SuccessConsole Output

Skipping 557 KB.. Full Log
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[info] - 1x1 patches convolutions
[info] - convolutions
[info] - convolutions should match scipy
[info] AnalysisUtilsSuite:
[info] - getChildren
[info] - getDescendents
[info] - getParents
[info] - getAncestors
[info] - linearize
[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
17/02/27 08:43:31 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
17/02/27 08:47:09 INFO ImageBenchMarkSuite: name,max(flops),median(flops),stddev(flops)
17/02/27 08:47:09 INFO ImageBenchMarkSuite: Cifar1000,2.428,2.243,0.452
17/02/27 08:47:09 INFO ImageBenchMarkSuite: Cifar10000,2.405,2.374,0.035
17/02/27 08:47:09 INFO ImageBenchMarkSuite: Cifar100,2.235,2.166,0.484
17/02/27 08:47:09 INFO ImageBenchMarkSuite: SolarFlares,1.692,1.683,0.025
17/02/27 08:47:09 INFO ImageBenchMarkSuite: ConvolvedSolarFlares,1.559,1.472,0.061
17/02/27 08:47:09 INFO ImageBenchMarkSuite: ImageNet,1.676,1.651,0.023
[info] ImageBenchMarkSuite:
[info] - Reverse map
[info] - Iteration Benchmarks
[info] - Convolution Benchmarks
[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
17/02/27 08:47:11 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 = -2.85135
     (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 = -2.55161 (+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 = -2.54327 (+0.027064)
     (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 = -2.53883 (+0.0142002)
     (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 = -2.53012 (+0.0271152)
     (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 = -2.5086 (+0.0627997)
     (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 = -2.44196 (+0.162767)
     (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 = -2.20396 (+0.367632)
     (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 = -1.78405 (+0.393438)
     (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 = -1.76471 (+0.0177955)
     (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 = -1.76471 (+1.21218e-07)
17/02/27 08:47:11 INFO EncEvalSuite: GMM means: 4.999446392059326,-0.996443510055542
17/02/27 08:47:11 INFO EncEvalSuite: GMM vars: 0.9810962677001953,0.25406503677368164
17/02/27 08:47:11 INFO EncEvalSuite: GMM weights: 0.5000002384185791,0.4999997019767761
[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 [0.8 sec].
Adding annotator lemma
Adding annotator ner
Loading classifier from edu/stanford/nlp/models/ner/english.all.3class.distsim.crf.ser.gz ... done [3.8 sec].
Loading classifier from edu/stanford/nlp/models/ner/english.muc.7class.distsim.crf.ser.gz ... done [2.0 sec].
Loading classifier from edu/stanford/nlp/models/ner/english.conll.4class.distsim.crf.ser.gz ... done [2.5 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
17/02/27 08:47:24 INFO AutoCacheRule: Starting pipeline profile
17/02/27 08:47:25 INFO AutoCacheRule: Finished pipeline profile
17/02/27 08:47:25 INFO AutoCacheRule: (NodeId(14),TransformerPlus(2),List(NodeId(7)),Some(Profile(15280736,48,0))),
(NodeId(7),TransformerPlus(1),List(NodeId(3)),Some(Profile(16633730,48,0))),
(NodeId(4),TransformerPlus(4),List(NodeId(14)),Some(Profile(16036181,48,0))),
(NodeId(8),TransformerPlus(11),List(NodeId(13)),None),
(NodeId(1),DatumOperator(5),List(),None),
(NodeId(6),workflow.DelegatingOperator@10da6e98,List(NodeId(5), NodeId(9)),None),
(NodeId(11),TransformerPlus(5),List(NodeId(10), NodeId(4)),Some(Profile(17873353,48,0))),
(NodeId(5),workflow.AutoCacheRuleSuite$$anon$1@1edfdc5e,List(NodeId(11)),None),
(NodeId(13),TransformerPlus(9),List(NodeId(12)),Some(Profile(1750,0,16))),
(NodeId(2),TransformerPlus(10),List(NodeId(13)),None),
(NodeId(10),TransformerPlus(3),List(NodeId(14)),Some(Profile(11958462,48,0))),
(NodeId(12),TransformerPlus(8),List(NodeId(1)),Some(Profile(34647,0,16))),
(NodeId(3),DatasetOperator(ParallelCollectionRDD[0] at parallelize at AutocCacheRuleSuite.scala:28),List(),Some(Profile(21737554,176,0))),
(NodeId(9),TransformerPlus(12),List(NodeId(2), NodeId(8)),None)
17/02/27 08:47:25 INFO AutoCacheRule: Starting cache selection
17/02/27 08:47:25 INFO AutoCacheRule: Finished cache selection
