-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathoutput_r.txt
5402 lines (5402 loc) · 229 KB
/
output_r.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
V jump indeces: [0, 399, 899, 1399, 1899, 2399, 2899, 3399, 3899, 4399, 4899, 5399, 5899, 6399, 6899, 7399, 7899, 8399, 8899, 9399, 10399, 10799]
Number of B-spline coeffs per segment: 13
lambda = 1
Costs at truth:
True theta: [-2.4384048161365803, -4.720581037499419, -5.268758564306492, -3.455231270154641]
Lambda: 1.000
Inner cost: 0.00174700 Data cost: 0.00058140 Gradient matching cost: 0.00116560
Iteration: 0
Best parameters: [-5.0614538 -7.67389311 -5.29493862 -5.92719919]
Best objective: 0.004856433584308041
Mean objective: 0.9116705618255355
Inner objective at best sample: 0.17218443382149343
Gradient objective at best sample: 0.1673280002371854
Time elapsed: 178.71689009666443
Iteration: 1
Best parameters: [-6.74059433 -6.31076531 -7.15208487 -4.20750967]
Best objective: 0.0019311702103464476
Mean objective: 3.427182858239667
Inner objective at best sample: 0.047595018797918276
Gradient objective at best sample: 0.04566384858757183
Time elapsed: 297.50144481658936
Iteration: 2
Best parameters: [-6.92098413 -8.15955694 -6.12816213 -5.25902375]
Best objective: 0.004022417314669788
Mean objective: 0.05402061622335242
Inner objective at best sample: 0.09107217467454029
Gradient objective at best sample: 0.0870497573598705
Time elapsed: 336.9399046897888
Iteration: 3
Best parameters: [-6.87033438 -8.80279879 -7.88556147 -3.87224224]
Best objective: 0.0012965661386117643
Mean objective: 0.048976654159948715
Inner objective at best sample: 0.03535132060895556
Gradient objective at best sample: 0.03405475447034379
Time elapsed: 386.96165585517883
Iteration: 4
Best parameters: [-5.61308981 -9.40411581 -5.82024566 -4.13574488]
Best objective: 0.005573603141788629
Mean objective: 0.019317414228253487
Inner objective at best sample: 0.12499808630907154
Gradient objective at best sample: 0.11942448316728291
Time elapsed: 398.39173126220703
Iteration: 5
Best parameters: [-8.11418206 -4.85379798 -8.17531847 -4.34542435]
Best objective: 0.0015371841592183254
Mean objective: 0.02073056946846146
Inner objective at best sample: 0.032237472289846233
Gradient objective at best sample: 0.030700288130627907
Time elapsed: 413.3828806877136
Iteration: 6
Best parameters: [-9.61097471 -7.16161925 -8.5307097 -4.0878845 ]
Best objective: 0.0013770440291759418
Mean objective: 0.01074254797864545
Inner objective at best sample: 0.03343501301240835
Gradient objective at best sample: 0.03205796898323241
Time elapsed: 377.3345293998718
Iteration: 7
Best parameters: [-6.72814517 -4.76827933 -6.90285114 -5.38327182]
Best objective: 0.0013484543318671481
Mean objective: 0.007268857285524187
Inner objective at best sample: 0.0420020686014914
Gradient objective at best sample: 0.04065361426962425
Time elapsed: 312.3167588710785
Iteration: 8
Best parameters: [-7.97314891 -5.80142781 -7.17287578 -5.42413588]
Best objective: 0.0013613856894465334
Mean objective: 0.004520838565820473
Inner objective at best sample: 0.04158949393151559
Gradient objective at best sample: 0.04022810824206906
Time elapsed: 227.79192805290222
Iteration: 9
Best parameters: [-8.72049471 -3.51827356 -7.29447927 -6.14306604]
Best objective: 0.001340407331859276
Mean objective: 0.0033781434210775492
Inner objective at best sample: 0.04037783755902684
Gradient objective at best sample: 0.03903743022716756
Time elapsed: 395.4121446609497
Iteration: 10
Best parameters: [-7.4660349 -2.95422055 -7.67477657 -5.17719486]
Best objective: 0.0013401589870404547
Mean objective: 0.00285487149776372
Inner objective at best sample: 0.03392117465104909
Gradient objective at best sample: 0.03258101566400863
Time elapsed: 392.5458161830902
Iteration: 11
Best parameters: [-8.69774815 -4.99031202 -8.0872706 -4.34068502]
Best objective: 0.0012994053938908325
Mean objective: 0.003699467764967032
Inner objective at best sample: 0.0366647560741148
Gradient objective at best sample: 0.035365350680223964
Time elapsed: 397.17622327804565
Iteration: 12
Best parameters: [-9.15872194 -3.48126433 -7.58467699 -5.31855714]
Best objective: 0.0013255262813213547
Mean objective: 0.002299772301826199
Inner objective at best sample: 0.036917899795189016
Gradient objective at best sample: 0.03559237351386766
Time elapsed: 402.6119575500488
Iteration: 13
Best parameters: [-8.95104022 -4.16613098 -7.88751371 -4.63535093]
Best objective: 0.0013083837775488953
Mean objective: 0.0025482498314354624
Inner objective at best sample: 0.03724981433571177
Gradient objective at best sample: 0.03594143055816287
Time elapsed: 350.20831632614136
Iteration: 14
Best parameters: [-9.10698194 -4.94094098 -7.57903314 -5.1216935 ]
Best objective: 0.0013162958169449749
Mean objective: 0.0020572869114452346
Inner objective at best sample: 0.037721500515065
Gradient objective at best sample: 0.03640520469812003
Time elapsed: 365.0492923259735
Iteration: 15
Best parameters: [-8.77096997 -4.05836471 -7.80356971 -4.76646323]
Best objective: 0.001307669960450351
Mean objective: 0.00170124088702392
Inner objective at best sample: 0.03703625212303442
Gradient objective at best sample: 0.03572858216258407
Time elapsed: 354.5801236629486
Iteration: 16
Best parameters: [-7.36253913 -4.55388664 -7.43965155 -4.91733318]
