ServerRun 16771
Creatorinternal
Programsc-MNBSVM
Datasetccs-challenge-dataset
Task typeBinaryClassification
Created5y80d ago
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BinaryClassification
7m36s
0.485
7m33s
0.540
1m39s

Log file

{
  trainp = 0
  testp = 0
  C =  1
  samplenum =  1
  samplerate =  1
  a =  1
  beta =  0.25000
  verbose = 0
}
Using type: MNBSVM
GNU Octave, version 3.2.4
Copyright (C) 2009 John W. Eaton and others.
This is free software; see the source code for copying conditions.
There is ABSOLUTELY NO WARRANTY; not even for MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE.

Octave was configured for "i686-pc-linux-gnu".

Additional information about Octave is available at http://www.octave.org.

Please contribute if you find this software useful.
For more information, visit http://www.octave.org/help-wanted.html

Report bugs to <bug@octave.org> (but first, please read
http://www.octave.org/bugs.html to learn how to write a helpful report).

ans = 0
usage: mkoctfile [options] file ...

ans =  1
classifiers
liblinear-1.8
metadata
run
setUpFuncs.m
util

ans = 0
make[1]: /usr/local/matlab/bin/mexext: Command not found
make[1]: Entering directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/matlab'
make[1]: /usr/local/matlab/bin/mexext: Command not found
make[1]: /usr/local/matlab/bin/mexext: Command not found
make[1]: /usr/local/matlab/bin/mexext: Command not found
make[1]: /usr/local/matlab/bin/mexext: Command not found
make[1]: /usr/local/matlab/bin/mexext: Command not found
make[1]: /usr/local/matlab/bin/mexext: Command not found
make[1]: /usr/local/matlab/bin/mexext: Command not found
cd blas;	make clean
make[1]: Entering directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/blas'
rm -f *.o
rm -f *.a
rm -f *~
make[1]: Leaving directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/blas'
cd matlab;	make clean
cd ../blas;	make clean
make[2]: Entering directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/blas'
rm -f *.o
rm -f *.a
rm -f *~
make[2]: Leaving directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/blas'
rm -f *~ *.o *.mex* *.obj ../linear.o ../tron.o
make[1]: Leaving directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/matlab'
rm -f *~ tron.o linear.o train predict liblinear.so.1

ans = 0
FINISHED CLEANING THE CORE LIB
linear.cpp: In function ‘model* load_model(const char*)’:
linear.cpp:2224:24: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:2227:25: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:2247:29: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:2252:31: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:2257:26: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:2269:38: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:2296:44: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:2297:19: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp: In function ‘void train_one(const problem*, const parameter*, double*, double, double)’:
linear.cpp:1092:9: warning: ‘loss_old’ may be used uninitialized in this function
linear.cpp:1090:9: warning: ‘Gnorm1_init’ may be used uninitialized in this function
linear.cpp:1376:9: warning: ‘Gnorm1_init’ may be used uninitialized in this function
g++ -Wall -Wconversion -O3 -fPIC -c -o tron.o tron.cpp
g++ -Wall -Wconversion -O3 -fPIC -c -o linear.o linear.cpp
cd blas; make OPTFLAGS='-Wall -Wconversion -O3 -fPIC' CC='cc';
make[1]: Entering directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/blas'
cc -Wall -Wconversion -O3 -fPIC  -c dnrm2.c
cc -Wall -Wconversion -O3 -fPIC  -c daxpy.c
cc -Wall -Wconversion -O3 -fPIC  -c ddot.c
cc -Wall -Wconversion -O3 -fPIC  -c dscal.c
ar rcv blas.a dnrm2.o daxpy.o ddot.o dscal.o   
a - dnrm2.o
a - daxpy.o
a - ddot.o
a - dscal.o
ranlib  blas.a
make[1]: Leaving directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/blas'
g++ -Wall -Wconversion -O3 -fPIC -o train train.c tron.o linear.o blas/blas.a
g++ -Wall -Wconversion -O3 -fPIC -o predict predict.c tron.o linear.o blas/blas.a

ans = 0
FINISHED MAKING THE CORE LIB
make: /usr/local/matlab/bin/mexext: Command not found
make: /usr/local/matlab/bin/mexext: Command not found
make: /usr/local/matlab/bin/mexext: Command not found
make: /usr/local/matlab/bin/mexext: Command not found
make: /usr/local/matlab/bin/mexext: Command not found
make: /usr/local/matlab/bin/mexext: Command not found
make: /usr/local/matlab/bin/mexext: Command not found
make: /usr/local/matlab/bin/mexext: Command not found
libsvmwrite.c: In function ‘void libsvmwrite(const char*, const mxArray*, const mxArray*)’:
libsvmwrite.c:67:45: warning: format ‘%ld’ expects type ‘long int’, but argument 3 has type ‘mwIndex’
make[1]: Entering directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/matlab'
g++ -Wall -O3 -fPIC -I/usr/include/octave -I.. -c linear_model_matlab.c
env CC=g++ mkoctfile --mex train.c ../tron.o ../linear.o linear_model_matlab.o ../blas/blas.a
env CC=g++ mkoctfile --mex predict.c ../tron.o ../linear.o linear_model_matlab.o ../blas/blas.a
env CC=g++ mkoctfile --mex libsvmread.c
env CC=g++ mkoctfile --mex libsvmwrite.c
make[1]: Leaving directory `/home/mlcomp/worker/scratch/program1/liblinear-1.8/matlab'

FINISHED MAKING OCTAVE
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
{
  trainp = 0
  testp = 0
  C =  1
  samplenum =  1
  samplerate =  1
  a =  1
  beta =  0.25000
  verbose = 0
}
Using type: MNBSVM
.....*
optimization finished, #iter = 51
Objective value = -2179.858140
nSV = 7806
Accuracy = 51.52% (5152/10000)
the train accuracy is 51.520000
=== END program1: ./run learn ../dataset2/train --- OK [456s]

===== 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
{
  trainp = 0
  testp = 0
  C =  1
  samplenum =  1
  samplerate =  1
  a =  1
  beta =  0.25000
  verbose = 0
}
Using type: MNBSVM
Accuracy = 44.18% (4418/10000)
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [453s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]

===== 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
{
  trainp = 0
  testp = 0
  C =  1
  samplenum =  1
  samplerate =  1
  a =  1
  beta =  0.25000
  verbose = 0
}
Using type: MNBSVM
Accuracy = 45.975% (1839/4000)
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [99s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	17m10.044s
user	10m34.984s
sys	6m7.487s

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