Datasets:

BasStein

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250000-randomfunctions-10d

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np.mean(10*(np.cos(2*np.pi*np.sqrt(abs(8.383955538447823-(np.dot(array_x, np.array([[0.22048689601780835, 0.2628805953233504, 0.40551918090578676, 0.39035089429967695, 0.8029312713900533, 0.7289902046426149, 0.09056922339485185, 0.46623513165626873, 0.6321797923306066, 0.6104202222392753], [0.5384470896714237, 0.20676153939321262, 0.19792955721725303, 0.42970853473221715, 0.12343754758952918, 0.4288850255090998, 0.8631234074694235, 0.6786534911998229, 0.9757255906470462, 0.29874122643007195], [0.8042344291240924, 0.652461514991909, 0.988663126945715, 0.9462308441945873, 0.9419855729679008, 0.22736623462374173, 0.5718061868884906, 0.46751110791921824, 0.8187449021174908, 0.6964278942805117], [0.11467356447258548, 0.0013310723421166015, 0.8394901421674353, 0.714733797155936, 0.3254946332117272, 0.5816119051744072, 0.7192199533552684, 0.8577485673782479, 0.5289186880109974, 0.8205382381881648], [0.07340903696311318, 0.6318053275700676, 0.25845859823440953, 0.20330715284359624, 0.21945660867960892, 0.3619711764783564, 0.7040293654845358, 0.04352001452590204, 0.7989321450999738, 0.6988819745050948], [0.5642176921362637, 0.6360210981424901, 0.034527594401739026, 0.19998798647301463, 0.5415040087860398, 0.9063637983507405, 0.7847785511249455, 0.3173859854311808, 0.06357474584917422, 0.5394191441943142], [0.9216629222958816, 0.7477632657247935, 0.3706943504962228, 0.5377765395753714, 0.8034407897638981, 0.5308689388686839, 0.6659832218105038, 0.8508138908754973, 0.1543076466933071, 0.4840693882544366], [0.7918969855941838, 0.1564660638300156, 0.1843597404093158, 0.9319377863825822, 0.5862107966043772, 0.15042266574635066, 0.3954001974090072, 0.11155652465849075, 0.43753995952576175, 0.8887405208674236], [0.5085437813262097, 0.49136265207941765, 0.36378077253150376, 0.3126006105977114, 0.5678309486260451, 0.780054096407738, 0.055427613028573686, 0.3568728416353171, 0.8075161331271664, 0.06959144272892037], [0.6952752413481025, 0.4267751004026896, 0.22682604511570637, 0.007534883396844161, 0.475984872446931, 0.5372644150839447, 0.25367091094319416, 0.26603609679313833, 0.5424330865541006, 0.03600741346927816]])))))-(np.dot(array_x, np.array([[0.007738623907620035, 0.19897202768491362, 0.5439811442248109, 0.4738069793134967, 0.3241728454243822, 0.7486488409528433, 0.6622592413393148, 0.4177671139215354, 0.5136682190040168, 0.31634349433013675], [0.7408636574321026, 0.8757687459783524, 0.6360113240005862, 0.3058646774450936, 0.11300946733985062, 0.6748418689185187, 0.40136504312158294, 0.15893056241984505, 0.12201876340879658, 0.45910097162296504], [0.16766728581407586, 0.5724452570916101, 0.6536625611359183, 0.450920365112669, 0.8487907162108426, 0.4379693146029654, 0.8690862503109921, 0.9370863986255263, 0.811930690873461, 0.3317784320245003], [0.49869267610585666, 0.5559923637632181, 0.08365059771899952, 0.2478114521265926, 0.7496519275905601, 0.48168574208473847, 0.7042689817338555, 0.764843087425473, 0.6895502306767751, 0.9298079883221944], [0.7777054628796113, 0.8023618341216948, 0.40695133781733084, 0.4774439100962906, 0.07477409904150223, 0.7759166045956242, 0.1811626178342861, 0.3790060016600767, 0.18124121601489485, 0.8710496369613936], [0.5466691088274704, 0.5164850604068919, 0.4939210897348585, 0.5188675866144593, 0.6537385004672219, 0.8865878461595421, 0.6848294115231446, 0.6068129466082406, 0.6173779531601353, 0.04425784325569804], [0.48033593094928084, 0.7827276029928387, 0.2432281596172886, 0.39255001199756634, 0.1116628829203904, 0.6871115584551424, 0.6201009732228115, 0.5897480974247219, 0.4708061353720775, 0.3418759920491252], [0.4511042093072182, 0.7782844839749817, 0.2929262784189409, 0.2477501348161668, 0.2699443073403981, 0.025770384507744826, 0.03452596752069259, 0.9486620464822488, 0.6857534841646125, 0.7337977994579673], [0.2868150428427869, 0.39978441398236564, 0.7940899663860838, 0.3197767439469983, 0.6201137729775471, 0.07194502086056853, 0.7974359137485404, 0.8872534918326946, 0.05673843677724488, 0.7863458231849909], [0.3837871285866655, 0.020838839488465544, 0.9645863869534548, 0.1907509002354567, 0.3893716532784488, 0.7905318279972972, 0.3088080984878645, 0.9657424025405036, 0.3116213864740496, 0.7675296116199178]])))+abs(5.9848777123445736))), axis=1)

4.096924957232973*array_x[:,0]-10*(3.277198616472126)-7.286773410404057*np.sum(array_x, axis=1)+np.sin(2*np.pi*8.145201439907112*array_x[:,0]-10*(9.072715765901469)-9.150224301153846*np.sum(array_x, axis=1))

np.mean(7.878806737559456*np.exp(1.286227406961273*7.713378802446854-(np.array(range(1, array_x.shape[1]+1)))*array_x)-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x+5.405541580350013), axis=1)

np.mean(np.round(array_x)-10*(np.sin(2*np.pi*7.49016216354217)-np.round(abs(array_x)+5.9948734671461095)), axis=1)

np.mean(10*(4.942791458183805+array_x*2.6937106351023834), axis=1)

np.sum(6.204107471627031+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)+10*(6.953374213894536)

np.mean(np.exp(9.988056242762834+1/(np.sqrt(abs(array_x-9.97994607184545)))), axis=1)

np.mean(10*(np.log(abs(4.011262885991744))+array_x*9.942321132834742), axis=1)

np.mean(np.exp(np.sin(2*np.pi*np.square(8.372946053131663)))+abs(np.square(7.685135707031815)*array_x+8.7875385277924), axis=1)

np.mean(np.sin(2*np.pi*np.sqrt(abs(4.21210493032891)))*np.sqrt(abs(array_x))+3.563274847215605/np.sin(2*np.pi*1.1276574595990865*np.log(abs(array_x-np.sqrt(abs(9.602154682664992))))), axis=1)

np.sum(np.sqrt(abs(np.exp(array_x)*np.exp(np.sqrt(abs(2.8307384824036497))))), axis=1)+np.sin(2*np.pi*np.sum(np.sqrt(abs(np.exp(array_x)*np.exp(np.sqrt(abs(8.125521122034858))))), axis=1))

np.prod(np.square(3.27866499089642)/np.square(9.840314900042)+array_x, axis=1)+10*(np.sin(2*np.pi*np.prod(np.square(7.223715166182683)/np.square(9.780262207191914)+array_x, axis=1)))

3.0522796176797162*np.sum(6.059013054148276+array_x, axis=1)*np.log(abs(9.81111653866474))

np.mean(5.919527831691617+array_x-np.exp(10*(array_x)-4.154097294078714), axis=1)

np.mean(np.exp(5.2367622762638675)*np.cos(2*np.pi*array_x-9.294259073742706), axis=1)

np.mean(np.exp(10*(1.9875820045128347-array_x))*np.square(-(np.sin(2*np.pi*array_x))+1/((np.array(range(1, array_x.shape[1]+1)))*7.9668927941605)), axis=1)+np.sin(2*np.pi*np.mean(np.exp(10*(9.318340378893453-array_x))*np.square(-(np.sin(2*np.pi*array_x))+1/((np.array(range(1, array_x.shape[1]+1)))*9.539782830445246)), axis=1))

np.mean(5.613880793042257-array_x/np.square(np.log(abs(array_x)))+np.sqrt(abs(np.square(3.962023100550104)-np.exp(5.040555713083887+array_x))), axis=1)