Best objective: 0.0013142895857944478
Mean objective: 0.00269031264014897
Inner objective at best sample: 0.037998142613699736
Gradient objective at best sample: 0.036683853027905285
Time elapsed: 390.47348856925964
Iteration: 17
Best parameters: [-8.47508604 -3.82700161 -7.70748414 -4.92957414]
Best objective: 0.0013101271772128537
Mean objective: 0.0014186884481189228
Inner objective at best sample: 0.03713895181242135
Gradient objective at best sample: 0.03582882463520849
Time elapsed: 404.68507838249207
Iteration: 18
Best parameters: [-7.94643494 -3.95327717 -7.68704698 -4.85413834]
Best objective: 0.001312209305554363
Mean objective: 0.003413301534638821
Inner objective at best sample: 0.03754928740823486
Gradient objective at best sample: 0.0362370781026805
Time elapsed: 393.80980253219604
Iteration: 19
Best parameters: [-8.58308175 -4.10978363 -7.8462526 -4.68318505]
Best objective: 0.0013058773626104328
Mean objective: 0.0013490436725265786
Inner objective at best sample: 0.036951491909649595
Gradient objective at best sample: 0.03564561454703916
Time elapsed: 411.2161457538605
Iteration: 20
Best parameters: [-8.72864923 -4.03974245 -7.87188492 -4.68305349]
Best objective: 0.0013054612311216704
Mean objective: 0.0013296020276302317
Inner objective at best sample: 0.03654521945664796
Gradient objective at best sample: 0.03523975822552629
Time elapsed: 391.5773243904114
Iteration: 21
Best parameters: [-7.48345076 -3.95280438 -7.74182155 -4.74369574]
Best objective: 0.001303948468158995
Mean objective: 0.03227083031391402
Inner objective at best sample: 0.03603630977657498
Gradient objective at best sample: 0.034732361308415984
Time elapsed: 305.1749587059021
Iteration: 22
Best parameters: [-8.61642605 -4.01463274 -7.93075697 -4.57045849]
Best objective: 0.00130715405297001
Mean objective: 0.0013894188990868563
Inner objective at best sample: 0.03713518037774304
Gradient objective at best sample: 0.035828026324773034
Time elapsed: 346.02663588523865
Iteration: 23
Best parameters: [-8.38134344 -4.18704866 -7.90075156 -4.58579024]
Best objective: 0.0013031288351519348
Mean objective: 0.0013127210657923809
Inner objective at best sample: 0.03676283634842756
Gradient objective at best sample: 0.03545970751327562
Time elapsed: 376.96726512908936
Iteration: 24
Best parameters: [-8.34689765 -4.44247004 -7.87404969 -4.60249194]
Best objective: 0.0013036106077756928
Mean objective: 0.0013268154925958342
Inner objective at best sample: 0.03636839369194906
Gradient objective at best sample: 0.035064783084173365
Time elapsed: 417.4498391151428
Iteration: 25
Best parameters: [-7.5676623 -4.24829617 -7.9943231 -4.3865667 ]
Best objective: 0.0012961811262565284
Mean objective: 0.0013168855599944343
Inner objective at best sample: 0.03543783153035363
Gradient objective at best sample: 0.034141650404097106
Time elapsed: 411.26936054229736
Iteration: 26
Best parameters: [-7.50748033 -3.60337457 -8.00381725 -4.47874942]
Best objective: 0.0012957629582088456
Mean objective: 0.001311332603504101
Inner objective at best sample: 0.0357895763696482
Gradient objective at best sample: 0.03449381341143935
Time elapsed: 416.0072891712189
Iteration: 27
Best parameters: [-6.45707567 -3.82768162 -8.12014453 -4.12306627]
Best objective: 0.0012906388566883435
Mean objective: 0.0013822681436841538
Inner objective at best sample: 0.035258246269722134
Gradient objective at best sample: 0.03396760741303379
Time elapsed: 419.16643738746643
Iteration: 28
Best parameters: [-5.70985865 -3.65651355 -8.03468833 -4.12967017]
Best objective: 0.0012539677927682871
Mean objective: 0.0013960864556846143
Inner objective at best sample: 0.03070980620050382
Gradient objective at best sample: 0.029455838407735534
Time elapsed: 380.5325860977173
Iteration: 29
Best parameters: [-6.26119934 -3.55239063 -8.08580177 -4.26383193]
Best objective: 0.001272390001911538
Mean objective: 0.0015946080380469837
Inner objective at best sample: 0.032975289234777085
Gradient objective at best sample: 0.031702899232865546
Time elapsed: 304.20105266571045
Iteration: 30
Best parameters: [-7.03150001 -3.29593974 -7.96013191 -4.56377945]
Best objective: 0.001294358224468664
Mean objective: 0.0014680713200940115
Inner objective at best sample: 0.03457940904444649
Gradient objective at best sample: 0.033285050819977825
Time elapsed: 400.2799096107483
Iteration: 31
Best parameters: [-5.89424332 -2.56586972 -7.90128565 -4.68098795]
Best objective: 0.0011810242968049323
Mean objective: 0.001408001308533539
Inner objective at best sample: 0.018704139326357422
Gradient objective at best sample: 0.01752311502955249
Time elapsed: 398.5474638938904
Iteration: 32
Best parameters: [-5.5027821 -2.62142561 -7.61057369 -5.10749987]
Best objective: 0.0011677425276288066
Mean objective: 0.7403727496467478
Inner objective at best sample: 0.01660010944213493
Gradient objective at best sample: 0.015432366914506125
Time elapsed: 420.999947309494
Iteration: 33
Best parameters: [-6.77173456 -2.18406347 -7.96458325 -4.76656389]
Best objective: 0.0011792300185069536
Mean objective: 0.004209734203113048
Inner objective at best sample: 0.014583605002042156
Gradient objective at best sample: 0.013404374983535201
Time elapsed: 399.69273471832275
Iteration: 34
Best parameters: [-6.57704344 -2.31194081 -7.67655663 -5.04773234]
