... (lines omitted) ...
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rnd 889: wh-err= 0.991982 th-err= 0.000160 test= nan train= 0.0000000
rnd 890: wh-err= 0.992234 th-err= 0.000159 test= nan train= 0.0000000
rnd 891: wh-err= 0.992936 th-err= 0.000158 test= nan train= 0.0000000
rnd 892: wh-err= 0.996485 th-err= 0.000157 test= nan train= 0.0000000
rnd 893: wh-err= 0.996997 th-err= 0.000157 test= nan train= 0.0000000
rnd 894: wh-err= 0.993774 th-err= 0.000156 test= nan train= 0.0000000
rnd 895: wh-err= 0.992246 th-err= 0.000154 test= nan train= 0.0000000
rnd 896: wh-err= 0.990637 th-err= 0.000153 test= nan train= 0.0000000
rnd 897: wh-err= 0.989832 th-err= 0.000151 test= nan train= 0.0000000
rnd 898: wh-err= 0.988354 th-err= 0.000150 test= nan train= 0.0000000
rnd 899: wh-err= 0.990717 th-err= 0.000148 test= nan train= 0.0000000
rnd 900: wh-err= 0.991758 th-err= 0.000147 test= nan train= 0.0000000
rnd 901: wh-err= 0.991999 th-err= 0.000146 test= nan train= 0.0000000
rnd 902: wh-err= 0.990941 th-err= 0.000144 test= nan train= 0.0000000
rnd 903: wh-err= 0.988788 th-err= 0.000143 test= nan train= 0.0000000
rnd 904: wh-err= 0.993296 th-err= 0.000142 test= nan train= 0.0000000
rnd 905: wh-err= 0.989810 th-err= 0.000140 test= nan train= 0.0000000
rnd 906: wh-err= 0.992265 th-err= 0.000139 test= nan train= 0.0000000
rnd 907: wh-err= 0.990453 th-err= 0.000138 test= nan train= 0.0000000
rnd 908: wh-err= 0.989201 th-err= 0.000137 test= nan train= 0.0000000
rnd 909: wh-err= 0.992326 th-err= 0.000135 test= nan train= 0.0000000
rnd 910: wh-err= 0.990130 th-err= 0.000134 test= nan train= 0.0000000
rnd 911: wh-err= 0.995455 th-err= 0.000134 test= nan train= 0.0000000
rnd 912: wh-err= 0.991118 th-err= 0.000132 test= nan train= 0.0000000
rnd 913: wh-err= 0.989610 th-err= 0.000131 test= nan train= 0.0000000
rnd 914: wh-err= 0.989443 th-err= 0.000130 test= nan train= 0.0000000
rnd 915: wh-err= 0.988219 th-err= 0.000128 test= nan train= 0.0000000
rnd 916: wh-err= 0.990896 th-err= 0.000127 test= nan train= 0.0000000
rnd 917: wh-err= 0.985858 th-err= 0.000125 test= nan train= 0.0000000
rnd 918: wh-err= 0.987908 th-err= 0.000124 test= nan train= 0.0000000
rnd 919: wh-err= 0.989040 th-err= 0.000122 test= nan train= 0.0000000
rnd 920: wh-err= 0.991720 th-err= 0.000121 test= nan train= 0.0000000
rnd 921: wh-err= 0.991953 th-err= 0.000120 test= nan train= 0.0000000
rnd 922: wh-err= 0.995915 th-err= 0.000120 test= nan train= 0.0000000
rnd 923: wh-err= 0.996207 th-err= 0.000119 test= nan train= 0.0000000
rnd 924: wh-err= 0.992920 th-err= 0.000118 test= nan train= 0.0000000
rnd 925: wh-err= 0.996468 th-err= 0.000118 test= nan train= 0.0000000
rnd 926: wh-err= 0.992719 th-err= 0.000117 test= nan train= 0.0000000
rnd 927: wh-err= 0.996022 th-err= 0.000117 test= nan train= 0.0000000
rnd 928: wh-err= 0.992910 th-err= 0.000116 test= nan train= 0.0000000
rnd 929: wh-err= 0.989459 th-err= 0.000115 test= nan train= 0.0000000
rnd 930: wh-err= 0.993354 th-err= 0.000114 test= nan train= 0.0000000
rnd 931: wh-err= 0.991402 th-err= 0.000113 test= nan train= 0.0000000
rnd 932: wh-err= 0.989520 th-err= 0.000112 test= nan train= 0.0000000
rnd 933: wh-err= 0.991940 th-err= 0.000111 test= nan train= 0.0000000
rnd 934: wh-err= 0.992167 th-err= 0.000110 test= nan train= 0.0000000
rnd 935: wh-err= 0.993031 th-err= 0.000109 test= nan train= 0.0000000
