SuccessConsole Output

Skipping 411 KB.. Full Log
.h
vlfeat-0.9.20/vl/qsort-def.h
vlfeat-0.9.20/vl/quickshift.c
vlfeat-0.9.20/vl/quickshift.h
vlfeat-0.9.20/vl/random.c
vlfeat-0.9.20/vl/random.h
vlfeat-0.9.20/vl/rodrigues.c
vlfeat-0.9.20/vl/rodrigues.h
vlfeat-0.9.20/vl/scalespace.c
vlfeat-0.9.20/vl/scalespace.h
vlfeat-0.9.20/vl/shuffle-def.h
vlfeat-0.9.20/vl/sift.c
vlfeat-0.9.20/vl/sift.h
vlfeat-0.9.20/vl/slic.c
vlfeat-0.9.20/vl/slic.h
vlfeat-0.9.20/vl/stringop.c
vlfeat-0.9.20/vl/stringop.h
vlfeat-0.9.20/vl/svm.c
vlfeat-0.9.20/vl/svm.h
vlfeat-0.9.20/vl/svmdataset.c
vlfeat-0.9.20/vl/svmdataset.h
vlfeat-0.9.20/vl/vlad.c
vlfeat-0.9.20/vl/vlad.h
vlfeat-0.9.20/vlfeat.sln
vlfeat-0.9.20/vlfeat.vcproj
vlfeat-0.9.20/vlfeat.xcodeproj/
vlfeat-0.9.20/vlfeat.xcodeproj/project.pbxproj
vlfeat-0.9.20/vlfeat.xcodeproj/xcshareddata/
vlfeat-0.9.20/vlfeat.xcodeproj/xcshareddata/xcschemes/
vlfeat-0.9.20/vlfeat.xcodeproj/xcshareddata/xcschemes/make doc-api.xcscheme
vlfeat-0.9.20/vlfeat.xcodeproj/xcshareddata/xcschemes/make info.xcscheme
vlfeat-0.9.20/vlfeat.xcodeproj/xcshareddata/xcschemes/make.xcscheme
mkdir -p /tmp/enceval
wget "http://www.robots.ox.ac.uk/~vgg/software/enceval_toolkit/downloads/enceval-toolkit-1.1.tar.gz" -O /tmp/enceval/enceval.tgz
--2016-04-06 17:32:44--  http://www.robots.ox.ac.uk/~vgg/software/enceval_toolkit/downloads/enceval-toolkit-1.1.tar.gz
Resolving www.robots.ox.ac.uk... 129.67.94.2
Connecting to www.robots.ox.ac.uk|129.67.94.2|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 220290 (215K) [application/x-gzip]
Saving to: `/tmp/enceval/enceval.tgz'

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2016-04-06 17:32:46 (167 KB/s) - `/tmp/enceval/enceval.tgz' saved [220290/220290]

cd /tmp/enceval && tar zxvf enceval.tgz
data/
data/codebooks/
data/dimred/
data/codes/
data/results/
data/compdata/
+featpipem/
+featpipem/+lib/
+featpipem/+lib/LLCEncodeHelper.mexa64
+featpipem/+lib/@LTMatrix/
+featpipem/+lib/@LTMatrix/LTMatrix.m
+featpipem/+lib/@LTMatrix/writeIndex.m
+featpipem/+lib/@LTMatrix/convToFullMatrix.m
+featpipem/+lib/@LTMatrix/readIndex.m
+featpipem/+lib/@LTMatrix/getIndex_.m
+featpipem/+lib/LLCEncodeHelper.cpp
+featpipem/+lib/KCBEncode.m
+featpipem/+lib/annkmeans.m
+featpipem/+lib/LLCEncode.m
+featpipem/+codebkgen/
+featpipem/+codebkgen/@GMMCodebkGen/
+featpipem/+codebkgen/@GMMCodebkGen/GMMCodebkGen.m
+featpipem/+codebkgen/@GMMCodebkGen/train.m
+featpipem/+codebkgen/@KmeansCodebkGen/
+featpipem/+codebkgen/@KmeansCodebkGen/train.m
+featpipem/+codebkgen/@KmeansCodebkGen/KmeansCodebkGen.m
+featpipem/+codebkgen/@GmmCodebkGen/
+featpipem/+codebkgen/@GmmCodebkGen/train.m
+featpipem/+codebkgen/@GmmCodebkGen/GmmCodebkGen.m
+featpipem/+codebkgen/@GenericCodebkGen/
+featpipem/+codebkgen/@GenericCodebkGen/GenericCodebkGen.m
+featpipem/+chunkio/
+featpipem/+chunkio/compWFromKT.m
+featpipem/+chunkio/loadChunksIntoMat.m
+featpipem/+chunkio/compKernel.m
+featpipem/+chunkio/testChunks.m
+featpipem/+chunkio/compChunksIMDB.m
+featpipem/+classification/
+featpipem/+classification/+svm/
+featpipem/+classification/+svm/@LibSvmDual/
+featpipem/+classification/+svm/@LibSvmDual/LibSvmDual.m
+featpipem/+classification/+svm/@LibSvmDual/test.m
+featpipem/+classification/+svm/@LibSvmDual/train.m
+featpipem/+classification/+svm/@LibSvmDual/getWMat.m
+featpipem/+classification/+svm/@LibSvm/
+featpipem/+classification/+svm/@LibSvm/test.m
+featpipem/+classification/+svm/@LibSvm/train.m
+featpipem/+classification/+svm/@LibSvm/getWMat.m
+featpipem/+classification/+svm/@LibSvm/LibSvm.m
+featpipem/+classification/+svm/@GenericSvm/
+featpipem/+classification/+svm/@GenericSvm/GenericSvm.m
+featpipem/+classification/+svm/@LinearSvm/
+featpipem/+classification/+svm/@LinearSvm/LinearSvm.m
+featpipem/+wrapper/
+featpipem/+wrapper/loaddimred.m
+featpipem/+wrapper/loadcodebook.m
+featpipem/+wrapper/dstest.m
+featpipem/+eval/
+featpipem/+eval/evalPrecRec.m
+featpipem/+eval/+VOCdevkit/
+featpipem/+eval/+VOCdevkit/VOCap.m
+featpipem/+utility/
+featpipem/+utility/getImdbGT.m
+featpipem/+utility/generateImdbFromFolders.m
+featpipem/+utility/set_class_properties.m
+featpipem/+utility/standardizeImage.m
+featpipem/+pooling/
+featpipem/+pooling/@GenericPooler/
+featpipem/+pooling/@GenericPooler/GenericPooler.m
+featpipem/+pooling/@SPMPooler/
+featpipem/+pooling/@SPMPooler/compute.m
+featpipem/+pooling/@SPMPooler/SPMPooler.m
+featpipem/class_handle.h
+featpipem/+dim_red/
+featpipem/+dim_red/@GenericDimRed/
+featpipem/+dim_red/@GenericDimRed/GenericDimRed.m
+featpipem/+dim_red/@PCADimRed/
+featpipem/+dim_red/@PCADimRed/train.m
+featpipem/+dim_red/@PCADimRed/PCADimRed.m
+featpipem/+features/
+featpipem/+features/@GenericFeatExtractor/
+featpipem/+features/@GenericFeatExtractor/GenericFeatExtractor.m
+featpipem/+features/@PhowExtractor/
+featpipem/+features/@PhowExtractor/compute.m
+featpipem/+features/@PhowExtractor/PhowExtractor.m
+featpipem/+encoding/
+featpipem/+encoding/@GenericEncoder/
+featpipem/+encoding/@GenericEncoder/GenericEncoder.m
+featpipem/+encoding/@FKEncoder/
+featpipem/+encoding/@FKEncoder/encode.m