Best objective: 0.0011536663048083446
Mean objective: 0.033151383779333354
Inner objective at best sample: 0.017267938120094195
Gradient objective at best sample: 0.016114271815285852
Time elapsed: 424.29779744148254
Iteration: 35
Best parameters: [-5.89558877 -2.43816878 -7.70663884 -4.94490181]
Best objective: 0.0011254038438563826
Mean objective: 0.005897819253925447
Inner objective at best sample: 0.015230355430436226
Gradient objective at best sample: 0.014104951586579843
Time elapsed: 400.3415985107422
Iteration: 36
Best parameters: [-6.24812886 -2.52987479 -7.85646638 -4.81853426]
Best objective: 0.0012113047056442887
Mean objective: 0.19446653256880048
Inner objective at best sample: 0.020859978597300065
Gradient objective at best sample: 0.019648673891655775
Time elapsed: 333.3893127441406
Iteration: 37
Best parameters: [-6.18482334 -2.30146968 -7.81846096 -4.80689226]
Best objective: 0.0010735175000079276
Mean objective: 0.002202142955135992
Inner objective at best sample: 0.01451433460778456
Gradient objective at best sample: 0.013440817107776634
Time elapsed: 300.06620836257935
Iteration: 38
Best parameters: [-6.02107702 -2.31842612 -7.9054086 -4.72425 ]
Best objective: 0.0010920088697412228
Mean objective: 0.0013818897816373897
Inner objective at best sample: 0.014863883928196377
Gradient objective at best sample: 0.013771875058455153
Time elapsed: 425.3720784187317
Iteration: 39
Best parameters: [-5.5192231 -2.51953479 -7.92195804 -4.63130042]
Best objective: 0.0010842246759790093
Mean objective: 0.005330073819580653
Inner objective at best sample: 0.014270743680298945
Gradient objective at best sample: 0.013186519004319936
Time elapsed: 425.41745138168335
Iteration: 40
Best parameters: [-5.2715683 -2.56348087 -7.68514351 -4.96199589]
Best objective: 0.0010869953374175428
Mean objective: 0.08759112574060138
Inner objective at best sample: 0.013918265403091903
Gradient objective at best sample: 0.01283127006567436
Time elapsed: 410.09865617752075
Iteration: 41
Best parameters: [-6.44922865 -2.29512193 -7.80318231 -4.85089121]
Best objective: 0.0010986177523434762
Mean objective: 0.002241789407339889
Inner objective at best sample: 0.015278986483043907
Gradient objective at best sample: 0.01418036873070043
Time elapsed: 420.6440291404724
Iteration: 42
Best parameters: [-5.64920456 -2.46351571 -7.91454088 -4.69529477]
Best objective: 0.0010668477924485596
Mean objective: 0.001280345914432259
Inner objective at best sample: 0.014111384429778537
Gradient objective at best sample: 0.013044536637329978
Time elapsed: 424.32499146461487
Iteration: 43
Best parameters: [-5.5312151 -2.47922285 -7.92975742 -4.66722369]
Best objective: 0.0010652351787307985
Mean objective: 0.014792548421067819
Inner objective at best sample: 0.014110010926574877
Gradient objective at best sample: 0.013044775747844078
Time elapsed: 328.9294195175171
Iteration: 44
Best parameters: [-5.66283225 -2.55697288 -7.82559423 -4.7966359 ]
Best objective: 0.0011252174038843517
Mean objective: 0.002154013096286113
Inner objective at best sample: 0.015910987099176637
Gradient objective at best sample: 0.014785769695292286
Time elapsed: 222.9526195526123
Iteration: 45
Best parameters: [-5.25226484 -2.54636185 -7.97859308 -4.56095883]
Best objective: 0.0010692711041510007
Mean objective: 0.0012007169865709505
Inner objective at best sample: 0.014209376857542996
Gradient objective at best sample: 0.013140105753391996
Time elapsed: 321.60575103759766
Iteration: 46
Best parameters: [-5.56388464 -2.47962493 -7.97504569 -4.59564354]
Best objective: 0.0010635550504884462
Mean objective: 0.0012395788130358356
Inner objective at best sample: 0.014107509619105042
Gradient objective at best sample: 0.013043954568616596
Time elapsed: 391.8457872867584
Iteration: 47
Best parameters: [-6.02765501 -2.35856991 -7.89729775 -4.72331723]
Best objective: 0.0010696571729764746
Mean objective: 0.0011698371064159077
Inner objective at best sample: 0.014335685619062826
Gradient objective at best sample: 0.013266028446086351
Time elapsed: 404.5774257183075
Iteration: 48
Best parameters: [-5.81969929 -2.4020784 -7.93499925 -4.66058207]
Best objective: 0.0010661991427004355
Mean objective: 0.0010963286028495673
Inner objective at best sample: 0.014255829342253104
Gradient objective at best sample: 0.013189630199552669
Time elapsed: 362.52544116973877
Iteration: 49
Best parameters: [-5.93521451 -2.37551018 -7.90564492 -4.70862665]
Best objective: 0.001068056571015244
Mean objective: 0.0010921576564517368
Inner objective at best sample: 0.014290785567642642
Gradient objective at best sample: 0.013222728996627398
Time elapsed: 332.51724314689636
Iteration: 50
Best parameters: [-5.81581332 -2.41808688 -7.95596488 -4.62622184]
Best objective: 0.0010675569899883256
Mean objective: 0.001080857028357386
Inner objective at best sample: 0.014256627510826373
Gradient objective at best sample: 0.013189070520838047
Time elapsed: 290.2314682006836
Iteration: 51
Best parameters: [-5.75861745 -2.42381758 -7.97349212 -4.60891863]
Best objective: 0.0010646448551551778
Mean objective: 0.0010705912571416672
Inner objective at best sample: 0.01420871723505421
Gradient objective at best sample: 0.013144072379899032
Time elapsed: 291.0555455684662
Iteration: 52
Best parameters: [-5.85825859 -2.39625768 -8.02661606 -4.53263993]
