ServerRun 38087
Creatorrichox
ProgramHmbFS R1
Datasetmaxgroup
Task typeMulticlassClassification
Created2y263d ago
Done! Flag_green
24s
1449M
MulticlassClassification
23s
0
3s
0.109
2s

Log file

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--[AT=1]--- Start Node with 1705 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.4841) name=[S:10>0.7305]

--[AT=1]--- Start Node with 1795 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.5059) name=[S:11>0.4425]
Run weak tree 168

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9976) name=[S:8>0.3515]

--[AT=1]--- Start Node with 2293 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.6649) name=[S:9>0.1305]

--[AT=1]--- Start Node with 1207 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.3256) name=[S:12>0.4375]
Run weak tree 169

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9979) name=[S:7>0.0595]

--[AT=1]--- Start Node with 3305 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9455) name=[S:12>0.0035]

--[AT=1]--- Start Node with 195 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0451) name=[S:3>0.2235]
Run weak tree 170

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9981) name=[S:2>0.0155]

--[AT=1]--- Start Node with 3447 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9848) name=[S:6>0.0025]

--[AT=1]--- Start Node with 53 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0089) name=[S:13>0.7535]
Run weak tree 171

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9983) name=[S:5>0.6085]

--[AT=1]--- Start Node with 1414 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.4092) name=[S:5>0.6265]

--[AT=1]--- Start Node with 2086 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.5825) name=[S:6>0.0595]
Run weak tree 172

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9980) name=[S:12>0.7475]

--[AT=1]--- Start Node with 853 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.2352) name=[S:2>0.9505]

--[AT=1]--- Start Node with 2647 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.7572) name=[S:12>0.6555]
Run weak tree 173

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9982) name=[S:1>0.9715]

--[AT=1]--- Start Node with 117 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0260) name=[S:11>0.9155]

--[AT=1]--- Start Node with 3383 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9678) name=[S:8>0.047]
Run weak tree 174

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9982) name=[S:13>0.4595]

--[AT=1]--- Start Node with 1857 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.5090) name=[S:13>0.4625]

--[AT=1]--- Start Node with 1643 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.4822) name=[S:7>0.8485]
Run weak tree 175

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9983) name=[S:11>0.2425]

--[AT=1]--- Start Node with 2600 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.7374) name=[S:15>0.7655]

--[AT=1]--- Start Node with 900 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.2535) name=[S:9>0.9445]
Run weak tree 176

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9981) name=[S:9>0.7695]

--[AT=1]--- Start Node with 793 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.2042) name=[S:15>0.6585]

--[AT=1]--- Start Node with 2707 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.7873) name=[S:3>0.1175]
Run weak tree 177

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9983) name=[S:12>0.265]

--[AT=1]--- Start Node with 2568 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.7461) name=[S:14>0.5375]

--[AT=1]--- Start Node with 932 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.2454) name=[S:10>0.8385]
Run weak tree 178

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9983) name=[S:1>0.7955]

--[AT=1]--- Start Node with 741 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1927) name=[S:4>0.6305]

--[AT=1]--- Start Node with 2759 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.7992) name=[S:1>0.7675]
Run weak tree 179

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9981) name=[S:8>0.6455]

--[AT=1]--- Start Node with 1275 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.3496) name=[S:7>0.5595]

--[AT=1]--- Start Node with 2225 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.6411) name=[S:15>0.6005]
Run weak tree 180

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9982) name=[S:12>0.9785]

--[AT=1]--- Start Node with 65 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0081) name=[S:10>0.2295]

--[AT=1]--- Start Node with 3435 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9843) name=[S:12>0.9595]
Run weak tree 181

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9982) name=[S:2>0.5275]

--[AT=1]--- Start Node with 1639 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.4419) name=[S:6>0.5005]

--[AT=1]--- Start Node with 1861 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.5479) name=[S:12>0.5095]
Run weak tree 182

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9982) name=[S:5>0.0335]

--[AT=1]--- Start Node with 3377 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9600) name=[S:5>0.0385]

--[AT=1]--- Start Node with 123 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0325) name=[S:15>0.4985]
Run weak tree 183

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9982) name=[S:11>0.6705]

--[AT=1]--- Start Node with 1125 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.3059) name=[S:6>0.7385]

--[AT=1]--- Start Node with 2375 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.6843) name=[S:11>0.6655]
Run weak tree 184

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9980) name=[S:5>0.3405]

--[AT=1]--- Start Node with 2326 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.6776) name=[S:1>0.4105]