+featpipem/+encoding/@FKEncoder/FKEncoder.m
+featpipem/+encoding/@LLCEncoder/
+featpipem/+encoding/@LLCEncoder/LLCEncoder.m
+featpipem/+encoding/@LLCEncoder/encode.m
+featpipem/+encoding/@KCBEncoder/
+featpipem/+encoding/@KCBEncoder/KCBEncoder.m
+featpipem/+encoding/@KCBEncoder/encode.m
+featpipem/+encoding/@KCBEncoder/get_assignments.m
+featpipem/+encoding/@VQEncoder/
+featpipem/+encoding/@VQEncoder/VQEncoder.m
+featpipem/+encoding/@VQEncoder/encode.m
+featpipem/+encoding/@VQEncoder/get_assignments.m
featpipem_addpaths.m
featpipem_setup.m
FKdemo.m
imdb/
imdb/imdb-VOC2007.mat
kcbdemo.m
lib/
lib/gmm-fisher/
lib/gmm-fisher/stat.cxx
lib/gmm-fisher/simd_math.cxx
lib/gmm-fisher/simd_math.h
lib/gmm-fisher/stat.h
lib/gmm-fisher/gmm.h
lib/gmm-fisher/matlab/
lib/gmm-fisher/matlab/setup.m
lib/gmm-fisher/matlab/setup_noopenmp.m
lib/gmm-fisher/matlab/mexGmmTrainSP.cxx
lib/gmm-fisher/matlab/mexFisherEncodeHelperSP.cxx
lib/gmm-fisher/matlab/setup_debug.m
lib/gmm-fisher/matlab/mexGmmTrainDP.cxx
lib/gmm-fisher/matlab/mexGmmTrainSP.mexa64
lib/gmm-fisher/matlab/mexFisherEncodeHelperSP.mexa64
lib/gmm-fisher/matlab/mexGmmTrainDP.mexa64
lib/gmm-fisher/matlab/@FisherEncoder/
lib/gmm-fisher/matlab/@FisherEncoder/FisherEncoder.m
lib/gmm-fisher/matlab/mlclasshandler/
lib/gmm-fisher/matlab/mlclasshandler/class_handle.h
lib/gmm-fisher/matlab/mlclasshandler/fisher_handle.h
lib/gmm-fisher/fisher.h
lib/gmm-fisher/gmm.cxx
lib/gmm-fisher/fisher.cxx
llcdemo.m
README.txt
utility/
utility/findbestperf.m
utility/parse_alt_res.m
vqdemo.m
g++ -I/tmp/enceval/lib/gmm-fisher -I/tmp/vlfeat/vlfeat-0.9.20 -I/home/jenkins/tools/hudson.model.JDK/JDK_7u60/include/ -I/home/jenkins/tools/hudson.model.JDK/JDK_7u60/include/linux -c -o /tmp/obj/VLFeat.o src/main/cpp/VLFeat.cxx -O2 -fPIC -fopenmp -shared
CLASSPATH=target/scala-2.10/keystoneml-assembly-0.3.0-SNAPSHOT.jar javah -o src/main/cpp/EncEval.h utils.external.EncEval
g++ -I/tmp/enceval/lib/gmm-fisher -I/tmp/vlfeat/vlfeat-0.9.20 -I/home/jenkins/tools/hudson.model.JDK/JDK_7u60/include/ -I/home/jenkins/tools/hudson.model.JDK/JDK_7u60/include/linux -c -o /tmp/obj/EncEval.o src/main/cpp/EncEval.cxx -O2 -fPIC -fopenmp -shared
make -C /tmp/vlfeat/vlfeat-0.9.20 ARCH=glnxa64 MEX= bin-all
Detected compiler: gcc 40407
GCC <= 4.6.0 detected, disabling AVX.
which: no  in (/home/jenkins/tools/hudson.model.JDK/JDK_7u60/bin:/home/anaconda/bin:/home/jenkins/tools/hudson.tasks.Maven_MavenInstallation/Maven_3.1.1/bin/:/home/android-sdk/:/usr/local/bin:/bin:/usr/bin)
The MALTAB version will be detected in the next phase of Make
make[1]: Entering directory `/tmp/vlfeat/vlfeat-0.9.20'
             MK bin/glnxa64/
             MK bin/glnxa64/objs/
             CC bin/glnxa64/test_vec_comp.d
             CC bin/glnxa64/test_threads.d
             CC bin/glnxa64/test_svd2.d
             CC bin/glnxa64/test_stringop.d
             CC bin/glnxa64/test_sqrti.d
             CC bin/glnxa64/test_rand.d
             CC bin/glnxa64/test_qsort-def.d
             CC bin/glnxa64/test_nan.d
             CC bin/glnxa64/test_mathop_abs.d
             CC bin/glnxa64/test_mathop.d
             CC bin/glnxa64/test_liop.d
             CC bin/glnxa64/test_kmeans.d
             CC bin/glnxa64/test_imopv.d
             CC bin/glnxa64/test_host.d
             CC bin/glnxa64/test_heap-def.d
             CC bin/glnxa64/test_gmm.d
             CC bin/glnxa64/test_getopt_long.d
             CC bin/glnxa64/test_gauss_elimination.d
             CC bin/glnxa64/sift.d
             CC bin/glnxa64/mser.d
             CC bin/glnxa64/aib.d
             CC bin/glnxa64/objs/vlad.d
             CC bin/glnxa64/objs/svmdataset.d
             CC bin/glnxa64/objs/svm.d
             CC bin/glnxa64/objs/stringop.d
             CC bin/glnxa64/objs/slic.d
             CC bin/glnxa64/objs/sift.d
             CC bin/glnxa64/objs/scalespace.d
             CC bin/glnxa64/objs/rodrigues.d
             CC bin/glnxa64/objs/random.d
             CC bin/glnxa64/objs/quickshift.d
             CC bin/glnxa64/objs/pgm.d
             CC bin/glnxa64/objs/mser.d
             CC bin/glnxa64/objs/mathop_sse2.d
             CC bin/glnxa64/objs/mathop_avx.d
             CC bin/glnxa64/objs/mathop.d
             CC bin/glnxa64/objs/liop.d
             CC bin/glnxa64/objs/lbp.d
             CC bin/glnxa64/objs/kmeans.d
             CC bin/glnxa64/objs/kdtree.d
             CC bin/glnxa64/objs/imopv_sse2.d
             CC bin/glnxa64/objs/imopv.d
             CC bin/glnxa64/objs/ikmeans.d
             CC bin/glnxa64/objs/host.d
             CC bin/glnxa64/objs/homkermap.d
             CC bin/glnxa64/objs/hog.d
             CC bin/glnxa64/objs/hikmeans.d
             CC bin/glnxa64/objs/gmm.d
             CC bin/glnxa64/objs/getopt_long.d
             CC bin/glnxa64/objs/generic.d
             CC bin/glnxa64/objs/fisher.d
             CC bin/glnxa64/objs/dsift.d
             CC bin/glnxa64/objs/covdet.d
             CC bin/glnxa64/objs/array.d
             CC bin/glnxa64/objs/aib.d
make[1]: Leaving directory `/tmp/vlfeat/vlfeat-0.9.20'
Detected compiler: gcc 40407
GCC <= 4.6.0 detected, disabling AVX.