Best objective: 0.0010651849462165101
Mean objective: 0.0010696198948264588
Inner objective at best sample: 0.01429215342083436
Gradient objective at best sample: 0.01322696847461785
Time elapsed: 363.7593207359314
Iteration: 53
Best parameters: [-5.50875691 -2.48795836 -8.02262322 -4.55359623]
Best objective: 0.001062458417670485
Mean objective: 0.15404647051879133
Inner objective at best sample: 0.014105696940602925
Gradient objective at best sample: 0.013043238522932439
Time elapsed: 409.8102812767029
Iteration: 54
Best parameters: [-5.57547527 -2.47417787 -8.10185027 -4.44432886]
Best objective: 0.0010610106085613296
Mean objective: 0.0010826226824099832
Inner objective at best sample: 0.014166366927873182
Gradient objective at best sample: 0.013105356319311853
Time elapsed: 419.44491815567017
Iteration: 55
Best parameters: [-5.05200715 -2.62793739 -8.04087692 -4.50443671]
Best objective: 0.0010549619076477492
Mean objective: 0.001067783899891243
Inner objective at best sample: 0.013880061305942246
Gradient objective at best sample: 0.012825099398294498
Time elapsed: 403.50194549560547
Iteration: 56
Best parameters: [-4.64215764 -2.76724652 -8.16250704 -4.32808577]
Best objective: 0.0010409615138102003
Mean objective: 0.0010801663673043213
Inner objective at best sample: 0.013965255210629229
Gradient objective at best sample: 0.012924293696819028
Time elapsed: 342.80512142181396
Iteration: 57
Best parameters: [-5.04582831 -2.64077959 -8.1730499 -4.36844074]
Best objective: 0.001049888373664842
Mean objective: 0.0011741594246991445
Inner objective at best sample: 0.013875072295761397
Gradient objective at best sample: 0.012825183922096554
Time elapsed: 352.35296511650085
Iteration: 58
Best parameters: [-4.45204093 -2.84631224 -8.27930028 -4.17386014]
Best objective: 0.0010236958645584958
Mean objective: 0.1603859682005609
Inner objective at best sample: 0.014159658187318853
Gradient objective at best sample: 0.013135962322760358
Time elapsed: 396.54005098342896
Iteration: 59
Best parameters: [-4.13624096 -2.97594569 -8.5424377 -3.88594369]
Best objective: 0.0010073552466213424
Mean objective: 0.0013834127834749426
Inner objective at best sample: 0.015570536569771464
Gradient objective at best sample: 0.014563181323150121
Time elapsed: 321.5893762111664
Iteration: 60
Best parameters: [-4.44300656 -2.88644382 -8.43217074 -4.04650899]
Best objective: 0.0010125738054256456
Mean objective: 0.0011859672226623126
Inner objective at best sample: 0.014002688526949877
Gradient objective at best sample: 0.012990114721524232
Time elapsed: 393.6600260734558
Iteration: 61
Best parameters: [-4.67196516 -2.75779252 -8.25421905 -4.23287301]
Best objective: 0.0010383676989933156
Mean objective: 0.0014492341500933494
Inner objective at best sample: 0.014052493489610253
Gradient objective at best sample: 0.013014125790616938
Time elapsed: 374.3841781616211
Iteration: 62
Best parameters: [-4.53945717 -2.81991059 -8.25663191 -4.24190171]
Best objective: 0.001045638382469212
Mean objective: 0.0011010752620188458
Inner objective at best sample: 0.013967350156849814
Gradient objective at best sample: 0.012921711774380602
Time elapsed: 329.2456142902374
Iteration: 63
Best parameters: [-4.36250135 -2.90092793 -8.42268951 -4.00705837]
Best objective: 0.001011684179331478
Mean objective: 0.0010465756045757403
Inner objective at best sample: 0.014297913867676273
Gradient objective at best sample: 0.013286229688344796
Time elapsed: 386.59506130218506
Iteration: 64
Best parameters: [-4.33253327 -2.89220818 -8.48327316 -3.9816532 ]
Best objective: 0.0010100223547178846
Mean objective: 0.0011061084478877008
Inner objective at best sample: 0.014710269282963269
Gradient objective at best sample: 0.013700246928245384
Time elapsed: 416.6835935115814
Iteration: 65
Best parameters: [-4.13532396 -2.95349287 -8.50138495 -3.90976771]
Best objective: 0.0010103041754087499
Mean objective: 0.17808817890905831
Inner objective at best sample: 0.01592325010022477
Gradient objective at best sample: 0.01491294592481602
Time elapsed: 422.1098909378052
Iteration: 66
Best parameters: [-4.30984564 -2.90346133 -8.48170649 -3.97150314]
Best objective: 0.0010068871989138883
Mean objective: 0.011816586721241869
Inner objective at best sample: 0.014731256226242682
Gradient objective at best sample: 0.013724369027328794
Time elapsed: 366.10861921310425
Iteration: 67
Best parameters: [-4.30432776 -2.89509703 -8.43730991 -4.01159615]
Best objective: 0.001014955181287527
Mean objective: 0.0010589837659054386
Inner objective at best sample: 0.01487570773218588
Gradient objective at best sample: 0.013860752550898353
Time elapsed: 352.9543218612671
Iteration: 68
Best parameters: [-4.08151054 -3.01355571 -8.70073065 -3.7344843 ]
Best objective: 0.0009738819606213837
Mean objective: 0.02010665340073719
Inner objective at best sample: 0.01585536782987491
Gradient objective at best sample: 0.014881485869253527
Time elapsed: 332.0563979148865
Iteration: 69
Best parameters: [-4.03519915 -3.04469357 -8.78668711 -3.65509365]
Best objective: 0.0009673325005053477
Mean objective: 0.0010910772446624582
Inner objective at best sample: 0.016185394965120326
Gradient objective at best sample: 0.01521806246461498
Time elapsed: 393.42984557151794
Iteration: 70
Best parameters: [-3.83278971 -3.16320881 -8.9317312 -3.46576185]