np.mean(abs(-(np.sqrt(abs(np.log(abs(array_x-5.28252651341934))))))-6.779654930233055-(np.dot(array_x, np.array([[0.6955036999960548, 0.8124163887455507, 0.7521911230865037, 0.28021754462494874, 0.3856018637458448, 0.018740641331853602, 0.6129177667451801, 0.10860653229440398, 0.06597489863766881, 0.6596107734443827], [0.23006609961122382, 0.27262932922259075, 0.03573857331472685, 0.20017052043476746, 0.3794095578005017, 0.10415810549377691, 0.1109020328773278, 0.6334106488442278, 0.05552088714514103, 0.04308317680818041], [0.026095307533934742, 0.48188340429634835, 0.8652389856583688, 0.8205008642132448, 0.9991726161255475, 0.7292453048865402, 0.05137057502587894, 0.6285544012948373, 0.6597131170390564, 0.6386818482693928], [0.8682669510024239, 0.08936825239109247, 0.45034043896325915, 0.45683392036705217, 0.12581792883193987, 0.6211436992000638, 0.30768448377620117, 0.11549558667072224, 0.7125570098916783, 0.17160021600551767], [0.9926605476158703, 0.09040127160899414, 0.9260038575183427, 0.1992798957603038, 0.4614762942796843, 0.20145795597706073, 0.7013702449990014, 0.6785910175586721, 0.9491099760936117, 0.5139824593208475], [0.901642455047346, 0.5585227382883863, 0.3871812737438014, 0.10580778785899525, 0.25427177576703286, 0.8803407258654283, 0.7965897355503306, 0.38240748073090425, 0.1259342495624325, 0.8248863533833689], [0.09343852143819797, 0.35304308369671933, 0.6102707288299964, 0.6315613877608116, 0.19765217865718765, 0.04836574796352411, 0.8331491725991388, 0.21775825122448267, 0.266490305788331, 0.07216188206728613], [0.04131434815964263, 0.04543146138182719, 0.7679520351686494, 0.6849992990751751, 0.4971631049366332, 0.2520198785066071, 0.859439872910267, 0.007973485902331712, 0.6036274881981001, 0.3795119345094883], [0.002904883962617122, 0.04634787336993096, 0.4684611040451163, 0.5964551840114108, 0.10142680011805694, 0.9277430399085972, 0.5983591510810532, 0.6818204166600957, 0.5015717166513307, 0.06107410142387537], [0.14647437492529458, 0.5460892737797615, 0.16053475907876347, 0.9943402783707579, 0.7908382118175058, 0.1999187888821483, 0.7951089282600802, 0.6660909650408287, 0.9355063443110162, 0.047468521136272424]])))-abs(array_x/np.cos(2*np.pi*6.768781515946753))+1.5352694127829336+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(abs(-(np.sqrt(abs(np.log(abs(array_x-7.268988204640495))))))-6.81006941872818-(np.dot(array_x, np.array([[0.24594379589066906, 0.024440479385893, 0.6226708103442312, 0.10870567249959673, 0.13636776836778686, 0.5497548880427392, 0.2497884997817993, 0.3596224060857408, 0.6742297063321406, 0.45477069498900446], [0.2138222217364797, 0.35398027560871637, 0.8392841352740447, 0.9826624942222107, 0.39877778853537527, 0.7817578136673563, 0.7395478464071025, 0.9000529200222839, 0.19010116483132578, 0.10823928645937497], [0.36389177984228016, 0.21202153947603353, 0.43577697900522205, 0.9703169247883399, 0.2443337528937577, 0.6605523027035317, 0.8951291920673184, 0.6003338811501046, 0.45372572004659695, 0.472909852605102], [0.34775166917477907, 0.2831772029876167, 0.8530386911288188, 0.6178037927368496, 0.812397711538062, 0.2246574738142355, 0.4585919559620746, 0.22843803738516122, 0.606420175964772, 0.7705245789870564], [0.10125296784766169, 0.8612852800543093, 0.5397908038714793, 0.6318623717254209, 0.24897189615751603, 0.5793346380361571, 0.728854757861617, 0.8608238597942965, 0.13347816006836744, 0.4419854968461693], [0.8905817792867939, 0.7357426236012237, 0.16697247375573676, 0.7182700197335238, 0.09587023889524648, 0.5029831843606326, 0.4219995761525269, 0.08263792800077785, 0.8045885240358349, 0.6448173257000577], [0.12528747695667708, 0.5814753736168826, 0.8813176087958758, 0.31707556809961135, 0.15477517297086518, 0.42219890729196263, 0.6338134474055029, 0.7711278616848484, 0.10310795527439076, 0.13613765397959], [0.9357651232470199, 0.19071309422542693, 0.23573464305523895, 0.3730458215681902, 0.2564750391061754, 0.1457225691702898, 0.44638995541943793, 0.9621525498908333, 0.05671888305795025, 0.7122142745226838], [0.09850637074280544, 0.507407257290047, 0.8334303672587696, 0.5109937976293708, 0.7631112480688694, 0.16598081521491592, 0.6252736684638095, 0.3041902967908994, 0.8746484310497443, 0.01731265359937806], [0.9944246167195, 0.14343340033799912, 0.4944405464731746, 0.19062243478861463, 0.8913378916063966, 0.4156464302271915, 0.8777097985183947, 0.7185480656883548, 0.044517323298032374, 0.18622014936950493]])))-abs(array_x/np.cos(2*np.pi*9.048402095151841))+1.0632450968939504+array_x, axis=1)))

np.sqrt(abs(np.mean(array_x, axis=1)))-np.log(abs(7.026813858433505))+10*(np.sin(2*np.pi*np.sqrt(abs(np.mean(array_x, axis=1)))-np.log(abs(1.6001900829393825))))

np.mean(7.403340208006231+10*(10*(1.963708707113167))*(np.dot(array_x, np.array([[0.2312192302386724, 0.7543807984971489, 0.36540513673731734, 0.1855946661144141, 0.1375882064896884, 0.09611186287871587, 0.3862660245018765, 0.5815617401502106, 0.6423354472486561, 0.7617571042168311], [0.8893725580610801, 0.20536729550405997, 0.580047678574721, 0.8232014196410188, 0.9148596369705988, 0.5705977999111091, 0.28879816720695284, 0.8995420431480436, 0.6927470865420888, 0.18105786419646241], [0.7836498803347892, 0.38839090525153075, 0.5321271323136173, 0.00410292834834991, 0.041882195606064876, 0.3410567203214726, 0.813470652454493, 0.6623474902322204, 0.07966963609658861, 0.805961006415691], [0.7703316405938124, 0.27352168131458865, 0.5182639597875891, 0.0526154798338172, 0.8088473265878234, 0.3033002903783517, 0.11048542827183416, 0.6882828321175591, 0.5897286968696185, 0.3154388399322643], [0.19735787539624172, 0.8086852406067978, 0.3658845606924196, 0.427961938477592, 0.1582065122738986, 0.4013129093137292, 0.5557994743143727, 0.3737853200371759, 0.6087084868954253, 0.015320417980313139], [0.5719935260713824, 0.3370599361240223, 0.8094041290923608, 0.6839555200137163, 0.9537385297227408, 0.08989305182326734, 0.6993928668739048, 0.7558433892118822, 0.13307449370399405, 0.518301257523953], [0.9007576174650992, 0.6353620236169576, 0.008939030275191384, 0.34624924870647145, 0.4322339251660211, 0.1885400383658572, 0.7775087900454982, 0.9913620655829375, 0.6804965624251357, 0.15184723670787303], [0.6841431871885567, 0.4544844407780354, 0.32871434556511636, 0.7741432518573781, 0.5324206725718555, 0.37953990647195635, 0.5987629430368644, 0.04042317708243248, 0.11739938038982722, 0.08361802327724255], [0.10766020059878678, 0.9865820814345807, 0.8463403420415597, 0.9806528444505522, 0.7866725603955252, 0.6922400390952417, 0.6789926708405827, 0.19147794614073965, 0.4188606875874116, 0.7852680254214798], [0.9681484234222807, 0.15611895249142127, 0.3623442927318874, 0.6750616628543817, 0.0679954337065054, 0.5388645813676775, 0.8039182449174762, 0.8869610383153969, 0.6358136607359465, 0.6653008354627643]])))-5.970415339508637, axis=1)

np.sum(array_x+np.sqrt(abs(8.117944148158038)), axis=1)-5.157554212945016*array_x[:,0]

np.mean(np.sqrt(abs(array_x/1.5305864828833848))/1.1115527262408773+np.round(np.square(np.exp(array_x)-8.3932416537365)), axis=1)

np.mean(np.cos(2*np.pi*np.exp(array_x))*np.square(5.154766534577702*array_x-7.460128511966447)-1.2945497937901949, axis=1)

np.mean(10*(np.square(np.cos(2*np.pi*np.sin(2*np.pi*array_x-4.154974425387034)-np.square(array_x)-9.824987712735672*np.sin(2*np.pi*3.8961827264148754)))), axis=1)

np.mean(1.2509848976802809/np.cos(2*np.pi*np.square(1.2194913244974688+np.sqrt(abs(array_x))*5.0709864436243555)), axis=1)

np.log(abs(np.sum(3.5114260000540476/np.cos(2*np.pi*array_x), axis=1)))/np.round(2.316733635963984)+10*(np.sin(2*np.pi*np.log(abs(np.sum(1.7470383083130905/np.cos(2*np.pi*array_x), axis=1)))/np.round(6.073310602767804)))

np.round(np.mean(np.exp(7.052901802982626-array_x)-8.393296128066972+array_x-2.5718933160318542, axis=1))

