ServerRun 39458
Creatorchuertas
Programlogreg-dis-python-1e-4
DatasetLos tres mosqueteros
Task typeMulticlassClassification
Created2y32d ago
Done! Flag_green
55s
95M
MulticlassClassification
44s
0.055
6s
0.045
3s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
d=162, n=7354 nd=1191348
datamatrix original dimension: (162, 7354)
using k=10
(162, 7354)
(1458, 7354)
datamatrix encoded dimension: (242, 7354)
(2, 242)
b is [[-2.57889822]
 [ 2.57889822]]
RUNNING THE L-BFGS-B CODE

           * * *

Machine precision = 1.084D-19
 N =          486     M =           10
 This problem is unconstrained.

At X0         0 variables are exactly at the bounds

At iterate    0    f=  6.89999D-01    |proj g|=  4.43088D-01

At iterate   10    f=  1.68391D-01    |proj g|=  3.44531D-03

At iterate   20    f=  1.58586D-01    |proj g|=  1.06373D-03

At iterate   30    f=  1.56836D-01    |proj g|=  3.38138D-04

At iterate   40    f=  1.56439D-01    |proj g|=  1.95242D-04

At iterate   50    f=  1.56359D-01    |proj g|=  1.45257D-04

At iterate   60    f=  1.56327D-01    |proj g|=  1.72141D-04

At iterate   70    f=  1.56313D-01    |proj g|=  6.84367D-05

At iterate   80    f=  1.56302D-01    |proj g|=  4.72993D-05

At iterate   90    f=  1.56297D-01    |proj g|=  3.41935D-05

At iterate  100    f=  1.56294D-01    |proj g|=  2.89812D-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
  486  104  112      1     0     0   8.766D-06   1.563D-01
  F =  0.15629352839518093     

CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL            

 Cauchy                time 0.000E+00 seconds.
 Subspace minimization time 8.000E-03 seconds.
 Line search           time 1.093E+01 seconds.

 Total User time 1.104E+01 seconds.

=== END program1: ./run learn ../dataset2/train --- OK [44s]

===== 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=162, n=7354 nd=1191348
datamatrix original dimension: (162, 7354)
datamatrix encoded dimension: (242, 7354)
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [6s]
=== 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 [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
d=162, n=3152 nd=510624
datamatrix original dimension: (162, 3152)
datamatrix encoded dimension: (242, 3152)
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [3s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	0m55.033s
user	0m30.434s
sys	0m23.593s

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