Best objective: 0.0009267960050252446
Mean objective: 0.001076838391101081
Inner objective at best sample: 0.018350465340692075
Gradient objective at best sample: 0.01742366933566683
Time elapsed: 405.9828579425812
Iteration: 71
Best parameters: [-3.95645683 -3.08938164 -8.79758326 -3.6242939 ]
Best objective: 0.0009558176678018958
Mean objective: 0.0012054368531960273
Inner objective at best sample: 0.016804694480921985
Gradient objective at best sample: 0.01584887681312009
Time elapsed: 377.2579982280731
Iteration: 72
Best parameters: [-3.59903874 -3.26590243 -9.13509774 -3.24829189]
Best objective: 0.0009353474078068022
Mean objective: 0.0010794543122020824
Inner objective at best sample: 0.023951562747327237
Gradient objective at best sample: 0.023016215339520436
Time elapsed: 392.54486107826233
Iteration: 73
Best parameters: [-3.63514443 -3.28672205 -9.06282736 -3.27562644]
Best objective: 0.0009368418103594742
Mean objective: 0.002122978265989337
Inner objective at best sample: 0.0226117453153077
Gradient objective at best sample: 0.021674903504948224
Time elapsed: 375.9669005870819
Iteration: 74
Best parameters: [-3.76756844 -3.22913628 -9.04814399 -3.34835817]
Best objective: 0.0009165378357332763
Mean objective: 0.0012139679917749148
Inner objective at best sample: 0.019494957376552154
Gradient objective at best sample: 0.018578419540818878
Time elapsed: 309.8702726364136
Iteration: 75
Best parameters: [-3.71823289 -3.25090909 -9.09740698 -3.30842524]
Best objective: 0.0009167029334400864
Mean objective: 0.0010288936970469455
Inner objective at best sample: 0.020600418955151606
Gradient objective at best sample: 0.01968371602171152
Time elapsed: 304.01666378974915
Iteration: 76
Best parameters: [-3.79334576 -3.18606351 -9.03589351 -3.39691746]
Best objective: 0.0009219897051098814
Mean objective: 0.20033710368167781
Inner objective at best sample: 0.01914976362554436
Gradient objective at best sample: 0.018227773920434478
Time elapsed: 333.5603675842285
Iteration: 77
Best parameters: [-3.74926045 -3.26823104 -9.05890663 -3.31280813]
Best objective: 0.0009147295794802774
Mean objective: 0.000960420405525027
Inner objective at best sample: 0.019868095905307376
Gradient objective at best sample: 0.0189533663258271
Time elapsed: 376.47365522384644
Iteration: 78
Best parameters: [-3.74892185 -3.24985487 -9.0557521 -3.32048903]
Best objective: 0.0009184223101487902
Mean objective: 0.0009500800210687727
Inner objective at best sample: 0.01985418395168429
Gradient objective at best sample: 0.0189357616415355
Time elapsed: 400.74625515937805
Iteration: 79
Best parameters: [-3.75564595 -3.24240528 -9.04552136 -3.34146356]
Best objective: 0.0009148715824066293
Mean objective: 0.000938624745064549
Inner objective at best sample: 0.019704443055793223
Gradient objective at best sample: 0.018789571473386592
Time elapsed: 393.9541392326355
Iteration: 80
Best parameters: [-3.76004871 -3.26381707 -9.01268952 -3.33950328]
Best objective: 0.0009162048097485689
Mean objective: 0.008593219341830922
Inner objective at best sample: 0.019557279647923517
Gradient objective at best sample: 0.018641074838174947
Time elapsed: 352.363219499588
Iteration: 81
Best parameters: [-3.7843234 -3.24224723 -9.03366345 -3.35165205]
Best objective: 0.0009158265459817458
Mean objective: 0.0009213433117376213
Inner objective at best sample: 0.01915755625313363
Gradient objective at best sample: 0.018241729707151884
Time elapsed: 233.71151876449585
Iteration: 82
Best parameters: [-3.75329658 -3.24526081 -9.05060066 -3.33773683]
Best objective: 0.0009152046527109911
Mean objective: 0.0009194819301967057
Inner objective at best sample: 0.019761012349779953
Gradient objective at best sample: 0.01884580769706896
Time elapsed: 415.54488825798035
Iteration: 83
Best parameters: [-3.73410945 -3.27397886 -8.98995444 -3.32574722]
Best objective: 0.0009138794088354745
Mean objective: 0.1344858642451536
Inner objective at best sample: 0.019996150687636174
Gradient objective at best sample: 0.0190822712788007
Time elapsed: 410.74104356765747
Iteration: 84
Best parameters: [-3.69673366 -3.2834931 -9.02254024 -3.29783884]
Best objective: 0.0009139713443188291
Mean objective: 0.0009169067802655313
Inner objective at best sample: 0.020861830106689197
Gradient objective at best sample: 0.019947858762370367
Time elapsed: 382.2047097682953
Iteration: 85
Best parameters: [-3.71342906 -3.27518833 -8.99829134 -3.31527737]
Best objective: 0.0009134673813188605
Mean objective: 0.0009165863527294462
Inner objective at best sample: 0.020430361449299096
Gradient objective at best sample: 0.019516894067980236
Time elapsed: 413.4132652282715
Iteration: 86
Best parameters: [-3.71665163 -3.26343685 -8.93902478 -3.34708707]
Best objective: 0.0009127371310430878
Mean objective: 0.0009165712850478467
Inner objective at best sample: 0.020207864541402224
Gradient objective at best sample: 0.019295127410359138
Time elapsed: 419.66126227378845
Iteration: 87
Best parameters: [-3.70751819 -3.26147345 -8.89647724 -3.36400631]
Best objective: 0.0009127805121755241
Mean objective: 0.0009164206349086383
Inner objective at best sample: 0.02029863935027256
Gradient objective at best sample: 0.01938585883809704
Time elapsed: 396.54182529449463
Iteration: 88
Best parameters: [-3.68560655 -3.27975216 -8.81665691 -3.37103926]