17/02/27 08:47:25 INFO Cacher: CACHING 27
17/02/27 08:47:25 INFO Cacher: CACHING 29
17/02/27 08:47:25 INFO Cacher: CACHING 2
17/02/27 08:47:25 INFO Cacher: CACHING 4
[info] AutoCacheRuleSuite:
[info] - End to end aggressive AutoCacheRule
[info] - End to end greedy AutoCacheRule
[info] - Aggressive 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] 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
[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
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=0, llh=5.767829102073351
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=1, llh=5.767829102073351
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: false
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=0, llh=7.615642942625182
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=1, llh=7.615642942625182
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: false
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=0, llh=-2.0250318846879742
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=1, llh=-2.0250318846879742
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: false
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=0, llh=-5.490827581227099
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=1, llh=-5.446995820356979
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=2, llh=-5.4350519473241095
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=3, llh=-5.429646583484984
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=4, llh=-5.426028608166554
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=5, llh=-5.422032553522298
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=6, llh=-5.414505514087402
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=7, llh=-5.396638079245855
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=8, llh=-5.358080184903084
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=9, llh=-5.277122007768408
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=10, llh=-5.121831540173761
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=11, llh=-4.971076345686689
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=12, llh=-4.91999330437809
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=13, llh=-4.912939121561565
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=14, llh=-4.9121155311405085
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=15, llh=-4.911960361364869
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=16, llh=-4.911917178258732
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=17, llh=-4.911903614114617
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=18, llh=-4.911899242816029
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=19, llh=-4.911897829148252
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=20, llh=-4.911897372845961
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=21, llh=-4.911897226032283
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=22, llh=-4.9118971790706185
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=23, llh=-4.911897164205195
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=24, llh=-4.911897159589299
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=25, llh=-4.9118971582081805
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=26, llh=-4.911897157825918
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=27, llh=-4.911897157739201
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=28, llh=-4.91189715773226
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: cost improving: true
17/02/27 08:47:30 INFO GaussianMixtureModelEstimator: iter=29, llh=-4.911897157742043
17/02/27 08:47:30 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
17/02/27 08:47:35 INFO LinearDiscriminantAnalysisSuite: 
-0.14977569639015229  0.009529304239957207  
-0.14817298129141612  0.32719336497541474   
0.8511218949751052    -0.5748203423144753   
0.48083628012042195   0.7499568443172159    
17/02/27 08:47:35 INFO LinearDiscriminantAnalysisSuite: Covar
1.0017254207221564      -8.829551482128998E-17   ... (5 total)
-8.829551482128998E-17  1.02180120333435         ...
6.964107302176681E-16   -2.1368181387367564E-16  ...