--[AT=1]--- Start Node with 1174 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.3119) name=[S:10>0.534]
Run weak tree 185

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9976) name=[S:13>0.7825]

--[AT=1]--- Start Node with 754 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1836) name=[S:14>0.8935]

--[AT=1]--- Start Node with 2746 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8080) name=[S:6>0.9305]
Run weak tree 186

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9978) name=[S:3>0.8315]

--[AT=1]--- Start Node with 568 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1540) name=[S:3>0.8445]

--[AT=1]--- Start Node with 2932 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8361) name=[S:5>0.0265]
Run weak tree 187

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9980) name=[S:4>0.0015]

--[AT=1]--- Start Node with 3493 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9953) name=[S:9>0.6545]

--[AT=1]--- Start Node with 7 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0000) name=[S:2>0.8175]
Run weak tree 188

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9981) name=[S:9>0.0045]

--[AT=1]--- Start Node with 3478 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9928) name=[S:8>0.0175]

--[AT=1]--- Start Node with 22 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0015) name=[S:9>0.0015]
Run weak tree 189

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9984) name=[S:7>0.8145]

--[AT=1]--- Start Node with 670 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1668) name=[S:9>0.3405]

--[AT=1]--- Start Node with 2830 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8236) name=[S:5>0.6525]
Run weak tree 190

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9979) name=[S:5>0.8395]

--[AT=1]--- Start Node with 587 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1501) name=[S:8>0.8385]

--[AT=1]--- Start Node with 2913 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8415) name=[S:11>0.9925]
Run weak tree 191

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9982) name=[S:9>0.1095]

--[AT=1]--- Start Node with 3099 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8959) name=[S:9>0.1155]

--[AT=1]--- Start Node with 401 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0937) name=[S:12>0.5765]
Run weak tree 192

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9979) name=[S:4>0.1535]

--[AT=1]--- Start Node with 2978 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8451) name=[S:10>0.0255]

--[AT=1]--- Start Node with 522 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1465) name=[S:2>0.3605]
Run weak tree 193

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9983) name=[S:15>0.0475]

--[AT=1]--- Start Node with 3349 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9582) name=[S:1>0.9965]

--[AT=1]--- Start Node with 151 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0343) name=[S:11>0.1445]
Run weak tree 194

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9984) name=[S:10>0.0615]

--[AT=1]--- Start Node with 3284 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.9352) name=[S:10>0.1005]

--[AT=1]--- Start Node with 216 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.0573) name=[S:9>0.2835]
Run weak tree 195

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9983) name=[S:8>0.4365]

--[AT=1]--- Start Node with 1992 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.5722) name=[S:6>0.5465]

--[AT=1]--- Start Node with 1508 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.4192) name=[S:13>0.1355]
Run weak tree 196

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9980) name=[S:1>0.6155]

--[AT=1]--- Start Node with 1369 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.3724) name=[S:2>0.3385]

--[AT=1]--- Start Node with 2131 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.6163) name=[S:9>0.9765]
Run weak tree 197

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9979) name=[S:11>0.1465]

--[AT=1]--- Start Node with 2954 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8386) name=[S:11>0.1505]

--[AT=1]--- Start Node with 546 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1519) name=[S:2>0.2485]
Run weak tree 198

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9970) name=[S:2>0.1615]

--[AT=1]--- Start Node with 2900 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8446) name=[S:2>0.1825]

--[AT=1]--- Start Node with 600 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1447) name=[S:12>0.1335]
Run weak tree 199

--[AT=1]--- Start Node with 3500 exemples, Depth=0
	xxx Q with gain(0.0000) error(0.9979) name=[S:15>0.8635]

--[AT=1]--- Start Node with 488 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.1316) name=[S:3>0.2345]

