Run 39458 |

Creator | chuertas |

Program | logreg-dis-python-1e-4 |

Dataset | Los tres mosqueteros |

Task type | MulticlassClassification |

Created | 3y1d ago |

Done!

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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