ServerRun 37939
Creatorchuertas
Programlogreg-dis-python-sqrt
DatasetAxon Cars
Task typeMulticlassClassification
Created2y262d ago
Done! Flag_green
50m15s
250M
MulticlassClassification
49m47s
0.673
21s
0.832
9s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
d=72, n=14000 nd=1008000
datamatrix original dimension: (72, 14000)
using k=10
(72, 14000)
(648, 14000)
datamatrix encoded dimension: (569, 14000)
mean lambda: 0.00238537208838
(100, 569)
b is [[-0.2005605 ]
 [ 0.17070322]
 [ 0.02014761]
 [-0.23341866]
 [ 0.10768803]
 [ 0.00633504]
 [ 0.3923433 ]
 [-0.36699484]
 [-0.20860899]
 [-0.53472823]
 [-0.1456161 ]
 [-0.19409926]
 [-0.18695952]
 [ 0.14480488]
 [ 0.07188993]
 [ 0.04043204]
 [-0.33693411]
 [-0.10871814]
 [-0.00763506]
 [-0.11384675]
 [-0.31108321]
 [-0.3503754 ]
 [ 0.05646978]
 [-0.30229469]
 [-0.01733305]
 [ 0.56885649]
 [ 0.35812167]
 [ 0.30903815]
 [ 0.00486044]
 [-0.07814678]
 [ 0.42424593]
 [-0.07830464]
 [-0.31818425]
 [-0.32958972]
 [ 0.38411974]
 [ 0.01811869]
 [-0.54647106]
 [ 0.16429872]
 [-0.01005094]
 [-0.30417317]
 [ 0.35934597]
 [ 0.04894254]
 [-0.04043158]
 [ 0.36384402]
 [ 0.06069451]
 [ 0.17307907]
 [ 0.53771103]
 [-0.05121211]
 [ 0.20419557]
 [-0.08401672]
 [-0.10055749]
 [-0.01333571]
 [-0.14863891]
 [-0.50050928]
 [-0.17736913]
 [ 0.24526006]
 [-0.07222526]
 [-0.07475565]
 [-0.00820158]
 [-0.55315006]
 [ 0.19870736]
 [-0.10114486]
 [ 0.01038492]
 [ 0.14591256]
 [ 0.26919598]
 [-0.10788333]
 [ 0.43370994]
 [ 0.39539874]
 [ 0.31326477]
 [ 0.176643  ]
 [ 0.23650972]
 [-0.09622286]
 [ 0.20568077]
 [ 0.0423407 ]
 [ 0.09519258]
 [ 0.28400382]
 [ 0.18568316]
 [ 0.11019483]
 [-0.05053011]
 [ 0.11146987]
 [-0.03130594]
 [-0.15769316]
 [-0.10066102]
 [-0.02322682]
 [ 0.33977081]
 [ 0.03624295]
 [-0.18671066]
 [ 0.19050606]
 [-0.28927841]
 [-0.50813098]
 [ 0.07304354]
 [ 0.48487384]
 [ 0.02925162]
 [ 0.27323426]
 [-0.37097748]
 [-0.51624949]
 [-0.42966173]
 [ 0.33312309]
 [-0.00436366]
 [-0.12731425]]
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=  4.60635D+00    |proj g|=  3.94043D-03

At iterate   10    f=  3.73221D+00    |proj g|=  4.82893D-04

At iterate   20    f=  3.73054D+00    |proj g|=  1.41438D-04

           * * *

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   29   32      1     0     0   5.494D-06   3.730D+00
  F =   3.7303695639195240     

CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL            

 Cauchy                time 0.000E+00 seconds.
 Subspace minimization time 5.640E-01 seconds.
 Line search           time 7.714E+01 seconds.

 Total User time 8.025E+01 seconds.

=== END program1: ./run learn ../dataset2/train --- OK [2987s]

===== 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 [21s]
=== 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 [9s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	50m20.674s
user	11m53.857s
sys	18m5.284s

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