rnd 936: wh-err= 0.988722 th-err= 0.000108 test= nan train= 0.0000000
rnd 937: wh-err= 0.992203 th-err= 0.000107 test= nan train= 0.0000000
rnd 938: wh-err= 0.993078 th-err= 0.000106 test= nan train= 0.0000000
rnd 939: wh-err= 0.993604 th-err= 0.000106 test= nan train= 0.0000000
rnd 940: wh-err= 0.996218 th-err= 0.000105 test= nan train= 0.0000000
rnd 941: wh-err= 0.997065 th-err= 0.000105 test= nan train= 0.0000000
rnd 942: wh-err= 0.993014 th-err= 0.000104 test= nan train= 0.0000000
rnd 943: wh-err= 0.991622 th-err= 0.000103 test= nan train= 0.0000000
rnd 944: wh-err= 0.990702 th-err= 0.000102 test= nan train= 0.0000000
rnd 945: wh-err= 0.988304 th-err= 0.000101 test= nan train= 0.0000000
rnd 946: wh-err= 0.992449 th-err= 0.000100 test= nan train= 0.0000000
rnd 947: wh-err= 0.991053 th-err= 0.000100 test= nan train= 0.0000000
rnd 948: wh-err= 0.989253 th-err= 0.000099 test= nan train= 0.0000000
rnd 949: wh-err= 0.990828 th-err= 0.000098 test= nan train= 0.0000000
rnd 950: wh-err= 0.990023 th-err= 0.000097 test= nan train= 0.0000000
rnd 951: wh-err= 0.993174 th-err= 0.000096 test= nan train= 0.0000000
rnd 952: wh-err= 0.990404 th-err= 0.000095 test= nan train= 0.0000000
rnd 953: wh-err= 0.990574 th-err= 0.000094 test= nan train= 0.0000000
rnd 954: wh-err= 0.991023 th-err= 0.000093 test= nan train= 0.0000000
rnd 955: wh-err= 0.994876 th-err= 0.000093 test= nan train= 0.0000000
rnd 956: wh-err= 0.991119 th-err= 0.000092 test= nan train= 0.0000000
rnd 957: wh-err= 0.995721 th-err= 0.000092 test= nan train= 0.0000000
rnd 958: wh-err= 0.992485 th-err= 0.000091 test= nan train= 0.0000000
rnd 959: wh-err= 0.992219 th-err= 0.000090 test= nan train= 0.0000000
rnd 960: wh-err= 0.988638 th-err= 0.000089 test= nan train= 0.0000000
rnd 961: wh-err= 0.992866 th-err= 0.000089 test= nan train= 0.0000000
rnd 962: wh-err= 0.992107 th-err= 0.000088 test= nan train= 0.0000000
rnd 963: wh-err= 0.996540 th-err= 0.000088 test= nan train= 0.0000000
rnd 964: wh-err= 0.996596 th-err= 0.000087 test= nan train= 0.0000000
rnd 965: wh-err= 0.993612 th-err= 0.000087 test= nan train= 0.0000000
rnd 966: wh-err= 0.995320 th-err= 0.000086 test= nan train= 0.0000000
rnd 967: wh-err= 0.991717 th-err= 0.000086 test= nan train= 0.0000000
rnd 968: wh-err= 0.991505 th-err= 0.000085 test= nan train= 0.0000000
rnd 969: wh-err= 0.991982 th-err= 0.000084 test= nan train= 0.0000000
rnd 970: wh-err= 0.994015 th-err= 0.000084 test= nan train= 0.0000000
rnd 971: wh-err= 0.992943 th-err= 0.000083 test= nan train= 0.0000000
rnd 972: wh-err= 0.990331 th-err= 0.000082 test= nan train= 0.0000000
rnd 973: wh-err= 0.993217 th-err= 0.000082 test= nan train= 0.0000000
rnd 974: wh-err= 0.991807 th-err= 0.000081 test= nan train= 0.0000000
rnd 975: wh-err= 0.990882 th-err= 0.000080 test= nan train= 0.0000000
rnd 976: wh-err= 0.992118 th-err= 0.000080 test= nan train= 0.0000000
rnd 977: wh-err= 0.993238 th-err= 0.000079 test= nan train= 0.0000000
rnd 978: wh-err= 0.993928 th-err= 0.000079 test= nan train= 0.0000000
rnd 979: wh-err= 0.991718 th-err= 0.000078 test= nan train= 0.0000000
rnd 980: wh-err= 0.992608 th-err= 0.000077 test= nan train= 0.0000000
rnd 981: wh-err= 0.996339 th-err= 0.000077 test= nan train= 0.0000000
rnd 982: wh-err= 0.993665 th-err= 0.000077 test= nan train= 0.0000000