which: no  in (/home/jenkins/tools/hudson.model.JDK/JDK_7u60/bin:/home/anaconda/bin:/home/jenkins/tools/hudson.tasks.Maven_MavenInstallation/Maven_3.1.1/bin/:/home/android-sdk/:/usr/local/bin:/bin:/usr/bin)
The MALTAB version will be detected in the next phase of Make
make[1]: Entering directory `/tmp/vlfeat/vlfeat-0.9.20'
             CC bin/glnxa64/objs/aib.o
             CC bin/glnxa64/objs/array.o
             CC bin/glnxa64/objs/covdet.o
vl/covdet.c: In function 'vl_covdet_extract_affine_shape_for_frame':
vl/covdet.c:2474: warning: 'referenceScale' may be used uninitialized in this function
             CC bin/glnxa64/objs/dsift.o
             CC bin/glnxa64/objs/fisher.o
             CC bin/glnxa64/objs/generic.o
             CC bin/glnxa64/objs/getopt_long.o
             CC bin/glnxa64/objs/gmm.o
             CC bin/glnxa64/objs/hikmeans.o
             CC bin/glnxa64/objs/hog.o
             CC bin/glnxa64/objs/homkermap.o
             CC bin/glnxa64/objs/host.o
             CC bin/glnxa64/objs/ikmeans.o
             CC bin/glnxa64/objs/imopv.o
             CC bin/glnxa64/objs/imopv_sse2.o
             CC bin/glnxa64/objs/kdtree.o
             CC bin/glnxa64/objs/kmeans.o
             CC bin/glnxa64/objs/lbp.o
             CC bin/glnxa64/objs/liop.o
             CC bin/glnxa64/objs/mathop.o
vl/mathop.c: In function 'vl_lapack_dlasv2':
vl/mathop.c:729: warning: 'tsign' may be used uninitialized in this function
             CC bin/glnxa64/objs/mathop_avx.o
             CC bin/glnxa64/objs/mathop_sse2.o
             CC bin/glnxa64/objs/mser.o
             CC bin/glnxa64/objs/pgm.o
             CC bin/glnxa64/objs/quickshift.o
             CC bin/glnxa64/objs/random.o
             CC bin/glnxa64/objs/rodrigues.o
             CC bin/glnxa64/objs/scalespace.o
             CC bin/glnxa64/objs/sift.o
             CC bin/glnxa64/objs/slic.o
vl/slic.c: In function 'vl_slic_segment':
vl/slic.c:188: warning: 'startingEnergy' may be used uninitialized in this function
             CC bin/glnxa64/objs/stringop.o
             CC bin/glnxa64/objs/svm.o
             CC bin/glnxa64/objs/svmdataset.o
             CC bin/glnxa64/objs/vlad.o
             CC bin/glnxa64/libvl.so
             CC bin/glnxa64/aib
             CC bin/glnxa64/mser
             CC bin/glnxa64/sift
             CC bin/glnxa64/test_gauss_elimination
             CC bin/glnxa64/test_getopt_long
             CC bin/glnxa64/test_gmm
             CC bin/glnxa64/test_heap-def
             CC bin/glnxa64/test_host
             CC bin/glnxa64/test_imopv
             CC bin/glnxa64/test_kmeans
             CC bin/glnxa64/test_liop
             CC bin/glnxa64/test_mathop
             CC bin/glnxa64/test_mathop_abs
             CC bin/glnxa64/test_nan
             CC bin/glnxa64/test_qsort-def
             CC bin/glnxa64/test_rand
             CC bin/glnxa64/test_sqrti
             CC bin/glnxa64/test_stringop
             CC bin/glnxa64/test_svd2
             CC bin/glnxa64/test_threads
             CC bin/glnxa64/test_vec_comp
make[1]: Leaving directory `/tmp/vlfeat/vlfeat-0.9.20'
g++ -c -o /tmp/enceval/lib/gmm-fisher/gmm.o /tmp/enceval/lib/gmm-fisher/gmm.cxx -O2 -fPIC -fopenmp -shared
g++ -c -o /tmp/enceval/lib/gmm-fisher/fisher.o /tmp/enceval/lib/gmm-fisher/fisher.cxx -O2 -fPIC -fopenmp -shared
g++ -c -o /tmp/enceval/lib/gmm-fisher/stat.o /tmp/enceval/lib/gmm-fisher/stat.cxx -O2 -fPIC -fopenmp -shared
g++ -c -o /tmp/enceval/lib/gmm-fisher/simd_math.o /tmp/enceval/lib/gmm-fisher/simd_math.cxx -O2 -fPIC -fopenmp -shared
g++ -dynamiclib -o lib/libImageFeatures.so /tmp/obj/VLFeat.o /tmp/obj/EncEval.o /tmp/enceval/lib/gmm-fisher/gmm.o /tmp/enceval/lib/gmm-fisher/fisher.o /tmp/enceval/lib/gmm-fisher/stat.o /tmp/enceval/lib/gmm-fisher/simd_math.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/homkermap.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/mathop_sse2.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/slic.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/liop.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/mser.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/imopv_sse2.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/aib.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/hikmeans.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/hog.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/mathop_avx.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/ikmeans.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/imopv.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/fisher.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/array.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/rodrigues.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/covdet.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/dsift.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/host.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/svm.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/svmdataset.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/stringop.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/quickshift.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/random.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/sift.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/kmeans.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/gmm.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/mathop.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/kdtree.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/vlad.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/getopt_long.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/generic.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/lbp.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/pgm.o /tmp/vlfeat/vlfeat-0.9.20/bin/glnxa64/objs/scalespace.o -O2 -fPIC -fopenmp -shared
[KeystoneML] $ /bin/sh -xe /tmp/hudson5114211136133165203.sh
+ sbt/sbt test
Launching sbt from sbt/sbt-launch-0.13.5.jar
[info] Loading project definition from /home/jenkins/workspace/KeystoneML/project
[info] Set current project to keystoneml (in build file:/home/jenkins/workspace/KeystoneML/)
[info] Compiling 1 Scala source to /home/jenkins/workspace/KeystoneML/target/scala-2.10/test-classes...