10*(np.square(3.1890536515028964))*np.mean(np.square(np.sin(2*np.pi*array_x+7.890474122723957)), axis=1)+4.999879463050059

np.sum(1.0761710127079849*3.2609777249987695-array_x, axis=1)-np.mean(np.exp(array_x), axis=1)/6.14812366909208/np.sin(2*np.pi*np.prod(array_x-7.318183493478955, axis=1)-4.461702160057083)+10*(np.sin(2*np.pi*np.sum(9.43884946800167*1.42644778760735-array_x, axis=1)-np.mean(np.exp(array_x), axis=1)/7.846377269319195/np.sin(2*np.pi*np.prod(array_x-5.003310471308313, axis=1)-8.775166483759993)))

np.mean(6.2632064040221485-(np.dot(array_x, np.array([[0.6067215620307087, 0.2683406711185613, 0.38287149315554125, 0.027726763663176945, 0.4352009552654077, 0.5912798601431483, 0.6562426871601242, 0.5877213092756702, 0.057038115457761385, 0.6849847560693685], [0.442724377161018, 0.4009377980240256, 0.35657954516521606, 0.12700350855269804, 0.8411892042032071, 0.7942601748114864, 0.22400593369528488, 0.40170695012239743, 0.5499293239301727, 0.05474744790002817], [0.7079476804075727, 0.8625924015853653, 0.7312641253342109, 0.5164980031451272, 0.13413692693569013, 0.5782274776350553, 0.8365807869017354, 0.776357859550656, 0.8563948092349493, 0.4992533561079632], [0.568138833853762, 0.40910064786067757, 0.3838413984752167, 0.6473899207140255, 0.7912040395760491, 0.7953648571186323, 0.29893861248096765, 0.8321778743335194, 0.34677203637385434, 0.8753599646666781], [0.2009016654226834, 0.23946034377642145, 0.7233307829730524, 0.22064361953559342, 0.07581014967520572, 0.45001842083108223, 0.5115752783098788, 0.7617519298758674, 0.20682181674741218, 0.824266722926075], [0.03912550672608062, 0.34165496774913406, 0.8208330527007478, 0.375369023353895, 0.8921539414479426, 0.9228641281427201, 0.5327100692853222, 0.33559232074308176, 0.18437062849281338, 0.48130647778672797], [0.9489983388243993, 0.12155371233400902, 0.2685940207579942, 0.25399866884705213, 0.6259581329422064, 0.7748880623887473, 0.5410866367406328, 0.04245866330086745, 0.9575294261289833, 0.37455277732692416], [0.622301356125789, 0.5344548610637722, 0.6520590297566554, 0.5332411208114444, 0.793832860519827, 0.2248185378183758, 0.032064311954712155, 0.20907399564105733, 0.4029082960211826, 0.44361520314626346], [0.4695930578502411, 0.20899573646420078, 0.4707975841620635, 0.14220381426760087, 0.19518104057404628, 0.15596526450532722, 0.2867654099791862, 0.7596296873625327, 0.9396805846185601, 0.3347981355697197], [0.9048646351826635, 0.2993837201104128, 0.5010991982778812, 0.32985129340834585, 0.7113214320198331, 0.38913445929900214, 0.25444291271671626, 0.9886886601866313, 0.8877066943006319, 0.9448947542372321]])))*np.round(6.938759501004459)-(np.dot(array_x, np.array([[0.8674468494285846, 0.6784776802035665, 0.935528970518758, 0.7201169473873401, 0.6613382480650101, 0.2474752559380603, 0.6756708436870457, 0.44040324896525895, 0.7330483547408712, 0.6870955472431918], [0.9874064992120777, 0.5891485576806335, 0.5811903707148198, 0.6148047809941506, 0.7111686741601093, 0.9332604702104418, 0.712675134686875, 0.05709206712910353, 0.9528107091971938, 0.08382364053275893], [0.4233167934860831, 0.257696992230695, 0.8519922869983791, 0.1827212620733979, 0.9524348001822872, 0.6155164916732081, 0.16190215545310216, 0.618850300427812, 0.37571632254354115, 0.3088584817312052], [0.7355166023250798, 0.9431371797193178, 0.27235149645418355, 0.37622620208870894, 0.202148651093499, 0.5291145458715469, 0.8074735292726688, 0.09411406732606309, 0.8346382231959957, 0.3059064064141751], [0.2280805492003557, 0.35020670003173404, 0.2338358448016833, 0.6710804849627754, 0.09790427806226931, 0.5121551820542385, 0.6532796182359502, 0.7577571995601055, 0.7984692076348476, 0.5299061184285531], [0.7682522245921001, 0.6729849374182194, 0.25595864589077066, 0.31377938254636517, 0.2638725649068405, 0.22171216386879333, 0.6878023715845762, 0.6790265487522847, 0.8749656783204209, 0.43457037168612245], [0.002029192279310865, 0.13757486410104347, 0.5129410243768974, 0.13479931677207146, 0.9911247384312964, 0.49129302220681925, 0.1849790178587728, 0.2940471515454891, 0.2577556497704784, 0.2790308065295223], [0.6492437922957891, 0.5283192627822144, 0.8304017431809793, 0.8291842079058401, 0.7565644552309677, 0.3665029157092382, 0.7908372559395895, 0.2400613698736257, 0.6199355206098335, 0.5622614958309975], [0.2802739139911773, 0.8019981644357881, 0.17034467860747637, 0.6748505757225909, 0.32971326156750547, 0.8348066298978573, 0.07605935658844465, 0.38492852849277026, 0.6595301183500425, 0.08936369044626402], [0.05913470901125861, 0.9811117743213387, 0.5442609886241747, 0.9400759461088983, 0.25634825053547794, 0.2786932670446074, 0.927463023407386, 0.25365021942799504, 0.7363561131030217, 0.08682259883681198]])))+np.exp(3.1805087849031723), axis=1)

np.exp(4.476147774492336)*np.mean(np.square((np.dot(array_x, np.array([[0.4498913334200402, 0.9835417265447309, 0.8241858667210065, 0.06943259380995737, 0.5959315003421375, 0.5858272490732274, 0.7995077540403288, 0.7361339772769064, 0.4583382988892375, 0.43664214851456096], [0.8651966354131545, 0.43194691106891603, 0.07823885979836454, 0.4431122220545124, 0.7288226439928419, 0.1466357831001882, 0.11769836299546566, 0.11054810487163058, 0.15795384057917117, 0.4096393835373039], [0.4765202839123194, 0.7281220135932048, 0.19554858460801072, 0.41170584278010414, 0.7460495926833216, 0.13973563066483252, 0.6660930201052507, 0.41610562424500686, 0.978813832021886, 0.3492972776900325], [0.012543359271920296, 0.6381347073997848, 0.2824491027231275, 0.39002415043570604, 0.04758056750706219, 0.9335995395436838, 0.9464689708456339, 0.8234022660661114, 0.9503057986941632, 0.26170675560931334], [0.24057555984724, 0.09343708171353848, 0.9671552609668252, 0.2384641504745737, 0.3092049273055837, 0.7731169895355626, 0.010674987418618942, 0.41121617726143767, 0.7254563263336183, 0.920144829868959], [0.14735189718642527, 0.43190429408639996, 0.009020670463382063, 0.32482628827354154, 0.6066548515306717, 0.15422888541513813, 0.9439274581352227, 0.9928966125689314, 0.2046401590090433, 0.9121224516268807], [0.5428626407199079, 0.6682876647970656, 0.010801533895833826, 0.7030875797585503, 0.49946495462044005, 0.8120899974741111, 0.08777444635677978, 0.013671565264857244, 0.44606496453139954, 0.2436806339898373], [0.5205448092775051, 0.8904687768149071, 0.4845414683902186, 0.4853845261560782, 0.7986211020885404, 0.5329585230714271, 0.6305830451020173, 0.9102908198384173, 0.595358934734476, 0.542303042318915], [0.9732630204100047, 0.9410091301504692, 0.7927481987922951, 0.09415693453900187, 0.38908604769622446, 0.1394800671252182, 0.09270295441011345, 0.28923483827626895, 0.5875884432517336, 0.7543084029931584], [0.5159788595995467, 0.2606926256150872, 0.11780398257885627, 0.37341728119608286, 0.8962576737892224, 0.8177732014417933, 0.5152356136168984, 0.7089669003773156, 0.25786626729898254, 0.7162348115184765]]))))-(np.dot(array_x, np.array([[0.892945181290531, 0.988572870430688, 0.821645312791513, 0.13229902489532974, 0.5408330280094413, 0.720370420061987, 0.4688943171099199, 0.9452260054349397, 0.5051583931172834, 0.5371591648071039], [0.48020658758182433, 0.48063906658301325, 0.04517219743343737, 0.15297277305756918, 0.4347654997991016, 0.2538816700329667, 0.17563429797743824, 0.7979131017713954, 0.6168975301059495, 0.3015846013047466], [0.5348167685065998, 0.26022343083139254, 0.8700235566644289, 0.8508680486505028, 0.39473267719412064, 0.1490331545967859, 0.8918123820102847, 0.5301898463731225, 0.31153840815198197, 0.37015126794663766], [0.1761416366300005, 0.8317000392448182, 0.17615196004665323, 0.6402018852518967, 0.3694791858761982, 0.627440282516195, 0.6667006692077605, 0.6321545116162803, 0.31349431325439425, 0.18009024616080171], [0.9710467985612544, 0.32702368886286126, 0.18033441375037151, 0.1810653613362322, 0.9212109210004916, 0.05905847978449186, 0.3316796458306728, 0.9883379524783205, 0.3289415229821412, 0.9805375870100609], [0.29798941848762095, 0.5103848521090435, 0.5622688940374083, 0.9207257593205925, 0.5241220486793209, 0.015467443970353822, 0.5625651243969845, 0.9030960623813201, 0.004023344665197048, 0.3302977234885419], [0.11566167905537061, 0.8141590180344559, 0.45088393478955513, 0.45246847360933473, 0.6052782418035725, 0.7184999812192001, 0.6550043786382233, 0.3863149663060199, 0.6297738055330069, 0.30534139324256604], [0.5263998418336058, 0.5027424479330339, 0.4312749509259389, 0.6517389768423599, 0.6042234634034704, 0.7592590072134072, 0.18794586144890113, 0.7147902348201403, 0.7965350059785425, 0.9933406200481854], [0.9887228391664096, 0.6441125180968058, 0.38661364404528165, 0.899564111526283, 0.31520221907420787, 0.8295557707766664, 0.09557125087033091, 0.9434802033908315, 0.5832146378650588, 0.9672379678153279], [0.14414411950127182, 0.8280412747336837, 0.5121389372752078, 0.6582863281356588, 0.2315104454356841, 0.26553389163492547, 0.8724655871959671, 0.16376654764917709, 0.020951555465653526, 0.9942094018278373]])))/8.178196399952094, axis=1)-2.1411505869796565-np.sum(8.80320033524289*6.922418077235337-array_x, axis=1)

np.round(np.sum(np.square(8.698484419712974-array_x/3.104514646693418), axis=1))

