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rnd 900: wh-err= 0.957768 th-err= 0.000000 test= nan train= 0.0000000
rnd 901: wh-err= 0.951859 th-err= 0.000000 test= nan train= 0.0000000
rnd 902: wh-err= 0.958997 th-err= 0.000000 test= nan train= 0.0000000
rnd 903: wh-err= 0.965120 th-err= 0.000000 test= nan train= 0.0000000
rnd 904: wh-err= 0.959268 th-err= 0.000000 test= nan train= 0.0000000
rnd 905: wh-err= 0.954509 th-err= 0.000000 test= nan train= 0.0000000
rnd 906: wh-err= 0.957255 th-err= 0.000000 test= nan train= 0.0000000
rnd 907: wh-err= 0.960709 th-err= 0.000000 test= nan train= 0.0000000
rnd 908: wh-err= 0.972022 th-err= 0.000000 test= nan train= 0.0000000
rnd 909: wh-err= 0.964989 th-err= 0.000000 test= nan train= 0.0000000
rnd 910: wh-err= 0.953640 th-err= 0.000000 test= nan train= 0.0000000
rnd 911: wh-err= 0.952844 th-err= 0.000000 test= nan train= 0.0000000
rnd 912: wh-err= 0.961290 th-err= 0.000000 test= nan train= 0.0000000
rnd 913: wh-err= 0.971348 th-err= 0.000000 test= nan train= 0.0000000
rnd 914: wh-err= 0.963702 th-err= 0.000000 test= nan train= 0.0000000
rnd 915: wh-err= 0.970343 th-err= 0.000000 test= nan train= 0.0000000
rnd 916: wh-err= 0.964341 th-err= 0.000000 test= nan train= 0.0000000
rnd 917: wh-err= 0.966135 th-err= 0.000000 test= nan train= 0.0000000
rnd 918: wh-err= 0.963440 th-err= 0.000000 test= nan train= 0.0000000
rnd 919: wh-err= 0.958226 th-err= 0.000000 test= nan train= 0.0000000
rnd 920: wh-err= 0.961222 th-err= 0.000000 test= nan train= 0.0000000
rnd 921: wh-err= 0.959003 th-err= 0.000000 test= nan train= 0.0000000
rnd 922: wh-err= 0.947709 th-err= 0.000000 test= nan train= 0.0000000
rnd 923: wh-err= 0.957325 th-err= 0.000000 test= nan train= 0.0000000
rnd 924: wh-err= 0.955778 th-err= 0.000000 test= nan train= 0.0000000
rnd 925: wh-err= 0.959055 th-err= 0.000000 test= nan train= 0.0000000
rnd 926: wh-err= 0.975377 th-err= 0.000000 test= nan train= 0.0000000
rnd 927: wh-err= 0.975653 th-err= 0.000000 test= nan train= 0.0000000
rnd 928: wh-err= 0.976670 th-err= 0.000000 test= nan train= 0.0000000
rnd 929: wh-err= 0.947157 th-err= 0.000000 test= nan train= 0.0000000
rnd 930: wh-err= 0.966865 th-err= 0.000000 test= nan train= 0.0000000
rnd 931: wh-err= 0.966834 th-err= 0.000000 test= nan train= 0.0000000
rnd 932: wh-err= 0.969774 th-err= 0.000000 test= nan train= 0.0000000
rnd 933: wh-err= 0.964911 th-err= 0.000000 test= nan train= 0.0000000
rnd 934: wh-err= 0.962753 th-err= 0.000000 test= nan train= 0.0000000
rnd 935: wh-err= 0.962200 th-err= 0.000000 test= nan train= 0.0000000
rnd 936: wh-err= 0.965453 th-err= 0.000000 test= nan train= 0.0000000
rnd 937: wh-err= 0.964596 th-err= 0.000000 test= nan train= 0.0000000
rnd 938: wh-err= 0.962623 th-err= 0.000000 test= nan train= 0.0000000
rnd 939: wh-err= 0.964342 th-err= 0.000000 test= nan train= 0.0000000
rnd 940: wh-err= 0.953252 th-err= 0.000000 test= nan train= 0.0000000
rnd 941: wh-err= 0.956342 th-err= 0.000000 test= nan train= 0.0000000
rnd 942: wh-err= 0.960190 th-err= 0.000000 test= nan train= 0.0000000
rnd 943: wh-err= 0.958041 th-err= 0.000000 test= nan train= 0.0000000
rnd 944: wh-err= 0.958338 th-err= 0.000000 test= nan train= 0.0000000
rnd 945: wh-err= 0.963279 th-err= 0.000000 test= nan train= 0.0000000
rnd 946: wh-err= 0.941255 th-err= 0.000000 test= nan train= 0.0000000
rnd 947: wh-err= 0.961187 th-err= 0.000000 test= nan train= 0.0000000
rnd 948: wh-err= 0.969537 th-err= 0.000000 test= nan train= 0.0000000
rnd 949: wh-err= 0.970908 th-err= 0.000000 test= nan train= 0.0000000
rnd 950: wh-err= 0.963666 th-err= 0.000000 test= nan train= 0.0000000
rnd 951: wh-err= 0.970770 th-err= 0.000000 test= nan train= 0.0000000
rnd 952: wh-err= 0.969869 th-err= 0.000000 test= nan train= 0.0000000
rnd 953: wh-err= 0.960094 th-err= 0.000000 test= nan train= 0.0000000