Best objective: 0.0009119502177004313
Mean objective: 0.0009157715172507434
Inner objective at best sample: 0.02053612777487805
Gradient objective at best sample: 0.019624177557177616
Time elapsed: 307.94816756248474
Iteration: 89
Best parameters: [-3.71697899 -3.27064815 -8.79546101 -3.39624706]
Best objective: 0.0009115037275607401
Mean objective: 0.005763307321862611
Inner objective at best sample: 0.019806828418648746
Gradient objective at best sample: 0.018895324691088004
Time elapsed: 324.3668088912964
Iteration: 90
Best parameters: [-3.67932451 -3.28334002 -8.62856547 -3.43085793]
Best objective: 0.0009093828914879804
Mean objective: 0.0009143001157676106
Inner objective at best sample: 0.02005260294690822
Gradient objective at best sample: 0.01914322005542024
Time elapsed: 405.9188368320465
Iteration: 91
Best parameters: [-3.67753405 -3.28976676 -8.59738439 -3.43794205]
Best objective: 0.0009084756353446882
Mean objective: 0.0009123766967801386
Inner objective at best sample: 0.01998086013567849
Gradient objective at best sample: 0.0190723845003338
Time elapsed: 420.69494223594666
Iteration: 92
Best parameters: [-3.65970845 -3.30421049 -8.4869296 -3.46249569]
Best objective: 0.0009066508653801323
Mean objective: 0.0009123632925715717
Inner objective at best sample: 0.019953742572631706
Gradient objective at best sample: 0.019047091707251572
Time elapsed: 417.140926361084
Iteration: 93
Best parameters: [-3.65705964 -3.32720037 -8.27423585 -3.52267272]
Best objective: 0.0009040120588078602
Mean objective: 0.43841954472668865
Inner objective at best sample: 0.019117445052431657
Gradient objective at best sample: 0.018213432993623796
Time elapsed: 413.09392166137695
Iteration: 94
Best parameters: [-3.5977229 -3.3246013 -8.23924093 -3.51364464]
Best objective: 0.0009038920858857105
Mean objective: 0.0034090123575915903
Inner objective at best sample: 0.02025799583859263
Gradient objective at best sample: 0.01935410375270692
Time elapsed: 420.8901379108429
Iteration: 95
Best parameters: [-3.48559487 -3.41652774 -7.49645882 -3.68389395]
Best objective: 0.0008854761956947626
Mean objective: 0.04254719342799331
Inner objective at best sample: 0.01763142602166881
Gradient objective at best sample: 0.01674594982597405
Time elapsed: 386.7081632614136
Iteration: 96
Best parameters: [-3.51532872 -3.43059636 -7.61711586 -3.62970546]
Best objective: 0.0008866705419947127
Mean objective: 0.000923434370552417
Inner objective at best sample: 0.018047602828475773
Gradient objective at best sample: 0.01716093228648106
Time elapsed: 284.3754644393921
Iteration: 97
Best parameters: [-3.44553831 -3.4257089 -7.52208976 -3.64532087]
Best objective: 0.0008818303976588054
Mean objective: 0.03591645218706552
Inner objective at best sample: 0.018899962910887306
Gradient objective at best sample: 0.0180181325132285
Time elapsed: 325.38103675842285
Iteration: 98
Best parameters: [-3.36702082 -3.53814918 -7.08534888 -3.68536304]
Best objective: 0.0008440335819861343
Mean objective: 0.0009065046821810294
Inner objective at best sample: 0.015829944840142295
Gradient objective at best sample: 0.01498591125815616
Time elapsed: 395.03396940231323
Iteration: 99
Best parameters: [-3.19248983 -3.64256556 -6.51413772 -3.7367715 ]
Best objective: 0.0008227251685082055
Mean objective: 0.0014454218515413662
Inner objective at best sample: 0.010873565946048288
Gradient objective at best sample: 0.010050840777540083
Time elapsed: 399.27139711380005
Iteration: 100
Best parameters: [-2.96018813 -3.74012304 -6.56182868 -3.53990107]
Best objective: 0.0007994276908839528
Mean objective: 0.0009774453153067616
Inner objective at best sample: 0.021047539599069028
Gradient objective at best sample: 0.020248111908185075
Time elapsed: 394.53311562538147
Iteration: 101
Best parameters: [-3.26821365 -3.65771082 -7.08889981 -3.51678819]
Best objective: 0.0008540134563413508
Mean objective: 0.0016546424999528992
Inner objective at best sample: 0.01888828490853635
Gradient objective at best sample: 0.018034271452194998
Time elapsed: 403.21118211746216
Iteration: 102
Best parameters: [-3.06261563 -3.8588368 -6.3520918 -3.55907543]
Best objective: 0.000734826189344355
Mean objective: 0.003403466797336277
Inner objective at best sample: 0.010415764872822074
Gradient objective at best sample: 0.009680938683477719
Time elapsed: 314.0583357810974
Iteration: 103
Best parameters: [-3.11704011 -3.68373235 -6.58930058 -3.64161586]
Best objective: 0.0007532746767182076
Mean objective: 0.0040713164962178595
Inner objective at best sample: 0.014248240749508447
Gradient objective at best sample: 0.01349496607279024
Time elapsed: 233.0507652759552
Iteration: 104
Best parameters: [-2.97045687 -3.75188191 -6.26719598 -3.66500394]
Best objective: 0.0006749982506429469
Mean objective: 0.0008372479260892015
Inner objective at best sample: 0.011617322731143073
Gradient objective at best sample: 0.010942324480500126
Time elapsed: 397.95104241371155
Iteration: 105
Best parameters: [-2.73887484 -3.93155282 -5.83424139 -3.58251968]
Best objective: 0.0007036874846996
Mean objective: 0.0018505987922786769
Inner objective at best sample: 0.007630929194410325
Gradient objective at best sample: 0.006927241709710725
Time elapsed: 398.9042966365814
Iteration: 106
Best parameters: [-2.91481026 -3.92676708 -6.04813291 -3.58929063]