1.6526453218013755-10*(np.mean((np.dot(array_x, np.array([[0.2641846026505861, 0.838426701925916, 0.7247714449514007, 0.012070322497921615, 0.915816791489989, 0.06781704707116498, 0.9825537545653593, 0.9220748875636773, 0.8639155531050611, 0.4631450892055875], [0.35421175380942715, 0.6940722719148693, 0.24885696016813486, 0.20342391387877512, 0.7093496682295718, 0.23874329363115987, 0.7756540055742939, 0.6027604890128234, 0.285589315492463, 0.5894715314961008], [0.6356429620714967, 0.10505604527989909, 0.835915059570373, 0.9248174681077443, 0.08081355727726813, 0.6562105117690735, 0.07723432171578193, 0.763583157405787, 0.6047615535062327, 0.7287596407912017], [0.5479533878723174, 0.2702404522118973, 0.8486953118301727, 0.9071403677169793, 0.8549790494461558, 0.6434801762275397, 0.7674305409710217, 0.7574811761755822, 0.46079990432155904, 0.8031730636771931], [0.12820768479306244, 0.8625554585563212, 0.14398893773436283, 0.19398101973173643, 0.19757502586811537, 0.3194490740137802, 0.31131394614404995, 0.8817763335177803, 0.2262858701964391, 0.49632594851704803], [0.6810410001880614, 0.2953757035171123, 0.5627018841618805, 0.8447223097121517, 0.03697290675516696, 0.5246985931620627, 0.13186271855231202, 0.3522978319216621, 0.8951657352837021, 0.21745974289569348], [0.3873547092750417, 0.5630477851774995, 0.8256046960332714, 0.8442844808352822, 0.14784758099150752, 0.6762015292893659, 0.30329721663608034, 0.6668818641932212, 0.19614877515288365, 0.20513984587867018], [0.6262655808574676, 0.824605498416413, 0.6102246396956627, 0.10589718145204163, 0.9286016507889886, 0.3685146586181456, 0.15414762363300116, 0.3162364015829613, 0.3508997126773904, 0.9903312109341239], [0.8478987484490774, 0.18097385202273852, 0.16976990097408817, 0.434945601000135, 0.12860653374579467, 0.863174318608732, 0.6444495620072312, 0.40868400390393134, 0.13806039382715696, 0.2695752005913751], [0.5463464224319498, 0.049231295439027045, 0.2646195714267333, 0.7643860832044763, 0.253156630400304, 0.7416559915964223, 0.08939668861376526, 0.9603707758518801, 0.38027221897731245, 0.5563030552459328]])))+np.exp((np.dot(array_x, np.array([[0.46252965351967235, 0.13437360786364128, 0.7220440992407289, 0.9920870288603192, 0.08931075214045414, 0.519250179711545, 0.6254696411367197, 0.33031914332115764, 0.9901374715274427, 0.23171416810191525], [0.9308633750562164, 0.08747455363519907, 0.7482691996705707, 0.9670335975988988, 0.5058104430619109, 0.46361186896885986, 0.7876234417187148, 0.6049040956596055, 0.03792701231873441, 0.33935110001608104], [0.5792578318492078, 0.5893095183193342, 0.1410086187681472, 0.3736119032218156, 0.40521294758443605, 0.7788213192263129, 0.41932506874887454, 0.7209935377174467, 0.9043396863901767, 0.8920564679216637], [0.7805969309105957, 0.7806710681213739, 0.16961243473337428, 0.7112876643519018, 0.48555729078535614, 0.9390208939610195, 0.4750198971096756, 0.58446669193136, 0.4559137051624249, 0.1289987737696502], [0.37296271673313197, 0.4022089074334343, 0.8240870484921017, 0.3819902896945986, 0.8322262307015439, 0.9451356095678382, 0.3607160259898051, 0.5621077220514061, 0.5016242253980971, 0.04486700270500643], [0.29796016825726046, 0.33749600731861507, 0.4035299506050394, 0.32124432069548114, 0.45763930852444445, 0.06804827701813887, 0.6327230767262609, 0.29385664024048497, 0.8375669789336412, 0.07564781634791817], [0.4511776632805298, 0.7496356487105869, 0.7097896373609748, 0.43181074580208745, 0.10922521280776065, 0.7928471187164714, 0.7814829039913128, 0.7189276402689083, 0.8858023434296196, 0.2833857461898651], [0.5863225376998615, 0.7247912391238874, 0.7734343919085418, 0.20482536189035339, 0.5051760127542675, 0.15706111133624745, 0.5550342010011747, 0.08958230327643557, 0.374965012934394, 0.6623261672001064], [0.871622716131839, 0.12077654791407433, 0.2123345971511048, 0.3016626128736658, 0.39483832144824993, 0.19936757229399238, 0.9657715404541238, 0.6594973401500394, 0.9626640211411178, 0.510117839608493], [0.9400251787012736, 0.2696518995167251, 0.9022341178720862, 0.6415768790999069, 0.5324695234843715, 0.49715615935207813, 0.3578229406354415, 0.7693951947332573, 0.032366585740361664, 0.7246924736587488]])))), axis=1))

np.mean(6.142100778230485+array_x/6.657244993597618+np.exp(np.cumsum(1.7220819782448282-array_x, axis=1)), axis=1)

np.mean(9.619214141659024*np.exp(np.sqrt(abs(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x))-4.215575722926856))), axis=1)

np.mean(np.sqrt(abs((np.dot(array_x, np.array([[0.7600727096711067, 0.230404048122124, 0.7452209979837804, 0.8617378601847995, 0.2828479229647024, 0.42002160609076056, 0.5032271177491254, 0.7385915532384455, 0.8051329615392167, 0.6071781597039451], [0.29936432241693267, 0.5672561523388312, 0.7105675029176769, 0.6943138647617602, 0.979886795354352, 0.5239370156542296, 0.20877592021807945, 0.7410911396050082, 0.6806489308370592, 0.12704303260375882], [0.042735996035444423, 0.9910698964398305, 0.6234097276762363, 0.5611910540561051, 0.11876777820961881, 0.9673055289233359, 0.11857427934750797, 0.8230925786703769, 0.574895355159265, 0.326091610158429], [0.09648204164415086, 0.6691649753627101, 0.3791363044106224, 0.843610681946185, 0.7027518243194139, 0.04508472601919189, 0.07933905200339031, 0.5866429705120976, 0.8626520732032853, 0.8340392815421641], [0.03952393531968257, 0.8072793257162867, 0.8865311296501796, 0.4722747689634731, 0.6117639349428303, 0.759259818044588, 0.4006000363736243, 0.47597885997476674, 0.1196477311258245, 0.6722647125758343], [0.7592351250708923, 0.297500141380485, 0.5042149897957618, 0.4885228530901382, 0.4653563804599685, 0.11087363264379557, 0.9892356984834193, 0.8452174160887451, 0.19827724184243922, 0.362174091705212], [0.5655976055651082, 0.10374182163894174, 0.9053901019240111, 0.958434583508377, 0.030002219014682807, 0.3165035286744621, 0.6672595440881018, 0.9080610575386213, 0.09778277560448123, 0.7258637505710712], [0.6954671879537395, 0.28596015575758005, 0.49026199955383565, 0.9504968907605419, 0.6209046625940182, 0.9992308835017272, 0.5256395720018847, 0.05486122800621418, 0.22404582779444182, 0.5388289356311285], [0.8296470129300999, 0.7553105369901976, 0.8299193215987596, 0.5087142116991783, 0.2071274801852332, 0.5738556835976536, 0.4656699411264409, 0.14792533986054635, 0.8371508069140775, 0.8379532230048591], [0.4286912430817854, 0.04122056516710482, 0.8302463760106782, 0.011664137742606107, 0.05581182329555445, 0.7961830775132025, 0.297535061130624, 0.386841788098901, 0.33063252733405746, 0.4441245073223141]])))+9.716116839140037))*np.square(1.4942837847585693-(np.dot(array_x, np.array([[0.1233549045700586, 0.05297854386089185, 0.10700508882919113, 0.165427779745286, 0.42719944952766775, 0.8570839021542553, 0.17443479017908314, 0.6806946356834244, 0.7123819063373998, 0.23463261932147272], [0.9007336627539942, 0.2759663221809906, 0.8922484832343647, 0.969034763737691, 0.8566483969562897, 0.4570307130042385, 0.3256828834300868, 0.4748256182871131, 0.5389921821979148, 0.07240951925409844], [0.11222519708190914, 0.24935991366080557, 0.9120647825681225, 0.3700470045437684, 0.7549441354557921, 0.9136708315634949, 0.8510371442028735, 0.7431851815829739, 0.7038869062857026, 0.4013675247410954], [0.4959103296082532, 0.9094306987959809, 0.7683355243511159, 0.9069708345915034, 0.7146202732990181, 0.09811910153186854, 0.5408298060510189, 0.6005852165561774, 0.25418497896566594, 0.5544552394923641], [0.6021020129124495, 0.8214805892243409, 0.23932175035649683, 0.041862435879987725, 0.11245007578208632, 0.6879157741957972, 0.9700137504893093, 0.72943404658684, 0.0069909550896920525, 0.1650518545759092], [0.12957647322713595, 0.4337366365338492, 0.5673452263318686, 0.36144856063522, 0.4659845869094086, 0.8033961045918001, 0.261271030695417, 0.2108076142705133, 0.5167429111761821, 0.36443158728079694], [0.4833671475169746, 0.07984705899804345, 0.9088048144975992, 0.21477615827092644, 0.5283398726881774, 0.7562207280859726, 0.401341427751818, 0.046164599339622026, 0.5444111839630071, 0.5936077527571063], [0.3453865846577586, 0.3578843777819877, 0.7963129215307992, 0.7888916907840466, 0.09618017901986509, 0.23903680379040926, 0.5208288914440893, 0.8332593859098074, 0.2730993433172769, 0.388831662642356], [0.09785807193080598, 0.5668879383130017, 0.883716618020363, 0.35573831543113354, 0.9536950797350394, 0.8464737849952515, 0.7717333262782679, 0.8599944495806043, 0.9892088598807014, 0.757727764687708], [0.26944624274804596, 0.6035247458421871, 0.38089077681733996, 0.1843331299705302, 0.6392181703400462, 0.3973506355698758, 0.3483827842227051, 0.8673099260215545, 0.2286521307797894, 0.545143484502925]])))+8.920324487772993), axis=1)