3.429577831825058E-16   1.4914107097567167E-16   ...
-3.248985998490135E-16  2.3393588256616163E-16   ...
[info] LinearDiscriminantAnalysisSuite:
[info] - Solve Linear Discriminant Analysis on the Iris Dataset
[info] - Check LDA output for a diagonal covariance
17/02/27 08:47:36 INFO Cacher: CACHING 1
17/02/27 08:47:36 INFO Cacher: CACHING 5
17/02/27 08:47:37 INFO Cacher: CACHING 1
17/02/27 08:47:37 INFO Cacher: CACHING 3
17/02/27 08:47:37 INFO Cacher: CACHING 2
17/02/27 08:47:37 INFO Cacher: CACHING 3
17/02/27 08:47:37 INFO Cacher: CACHING 9
17/02/27 08:47:38 INFO Cacher: CACHING 2
17/02/27 08:47:38 INFO Cacher: CACHING 4
17/02/27 08:47:38 INFO Cacher: CACHING 7
17/02/27 08:47:38 INFO Cacher: CACHING 9
17/02/27 08:47:38 INFO Cacher: CACHING 11
17/02/27 08:47:38 INFO Cacher: CACHING 2
17/02/27 08:47:38 INFO Cacher: CACHING 4
[info] PipelineSuite:
[info] - pipeline chaining
[info] - Do not fit estimators multiple times
[info] - estimator chaining
[info] - label estimator chaining
[info] - Incrementally update execution state variation 1
[info] - Incrementally update execution state variation 2
[info] - Incrementally update execution state with LabelEstimator
[info] - Incrementally update execution state when andThen is used
[info] - access features and final value
[info] - Pipeline gather
[info] - Pipeline gather incremental construction
[info] - Pipeline fit
[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
17/02/27 08:47:42 INFO KernelRidgeRegression: EPOCH_0_BLOCK_0 took 0.3670036 seconds
17/02/27 08:47:42 INFO KernelRidgeRegression: EPOCH_0_BLOCK_0 kernelGen: 0.188088828 residual: 0.031501654 collect: 0.106904726 localSolve: 0.005547437 modelUpdate: 0.033952055
17/02/27 08:47:42 INFO KernelRidgeRegression: EPOCH_1_BLOCK_0 took 0.270549043 seconds
17/02/27 08:47:42 INFO KernelRidgeRegression: EPOCH_1_BLOCK_0 kernelGen: 0.168176652 residual: 0.026672304 collect: 0.048537162 localSolve: 0.004401586 modelUpdate: 0.022748254
SUM OF DELTA1 2.3517130792981078E-104
17/02/27 08:47:43 INFO KernelRidgeRegression: EPOCH_0_BLOCK_0 took 0.414389142 seconds
17/02/27 08:47:43 INFO KernelRidgeRegression: EPOCH_0_BLOCK_0 kernelGen: 0.200196081 residual: 0.035295105 collect: 0.146044584 localSolve: 0.003957238 modelUpdate: 0.028880692
17/02/27 08:47:44 INFO KernelRidgeRegression: EPOCH_0_BLOCK_1 took 0.317390315 seconds
17/02/27 08:47:44 INFO KernelRidgeRegression: EPOCH_0_BLOCK_1 kernelGen: 0.190600592 residual: 0.029756041 collect: 0.055300916 localSolve: 0.004999014 modelUpdate: 0.036721009
17/02/27 08:47:44 INFO KernelRidgeRegression: EPOCH_1_BLOCK_0 took 0.277156325 seconds
17/02/27 08:47:44 INFO KernelRidgeRegression: EPOCH_1_BLOCK_0 kernelGen: 0.18148951 residual: 0.024694629 collect: 0.045039702 localSolve: 0.003423541 modelUpdate: 0.022496564
17/02/27 08:47:44 INFO KernelRidgeRegression: EPOCH_1_BLOCK_1 took 0.267991559 seconds
17/02/27 08:47:44 INFO KernelRidgeRegression: EPOCH_1_BLOCK_1 kernelGen: 0.177571192 residual: 0.021973957 collect: 0.043330956 localSolve: 0.004189987 modelUpdate: 0.020909961
[info] KernelModelSuite:
[info] - KernelModel XOR test
[info] - KernelModel XOR blocked test
[info] MaxClassifierSuite:
[info] - max classifier
[info] OperatorSuite:
[info] - DatumOperator
[info] - DatasetOperator
[info] - TransformerOperator single datums
[info] - TransformerOperator batch datasets
[info] - TransformerOperator test invalid inputs
[info] - EstimatorOperator
[info] - DelegatingOperator single datums
[info] - DelegatingOperator batch datasets
[info] - DelegatingOperator test invalid inputs
[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.
17/02/27 08:47:49 WARN LogisticRegressionEstimator$LogisticRegressionWithLBFGS: The input data is not directly cached, which may hurt performance if its parent RDDs are also uncached.
17/02/27 08:47:50 WARN LogisticRegressionEstimator$LogisticRegressionWithLBFGS: The input data was not directly cached, which may hurt performance if its parent RDDs are also uncached.
17/02/27 08:47:51 WARN LogisticRegressionEstimator$LogisticRegressionWithLBFGS: The input data is not directly cached, which may hurt performance if its parent RDDs are also uncached.
