53%
=== END _one-vs-all-learner5: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y5 --- OK [1s]
=== START _one-vs-all-learner6: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y6
Reading model...OK. (4184 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 21.20% (3935 correct, 14628 incorrect, 18563 total)
Precision/recall on test set: 100.00%/21.20%
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y6 --- OK [1s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y7
Reading model...OK. (3362 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 1.66% (308 correct, 18255 incorrect, 18563 total)
Precision/recall on test set: 100.00%/1.66%
=== END _one-vs-all-learner7: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y7 --- OK [2s]
=== START _one-vs-all-learner8: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y8
Reading model...OK. (4214 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 12.26% (2276 correct, 16287 incorrect, 18563 total)
Precision/recall on test set: 100.00%/12.26%
=== END _one-vs-all-learner8: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y8 --- OK [1s]
=== START _one-vs-all-learner9: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y9
Reading model...OK. (3488 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..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 6.48% (1203 correct, 17360 incorrect, 18563 total)
Precision/recall on test set: 100.00%/6.48%
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y9 --- OK [1s]
18563 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [15s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [1s]


real	3m58.471s
user	3m44.126s
sys	0m9.897s

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