np.mean(array_x*9.54601666751609-9.712588011182332+(np.dot(array_x, np.array([[0.37040017100634404, 0.8710233361261983, 0.20560166047158956, 0.8524041541603229, 0.807963479808982, 0.10989166517367788, 0.16301873976450065, 0.19890583548640162, 0.7483159036398407, 0.2356061354315937], [0.9107360917729409, 0.8551339088279027, 0.22968340409960764, 0.5606695724374398, 0.7787246987389523, 0.7288751866110966, 0.09202548246135223, 0.13021332224048143, 0.3149241427145091, 0.9803069010644557], [0.286695745794062, 0.41971681427634544, 0.35342415042782505, 0.1744193097842497, 0.7661807065088361, 0.9233171454806648, 0.13133986359812333, 0.9859160193628461, 0.5855206280203493, 0.3385522434853374], [0.8146787953531697, 0.8780190128565482, 0.7970299030279777, 0.5404272240780499, 0.6787230730201049, 0.25786362878410873, 0.18388260798012723, 0.6529923377129716, 0.5450400375105998, 0.7972540096175587], [0.5921025258351166, 0.5728581494183578, 0.23302829516371693, 0.25702250026347984, 0.43878388085174447, 0.6677852735873852, 0.39527314956609516, 0.6556546059680401, 0.9337216794830353, 0.8970311213736909], [0.11460011903080569, 0.871761844631993, 0.8079952052757399, 0.8869035146364894, 0.0842141894160292, 0.23006625159676175, 0.2889066434253643, 0.8825767965021655, 0.10921250372142854, 0.33668779293703766], [0.6147497462199188, 0.20637410865665717, 0.49081280728749277, 0.2685306902022405, 0.20709657845181406, 0.5627942580383483, 0.3361019578560015, 0.3196338906541232, 0.9966604390827638, 0.218810762589575], [0.3952634889921952, 0.050235054166621884, 0.7385360907315772, 0.8656855311858803, 0.8910580129655342, 0.3702538966536504, 0.33550570508622457, 0.2744446910663809, 0.41558401382199506, 0.8976654193922498], [0.6349534775834154, 0.7666670772736675, 0.6967957381217669, 0.10746976080660497, 0.8043978371373078, 0.6545649888760455, 0.4714507748665201, 0.37649748510237624, 0.8670236330616308, 0.3298720117839008], [0.2656079840945562, 0.466059845286914, 0.5104501600187148, 0.5837701548988609, 0.3583335333313258, 0.265633813719961, 0.22301307332245646, 0.2573514062871868, 0.5253832042283503, 0.9968791810620148]])))*array_x-(np.array(range(1, array_x.shape[1]+1))), axis=1)

np.mean(10*(np.square(array_x))-8.713734423856003-(np.dot(array_x, np.array([[0.09474537504348368, 0.3837414368228852, 0.03683083430513934, 0.7517962422707039, 0.9087263535110232, 0.9362287240067558, 0.7956073069893695, 0.5755324486404493, 0.7724033396771451, 0.13986542320187323], [0.6266777118577661, 0.2611190974554405, 0.7990412716076534, 0.7897154290655898, 0.16356186408217444, 0.714916354148128, 0.798669905557683, 0.8817968703708458, 0.9557723999928224, 0.8480646524183173], [0.31382506837412405, 0.6943393134285822, 0.9110655653327476, 0.9803311732208281, 0.7022843319204636, 0.23703788025356853, 0.25984631181464435, 0.62478851896639, 0.905897776649061, 0.6644490795991314], [0.1404095798594892, 0.4853423077134278, 0.7526108067286882, 0.6459682698204032, 0.8550479809562426, 0.8766705632765487, 0.687297200149213, 0.6531381802725861, 0.5936060425011629, 0.3483605932833742], [0.03277378381899798, 0.49909049175096687, 0.4110838624151174, 0.02400661293206907, 0.3926373130347123, 0.815897681341095, 0.5147939982348146, 0.12762716866536972, 0.2796736885981155, 0.47979141779396695], [0.19218955849929908, 0.7402940927416629, 0.7795933974945002, 0.030407849430545197, 0.32046178653759194, 0.6178057974183435, 0.00929652584323537, 0.16135023460606268, 0.22859368149654202, 0.023384668301880573], [0.7028364259700124, 0.23221163529798128, 0.564372315445907, 0.6373390455022738, 0.12628464125583216, 0.08184577805858839, 0.5233346352618572, 0.8530245347828325, 0.13790246231555037, 0.09581446912533009], [0.2231149120271304, 0.4092442290098538, 0.4394522641296239, 0.555695369749864, 0.6599226128404974, 0.43595917442153886, 0.6351912100437718, 0.5879282160980086, 0.7681698220743352, 0.0036269745578213497], [0.8980451703145023, 0.19571320840714101, 0.8908957082486304, 0.4527064803315457, 0.35226073423503257, 0.389205258440114, 0.5685400519097603, 0.2974185686476719, 0.6094720576390237, 0.5593233899117079], [0.6726110057356159, 0.3927588505039302, 0.3096591274659791, 0.06756426927669978, 0.7383904248008859, 0.8277712517712734, 0.15564964805044368, 0.7501971971416029, 0.33125965572943905, 0.5756173693219336]])))*7.779972295101902, axis=1)

np.mean(9.8484231876948*(np.dot(array_x, np.array([[0.19573055131549044, 0.6760306637150709, 0.22808179058942868, 0.28743591348511877, 0.36258342693127443, 0.5337693480616038, 0.985388000278377, 0.14800605307931747, 0.806178313720928, 0.7376277416547137], [0.4163705051542046, 0.30619611644473177, 0.5658078326380839, 0.7888400492712059, 0.624953216999929, 0.7712321476005353, 0.7727966958708491, 0.8179309760081666, 0.0842903302048027, 0.4932807226922059], [0.8615804408915357, 0.12646934559510847, 0.6482611044385269, 0.8868855490476124, 0.8825751067828638, 0.4202270788537663, 0.605817814775023, 0.9333193253310588, 0.9274531588412633, 0.8884886800289439], [0.2877885165453282, 0.7363020363775177, 0.23999804257825075, 0.2788249592297629, 0.855369460730357, 0.45090812234486133, 0.8509110170047532, 0.4829228242742084, 0.8829409203957221, 0.7030006231155096], [0.4391648567702735, 0.8131271774845986, 0.7569408618426987, 0.09976963093015867, 0.33091232336857346, 0.5633003785030505, 0.3649470114132165, 0.8087480828366848, 0.28654287609853046, 0.5325862908442974], [0.039451989423962996, 0.5634563135922879, 0.8960894201186214, 0.40076467515376624, 0.1847136210164847, 0.10442293086437071, 0.8836518674819948, 0.7725147746554352, 0.1855708370130873, 0.6901845658317352], [0.3955594846870022, 0.12514860838611575, 0.9390997417765716, 0.7882218047401958, 0.5741708490616327, 0.6639274515047767, 0.2849372134644911, 0.12805278093165162, 0.8623677619781113, 0.9078151718636157], [0.6540851480774972, 0.3371989447173084, 0.903937855954218, 0.9656941945229818, 0.5264662585209314, 0.5675521206910421, 0.007447289384648226, 0.9711921102988739, 0.3078469428377377, 0.4819608766761372], [0.8699087275719747, 0.504080506094045, 0.5331035723166451, 0.09993262901694988, 0.47994126283363103, 0.4957158620236187, 0.5096762319027897, 0.2314530080809899, 0.7860537116952809, 0.8495948019473333], [0.44366562796382436, 0.7699644962631067, 0.6301463907776292, 0.5280527659722821, 0.6101694936298999, 0.7990164745427826, 0.09828976025732028, 0.3550545375769969, 0.2558217956721196, 0.6565713006517901]])))-1/(np.sin(2*np.pi*np.cos(2*np.pi*(np.dot(array_x, np.array([[0.37546796333275634, 0.008376228825519472, 0.5577388843684131, 0.8826822784198549, 0.042961708208096194, 0.0994284544081897, 0.38706839082762823, 0.21941884104512144, 0.5150103014431879, 0.20433922024780904], [0.5018057838859726, 0.0962250476855272, 0.11835927762449894, 0.01153093247850523, 0.12728358546152752, 0.7827073296994912, 0.6986363123811754, 0.0697115100569049, 0.6866056776551986, 0.30085091657514285], [0.014552056195715757, 0.26329296525306756, 0.17646888787327397, 0.9519373678928715, 0.9934456856762884, 0.4016571918299352, 0.904321531222396, 0.7775208509384274, 0.6618178307586985, 0.06859574172465455], [0.168293912901886, 0.47671356829616507, 0.48760666781047124, 0.1583661258633, 0.17366008998540916, 0.4676378057138346, 0.9822293295538996, 0.6947366388941462, 0.09194740877080432, 0.9889578970988052], [0.9003328127665224, 0.08050232257565959, 0.7613584501765532, 0.9818075715949199, 0.5542848810678084, 0.43165328659540836, 0.31467540399408667, 0.4376846622330254, 0.6564040598192469, 0.8801365115144639], [0.09397727682767898, 0.7446715545126823, 0.6943531287659964, 0.6950121480850536, 0.21172671923750985, 0.2791330625366002, 0.17486154406491028, 0.7074910833235009, 0.6412762538101058, 0.5156180541679176], [0.2606212722953264, 0.5538677046534098, 0.3013898877915968, 0.22488114078570864, 0.20397161429321808, 0.5147613519452786, 0.3651240121242175, 0.32388369070167533, 0.803255481458446, 0.22502772687196315], [0.7084602577645037, 0.9292826654558708, 0.08824962580794904, 0.94000881951839, 0.9020157108850526, 0.49198301437908065, 0.6304026000697537, 0.9424422849269265, 0.6167829145059237, 0.4711311637881218], [0.6046981828292765, 0.17037344230828944, 0.8939362336641806, 0.8846548194855691, 0.629709269987009, 0.7727997253684219, 0.17296889489566303, 0.022289238252113486, 0.32607989296960804, 0.6272278014336218], [0.7277287620911367, 0.866505187902792, 0.024937986688492564, 0.44993644441647773, 0.4418623104803995, 0.8907796181383452, 0.08231113898078912, 0.8563339365862885, 0.8526333453964368, 0.9697352411596598]]))))+8.037200810277968)), axis=1)