17/02/27 08:47:53 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
17/02/27 08:47:56 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
17/02/27 08:47:57 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
17/02/27 08:47:59 INFO LBFGSwithL2: LBFGS.runLBFGS finished. Last 10 losses 9.482245383058557E-11, 5.5585233155622565E-12, 4.848292028543071E-13, 1.6060531958843392E-13, 3.706428815898728E-15, 1.784321574378326E-15, 9.2500622904722E-16, 2.1841342528114271E-16, 1.3162602275675318E-16, 8.38210118854565E-18
17/02/27 08:48:00 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)
17/02/27 08:48:01 WARN BlockWeightedLeastSquaresEstimator: Partitions do not contain elements of the same class. Re-shuffling
17/02/27 08:48:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
17/02/27 08:48:03 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
17/02/27 08:48:04 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
17/02/27 08:48:05 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
17/02/27 08:48:06 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
17/02/27 08:48:07 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
17/02/27 08:48:08 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
17/02/27 08:48:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
17/02/27 08:48:10 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
17/02/27 08:48:11 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
17/02/27 08:48:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
17/02/27 08:48:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
17/02/27 08:48:14 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
17/02/27 08:48:15 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
17/02/27 08:48:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
17/02/27 08:48:17 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
17/02/27 08:48:18 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
17/02/27 08:48:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
17/02/27 08:48:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
17/02/27 08:48:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
17/02/27 08:48:22 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
17/02/27 08:48:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
17/02/27 08:48:24 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
17/02/27 08:48:25 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
17/02/27 08:48:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
17/02/27 08:48:28 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
17/02/27 08:48:29 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
17/02/27 08:48:30 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
17/02/27 08:48:31 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
17/02/27 08:48:33 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of gradient is 0.678415304455423
17/02/27 08:48:34 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
17/02/27 08:48:35 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
17/02/27 08:48:36 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
17/02/27 08:48:37 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
17/02/27 08:48:39 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
17/02/27 08:48:40 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
17/02/27 08:48:41 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
17/02/27 08:48:42 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
17/02/27 08:48:43 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
17/02/27 08:48:44 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
17/02/27 08:48:45 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
17/02/27 08:48:46 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
17/02/27 08:48:47 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
17/02/27 08:48:48 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
17/02/27 08:48:50 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
17/02/27 08:48:58 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
17/02/27 08:49:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
17/02/27 08:49:04 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
17/02/27 08:49:06 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
17/02/27 08:49:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
17/02/27 08:49:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
17/02/27 08:49:14 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
17/02/27 08:49:17 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
17/02/27 08:49:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
17/02/27 08:49:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
17/02/27 08:49:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
17/02/27 08:49:24 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
17/02/27 08:49:25 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
17/02/27 08:49:26 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
17/02/27 08:49:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
17/02/27 08:49:28 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
17/02/27 08:49:29 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
17/02/27 08:49:30 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
17/02/27 08:49:31 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
17/02/27 08:49:32 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
17/02/27 08:49:34 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
17/02/27 08:49:35 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
17/02/27 08:49:35 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
17/02/27 08:49:36 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
17/02/27 08:49:37 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
17/02/27 08:49:38 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
17/02/27 08:49:39 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
17/02/27 08:49:40 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
17/02/27 08:49:41 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
17/02/27 08:49:42 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of gradient is 0.008125665854027618
17/02/27 08:49:43 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
17/02/27 08:49:44 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
17/02/27 08:49:45 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
17/02/27 08:49:46 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
17/02/27 08:49:47 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
17/02/27 08:49:48 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
17/02/27 08:49:49 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
17/02/27 08:49:50 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
17/02/27 08:49:51 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
17/02/27 08:49:52 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
17/02/27 08:49:53 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
17/02/27 08:49:54 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
17/02/27 08:49:55 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
17/02/27 08:49:56 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
17/02/27 08:49:57 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
17/02/27 08:49:58 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
17/02/27 08:49:59 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