Best objective: 0.0006235220794517843
Mean objective: 0.001001899894363752
Inner objective at best sample: 0.007209831934690078
Gradient objective at best sample: 0.006586309855238294
Time elapsed: 353.73705887794495
Iteration: 107
Best parameters: [-3.06707026 -3.75897642 -6.31419677 -3.70448277]
Best objective: 0.0006679990034558481
Mean objective: 0.0008579980867942158
Inner objective at best sample: 0.00982482496296366
Gradient objective at best sample: 0.009156825959507812
Time elapsed: 375.5787355899811
Iteration: 108
Best parameters: [-2.77603683 -3.88737217 -5.90236559 -3.63961816]
Best objective: 0.0005848927369105836
Mean objective: 0.0011638904081326105
Inner objective at best sample: 0.008285299822204004
Gradient objective at best sample: 0.00770040708529342
Time elapsed: 508.7925977706909
Iteration: 109
Best parameters: [-2.75231709 -3.96728682 -5.86875884 -3.5728571 ]
Best objective: 0.0005647927853787638
Mean objective: 0.0007258229695474855
Inner objective at best sample: 0.00748003032475228
Gradient objective at best sample: 0.006915237539373517
Time elapsed: 346.5067386627197
Iteration: 110
Best parameters: [-2.76703442 -3.94172578 -6.01282049 -3.54696494]
Best objective: 0.0005814732862209832
Mean objective: 0.0026986271841247206
Inner objective at best sample: 0.011892516906911366
Gradient objective at best sample: 0.011311043620690383
Time elapsed: 277.2683708667755
Iteration: 111
Best parameters: [-2.72635854 -4.03796454 -5.78540478 -3.568797 ]
Best objective: 0.0004981717503014248
Mean objective: 0.000663958225409487
Inner objective at best sample: 0.005767507831474901
Gradient objective at best sample: 0.005269336081173476
Time elapsed: 319.1662847995758
Iteration: 112
Best parameters: [-2.81759392 -3.96480233 -5.99954485 -3.56249324]
Best objective: 0.0005382312046796216
Mean objective: 0.000644705295042832
Inner objective at best sample: 0.00887007859782303
Gradient objective at best sample: 0.008331847393143408
Time elapsed: 357.6291084289551
Iteration: 113
Best parameters: [-2.76904389 -4.05737354 -5.84108962 -3.55488059]
Best objective: 0.0004842526101791439
Mean objective: 0.000617205910327744
Inner objective at best sample: 0.005743168339045122
Gradient objective at best sample: 0.005258915728865977
Time elapsed: 411.9691410064697
Iteration: 114
Best parameters: [-2.76806923 -4.21303552 -5.85159583 -3.45908328]
Best objective: 0.00047037808210305626
Mean objective: 0.0007795273194541724
Inner objective at best sample: 0.005751582893718302
Gradient objective at best sample: 0.005281204811615246
Time elapsed: 412.5375814437866
Iteration: 115
Best parameters: [-2.78715429 -4.16981448 -6.00906901 -3.41569285]
Best objective: 0.0005336086576019408
Mean objective: 0.0008047962157872714
Inner objective at best sample: 0.00978432069598362
Gradient objective at best sample: 0.00925071203838168
Time elapsed: 415.6607036590576
Iteration: 116
Best parameters: [-2.84012413 -4.14525336 -5.82050171 -3.58020689]
Best objective: 0.00048097617702992434
Mean objective: 0.0007779451718522622
Inner objective at best sample: 0.003931355678448512
Gradient objective at best sample: 0.0034503795014185876
Time elapsed: 377.37612104415894
Iteration: 117
Best parameters: [-2.72811051 -4.22775107 -5.7523105 -3.49500321]
Best objective: 0.0004186385591415432
Mean objective: 0.0006872294494847671
Inner objective at best sample: 0.004284017511254839
Gradient objective at best sample: 0.003865378952113296
Time elapsed: 381.1301534175873
Iteration: 118
Best parameters: [-2.70637058 -4.24769319 -5.63599284 -3.55800408]
Best objective: 0.00044730077264799276
Mean objective: 0.0005950419079987937
Inner objective at best sample: 0.0026555017594985684
Gradient objective at best sample: 0.0022082009868505757
Time elapsed: 416.45494389533997
Iteration: 119
Best parameters: [-2.69091987 -4.17986876 -5.74389241 -3.49607782]
Best objective: 0.00043864138603067284
Mean objective: 0.0005218655556608904
Inner objective at best sample: 0.005187231465722131
Gradient objective at best sample: 0.004748590079691458
Time elapsed: 379.67007517814636
Iteration: 120
Best parameters: [-2.67726994 -4.23094072 -5.73508603 -3.47706248]
Best objective: 0.00041636893446365264
Mean objective: 0.00045982022473829286
Inner objective at best sample: 0.0052352794840482275
Gradient objective at best sample: 0.004818910549584575
Time elapsed: 406.2537729740143
Iteration: 121
Best parameters: [-2.63733148 -4.3049369 -5.57021616 -3.50972922]
Best objective: 0.0003993817602886448
Mean objective: 0.014063263863996011
Inner objective at best sample: 0.0026010401477772303
Gradient objective at best sample: 0.0022016583874885855
Time elapsed: 399.09394669532776
Iteration: 122
Best parameters: [-2.60817386 -4.35152691 -5.55340693 -3.47472244]
Best objective: 0.0003920740080775585
Mean objective: 0.0004987683180529104
Inner objective at best sample: 0.0029393406890942026
Gradient objective at best sample: 0.002547266681016644
Time elapsed: 377.44589138031006
Iteration: 123
Best parameters: [-2.64916058 -4.38981863 -5.64280244 -3.43645209]
Best objective: 0.00038061761735212995
Mean objective: 0.00046436823136948607
Inner objective at best sample: 0.003615801624765996
Gradient objective at best sample: 0.003235184007413866
Time elapsed: 338.7684853076935
Iteration: 124