16/04/06 17:33:12 INFO ConcretePipeline: Fitting '$anonfun$1$$anon$1' [2]
16/04/06 17:33:13 INFO ConcretePipeline: Finished fitting '$anonfun$1$$anon$1' [2]
[info] LabelEstimatorSuite:
[info] - estimator withData
[info] TopKClassifierSuite:
[info] - top k classifier, k <= vector size
[info] - top k classifier, k > vector size
[info] SparseFeatureVectorizerSuite:
[info] - sparse feature vectorization
[info] - all sparse feature selection
[info] - common sparse feature selection
16/04/06 17:33:18 INFO SIFTExtractorSuite: Scale 1 shape is: 128x35841
16/04/06 17:33:18 INFO SIFTExtractorSuite: Scale 0 shape is: 128x64990
[info] SIFTExtractorSuite:
[info] - Test Sift on a single image RDD, scaleStep=1 and scaleStep=0, 0 should have more descriptors
16/04/06 17:33:18 INFO ConvolverSuite: Image: 0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0,0.30151134457776363,0.0
16/04/06 17:33:18 INFO ConvolverSuite: Image Dimensions 4 4 2
16/04/06 17:33:18 INFO ConvolverSuite: Image: -0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0,-0.11858541225631422,0.0
16/04/06 17:33:18 INFO ConvolverSuite: Image Dimensions 8 8 2
[info] ConvolverSuite:
[info] - 1x1 patches convolutions
[info] - convolutions
[info] - convolutions should match scipy
[info] ZCAWhiteningSuite:
[info] - whitening with small epsilon
[info] - whitening with large epsilon
[info] PaddedFFTSuite:
[info] - Test PaddedFFT node
[info] StupidBackoffSuite:
[info] - end-to-end InitialBigramPartitioner
[info] - Stupid Backoff calculates correct scores
[info] WordFrequencyEncoderSuite:
[info] - WordFrequencyEncoder
[info] BinaryClassifierEvaluatorSuite:
[info] - Multiclass evaluation metrics
[info] RandomSignNodeSuite:
[info] - RandomSignNode
[info] - RandomSignNode.create
16/04/06 17:33:23 INFO PCASuite: -0.2241642086975278   -1.396257788109452   0.3610534707559571     ... (6 total)
0.22546342395914792   -1.732342707765178   0.27330627270648383    ...
-0.11435955581586561  0.24382802663441877  -0.6170888356199791    ...
-1.0065037880882655   0.39304507131971606  -0.020372318920404222  ...
1.0732787419810632    -2.4027497548882017  -0.20015552105797807   ...
1.6012312541480742    -1.8985624240216878  0.4250082584252659     ...
[info] PCASuite:
[info] - PCA matrix transformation
[info] - PCA Estimation
[info] - Covariance Matrix of Distributed PCA should match local one
[info] - Sketch algorithm should produce a valid sketch of the matrix
[info] - Singular values of low-rank projection should be similar regardless of method used.
[info] - Approximate PCA application should result in a matrix that's basically diagonal covariance.
[info] - small n small d dense column pca
[info] - big n big d dense column pca
[info] NaiveBayesModelSuite:
[info] - Naive Bayes Multinomial
[info] BlockLinearMapperSuite:
[info] - BlockLinearMapper transformation
[info] ImageSuite:
[info] - Vectorized Image Coordinates Should be Correct
[info] ImageNetLoaderSuite:
[info] - load a sample of imagenet data
[info] SignedHellingerMapperSuite:
[info] - signed hellinger mapper
[info] NGramsHashingTFSuite:
[info] - NGramsHashingTF 1 to 1
[info] - NGramsHashingTF 1 to 3
[info] - NGramsHashingTF 2 to 3
[info] - NGramsHashingTF with collisions 1 to 3
[info] VectorSplitterSuite:
[info] - vector splitter
[info] - vector splitter maintains order
16/04/06 17:37:06 INFO ImageBenchMarkSuite: name,max(flops),median(flops),stddev(flops)
16/04/06 17:37:06 INFO ImageBenchMarkSuite: Cifar1000,2.430,2.420,0.106
16/04/06 17:37:06 INFO ImageBenchMarkSuite: Cifar10000,2.394,2.384,0.019
16/04/06 17:37:06 INFO ImageBenchMarkSuite: Cifar100,2.222,2.215,0.536
16/04/06 17:37:06 INFO ImageBenchMarkSuite: SolarFlares,1.607,1.502,0.057
16/04/06 17:37:06 INFO ImageBenchMarkSuite: ConvolvedSolarFlares,1.564,1.377,0.114
16/04/06 17:37:06 INFO ImageBenchMarkSuite: ImageNet,1.523,1.399,0.074
[info] ImageBenchMarkSuite:
[info] - Reverse map
[info] - Iteration Benchmarks
[info] - Convolution Benchmarks
16/04/06 17:37:06 INFO ConcretePipeline: Fitting 'x + data.first()' [2]
16/04/06 17:37:07 INFO ConcretePipeline: Finished fitting 'x + data.first()' [2]
16/04/06 17:37:07 INFO ConcretePipeline: Fitting 'x + data.first() + label.first()' [5]
16/04/06 17:37:07 INFO ConcretePipeline: Fitting 'x' [2]
16/04/06 17:37:07 INFO ConcretePipeline: Finished fitting 'x' [2]
16/04/06 17:37:07 INFO ConcretePipeline: Finished fitting 'x + data.first() + label.first()' [5]
16/04/06 17:37:07 INFO ConcretePipeline: Fitting 'x + data.first()' [4]
16/04/06 17:37:07 INFO ConcretePipeline: Finished fitting 'x + data.first()' [4]
16/04/06 17:37:07 INFO ConcretePipeline: Fitting 'x + data.first() + label.first()' [10]
16/04/06 17:37:07 INFO ConcretePipeline: Fitting 'x' [7]
16/04/06 17:37:07 INFO ConcretePipeline: Finished fitting 'x' [7]
16/04/06 17:37:07 INFO ConcretePipeline: Finished fitting 'x + data.first() + label.first()' [10]
[info] WorkflowUtilsSuite:
[info] - Pipeline to Instructions to Pipeline
[info] - Instruction removal test
[info] - Instruction splicing
[info] ClassLabelIndicatorsSuite:
[info] - single label indicators
[info] - multiple label indicators without validation
[info] - multiple label indicators with validation
input to vectors
make gmm
  grad_weights = 0
  grad_means = 1
  grad_variances = 1
  alpha = 1
  pnorm = 0
make handle 
.. and set gmm model to handle 
descriptors to vector 
descriptors length: 20309
encode without weights 
Copy to JNI return memory
Calling free on fvenc
16/04/06 17:37:10 INFO EncEvalSuite: Fisher Vector is 40.109097
Computing variance floor...
