Run 39457 |

Creator | chuertas |

Program | logreg-dis-python-sqrt |

Dataset | Los tres mosqueteros |

Task type | MulticlassClassification |

Created | 1y336d ago |

Done!

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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