ServerRun 37997
Creatorchuertas
ProgramLR-sbc-nbr f4
DatasetAxon Cars
Task typeMulticlassClassification
Created2y260d ago
Done! Flag_green
51m5s
251M
MulticlassClassification
50m36s
0.614
19s
0.830
8s

Log file

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   1.90133468e-01   4.00460100e-01   4.70206967e-01   6.51858904e-01
   4.60925296e-01   4.47808996e-01   9.65793510e-01   5.72713464e-01
   5.50921951e-01   3.97398034e-01   3.64800079e-01   1.41656528e-01
   3.93124784e-01   1.26456508e+00   1.94247007e-01   4.58706628e-02
   1.33700675e-02   6.96662985e-01   2.56050590e-01   3.55277877e-01
   5.44993080e-01   6.99341957e-01   2.12551362e-01   1.55748529e-01
   2.98677406e-01   8.42624779e-02   1.05660560e-01   2.17617107e-01
   2.41811410e-01   4.78404226e-01   5.57794831e-01   5.01366266e-01
   7.22419526e-02   3.43459406e-01   2.08917197e-01   3.82299239e-01
   1.15851944e-01   4.18631125e-02   1.97763251e-02   5.52138522e-02
   2.81456808e-01   1.45456843e-01   5.86021938e-02   2.67651002e-01
   6.11140811e-01   1.51573793e-02   1.71142922e-01   1.02624643e-02
   2.34895219e-01   2.21119172e-01   3.51782650e-01   5.17360074e-01
   2.88362128e-01   3.24672186e-01   5.60757081e-01   3.94525365e-01
   2.03252649e+00   4.74568072e-01   8.59926793e-01   2.33951794e-01
   1.09536534e-01   1.81174926e-01   1.25322251e+00   9.29304230e-01
   4.11232197e-01   6.55592574e-01   5.40523244e-01   6.80133683e-01
   2.90434730e-01]
lambs, mean: 0.00238537208838, var:1.99978071736e-05
mean lambda: 0.00238537208838
(100, 569)
b is [[-0.12415747]
 [ 0.1443143 ]
 [-0.52350597]
 [ 0.37575328]
 [ 0.14393721]
 [ 0.51127555]
 [ 0.13883383]
 [-0.54060487]
 [ 0.49150973]
 [ 0.0535558 ]
 [-0.00754158]
 [-0.05368383]
 [ 0.10761907]
 [ 0.16045188]
 [ 0.10691471]
 [ 0.14710737]
 [-0.24227738]
 [-0.29642259]
 [-0.59306002]
 [ 0.0327728 ]
 [-0.79004406]
 [-0.41044491]
 [ 0.52660476]
 [-0.46396273]
 [-0.26510062]
 [ 0.32453481]
 [ 0.04675254]
 [ 0.18266686]
 [-0.29145649]
 [-0.00427053]
 [ 0.11978391]
 [-0.24873804]
 [-0.26326518]
 [-0.39675024]
 [ 0.3339346 ]
 [ 0.08750516]
 [-0.23255348]
 [ 0.19231115]
 [ 0.54116341]
 [-0.15808848]
 [-0.11716728]
 [-0.2751899 ]
 [-0.06084501]
 [ 0.41864076]
 [ 0.14056934]
 [ 0.41511964]
 [-0.03006025]
 [-0.07376309]
 [ 0.14021325]
 [ 0.19824422]
 [-0.04776014]
 [-0.16291943]
 [ 0.37916814]
 [-0.52774843]
 [-0.01669779]
 [ 0.1023751 ]
 [-0.11647614]
 [-0.00734862]
 [-0.12388557]
 [ 0.1493809 ]
 [ 0.20965602]
 [-0.58205636]
 [-0.29609578]
 [ 0.14041916]
 [-0.04188174]
 [ 0.0179993 ]
 [-0.16845291]
 [-0.13478032]
 [ 0.47552018]
 [-0.05715799]
 [-0.3466671 ]
 [-0.10204719]
 [-0.40840161]
 [-0.00507064]
 [ 0.41419228]
 [-0.04982188]
 [ 0.23860064]
 [ 0.40912099]
 [ 0.04622567]
 [ 0.63790325]
 [-0.20497565]
 [-0.1003167 ]
 [-0.07335133]
 [-0.11774043]
 [-0.29316146]
 [ 0.35747305]
 [ 0.24396156]
 [ 0.04971009]
 [-0.15942496]
 [-0.26719362]
 [ 0.64027148]
 [ 0.54506961]
 [ 0.47691769]
 [ 0.04773828]
 [-0.45931148]
 [-0.51018305]
 [-0.44140226]
 [ 0.18869872]
 [ 0.38719112]
 [ 0.04560138]]
RUNNING THE L-BFGS-B CODE

           * * *

Machine precision = 1.084D-19
 N =        57000     M =           10
 This problem is unconstrained.

At X0         0 variables are exactly at the bounds

At iterate    0    f=  4.60690D+00    |proj g|=  4.13357D-03

At iterate   10    f=  3.21299D+00    |proj g|=  3.19768D-03

At iterate   20    f=  3.15674D+00    |proj g|=  6.36196D-04

At iterate   30    f=  3.15042D+00    |proj g|=  1.48514D-04

At iterate   40    f=  3.14937D+00    |proj g|=  1.80105D-04

At iterate   50    f=  3.14915D+00    |proj g|=  6.72564D-05

At iterate   60    f=  3.14909D+00    |proj g|=  1.82119D-05

At iterate   70    f=  3.14907D+00    |proj g|=  2.27270D-05

           * * *

Tit   = total number of iterations
Tnf   = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip  = number of BFGS updates skipped
Nact  = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F     = final function value

           * * *

   N   Tit  Tnf  Tnint  Skip  Nact     Projg        F
57000   78   82      1     0     0   5.795D-06   3.149D+00
  F =   3.1490659463113255     

CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL            

 Cauchy                time 8.057E-03 seconds.
 Subspace minimization time 2.113E+00 seconds.
 Line search           time 2.552E+02 seconds.

 Total User time 2.610E+02 seconds.

=== END program1: ./run learn ../dataset2/train --- OK [3036s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
d=72, n=14000 nd=1008000
datamatrix original dimension: (72, 14000)
datamatrix encoded dimension: (569, 14000)
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [19s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
d=72, n=6000 nd=432000
datamatrix original dimension: (72, 6000)
datamatrix encoded dimension: (569, 6000)
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [8s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	51m7.690s
user	23m9.391s
sys	27m0.409s

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