rnd 954: wh-err= 0.962710 th-err= 0.000000 test= nan train= 0.0000000
rnd 955: wh-err= 0.967549 th-err= 0.000000 test= nan train= 0.0000000
rnd 956: wh-err= 0.955729 th-err= 0.000000 test= nan train= 0.0000000
rnd 957: wh-err= 0.961858 th-err= 0.000000 test= nan train= 0.0000000
rnd 958: wh-err= 0.964469 th-err= 0.000000 test= nan train= 0.0000000
rnd 959: wh-err= 0.953272 th-err= 0.000000 test= nan train= 0.0000000
rnd 960: wh-err= 0.947857 th-err= 0.000000 test= nan train= 0.0000000
rnd 961: wh-err= 0.965736 th-err= 0.000000 test= nan train= 0.0000000
rnd 962: wh-err= 0.954304 th-err= 0.000000 test= nan train= 0.0000000
rnd 963: wh-err= 0.955631 th-err= 0.000000 test= nan train= 0.0000000
rnd 964: wh-err= 0.959956 th-err= 0.000000 test= nan train= 0.0000000
rnd 965: wh-err= 0.967215 th-err= 0.000000 test= nan train= 0.0000000
rnd 966: wh-err= 0.958756 th-err= 0.000000 test= nan train= 0.0000000
rnd 967: wh-err= 0.966139 th-err= 0.000000 test= nan train= 0.0000000
rnd 968: wh-err= 0.964637 th-err= 0.000000 test= nan train= 0.0000000
rnd 969: wh-err= 0.970289 th-err= 0.000000 test= nan train= 0.0000000
rnd 970: wh-err= 0.972116 th-err= 0.000000 test= nan train= 0.0000000
rnd 971: wh-err= 0.963836 th-err= 0.000000 test= nan train= 0.0000000
rnd 972: wh-err= 0.957918 th-err= 0.000000 test= nan train= 0.0000000
rnd 973: wh-err= 0.947993 th-err= 0.000000 test= nan train= 0.0000000
rnd 974: wh-err= 0.949054 th-err= 0.000000 test= nan train= 0.0000000
rnd 975: wh-err= 0.968539 th-err= 0.000000 test= nan train= 0.0000000
rnd 976: wh-err= 0.957714 th-err= 0.000000 test= nan train= 0.0000000
rnd 977: wh-err= 0.951100 th-err= 0.000000 test= nan train= 0.0000000
rnd 978: wh-err= 0.953103 th-err= 0.000000 test= nan train= 0.0000000
rnd 979: wh-err= 0.945037 th-err= 0.000000 test= nan train= 0.0000000
rnd 980: wh-err= 0.964495 th-err= 0.000000 test= nan train= 0.0000000
rnd 981: wh-err= 0.965646 th-err= 0.000000 test= nan train= 0.0000000
rnd 982: wh-err= 0.951492 th-err= 0.000000 test= nan train= 0.0000000
rnd 983: wh-err= 0.970384 th-err= 0.000000 test= nan train= 0.0000000
rnd 984: wh-err= 0.962170 th-err= 0.000000 test= nan train= 0.0000000
rnd 985: wh-err= 0.971575 th-err= 0.000000 test= nan train= 0.0000000
rnd 986: wh-err= 0.959708 th-err= 0.000000 test= nan train= 0.0000000
rnd 987: wh-err= 0.971908 th-err= 0.000000 test= nan train= 0.0000000
rnd 988: wh-err= 0.969685 th-err= 0.000000 test= nan train= 0.0000000
rnd 989: wh-err= 0.965429 th-err= 0.000000 test= nan train= 0.0000000
rnd 990: wh-err= 0.962935 th-err= 0.000000 test= nan train= 0.0000000
rnd 991: wh-err= 0.967717 th-err= 0.000000 test= nan train= 0.0000000
rnd 992: wh-err= 0.963308 th-err= 0.000000 test= nan train= 0.0000000
rnd 993: wh-err= 0.969172 th-err= 0.000000 test= nan train= 0.0000000
rnd 994: wh-err= 0.962263 th-err= 0.000000 test= nan train= 0.0000000
rnd 995: wh-err= 0.970418 th-err= 0.000000 test= nan train= 0.0000000
rnd 996: wh-err= 0.963714 th-err= 0.000000 test= nan train= 0.0000000
rnd 997: wh-err= 0.961943 th-err= 0.000000 test= nan train= 0.0000000
rnd 998: wh-err= 0.970804 th-err= 0.000000 test= nan train= 0.0000000
rnd 999: wh-err= 0.964178 th-err= 0.000000 test= nan train= 0.0000000
rnd 1000: wh-err= 0.967448 th-err= 0.000000 test= nan train= 0.0000000
=== END program1: ./run learn ../dataset2/train --- OK [14s]
===== 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 = 333.000000 / 333 = 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 = 143.000000 / 143 = 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 [0s]
real 0m14.839s
user 0m6.184s
sys 0m0.172s
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) musk-1: 476 examples, 166 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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