ServerRun 37995
Creatorchuertas
ProgramLR-sbc-nbr f72
Datasetmulticlass-sample
Task typeMulticlassClassification
Created2y50d ago
Done! Flag_green
1s
33M
MulticlassClassification
1s
0
1s
0.500
0s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
d=3, n=4 nd=12
datamatrix original dimension: (3, 4)
using k=10
(3, 4)
(27, 4)
datamatrix encoded dimension: (3, 4)
[ 0.47000363  0.47000363  0.91629073]
[ 0.47000363  0.47000363  0.91629073]
lambs, mean: 0.000173205080757, var:2.19636710771e-09
mean lambda: 0.000173205080757
(2, 3)
b is [[ 3.37617685]
 [-3.37617685]]
RUNNING THE L-BFGS-B CODE

           * * *

Machine precision = 1.084D-19
 N =            8     M =           10
 This problem is unconstrained.

At X0         0 variables are exactly at the bounds

At iterate    0    f=  6.85614D-01    |proj g|=  2.45521D-01

At iterate   10    f=  5.52820D-03    |proj g|=  1.15934D-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
    8   12   13      1     0     0   6.591D-06   5.520D-03
  F =  5.52045205104052025E-003

CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL            

 Cauchy                time 0.000E+00 seconds.
 Subspace minimization time 4.001E-03 seconds.
 Line search           time 0.000E+00 seconds.

 Total User time 4.001E-03 seconds.

=== END program1: ./run learn ../dataset2/train --- OK [1s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
d=3, n=4 nd=12
datamatrix original dimension: (3, 4)
datamatrix encoded dimension: (3, 4)
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
d=3, n=2 nd=6
datamatrix original dimension: (3, 2)
datamatrix encoded dimension: (3, 2)
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	0m2.363s
user	0m1.528s
sys	0m0.728s

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