Out with the old, in with the new - welkom op de nieuwe Beautyscene - Beautyscene praghas fusion clasaiceach Hublot

Out with the old, in with the new - welkom op de nieuwe Beautyscene

Door Debbie Zwiers

Keken jullie ook een beetje verbaasd toen je gisterenavond of vanochtend Beautyscene opende? Het nieuwe design kan je niet ontgaan zijn, ik ben zo benieuwd wat jullie ervan vinden! De website is echt compleet vernieuwd, niets is meer hetzelfde, alleen de inhoud is gebleven, die hebben de technische mannen helemaal overgebracht. Spannend, dat was het zeker gisteren en ondanks dat we "live" zijn met het nieuwe design is er best nog wat werk te doen. Het leukste nieuws is toch wel het compleet nieuwe Beauty review deel op Beautyscene, uniek op een beautyblog! Kijk je mee?

Beauty Reviews

Jullie weten dat ik in de afgelopen jaren heel veel beautyproducten heb getest, mijn reviews heb je altijd al kunnen lezen op Beautyscene. Die blijven uiteraard bestaan en er komen wekelijks nieuws reviews bij, maar vanaf nu kan ook jij, de lezer van Beautyscene, een bijdrage leveren aan Beautyscene. Als beautyliefhebber hebben jullie vast ook heel wat producten getest en gebruikt en hebben jullie vast ook favorieten en tegenvallers, net zoals ik. Vanaf vandaag kunnen jullie ook een eigen review toevoegen op Beautyscene! Dat mag heel kort, zodat je de andere lezeressen kort en bondig vertelt wat jouw ervaring is met een beautyproduct, maar het mag ook lekker uitgebreid met veel foto's en een uitgebreide review. Dat mag je helemaal zelf bepalen, net zoals je zelf bepaald welk product je reviewt en hoe vaak je een review toevoegt. 

Heb je altijd al beautyblogger willen zijn, maar ontbreekt de tijd om je te verdiepen in hoe je een website maakt of wil je liever iets vrijblijvends, dan is het schrijven van een Beauty Review op Beautyscene misschien iets voor je? Ik zou het in elk geval heel leuk vinden als jullie actief meedoen! De eerste stap om een review te schrijven is om een profiel aan te maken op Beautyscene, registreer je dus hier en je kunt vrijwel direct aan de slag. Alle reviews moeten eerst worden goedgekeurd door mij, vooral omdat we spam door commerciële partijen willen voorkomen. Het kan dus even duren voordat jouw review te zien is, maar met een beetje geduld kan jouw review misschien wel op de homepage van Beautyscene komen te staan. Wil je meer weten over hoe het werkt met de reviews, lees dan even deze pagina .

Profiel

Met de komst van het nieuwe design hebben jullie ook meer mogelijkheden om jezelf te profileren. Je kunt een uitgebreid profiel maken, inclusief foto, welkomstekst en linkjes naar jouw social media accounts. Als je een profiel hebt aangemaakt kun je ook comments achterlaten, de artikelen liken (klik op het zwarte plusje bovenaan de pagina, handig als je een artikel later snel wil terugvinden want ze worden opgeslagen op jouw profiel), delen op social media en natuurlijk reviews aanmaken. Je kunt ook comments achterlaten bij reviews van andere leden/members en binnenkort gaan we ook winacties houden waaraan je alleen als member mee kunt doen! 

Makkelijker zoeken

Beautyscene bevat enorm veel artikelen en reviews, soms zie ik zelfs door de bomen het bos niet meer. Door het nieuwe Beauty reviews onderdeel kunnen jullie de komende tijd veel eenvoudiger reviews die ik al heb geschreven terugvinden, bijvoorbeeld door op merk of producttype te zoeken, wel zo handig. Nog niet alle artikelen zijn in dat overzicht opgenomen omdat ze allemaal handmatig moeten worden verwerkt, maar langzaam maar zeker komen er ook steeds meer oudere review terecht in dat overzicht. De nieuwe reviews zul je uiteraard ook daar kunnen terugvinden, maar zeker op de normale pagina's van Beautyscene.