  Number of Gaussians: 2
  Number of samples: 20000
  Sample dimensions: 1

     (e-step): updating statistics for sample 0 of 20000...
     (e-step): updating statistics for sample 5000 of 20000...
     (e-step): updating statistics for sample 10000 of 20000...
     (e-step): updating statistics for sample 15000 of 20000...
  iter 0, avg. llh = -2.83521
     (m-step): updating model...
     (e-step): updating statistics for sample 0 of 20000...
     (e-step): updating statistics for sample 5000 of 20000...
     (e-step): updating statistics for sample 10000 of 20000...
     (e-step): updating statistics for sample 15000 of 20000...
  iter 1, avg. llh = -2.55379 (+1)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 2, avg. llh = -2.54755 (+0.0216977)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 3, avg. llh = -2.54466 (+0.00992943)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 4, avg. llh = -2.53948 (+0.0175283)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 5, avg. llh = -2.52817 (+0.0368324)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 6, avg. llh = -2.49829 (+0.0887044)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 7, avg. llh = -2.39918 (+0.227294)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 8, avg. llh = -2.06492 (+0.433945)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 9, avg. llh = -1.76987 (+0.276952)
     (m-step): updating model...
     (e-step): updating statistics for sample +0 of +20000...
     (e-step): updating statistics for sample +5000 of +20000...
     (e-step): updating statistics for sample +10000 of +20000...
     (e-step): updating statistics for sample +15000 of +20000...
  iter 10, avg. llh = -1.7689 (+0.000911264)
16/04/06 17:37:10 INFO EncEvalSuite: GMM means: 5.005826950073242,-1.0036431550979614
16/04/06 17:37:10 INFO EncEvalSuite: GMM vars: 1.0079326629638672,0.2514866590499878
16/04/06 17:37:10 INFO EncEvalSuite: GMM weights: 0.5000001192092896,0.49999985098838806
[info] EncEvalSuite:
[info] - Load SIFT Descriptors and compute Fisher Vector Features
[info] - Compute a GMM from scala
[info] NGramIndexerSuite:
[info] - pack()
[info] - removeFarthestWord()
[info] - removeCurrentWord()
VALUES: 1 14 7 8 6
VALUES: 2 13 7 8 5
VALUES: 3 12 6 6 6
VALUES: 4 11 6 6 5
VALUES: 6 9 5 6 3
VALUES: 8 7 4 4 3
[info] PoolingSuite:
[info] - pooling
[info] - pooling odd
[info] LCSExtractorSuite:
[info] - Load an Image and compute LCS Features
Adding annotator tokenize
Adding annotator ssplit
Adding annotator pos
Reading POS tagger model from edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger ... done [0.7 sec].
Adding annotator lemma
Adding annotator ner
Loading classifier from edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz ... done [3.0 sec].
Loading classifier from edu/stanford/nlp/models/ner/english.muc.7class.distsim.crf.ser.gz ... done [1.8 sec].
Loading classifier from edu/stanford/nlp/models/ner/english.conll.4class.distsim.crf.ser.gz ... done [2.4 sec].
Initializing JollyDayHoliday for sutime
Reading TokensRegex rules from edu/stanford/nlp/models/sutime/defs.sutime.txt
Reading TokensRegex rules from edu/stanford/nlp/models/sutime/english.sutime.txt
Reading TokensRegex rules from edu/stanford/nlp/models/sutime/english.holidays.sutime.txt
Adding annotator tokenize
Adding annotator ssplit
Adding annotator pos
Adding annotator lemma
Adding annotator ner
Adding annotator tokenize
Adding annotator ssplit
Adding annotator pos
Adding annotator lemma
Adding annotator ner
[info] CoreNLPFeatureExtractorSuite:
[info] - lemmatization
[info] - entity extraction
[info] - 1-2-3-grams
[info] MLlibUtilsSuite:
[info] - dense vector to breeze dense
[info] - sparse vector to breeze dense
[info] - dense breeze to vector
[info] - sparse breeze to vector
[info] - sparse breeze with partially-used arrays to vector
[info] - dense matrix to breeze dense
[info] - sparse matrix to breeze dense
[info] CosineRandomFeaturesSuite:
[info] - Guassian cosine random features
[info] - Cauchy cosine random features
[info] WindowingSuite:
[info] - windowing
[info] - 1x1 windowing
[info] - 2x2 windowing
[info] - nxn windowing with step=1
16/04/06 17:37:23 INFO ConcretePipeline: Fitting 'anon$1' [12]
16/04/06 17:37:23 INFO ConcretePipeline: Fitting 'anon$1' [4]
16/04/06 17:37:23 INFO ConcretePipeline: Finished fitting 'anon$1' [4]
16/04/06 17:37:23 INFO ConcretePipeline: Finished fitting 'anon$1' [12]
16/04/06 17:37:23 INFO ConcretePipeline: Fitting 'anon$1' [10]
16/04/06 17:37:23 INFO ConcretePipeline: Finished fitting 'anon$1' [10]
16/04/06 17:37:26 INFO ConcretePipeline: Fitting 'anon$1' [12]
16/04/06 17:37:26 INFO ConcretePipeline: Fitting 'anon$1' [4]
16/04/06 17:37:26 INFO ConcretePipeline: Finished fitting 'anon$1' [4]
16/04/06 17:37:26 INFO ConcretePipeline: Finished fitting 'anon$1' [12]
16/04/06 17:37:26 INFO ConcretePipeline: Fitting 'anon$1' [10]
16/04/06 17:37:26 INFO ConcretePipeline: Finished fitting 'anon$1' [10]
16/04/06 17:37:26 INFO ConcretePipeline: Fitting 'anon$1' [7]
16/04/06 17:37:26 INFO ConcretePipeline: Fitting 'anon$1' [2]
16/04/06 17:37:26 INFO ConcretePipeline: Finished fitting 'anon$1' [2]
16/04/06 17:37:26 INFO ConcretePipeline: Finished fitting 'anon$1' [7]
16/04/06 17:37:26 INFO ConcretePipeline: Fitting 'anon$1' [10]
16/04/06 17:37:26 INFO ConcretePipeline: Finished fitting 'anon$1' [10]
16/04/06 17:37:27 INFO ConcretePipeline: Fitting 'anon$1' [12]
16/04/06 17:37:27 INFO ConcretePipeline: Fitting 'anon$1' [2]
16/04/06 17:37:27 INFO ConcretePipeline: Finished fitting 'anon$1' [2]
16/04/06 17:37:27 INFO ConcretePipeline: Finished fitting 'anon$1' [12]
16/04/06 17:37:27 INFO ConcretePipeline: Fitting 'anon$1' [9]
16/04/06 17:37:27 INFO ConcretePipeline: Finished fitting 'anon$1' [9]
[info] NodeOptimizationRuleSuite:
[info] - Test node level optimizations choice some false
[info] - Test node level optimizations choice all true
[info] - Test node level optimizations with no opts to make
[info] - Test node level optimizations with one opt to make
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: iter=0, llh=5.767829102073352
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: iter=1, llh=5.767829102073352
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: cost improving: false
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: iter=0, llh=7.615642942625183
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: iter=1, llh=7.615642942625183
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: cost improving: false
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: iter=0, llh=-2.0250318846879747
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: iter=1, llh=-2.0250318846879747
16/04/06 17:37:28 INFO GaussianMixtureModelEstimator: cost improving: false