Best parameters: [-2.64199288 -4.42679805 -5.60425383 -3.44328457]
Best objective: 0.00036880454270096774
Mean objective: 0.00042052071942387363
Inner objective at best sample: 0.00291810457359584
Gradient objective at best sample: 0.002549300030894872
Time elapsed: 299.02937293052673
Iteration: 125
Best parameters: [-2.69669922 -4.36885338 -5.69681496 -3.4561091 ]
Best objective: 0.00038618051732379894
Mean objective: 0.0004604627476377938
Inner objective at best sample: 0.0036052365502848302
Gradient objective at best sample: 0.003219056032961031
Time elapsed: 318.72996258735657
Iteration: 126
Best parameters: [-2.67488702 -4.35365677 -5.70971369 -3.4433908 ]
Best objective: 0.00038843037405743953
Mean objective: 0.00046460914130410464
Inner objective at best sample: 0.004458366554402743
Gradient objective at best sample: 0.0040699361803453035
Time elapsed: 396.55458188056946
Iteration: 127
Best parameters: [-2.6509991 -4.59746829 -5.61242893 -3.38705075]
Best objective: 0.000355427970987405
Mean objective: 0.0004198824635396163
Inner objective at best sample: 0.0030735232422117926
Gradient objective at best sample: 0.002718095271224388
Time elapsed: 358.9517753124237
Iteration: 128
Best parameters: [-2.65327637 -4.55921485 -5.60432217 -3.40670751]
Best objective: 0.0003567124066712975
Mean objective: 0.00042046821728177077
Inner objective at best sample: 0.0027627040455330976
Gradient objective at best sample: 0.0024059916388618
Time elapsed: 412.2439920902252
Iteration: 129
Best parameters: [-2.63496398 -4.62501051 -5.61387735 -3.36516381]
Best objective: 0.00036145429516747416
Mean objective: 0.000436350265665576
Inner objective at best sample: 0.0036536372725287447
Gradient objective at best sample: 0.0032921829773612707
Time elapsed: 423.16627383232117
Iteration: 130
Best parameters: [-2.5823653 -4.62572979 -5.53158991 -3.38093562]
Best objective: 0.0003491383181230161
Mean objective: 0.00038366830979056553
Inner objective at best sample: 0.0030534199495198186
Gradient objective at best sample: 0.0027042816313968027
Time elapsed: 399.12262511253357
Iteration: 131
Best parameters: [-2.55598307 -4.50681774 -5.50244463 -3.42335544]
Best objective: 0.00035734660940725304
Mean objective: 0.00037727975961364776
Inner objective at best sample: 0.002774263826012502
Gradient objective at best sample: 0.0024169172166052487
Time elapsed: 322.6553485393524
Iteration: 132
Best parameters: [-2.58425537 -4.61528628 -5.56361585 -3.37171532]
Best objective: 0.00034657973472928985
Mean objective: 0.0003663134875847659
Inner objective at best sample: 0.0034824194387405917
Gradient objective at best sample: 0.003135839704011302
Time elapsed: 343.82129740715027
Iteration: 133
Best parameters: [-2.53329602 -4.66223443 -5.51335703 -3.35568985]
Best objective: 0.0003495275553134364
Mean objective: 0.00036001775818025547
Inner objective at best sample: 0.003894825339088509
Gradient objective at best sample: 0.003545297783775073
Time elapsed: 401.4251718521118
Iteration: 134
Best parameters: [-2.55961886 -4.64794181 -5.50473133 -3.38446474]
Best objective: 0.0003405341430641068
Mean objective: 0.02167741482414103
Inner objective at best sample: 0.0026782264297268866
Gradient objective at best sample: 0.0023376922866627796
Time elapsed: 365.743852853775
Iteration: 135
Best parameters: [-2.51160531 -4.67675839 -5.48274345 -3.36305303]
Best objective: 0.00034520404234446734
Mean objective: 0.00036606300900852745
Inner objective at best sample: 0.0034746944333598215
Gradient objective at best sample: 0.003129490391015354
Time elapsed: 342.57799768447876
Iteration: 136
Best parameters: [-2.57733814 -4.63947228 -5.54282734 -3.37756589]
Best objective: 0.00034064199637788057
Mean objective: 0.00036168656940158584
Inner objective at best sample: 0.0030094380792586427
Gradient objective at best sample: 0.0026687960828807623
Time elapsed: 386.71368527412415
Iteration: 137
Best parameters: [-2.53001696 -4.75541043 -5.47528902 -3.3599811 ]
Best objective: 0.0003386818586861006
Mean objective: 0.014385450880279333
Inner objective at best sample: 0.002741218335782452
Gradient objective at best sample: 0.0024025364770963514
Time elapsed: 360.0741262435913
Iteration: 138
Best parameters: [-2.60630524 -4.78245763 -5.57376877 -3.3421376 ]
Best objective: 0.00033872004592251393
Mean objective: 0.000370064846397808
Inner objective at best sample: 0.0032086380683915682
Gradient objective at best sample: 0.0028699180224690543
Time elapsed: 290.5573055744171
Iteration: 139
Best parameters: [-2.58671576 -4.90004232 -5.5390294 -3.32048707]
Best objective: 0.0003347805633197476
Mean objective: 0.00035238022990537524
Inner objective at best sample: 0.0032892903099592625
Gradient objective at best sample: 0.002954509746639515
Time elapsed: 274.0570111274719
Iteration: 140
Best parameters: [-2.56000392 -4.83254799 -5.50691669 -3.33704473]
Best objective: 0.0003330843291835261
Mean objective: 0.000346700407510452
Inner objective at best sample: 0.0030535961265563223
Gradient objective at best sample: 0.0027205117973727964
Time elapsed: 290.6602463722229
Iteration: 141
Best parameters: [-2.56773688 -4.80260347 -5.52634057 -3.33757979]
Best objective: 0.00033422305643394506
Mean objective: 0.00034410439121491276
Inner objective at best sample: 0.0032413047631628506
Gradient objective at best sample: 0.0029070817067289056