--[AT=1]--- Start Node with 3012 exemples, Depth=1
	xxx Q with gain(0.0000) error(0.8589) name=[S:4>0.8525]
run finished
=== END program1: ./run learn ../dataset2/train --- OK [23s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Using Java: 1.7.0-ea
Converting Test Data...
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 
Test data generated in: 601ms
Read on stdin:     0   10   20   30   40   50   60   70   80   90  100  110  120  130  140  150  160  170  180  190  200  210  220  230  240  250  260  270  280  290  300  310  320  330  340  350  360  370  380  390  400  410  420  430  440  450  460  470  480  490  500  510  520  530  540  550  560  570  580  590  600  610  620  630  640  650  660  670  680  690  700  710  720  730  740  750  760  770  780  790  800  810  820  830  840  850  860  870  880  890  900  910  920  930  940  950  960  970  980  990 1000 1010 1020 1030 1040 1050 1060 1070 1080 1090 1100 1110 1120 1130 1140 1150 1160 1170 1180 1190 1200 1210 1220 1230 1240 1250 1260 1270 1280 1290 1300 1310 1320 1330 1340 1350 1360 1370 1380 1390 1400 1410 1420 1430 1440 1450 1460 1470 1480 1490 1500 1510 1520 1530 1540 1550 1560 1570 1580 1590 1600 1610 1620 1630 1640 1650 1660 1670 1680 1690 1700 1710 1720 1730 1740 1750 1760 1770 1780 1790 1800 1810 1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150 2160 2170 2180 2190 2200 2210 2220 2230 2240 2250 2260 2270 2280 2290 2300 2310 2320 2330 2340 2350 2360 2370 2380 2390 2400 2410 2420 2430 2440 2450 2460 2470 2480 2490 2500 2510 2520 2530 2540 2550 2560 2570 2580 2590 2600 2610 2620 2630 2640 2650 2660 2670 2680 2690 2700 2710 2720 2730 2740 2750 2760 2770 2780 2790 2800 2810 2820 2830 2840 2850 2860 2870 2880 2890 2900 2910 2920 2930 2940 2950 2960 2970 2980 2990 3000 3010 3020 3030 3040 3050 3060 3070 3080 3090 3100 3110 3120 3130 3140 3150 3160 3170 3180 3190 3200 3210 3220 3230 3240 3250 3260 3270 3280 3290 3300 3310 3320 3330 3340 3350 3360 3370 3380 3390 3400 3410 3420 3430 3440 3450 3460 3470 3480 3490 3500Total features:15
Text features:0
Num features:0
Scored features:15
Load data:        0        0     1000     2000     3000     3500

0 Go 0 Mo 56 Ko 0 octets
Load round:    0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

Example      
Error statistics
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
| Label |        HYP |        REF |    Correct |        Err |  Precision |     Recall |  F-Measure |        CER |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|     1 |       1168 |       3500 |       1168 |       2332 |     100.00 |      33.37 |      50.04 |      66.63 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|     2 |       1179 |          0 |          0 |       1179 |       0.00 |     100.00 |       0.00 |  117900.00 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|     3 |       1153 |          0 |          0 |       1153 |       0.00 |     100.00 |       0.00 |  115300.00 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|   All |       3500 |       3500 |       1168 |       2332 |      33.37 |      33.37 |      33.37 |      66.63 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Using Java: 1.7.0-ea
Converting Test Data...
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 
Test data generated in: 520ms
Read on stdin:     0   10   20   30   40   50   60   70   80   90  100  110  120  130  140  150  160  170  180  190  200  210  220  230  240  250  260  270  280  290  300  310  320  330  340  350  360  370  380  390  400  410  420  430  440  450  460  470  480  490  500  510  520  530  540  550  560  570  580  590  600  610  620  630  640  650  660  670  680  690  700  710  720  730  740  750  760  770  780  790  800  810  820  830  840  850  860  870  880  890  900  910  920  930  940  950  960  970  980  990 1000 1010 1020 1030 1040 1050 1060 1070 1080 1090 1100 1110 1120 1130 1140 1150 1160 1170 1180 1190 1200 1210 1220 1230 1240 1250 1260 1270 1280 1290 1300 1310 1320 1330 1340 1350 1360 1370 1380 1390 1400 1410 1420 1430 1440 1450 1460 1470 1480 1490 1500Total features:15
Text features:0
Num features:0
Scored features:15
Load data:        0        0     1000     1500

0 Go 0 Mo 24 Ko 0 octets
Load round:    0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

Example      
Error statistics
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
| Label |        HYP |        REF |    Correct |        Err |  Precision |     Recall |  F-Measure |        CER |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|     1 |        454 |       1500 |        454 |       1046 |     100.00 |      30.27 |      46.47 |      69.73 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|     2 |        539 |          0 |          0 |        539 |       0.00 |     100.00 |       0.00 |   53900.00 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|     3 |        507 |          0 |          0 |        507 |       0.00 |     100.00 |       0.00 |   50700.00 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
|   All |       1500 |       1500 |        454 |       1046 |      30.27 |      30.27 |      30.27 |      69.73 |
|-------|------------|------------|------------|------------|------------|------------|------------|------------|
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	0m28.375s
user	0m26.070s
sys	0m1.820s

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