np.mean(np.round((np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x)-1.7467425824580904-(np.dot(array_x, np.array([[0.9875654945779635, 0.7740946217089961, 0.8918127639031174, 0.12513788031782513, 0.8845162169057461, 0.5489634157084331, 0.7072602696259169, 0.7798804529116237, 0.886898255046594, 0.5391546044092996], [0.1102619524270747, 0.33540637531073236, 0.33292935505289223, 0.025090464593754302, 0.37034621871178997, 0.945541678083939, 0.5974054286448002, 0.8046300588807517, 0.28843489173808756, 0.7682741160266429], [0.6736692077363154, 0.8007068146626491, 0.7763266152691197, 0.05250689183411683, 0.6415027253342923, 0.07730421862613479, 0.12124290754263833, 0.2351566656104005, 0.17931432208234233, 0.7802628421645569], [0.681077152511961, 0.4654739879832578, 0.9445692778098123, 0.7599076890261591, 0.3466316120302618, 0.7153250759971246, 0.6761517455370276, 0.4501430861522755, 0.08127817006106952, 0.4332125936747975], [0.6691394855836096, 0.04993348828563626, 0.4867577835580058, 0.6394911943193896, 0.21679100604530932, 0.8941231938603967, 0.13141704405528665, 0.7021283299862738, 0.40526780774499294, 0.06878345554786425], [0.27316765211256555, 0.6465348017101112, 0.9651980814990908, 0.950592791508274, 0.4395173230474615, 0.09355453800748326, 0.3538988605578498, 0.8511006847380189, 0.2228679791197059, 0.5966994780987646], [0.5847389401190756, 0.6213769693576748, 0.6989382766203693, 0.6809919629512557, 0.9118755413047995, 0.09786165432296245, 0.3167417705968457, 0.0781235775342779, 0.8425025637565796, 0.6416178729106113], [0.9251279329300155, 0.13702955218274926, 0.049109117671218616, 0.2937656162511212, 0.336609310545539, 0.49344046756010007, 0.4668018120867711, 0.31509705205294214, 0.8303886890347477, 0.22447941886283396], [0.6367598206700487, 0.3917107915596897, 0.3874210650294613, 0.8379149616830354, 0.11868214588421733, 0.9361769964117562, 0.2812526077993186, 0.08521494644176053, 0.8917992690068531, 0.37389737172030213], [0.14435807572083326, 0.7274618538202702, 0.7454706987382067, 0.6884081130483023, 0.9337263195390613, 0.8630459852120839, 0.561054310146718, 0.22355767093896584, 0.07051075773649074, 0.7277652230743867]])))*7.074555512587399*(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.mean(10*(1.002364599642993-np.exp(array_x+6.084555367728387))/9.168286819248387, axis=1)

np.mean(8.69683152604745-(np.dot(array_x, np.array([[0.04173042041057029, 0.19718956248402808, 0.8588015526654322, 0.9245142297542972, 0.5440987740441166, 0.6063548942716042, 0.7186832773262083, 0.8190286364482348, 0.3624909009956032, 0.7480060303922513], [0.07512844101075045, 0.6112790794536349, 0.7311944008323, 0.22529746645561965, 0.6925775057129235, 0.9529816856725045, 0.6298919176934371, 0.8981090248988286, 0.48167786764773957, 0.8846852436708093], [0.08373331428107489, 0.23638311503996468, 0.6523930258170352, 0.9627576783473347, 0.5604636830935118, 0.3093697895129368, 0.825175409442677, 0.7161235761300558, 0.48007248700721317, 0.6217695442339453], [0.21346108313118417, 0.2266101086455684, 0.8099606099191008, 0.7903327956985466, 0.5823368129270411, 0.4026067987729962, 0.9550587891118432, 0.24287258447482007, 0.052356206739543554, 0.2376120384792142], [0.6715690030264276, 0.9910786016260532, 0.7319302345154872, 0.8823791848179652, 0.6620952759472432, 0.5229187158198314, 0.24207636642480956, 0.08246068113707183, 0.33760968275761616, 0.9251894963479276], [0.6130326034674065, 0.042931096044394934, 0.928958486978191, 0.644378193140643, 0.5233965548660361, 0.3168273903844744, 0.3612763674681023, 0.060979420641586346, 0.08943491258464353, 0.8356036143567402], [0.4122849994514852, 0.36620796887434803, 0.08723882941670225, 0.9336093188215976, 0.7810411042644508, 0.007667273026757293, 0.8404929190004363, 0.18053750843980787, 0.9463538389688999, 0.9354395765547558], [0.4413087620390115, 0.9290707279410888, 0.7527299553874547, 0.6150270113485053, 0.3338251484535011, 0.3414530140801131, 0.1341377166352855, 0.8839233600352878, 0.14116905360405396, 0.8150848346285486], [0.9428590819745022, 0.806782567654609, 0.5679413373876299, 0.38656864894107257, 0.1475910988317839, 0.061796987582319995, 0.6011852907625792, 0.09266274411188358, 0.5718559522901335, 0.07814285339699178], [0.4521749218656417, 0.28167152807970164, 0.17772370667198967, 0.6956851493115098, 0.15832266622929447, 0.8398042523933493, 0.5914640133836695, 0.6553116975110092, 0.5554270540081735, 0.9655390622474121]])))/np.cos(2*np.pi*9.946309927303004)-array_x/np.square(9.710742208244124)+array_x*np.sqrt(abs(2.0857622462377634)), axis=1)+10*(np.sin(2*np.pi*np.mean(6.589826509817288-(np.dot(array_x, np.array([[0.848917306805794, 0.5870353540307224, 0.6357458464464414, 0.01745713768589985, 0.8316782911856035, 0.6869518335755567, 0.8388086460665785, 0.926077253201981, 0.8574766313198744, 0.4134154801413854], [0.9182007560371779, 0.1948042237400266, 0.014113669976052567, 0.949370612890226, 0.3186288662647856, 0.943794694438188, 0.5213625582537619, 0.9789230643449115, 0.5059295442923888, 0.7337140338931536], [0.2663749605659428, 0.4901671855392209, 0.3163294464852928, 0.3686703569248393, 0.8525733835682276, 0.05857392717439913, 0.613859604717903, 0.15409122247722684, 0.14421114331247986, 0.8801311317260709], [0.8980356685080038, 0.20584656737414242, 0.753816479955479, 0.10224434426174578, 0.17300823841445945, 0.847093549128241, 0.6368578921845264, 0.18208581915140176, 0.4286532490849827, 0.9555998369022627], [0.5579980048531556, 0.01397422939191828, 0.6185056589678003, 0.4689177634879451, 0.8143727413804833, 0.1755855071778839, 0.20494935723090724, 0.23122078418395664, 0.008702982823452055, 0.7162693099956148], [0.8927070042593956, 0.9834049848856526, 0.5216335095053033, 0.3357541650706516, 0.7392124759190601, 0.03629860911794769, 0.5626804365924637, 0.44872348937034434, 0.6713476131023735, 0.20545880999723942], [0.5229594908962625, 0.3854229000626518, 0.5835625171243719, 0.6260618667866794, 0.7920379064419264, 0.6382915685586871, 0.4003508264883864, 0.3019143608233338, 0.7414809989598096, 0.6015645852690521], [0.141472954259253, 0.3719034400468201, 0.7703262936301766, 0.10010160367743726, 0.06931123191625499, 0.5374643338519922, 0.12295112782794071, 0.7894950037803942, 0.080998356932516, 0.5327400713662578], [0.7270552624195671, 0.8166829130631809, 0.648030610875395, 0.37748187187160454, 0.9664747344660343, 0.532125235333256, 0.4587372348561901, 0.6102411146807255, 0.14701374442312565, 0.47983346292203455], [0.963122316340995, 0.2527151877454801, 0.17269661408968973, 0.9015815921018441, 0.23170637614606981, 0.5989385924718971, 0.6568897109476344, 0.6725084977985873, 0.3449093837262488, 0.9486313177971885]])))/np.cos(2*np.pi*2.668670889147414)-array_x/np.square(6.411682574185737)+array_x*np.sqrt(abs(3.5923359082722737)), axis=1)))