rnd 983: wh-err= 0.996457 th-err= 0.000076 test= nan train= 0.0000000
rnd 984: wh-err= 0.993653 th-err= 0.000076 test= nan train= 0.0000000
rnd 985: wh-err= 0.993656 th-err= 0.000075 test= nan train= 0.0000000
rnd 986: wh-err= 0.990588 th-err= 0.000075 test= nan train= 0.0000000
rnd 987: wh-err= 0.991792 th-err= 0.000074 test= nan train= 0.0000000
rnd 988: wh-err= 0.991249 th-err= 0.000073 test= nan train= 0.0000000
rnd 989: wh-err= 0.992409 th-err= 0.000073 test= nan train= 0.0000000
rnd 990: wh-err= 0.991699 th-err= 0.000072 test= nan train= 0.0000000
rnd 991: wh-err= 0.989666 th-err= 0.000072 test= nan train= 0.0000000
rnd 992: wh-err= 0.990368 th-err= 0.000071 test= nan train= 0.0000000
rnd 993: wh-err= 0.994113 th-err= 0.000070 test= nan train= 0.0000000
rnd 994: wh-err= 0.991222 th-err= 0.000070 test= nan train= 0.0000000
rnd 995: wh-err= 0.988598 th-err= 0.000069 test= nan train= 0.0000000
rnd 996: wh-err= 0.991216 th-err= 0.000068 test= nan train= 0.0000000
rnd 997: wh-err= 0.992493 th-err= 0.000068 test= nan train= 0.0000000
rnd 998: wh-err= 0.990517 th-err= 0.000067 test= nan train= 0.0000000
rnd 999: wh-err= 0.995246 th-err= 0.000067 test= nan train= 0.0000000
rnd 1000: wh-err= 0.991512 th-err= 0.000066 test= nan train= 0.0000000
=== END program1: ./run learn ../dataset2/train --- OK [1s]
===== 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
Copyright 2001 AT&T. All rights reserved.
Test error = 189.000000 / 189 = 1.000000
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== 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
Copyright 2001 AT&T. All rights reserved.
Test error = 81.000000 / 81 = 1.000000
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]
real 0m1.536s
user 0m0.552s
sys 0m0.104s
Run specification
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) boostexter: Adaboost on single level decision trees. AT&T's closed-source implementation. See http://www.cs.princeton.edu/~schapire/boostexter.html.
(dataset:Dataset) statlog-heart: 270 examples, 13 features
(stripper:Program[Strip]) multiclass-utils: Validates and inspects a dataset in MulticlassClassification format.
(evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).
When you generate a run, you can set a time limit for the run (no more than 24 hours). After that point, we will terminate the program.
Your program can use 1.5GB of memory. More information here.
Go to the page for the run and look at the log file for signs of the responsible error.
You can also download the run and run it locally on your machine (a README file should
be included in the download which provides more information).
We said that a run was simply a program/dataset pair, but that's not the full story.
A run actually includes other helper programs such as the evaluation program and
various programs for reductions (e.g., one-versus-all, hyperparameter tuning).
More formally, a run is a given by a run specification,
which can be found on the page for any run.
A run specification is a tree where each internal node represents a program
and its children represents the arguments to be passed into its constructor.
For example, the one-versus-all program takes your binary classification program
as a constructor argument and behaves like a multiclass classification program.
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