17/02/27 08:50:00 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
17/02/27 08:50:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
17/02/27 08:50:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
17/02/27 08:50:02 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
17/02/27 08:50:03 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
17/02/27 08:50:05 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
17/02/27 08:50:05 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
17/02/27 08:50:06 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
17/02/27 08:50:07 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
17/02/27 08:50:08 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
17/02/27 08:50:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
17/02/27 08:50:10 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
17/02/27 08:50:11 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
17/02/27 08:50:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
17/02/27 08:50:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
17/02/27 08:50:14 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
17/02/27 08:50:15 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
17/02/27 08:50:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
17/02/27 08:50:17 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
17/02/27 08:50:18 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
17/02/27 08:50:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
17/02/27 08:50:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
17/02/27 08:50:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
17/02/27 08:50:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
17/02/27 08:50:22 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
17/02/27 08:50:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
17/02/27 08:50:25 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
17/02/27 08:50:26 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
17/02/27 08:50:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
17/02/27 08:50:28 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
17/02/27 08:50:30 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
17/02/27 08:50:31 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
17/02/27 08:50:32 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
17/02/27 08:50:33 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
17/02/27 08:50:34 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
17/02/27 08:50:36 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
17/02/27 08:50:37 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
17/02/27 08:50:38 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
17/02/27 08:50:39 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
17/02/27 08:50:40 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
17/02/27 08:50:42 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
17/02/27 08:50:43 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
17/02/27 08:50:44 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of WLS gradient is 0.018370577718911624
norm of PCS gradient is 0.01837057771891162
17/02/27 08:50:58 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 0
17/02/27 08:51:01 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 1
17/02/27 08:51:02 INFO BlockWeightedLeastSquaresEstimator: Running pass 0 block 2
17/02/27 08:51:03 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 0
17/02/27 08:51:04 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 1
17/02/27 08:51:05 INFO BlockWeightedLeastSquaresEstimator: Running pass 1 block 2
17/02/27 08:51:06 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 0
17/02/27 08:51:07 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 1
17/02/27 08:51:09 INFO BlockWeightedLeastSquaresEstimator: Running pass 2 block 2
17/02/27 08:51:10 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 0
17/02/27 08:51:11 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 1
17/02/27 08:51:12 INFO BlockWeightedLeastSquaresEstimator: Running pass 3 block 2
17/02/27 08:51:13 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 0
17/02/27 08:51:14 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 1
17/02/27 08:51:15 INFO BlockWeightedLeastSquaresEstimator: Running pass 4 block 2
17/02/27 08:51:16 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 0
17/02/27 08:51:17 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 1
17/02/27 08:51:18 INFO BlockWeightedLeastSquaresEstimator: Running pass 5 block 2
17/02/27 08:51:19 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 0
17/02/27 08:51:20 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 1
17/02/27 08:51:21 INFO BlockWeightedLeastSquaresEstimator: Running pass 6 block 2
17/02/27 08:51:22 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 0
17/02/27 08:51:23 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 1
17/02/27 08:51:24 INFO BlockWeightedLeastSquaresEstimator: Running pass 7 block 2
17/02/27 08:51:25 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 0
17/02/27 08:51:26 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 1
17/02/27 08:51:27 INFO BlockWeightedLeastSquaresEstimator: Running pass 8 block 2
17/02/27 08:51:29 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 0
17/02/27 08:51:30 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 1
17/02/27 08:51:31 INFO BlockWeightedLeastSquaresEstimator: Running pass 9 block 2
norm of gradient is 0.008125665854027618
[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] - PerClass WeightedLeastSquares should work with empty partitions
[info] MeanAveragePrecisionSuite:
[info] - random map test
[info] HogExtractorSuite:
[info] - Load an Image and compute Hog Features
17/02/27 08:51:39 INFO KMeansPlusPlusEstimator: Iteration: 1 current cost 4.333333333333333 imp true
17/02/27 08:51:39 INFO KMeansPlusPlusEstimator: Iteration: 2 current cost 4.333333333333333 imp false
17/02/27 08:51:39 INFO KMeansPlusPlusEstimator: Iteration: 1 current cost 0.5 imp true
17/02/27 08:51:39 INFO KMeansPlusPlusEstimator: Iteration: 2 current cost 0.5 imp false
17/02/27 08:51:39 INFO KMeansPlusPlusEstimator: Iteration: 1 current cost 0.5 imp true
17/02/27 08:51:39 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
[info] EstimatorSuite:
[info] - Estimator fit RDD
[info] - Estimator fit Pipeline Data
[info] GraphSuite:
[info] - nodes
[info] - sinks
[info] - getDependencies
[info] - getSinkDependency
[info] - getOperator
[info] - addNode
[info] - addNode on empty graph
[info] - addSource on empty graph
[info] - addSink
[info] - addSource
[info] - setDependencies
[info] - setOperator
[info] - setSinkDependency
[info] - removeSink
[info] - removeSource
[info] - removeNode
[info] - replaceDependency
[info] - addGraph
[info] - connectGraph
[info] - connectGraph argument checks
[info] - replaceNodes
[info] - replaceNodes argument checks
[info] RandomPatcherSuite:
[info] - patch dimensions, number
[info] StringUtilsSuite:
[info] - trim
[info] - lower case
[info] - tokenizer
[info] Run completed in 8 minutes, 18 seconds.
[info] Total number of tests run: 193
[info] Suites: completed 62, aborted 0
[info] Tests: succeeded 193, failed 0, canceled 0, ignored 0, pending 0
[info] All tests passed.
[success] Total time: 534 s, completed Feb 27, 2017 8:51:45 AM
Finished: SUCCESS