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=0, llh=-5.490827581227109
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=1, llh=-5.446995820356972
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=2, llh=-5.435051947324123
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=3, llh=-5.429646583484977
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=4, llh=-5.426028608166569
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=5, llh=-5.422032553522312
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=6, llh=-5.414505514087394
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=7, llh=-5.39663807924585
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=8, llh=-5.358080184903091
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=9, llh=-5.277122007768411
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=10, llh=-5.121831540173776
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=11, llh=-4.971076345686694
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=12, llh=-4.91999330437808
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=13, llh=-4.912939121561559
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=14, llh=-4.9121155311405085
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=15, llh=-4.91196036136487
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=16, llh=-4.9119171782587365
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=17, llh=-4.911903614114612
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=18, llh=-4.911899242816032
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=19, llh=-4.911897829148268
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=20, llh=-4.911897372845954
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=21, llh=-4.9118972260322815
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=22, llh=-4.911897179070608
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=23, llh=-4.91189716420518
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=24, llh=-4.911897159589295
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=25, llh=-4.911897158208207
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=26, llh=-4.911897157825921
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=27, llh=-4.91189715773921
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=28, llh=-4.911897157732262
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: true
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: iter=29, llh=-4.911897157742046
16/04/06 17:37:29 INFO GaussianMixtureModelEstimator: cost improving: false
[info] GaussianMixtureModelSuite:
[info] - GMM Single Center
[info] - GMM Two Centers dataset 1
[info] - GMM Two Centers dataset 2
[info] - GMM Two Centers dataset 3
[info] - GaussianMixtureModel test
[info] VLFeatSuite:
[info] - Load an Image and compute SIFT Features
[info] HashingTFSuite:
[info] - HashingTF with no collisions
[info] - HashingTF with collisions
16/04/06 17:37:34 INFO LinearDiscriminantAnalysisSuite: 
-0.1497756963901527   0.009529304239967146  
-0.14817298129141573  0.3271933649754069    
0.8511218949751058    -0.5748203423144842   
0.4808362801204207    0.7499568443172125    
16/04/06 17:37:35 INFO LinearDiscriminantAnalysisSuite: Covar
1.0080270912919727       1.9609494764275162E-16  ... (5 total)
1.9609494764275162E-16   1.0560447088288791      ...
4.011917035932748E-17    -9.583869678289483E-17  ...
-2.1982193620706303E-16  -3.247180080156786E-16  ...
-5.331070920046923E-16   7.438994365450092E-17   ...
[info] LinearDiscriminantAnalysisSuite:
[info] - Solve Linear Discriminant Analysis on the Iris Dataset
[info] - Check LDA output for a diagonal covariance
16/04/06 17:37:35 INFO ConcretePipeline: Fitting '$anonfun$4$$anon$1' [2]
16/04/06 17:37:35 INFO ConcretePipeline: Finished fitting '$anonfun$4$$anon$1' [2]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$6$$anon$2' [2]
16/04/06 17:37:36 INFO ConcretePipeline: Finished fitting '$anonfun$6$$anon$2' [2]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$6$$anon$2' [2]
16/04/06 17:37:36 INFO ConcretePipeline: Finished fitting '$anonfun$6$$anon$2' [2]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$8$$anon$3' [3]
16/04/06 17:37:36 INFO ConcretePipeline: Finished fitting '$anonfun$8$$anon$3' [3]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$8$$anon$3' [3]
16/04/06 17:37:36 INFO ConcretePipeline: Finished fitting '$anonfun$8$$anon$3' [3]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$10$$anon$4' [2]
16/04/06 17:37:36 INFO ConcretePipeline: Finished fitting '$anonfun$10$$anon$4' [2]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$10$$anon$5' [3]
16/04/06 17:37:36 INFO ConcretePipeline: Finished fitting '$anonfun$10$$anon$5' [3]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$10$$anon$4' [4]
16/04/06 17:37:36 INFO ConcretePipeline: Finished fitting '$anonfun$10$$anon$4' [4]
16/04/06 17:37:36 INFO ConcretePipeline: Fitting '$anonfun$10$$anon$5' [10]
16/04/06 17:37:37 INFO ConcretePipeline: Finished fitting '$anonfun$10$$anon$5' [10]
16/04/06 17:37:37 INFO ConcretePipeline: Fitting 'anon$1' [1]
16/04/06 17:37:37 INFO ConcretePipeline: Finished fitting 'anon$1' [1]
16/04/06 17:37:37 INFO ConcretePipeline: Fitting 'anon$1' [1]
16/04/06 17:37:37 INFO ConcretePipeline: Finished fitting 'anon$1' [1]
16/04/06 17:37:37 INFO ConcretePipeline: Fitting 'anon$1' [3]
16/04/06 17:37:37 INFO ConcretePipeline: Finished fitting 'anon$1' [3]
[info] PipelineSuite:
[info] - pipeline chaining
[info] - Do not fit estimators multiple times
[info] - estimator chaining
[info] - label estimator chaining
[info] - Pipeline gather
[info] - Lazy execution
[info] AutoCacheRuleSuite:
[info] - Naive cacher
[info] - Greedy cacher, max mem 10
[info] - Greedy cacher, max mem 75
[info] - Greedy cacher, max mem 125
[info] - Greedy cacher, max mem 175
[info] - Greedy cacher, max mem 350
[info] - Greedy cacher, max mem 10000
[info] MatrixUtilsSuite:
[info] - computeMean works correctly
[info] LeastSquaresEstimatorSuite:
[info] - Big n small d dense
[info] - big n big d dense
[info] - big n big d sparse
[info] VOCLoaderSuite:
[info] - load a sample of VOC data
[info] MulticlassClassifierEvaluatorSuite:
[info] - Multiclass evaluation metrics
[info] StandardScalerSuite:
[info] - Standardization with dense input when means and stds are provided
[info] - Standardization with dense input
[info] - Standardization with constant input when means and stds are provided
[info] - Standardization with constant input
[info] MaxClassifierSuite:
[info] - max classifier
[info] TermFrequencySuite:
[info] - term frequency of simple strings
[info] - term frequency of varying types
[info] - log term frequency
[info] DaisyExtractorSuite:
[info] - Load an Image and compute Daisy Features
[info] - Daisy and SIFT extractors should have same row/column ordering.
