ServerRun 39457
Creatorchuertas
Programlogreg-dis-python-sqrt
DatasetLos tres mosqueteros
Task typeMulticlassClassification
Created2y32d ago
Done! Flag_green
33s
95M
MulticlassClassification
33s
0.055
7s
0.046
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)
mean lambda: 0.00155563491861
(2, 242)
b is [[-1.91923846]
 [ 1.91923846]]
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=  6.93727D-01    |proj g|=  4.44979D-01

At iterate   10    f=  1.72848D-01    |proj g|=  2.81404D-03

At iterate   20    f=  1.70994D-01    |proj g|=  8.39394D-04

At iterate   30    f=  1.70880D-01    |proj g|=  3.32814D-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   34   36      1     0     0   6.928D-06   1.709D-01
  F =  0.17087794700846695     

CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL            

 Cauchy                time 0.000E+00 seconds.
 Subspace minimization time 8.000E-03 seconds.
 Line search           time 3.924E+00 seconds.

 Total User time 3.968E+00 seconds.

=== END program1: ./run learn ../dataset2/train --- OK [33s]

===== 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 [7s]
=== 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 [1s]
=== 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	0m43.787s
user	0m16.529s
sys	0m9.073s

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