10*(-(np.square(np.sin(2*np.pi*np.sum(np.log(abs(7.4151071946146905))-(np.dot(array_x, np.array([[0.5621840342068737, 0.8345929715007915, 0.45369610527053794, 0.9415645409359751, 0.015256923698117708, 0.0895265924377997, 0.2273277265037339, 0.2371265552803491, 0.7943468466303119, 0.3123254498655955], [0.9978687288049923, 0.06838444642905306, 0.4081690902365298, 0.6514189723610156, 0.39528644569994487, 0.49420143295602004, 0.22436951402699246, 0.36523189649480403, 0.5032166510580927, 0.539417700262963], [0.9789820297897355, 0.12538123984883065, 0.8731989388263949, 0.8043252685753366, 0.12167317280750667, 0.02669377638983017, 0.656986282548896, 0.41381721491849655, 0.9529376107291725, 0.39543490204889153], [0.36506242718670745, 0.9737996497612367, 0.04447622601391821, 0.2582481796505348, 0.1511186833126259, 0.06274427955204565, 0.29148938646671885, 0.24071726054017295, 0.9067249947673971, 0.170745855240794], [0.008635754393922723, 0.15600860134440497, 0.9476499145752431, 0.3853430961968898, 0.2580380559610891, 0.45185422607537085, 0.04863152780368363, 0.557265416475952, 0.9256881502809391, 0.766855130393526], [0.005295498611500693, 0.5809017518550189, 0.23923616005015658, 0.763410941474277, 0.901102303542462, 0.3747617121439927, 0.5961476620930364, 0.983626131752899, 0.5743632447630214, 0.2954518278088243], [0.04413058110380763, 0.9402456830387876, 0.09233757958888122, 0.7285109214782474, 0.5169560441146596, 0.7227605763814302, 0.5727039757717355, 0.3978189198651074, 0.3677068253792807, 0.12109120591551503], [0.09496683475823764, 0.9413584963286001, 0.8610915420213248, 0.8766206693948048, 0.14319643860639664, 0.6506605984536548, 0.6879335041094783, 0.4609561135034389, 0.9405423097461307, 0.7417420217545773], [0.534141498071501, 0.4140481978415934, 0.14004076496046503, 0.5254280569673007, 0.005544241655093485, 0.19397502085939666, 0.450039577098735, 0.9600329099207199, 0.15999614246706917, 0.08339602197437934], [0.889587498964344, 0.7757344104923092, 0.296758932831369, 0.596397105653456, 0.22124071721898775, 0.6377048071682324, 0.6627601956544223, 0.13630973651882627, 0.760550164169886, 0.21736982628173596]]))), axis=1)))))

np.mean(np.square(np.round(8.877342208650244+array_x)), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.round(1.0345119346840932+array_x)), axis=1))

np.mean(10*(np.square(array_x)-5.434695937077585), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.square(array_x)-6.997605032008344), axis=1)))

np.sum(2.6499781793223276+(np.dot(array_x, np.array([[0.9090192241476469, 0.40570920088385454, 0.19537092820128488, 0.3196053355561893, 0.31645964460323184, 0.4859415650649811, 0.7445160176344934, 0.9867573603825863, 0.13857670932414634, 0.018555256804236087], [0.657716385975764, 0.973247557209081, 0.7895332941758916, 0.5041931936401132, 0.3437024939969566, 0.944738992840374, 0.977934749060538, 0.20257985834662318, 0.9953160758572397, 0.28336002803420224], [0.43796022334720874, 0.9189901126509541, 0.37223448977933593, 0.37420837951949104, 0.8755897922023618, 0.6526468795127256, 0.20197728272308324, 0.5883010433556219, 0.2385917145204206, 0.9171728484259521], [0.5531808487903879, 0.8236934993203971, 0.025194066478001376, 0.9851950158553766, 0.7838133835904251, 0.6324526391440872, 0.9220213322296986, 0.8438524326849637, 0.9460547257927973, 0.41830900243282687], [0.5389724655048796, 0.5013103575191228, 0.44487902796923995, 0.2780955110762572, 0.508152818274498, 0.9623525076980385, 0.12949339910300872, 0.5150916710851553, 0.6870179660293871, 0.557412669251453], [0.21103809240872962, 0.8962291816888038, 0.29716671634359815, 0.5533396563637378, 0.4879708600189291, 0.5949827409471755, 0.8007858095404775, 0.5237751020827706, 0.3205349452699203, 0.356185536321561], [0.6129523687174951, 0.06719986146709689, 0.8168778635671038, 0.4378570491221555, 0.248751584951878, 0.15506507698437422, 0.7617283568298929, 0.9880631856041505, 0.010630615782057928, 0.7766660848357111], [0.5898373062875417, 0.254758778298518, 0.42445161029680956, 0.2746124850148435, 0.561865080880105, 0.4381271449367532, 0.7047757808674945, 0.018524914013737903, 0.07083749258288019, 0.5574764303950257], [0.7280790038842945, 0.6952734478408612, 0.6088922145673411, 0.9013285450629162, 0.3751382368876216, 0.2857334561180519, 0.09687094252804607, 0.104289723225095, 0.8214239024580838, 0.6482284406350407], [0.8983886754237521, 0.1960503807760623, 0.6405956192698928, 0.5758027403792066, 0.310983530430603, 0.3643850151375777, 0.9803313002030721, 0.057028721302505025, 0.9346865206015097, 0.5520933725969727]])))+np.sqrt(abs(np.square(-(np.sqrt(abs((np.dot(array_x, np.array([[0.4224849583961119, 0.8315706430642198, 0.04824273234430765, 0.018028055918635433, 0.49392535055155407, 0.39977833389527584, 0.5281784926931903, 0.7802371374816343, 0.29984374791081014, 0.12947113277679922], [0.09543138058416722, 0.8416436219647319, 0.4007743074297939, 0.02297566612603852, 0.707384371280267, 0.717322477787207, 0.9597101109087669, 0.11119804376105813, 0.9949834390094958, 0.5494366047505166], [0.49511994840880647, 0.6230939993062868, 0.6868251130424063, 0.8932448823319813, 0.9113873006753832, 0.48375909383969773, 0.7741170847105027, 0.8160374965103057, 0.47700590632834505, 0.8632514766060829], [0.029864950082089115, 0.9628570204457104, 0.7638318388828484, 0.5136107005175056, 0.4193679279057714, 0.1564271181495599, 0.4835457229463809, 0.5901341105027449, 0.7518343122848348, 0.934651859041891], [0.20409384983273593, 0.8675727839510201, 0.6814725569833152, 0.2863309381221264, 0.5675326100722397, 0.4500355488214638, 0.7843752591896657, 0.13336965388535704, 0.3518007141795617, 0.603874164581196], [0.3777618572195127, 0.7055150114251497, 0.7674929743294834, 0.7624828783664793, 0.1871469786873583, 0.9201518719170428, 0.0727546313502162, 0.07125702310594928, 0.7913889491587365, 0.7775307310106043], [0.45726837066747017, 0.37885110893105634, 0.5887070595217149, 0.05551079015560134, 0.9651884034669957, 0.9194111427950323, 0.10224791548208001, 0.48192853917514433, 0.3432776916472151, 0.09600485483805332], [0.6439548162900246, 0.22144794576044835, 0.8377441046828846, 0.143835627245546, 0.4951747426987986, 0.43015304382939945, 0.5049890971578589, 0.3408600615996584, 0.4628040302677764, 0.21157345967113028], [0.483703658746364, 0.3163825944989246, 0.6463264386329418, 0.7320041968570447, 0.3657330270696131, 0.838319466465708, 0.3718787736743939, 0.6982686917618915, 0.8064878931533436, 0.7086363966009791], [0.5323691431741319, 0.2017546879859159, 0.04873040972788467, 0.8405067110546397, 0.05089331501985006, 0.9642742956701434, 0.9549226417525016, 0.17619024646958026, 0.035816002892882004, 0.2234622908589985]])))))))-4.3192189797004605)), axis=1)

np.mean(np.exp(-(np.round(np.cos(2*np.pi*np.sin(2*np.pi*8.783629785023813-array_x))-(np.array(range(1, array_x.shape[1]+1)))))), axis=1)

np.cos(2*np.pi*7.058371389399362*np.sum(array_x+abs(array_x), axis=1))*3.1140620954381886