16/04/06 17:37:44 WARN LogisticRegressionEstimator$LogisticRegressionWithLBFGS: The input data is not directly cached, which may hurt performance if its parent RDDs are also uncached.
16/04/06 17:37:45 WARN LogisticRegressionEstimator$LogisticRegressionWithLBFGS: The input data was not directly cached, which may hurt performance if its parent RDDs are also uncached.
16/04/06 17:37:46 WARN LogisticRegressionEstimator$LogisticRegressionWithLBFGS: The input data is not directly cached, which may hurt performance if its parent RDDs are also uncached.
16/04/06 17:37:47 WARN LogisticRegressionEstimator$LogisticRegressionWithLBFGS: The input data was not directly cached, which may hurt performance if its parent RDDs are also uncached.
[info] LogisticRegressionModelSuite:
[info] - logistic regression with LBFGS
[info] - multinomial logistic regression with LBFGS
[info] NGramSuite:
[info] - NGramsFeaturizer
[info] - NGramsCounts
[info] - NGramsCounts (noAdd)
[info] CenterCornerPatcherSuite:
[info] - check number and dimension of patches
[info] - 1x1 image patches
[info] LinearRectifierSuite:
[info] - Test MaxVal
[info] LinearMapperSuite:
[info] - Solve and apply a linear system
[info] - LocalLeastSquaresEstimator doesn't crash
[info] - Solve a dense linear system (fit intercept) using local least squares
[info] ImageUtilsSuite:
[info] - crop
[info] - flipHorizontal
16/04/06 17:37:51 INFO LBFGSwithL2: LBFGS.runLBFGS finished. Last 10 losses 39.51089035554481, 30.80427506901725, 0.015467235910564396, 0.0012568075194770206, 3.185640802536321E-6, 1.0683057088680965E-6, 3.126042226288474E-10, 9.828354870550519E-12, 2.7565665154724147E-17
16/04/06 17:37:52 INFO LBFGSwithL2: LBFGS.runLBFGS finished. Last 10 losses 39.61692873355631, 30.885864138919136, 0.015034566347342758, 0.0010917532728325296, 3.6680987629989183E-6, 1.0075144116121223E-6, 7.770041550541499E-10, 2.0280384993784464E-11, 1.4529816848056907E-17
16/04/06 17:37:54 INFO LBFGSwithL2: LBFGS.runLBFGS finished. Last 10 losses 0.0023902454742197442, 2.5047836706518255E-4, 6.503740587417343E-5, 4.7404603123716296E-6, 2.2500252130263825E-6, 4.5918167613319965E-8, 1.9464994120619726E-8, 2.3144799988422276E-10, 9.482245383058557E-11, 5.5585233155622565E-12
16/04/06 17:37:55 INFO LBFGSwithL2: LBFGS.runLBFGS finished. Last 10 losses 39.61692873355631, 30.885864138919125, 0.015034566347342754, 0.0010917532728325296, 3.6680987629989187E-6, 1.0075144116121223E-6, 7.7700415505415E-10, 2.028038499378446E-11, 1.4529816848056907E-17
[info] LBFGSSuite:
[info] - Solve a dense linear system (fit intercept)
[info] - Solve a dense linear system (no fit intercept)
[info] - Solve a sparse linear system (fit intercept)
[info] - Solve a sparse linear system (no fit intercept)
16/04/06 17:37:55 WARN BlockWeightedLeastSquaresEstimator: Partitions do not contain elements of the same class. Re-shuffling
16/04/06 17:37:55 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
16/04/06 17:37:57 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
16/04/06 17:37:58 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
16/04/06 17:37:59 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
16/04/06 17:38:00 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
16/04/06 17:38:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
16/04/06 17:38:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
16/04/06 17:38:02 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
16/04/06 17:38:03 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
16/04/06 17:38:04 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
16/04/06 17:38:05 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
16/04/06 17:38:06 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
16/04/06 17:38:07 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
16/04/06 17:38:08 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
16/04/06 17:38:08 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
16/04/06 17:38:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
16/04/06 17:38:10 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
16/04/06 17:38:11 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
16/04/06 17:38:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
16/04/06 17:38:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
16/04/06 17:38:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
16/04/06 17:38:14 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
16/04/06 17:38:15 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
16/04/06 17:38:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
16/04/06 17:38:17 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
16/04/06 17:38:18 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
16/04/06 17:38:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
16/04/06 17:38:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
16/04/06 17:38:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
16/04/06 17:38:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of gradient is 0.6784153044554231
16/04/06 17:38:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
16/04/06 17:38:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
16/04/06 17:38:24 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
16/04/06 17:38:25 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
16/04/06 17:38:26 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
16/04/06 17:38:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
16/04/06 17:38:28 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
16/04/06 17:38:28 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
16/04/06 17:38:29 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
16/04/06 17:38:30 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
16/04/06 17:38:31 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
16/04/06 17:38:32 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
16/04/06 17:38:33 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
16/04/06 17:38:33 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
16/04/06 17:38:34 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
16/04/06 17:38:41 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
16/04/06 17:38:43 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
16/04/06 17:38:43 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
16/04/06 17:38:44 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
16/04/06 17:38:45 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
16/04/06 17:38:46 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
16/04/06 17:38:47 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
16/04/06 17:38:48 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
16/04/06 17:38:48 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
16/04/06 17:38:49 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
16/04/06 17:38:50 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
16/04/06 17:38:51 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
16/04/06 17:38:52 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
16/04/06 17:38:53 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
16/04/06 17:38:53 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
16/04/06 17:38:54 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
16/04/06 17:38:55 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
16/04/06 17:38:56 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
16/04/06 17:38:57 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
16/04/06 17:38:57 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
16/04/06 17:38:58 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
16/04/06 17:38:59 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
16/04/06 17:39:00 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
16/04/06 17:39:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
16/04/06 17:39:02 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
16/04/06 17:39:02 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
16/04/06 17:39:03 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
16/04/06 17:39:04 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
16/04/06 17:39:05 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
16/04/06 17:39:06 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of gradient is 0.008125665854027573
16/04/06 17:39:07 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