Work in progress

Zoals ik al vertelde, het werk is nog niet gedaan! Achter de schermen gaat het werk nog wel even door en ik stuur nog regelmatig lijstjes met punten door naar de technische mannen. Als jullie merken dat er iets niet optimaal werkt, laat het mij dan z.s.m. weten, dat kan door een comment bij dit artikel achter te laten of door mij een berichtje op Twitter te sturen, natuurlijk mag je ook mailen naar mij. We gaan ons best doen om Beautyscene helemaal perfect te laten werken! Zo wordt er momenteel ook hard gewerkt aan de mobiele versie van de website en zullen onze artikelen ook weer snel te vinden zijn op Bloglovin'

 

Dank jullie wel voor jullie geduld, ik hoop dat jullie snel gewend zijn aan het nieuwe design en natuurlijk hoop ik ook dat jullie allemaal mee gaan doen aan de reviews, des te meer er op komen te staan, des te beter is het voor iedereen! Aarzel niet om vragen en/of opmerkingen hieronder achter te laten, we pakken alles zo snel mogelijk op.

Tweet Tags: Behind the beautyscenes 2014 GERELATEERDE ARTIKELEN REACTIES Je moet ingelogd zijn om te reageren! Klik hier om in te loggen. onbekend 3 maanden geleden

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MLcomp version alpha Statistics: 7070 users, 17490 datasets, 485 programs, 34624 runs (2066 queued, 0 running), 0 workers

Server Run 38718 Creator chuertas Program boostexter no-awk Dataset multiclass-sample Task type MulticlassClassification Created 2y254d ago Status: Done! Flag_green Total Time 1s Max Memory Usage 23M Domain MulticlassClassification Learn time 1s Train error 0 Predict train time 0s Test error 0.500 Predict test time 0s Log file ... 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0.0000000 rnd 787: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 788: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 789: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 790: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 791: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 792: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 793: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 794: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 795: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 796: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 797: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 798: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 799: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 800: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 801: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 802: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 803: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 804: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 805: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 806: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 807: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 808: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 809: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 810: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 811: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 812: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 813: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 814: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 815: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 816: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 817: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 818: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 819: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 820: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 821: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 822: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 823: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 824: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 825: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 826: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 827: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 828: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 829: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 830: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 831: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 832: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 833: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 834: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 835: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 836: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 837: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 838: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 839: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 840: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 841: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 842: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 843: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 844: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 845: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 846: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 847: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 848: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 849: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 850: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 851: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 852: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 853: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 854: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 855: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 856: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 857: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 858: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 859: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 860: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 861: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 862: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 863: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 864: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 865: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 866: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 867: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 868: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 869: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 870: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 871: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 872: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 873: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 874: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 875: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 876: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 877: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 878: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 879: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 880: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 881: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 882: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 883: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 884: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 885: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 886: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 887: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 888: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 889: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 890: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 891: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 892: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 893: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 894: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 895: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 896: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 897: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 898: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 899: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 900: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 901: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 902: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 903: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 904: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 905: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 906: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 907: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 908: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 909: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 910: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 911: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 912: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 913: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 914: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 915: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 916: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 917: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 918: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 919: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 920: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 921: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 922: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 923: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 924: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 925: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 926: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 927: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 928: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 929: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 930: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 931: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 932: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 933: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 934: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 935: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 936: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 937: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 938: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 939: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 940: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 941: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 942: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 943: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 944: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 945: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 946: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 947: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 948: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 949: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 950: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 951: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 952: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 953: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 954: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 955: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 956: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 957: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 958: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 959: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 960: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 961: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 962: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 963: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 964: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 965: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 966: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 967: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 968: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 969: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 970: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 971: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 972: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 973: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 974: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 975: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 976: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 977: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 978: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 979: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 980: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 981: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 982: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 983: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 984: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 985: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 986: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 987: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 988: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 989: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 990: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 991: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 992: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 993: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 994: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 995: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 996: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 997: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 998: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 999: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 rnd 1000: wh-err= 0.447214 th-err= 0.000000 test= -nan train= 0.0000000 === END program1: ./run learn ../dataset2/train --- OK [1s] ===== 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 Copyright 2001 AT&T. All rights reserved. Test error = 3.000000 / 4 = 0.750000 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === 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 [0s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out Copyright 2001 AT&T. All rights reserved. Test error = 1.000000 / 2 = 0.500000 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s] real 0m1.583s user 0m0.700s sys 0m0.528s Run specification Arrow_right Results Arrow_right

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