16/04/06 17:39:08 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
16/04/06 17:39:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
16/04/06 17:39:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
16/04/06 17:39:10 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
16/04/06 17:39:11 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
16/04/06 17:39:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
16/04/06 17:39:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
16/04/06 17:39:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
16/04/06 17:39:14 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
16/04/06 17:39:15 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
16/04/06 17:39:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
16/04/06 17:39:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
16/04/06 17:39:17 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
16/04/06 17:39:18 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
16/04/06 17:39:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
16/04/06 17:39:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
16/04/06 17:39:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
16/04/06 17:39:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
16/04/06 17:39:22 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
16/04/06 17:39:22 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
16/04/06 17:39:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
16/04/06 17:39:24 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
16/04/06 17:39:25 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
16/04/06 17:39:26 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
16/04/06 17:39:26 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
16/04/06 17:39:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
16/04/06 17:39:28 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
16/04/06 17:39:29 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
16/04/06 17:39:30 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
16/04/06 17:39:31 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
16/04/06 17:39:32 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
16/04/06 17:39:33 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
16/04/06 17:39:33 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
16/04/06 17:39:34 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
16/04/06 17:39:35 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
16/04/06 17:39:36 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
16/04/06 17:39:37 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
16/04/06 17:39:37 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
16/04/06 17:39:38 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
16/04/06 17:39:39 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
16/04/06 17:39:40 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
16/04/06 17:39:41 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
16/04/06 17:39:42 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
16/04/06 17:39:42 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
16/04/06 17:39:43 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
16/04/06 17:39:44 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
16/04/06 17:39:45 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
16/04/06 17:39:46 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
16/04/06 17:39:47 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
16/04/06 17:39:47 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
16/04/06 17:39:48 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
16/04/06 17:39:49 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
16/04/06 17:39:50 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
16/04/06 17:39:51 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
16/04/06 17:39:52 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
16/04/06 17:39:52 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
16/04/06 17:39:53 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
16/04/06 17:39:54 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
16/04/06 17:39:55 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of WLS gradient is 0.018370577718911645
norm of PCS gradient is 0.01837057771891162
16/04/06 17:40:06 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
16/04/06 17:40:07 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
16/04/06 17:40:08 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
16/04/06 17:40:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
16/04/06 17:40:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
16/04/06 17:40:10 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
16/04/06 17:40:11 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
16/04/06 17:40:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
16/04/06 17:40:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
16/04/06 17:40:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
16/04/06 17:40:14 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
16/04/06 17:40:15 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
16/04/06 17:40:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
16/04/06 17:40:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
16/04/06 17:40:17 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
16/04/06 17:40:18 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
16/04/06 17:40:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
16/04/06 17:40:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
16/04/06 17:40:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
16/04/06 17:40:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
16/04/06 17:40:22 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
16/04/06 17:40:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
16/04/06 17:40:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
16/04/06 17:40:24 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
16/04/06 17:40:25 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
16/04/06 17:40:26 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
16/04/06 17:40:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
16/04/06 17:40:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
16/04/06 17:40:28 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
16/04/06 17:40:29 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of gradient is 0.008125665854027573
[info] BlockWeightedLeastSquaresSuite:
[info] - BlockWeighted solver solution should work with empty partitions
[info] - Per-class solver solution should match BlockWeighted solver
[info] - BlockWeighted solver solution should have zero gradient
[info] - BlockWeighted solver should work with 1 class only
[info] - BlockWeighted solver should work with nFeatures not divisible by blockSize
[info] - groupByClasses should work correctly
[info] MeanAveragePrecisionSuite:
[info] - random map test
[info] HogExtractorSuite:
[info] - Load an Image and compute Hog Features
16/04/06 17:40:31 INFO KMeansPlusPlusEstimator: Iteration: 1 current cost 4.333333333333333 imp true
16/04/06 17:40:31 INFO KMeansPlusPlusEstimator: Iteration: 2 current cost 4.333333333333333 imp false
16/04/06 17:40:31 INFO KMeansPlusPlusEstimator: Iteration: 1 current cost 0.5 imp true
16/04/06 17:40:31 INFO KMeansPlusPlusEstimator: Iteration: 2 current cost 0.5 imp false
16/04/06 17:40:31 INFO KMeansPlusPlusEstimator: Iteration: 1 current cost 0.5 imp true
16/04/06 17:40:31 INFO KMeansPlusPlusEstimator: Iteration: 2 current cost 0.5 imp false
[info] KMeansPlusPlusSuite:
[info] - K-Means++ Single Center
[info] - K-Means++ Two Centers
[info] - K-Means Transformer
16/04/06 17:40:32 INFO ConcretePipeline: Fitting '$anonfun$1$$anon$1' [1]
16/04/06 17:40:32 INFO ConcretePipeline: Finished fitting '$anonfun$1$$anon$1' [1]
[info] EstimatorSuite:
[info] - estimator withData
[info] RandomPatcherSuite:
[info] - patch dimensions, number
[info] StringUtilsSuite:
[info] - trim
[info] - lower case
[info] - tokenizer
[info] Passed: Total 147, Failed 0, Errors 0, Passed 147
[success] Total time: 450 s, completed Apr 6, 2016 5:40:33 PM
Sending e-mails to: sparks@cs.berkeley.edu shivaram@cs.berkeley.edu tomerk11@berkeley.edu tomer.kaftan@gmail.com evan.sparks@gmail.com
Finished: SUCCESS