np.mean(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.8741827767628184, 0.9448629152691872, 0.3923864466985947, 0.959206979806415, 0.43313229303449663, 0.7301843015902576, 0.1580400544244741, 0.8556040111609595, 0.7148136791208596, 0.13941199904917667], [0.7701553619526068, 0.9397431201962662, 0.8343387875365327, 0.6901113706587908, 0.2500173216155528, 0.6997328980806701, 0.5589392845646137, 0.8278767199114084, 0.4123404904739236, 0.5853682873380116], [0.5189136941448009, 0.601412824766294, 0.28924070552173486, 0.9376524598977275, 0.9960935562146833, 0.2340287177046747, 0.8067895567711211, 0.1021096477475516, 0.7732763185238198, 0.04952207316136292], [0.596270414056359, 0.6820963979548237, 0.7295578062029725, 0.7761052232146439, 0.5511393219037213, 0.955379943522989, 0.5678353973703711, 0.8218091800465632, 0.1955847969280049, 0.9058121008957644], [0.40516201051376644, 0.9451166402431732, 0.7653236906960204, 0.7451517050795736, 0.8956836618984374, 0.6362119233991346, 0.2747119428035574, 0.47931373380960196, 0.5109379518609563, 0.8236857528507441], [0.45771747617206615, 0.5941767196123274, 0.042151575189205204, 0.07389470041982205, 0.5432838213234843, 0.2789161016326309, 0.8291816394126821, 0.8073265833697458, 0.7741025965147809, 0.4631885282902003], [0.8766229640876236, 0.04031484601423874, 0.29433227843403764, 0.7240375331198678, 0.4704333682908465, 0.6532900560681189, 0.26976936720815803, 0.15135307942446397, 0.9021631329135427, 0.4295159182189461], [0.15440112820865237, 0.3325468362102043, 0.06892056907886612, 0.6388700881859576, 0.3723968584512376, 0.5672548881703946, 0.313169828179262, 0.8614268242088277, 0.22790582362673517, 0.785760850528969], [0.6406034774591651, 0.47017400792265607, 0.55809446241519, 0.9140065832016258, 0.5256638119783598, 0.5981722964489437, 0.704548747524734, 0.96830286316818, 0.7117799596220165, 0.1558803483411293], [0.790331321957654, 0.00827491607216424, 0.33009183041691015, 0.2913046454379412, 0.8157382214929317, 0.5350983713749727, 0.5098881813614541, 0.39503926937042955, 0.3401365636583833, 0.016595032425821943]]))))-1.1734256765417945+np.cumsum(10*(array_x), axis=1)-array_x+4.209973603500198*array_x, axis=1)+np.sin(2*np.pi*np.mean(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.7648035546734987, 0.4531139253698456, 0.27535301319912586, 0.045334928505297745, 0.12532510084887338, 0.5672215218642548, 0.5233073916384433, 0.49849782860449143, 0.9697431453342407, 0.9913879326471998], [0.22453219443850514, 0.4414564184065174, 0.296333450531737, 0.6111626407810326, 0.5204479387019166, 0.29692658261836014, 0.580402084279832, 0.6455138721951839, 0.027349416818083894, 0.47891562299997303], [0.7494849502634577, 0.20161950046262478, 0.9594504071133922, 0.5870797342316322, 0.8020706936302057, 0.8386092042619586, 0.021974608000460805, 0.9226998661341282, 0.21038411113984012, 0.617033704260876], [0.1693990480070683, 0.3440995030438264, 0.7383207683843268, 0.6734727642722101, 0.8566484324721184, 0.8887784686956554, 0.4588594774930398, 0.531443677430913, 0.7224269019499441, 0.2678583532706613], [0.5721253935114116, 0.8291661359925018, 0.3337453276357205, 0.6844444412912428, 0.370393659925503, 0.2939916392107075, 0.8605314218412334, 0.2869098515433832, 0.199120947641528, 0.3256438993390496], [0.7598552263523242, 0.8458733785217399, 0.6273699078786283, 0.9037812504607553, 0.9011428519597221, 0.10727418953219703, 0.5759317110316977, 0.1343646110922575, 0.8228416570024164, 0.5443619139356562], [0.637875953535884, 0.47572660849257786, 0.7506690255306662, 0.02081851286910552, 0.5560843465130108, 0.6321995771424355, 0.9472469704789334, 0.206108559908309, 0.00020200959338523283, 0.5421688128728481], [0.2549703882263421, 0.23676199392494546, 0.29861821322561277, 0.34108231765346253, 0.4578680112365894, 0.7567920622489226, 0.2632965946019731, 0.8395443599184826, 0.7863556013379237, 0.5664191113878583], [0.771611708731022, 0.037994377630601206, 0.4961974487226487, 0.7739961771496399, 0.48386699075895423, 0.23678362982039036, 0.49779068844986196, 0.06617848926738246, 0.21925364173766515, 0.07766313400342795], [0.40003065396038606, 0.45302559352773175, 0.24651854757741465, 0.818864636182807, 0.0474336791758484, 0.356148442544415, 0.4501480408330959, 0.31454431031663976, 0.1239130725995834, 0.24099715613412886]]))))-4.2515553289357+np.cumsum(10*(array_x), axis=1)-array_x+3.5118898222358257*array_x, axis=1))

3.0934544106816424*10*(np.mean((np.dot(array_x, np.array([[0.2002694403018691, 0.8074023356695799, 0.339543449444755, 0.5616004903390279, 0.6439136998741912, 0.12520953089239317, 0.7263922605892678, 0.3704272768384459, 0.3207444253740931, 0.6836191680694871], [0.22917521627913107, 0.6673300315836751, 0.7693378524191226, 0.17581721116294935, 0.663307889855623, 0.029169894921902473, 0.8662319695386439, 0.9258633939146543, 0.010094738300717498, 0.6766633501525767], [0.7690091566810472, 0.8536091929975976, 0.3260495679253085, 0.31525030124767295, 0.6888074781829241, 0.5284131392841612, 0.07637831645379445, 0.7807071648621768, 0.1752840662582693, 0.7947907304549886], [0.07621785079910304, 0.6874807731702158, 0.07733491842958584, 0.5611188406821636, 0.45319467913044553, 0.37690285954993674, 0.11193734788069265, 0.2507660473681175, 0.7727224635943064, 0.9374462216550289], [0.6767501495613718, 0.5286092963764187, 0.13172012330500993, 0.7274909672394821, 0.5094326262960899, 0.5026765743502911, 0.0795606681080987, 0.8251972847276926, 0.8080103140091626, 0.5628942220376004], [0.3609419014416455, 0.9891872070447825, 0.6940462407246387, 0.7275410683868815, 0.33709472246940786, 0.4755817349540721, 0.7296736602023303, 0.5663670743127155, 0.28158630788473127, 0.8354423467234297], [0.22396436843331868, 0.4364932363924392, 0.2596618014212342, 0.39163553913517957, 0.6854824753822747, 0.043831678062764445, 0.23751607828682475, 0.6659398583939438, 0.6979913236604068, 0.7172643339243978], [0.9219402556389462, 0.3359393962926881, 0.705153590670661, 0.38849168575170856, 0.8769897936823426, 0.12367995032653067, 0.07405944301817247, 0.46840942062500435, 0.7211542909387166, 0.9152566734948924], [0.49150258108323, 0.32869775334560125, 0.9279704063622359, 0.010075152379439789, 0.1389724645300363, 0.35371703799042664, 0.6814787938966075, 0.903037991757145, 0.13653119163569838, 0.974596411411362], [0.46022920802387857, 0.0511829573329059, 0.307469861385293, 0.6174507467749474, 0.9718466216387042, 0.5651542104989709, 0.8431515654610776, 0.9270253595793172, 0.15717451950147687, 0.7197983783625891]])))*3.537524648729827, axis=1)+4.686102648061006)+np.sum(array_x+array_x, axis=1)

np.mean(5.775899788474138+array_x/np.sin(2*np.pi*-(5.610103092739245))+array_x+9.33570409733007-np.square(7.082025031834862+array_x)-2.174504887580863, axis=1)

np.mean(np.cos(2*np.pi*np.sin(2*np.pi*array_x))/(np.array(range(1, array_x.shape[1]+1)))/1.3451761407123346*10*(8.143139481356707), axis=1)

np.mean(np.cumsum(array_x, axis=1)*3.3409081115801254+np.round(abs(4.255736382655558+array_x))*9.271896747845211, axis=1)

np.exp(5.56965419373446)*np.sum(-(np.square(3.032376665736118))-array_x-array_x, axis=1)

np.mean(10*(np.exp(array_x)*6.9014455545064575), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.exp(array_x)*5.632345302820381), axis=1)))

np.mean(np.exp(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.32868714982576364, 0.3466955129531627, 0.4087054741377347, 0.6322754714130181, 0.7294584274784499, 0.14632044739794547, 0.1852248324543584, 0.9561191321340944, 0.9741040123417802, 0.33159920137430354], [0.9858889093483544, 0.8005943038074833, 0.7819426196630612, 0.7662234205850221, 0.07706207865015402, 0.184036653087445, 0.2730556816884506, 0.804264939224569, 0.08018257100907356, 0.5446866322592402], [0.3721212708068954, 0.6764034837307581, 0.3375432567019365, 0.4542323296730352, 0.837891613937136, 0.628160141219244, 0.9833834879611741, 0.970823153437311, 0.004104397233330115, 0.07918009741191157], [0.6596469799187099, 0.7161881837320967, 0.8685152470230455, 0.8625233716453388, 0.42151430106296517, 0.4535736987291672, 0.14576514183616374, 0.894656258811393, 0.016896558716789745, 0.36979801992684735], [0.9225149125594503, 0.5422446699188371, 0.812867616724101, 0.6967681486385203, 0.79908833987506, 0.9770801125956148, 0.35610250842800206, 0.09157342251580958, 0.8492763626717618, 0.8689733807093907], [0.11984719735134552, 0.25809398053587973, 0.47946182752493793, 0.09712614240596273, 0.07764313332031414, 0.6589253988965531, 0.32762162245151705, 0.5878137752083181, 0.28588121412811895, 0.9533662686348036], [0.2094099522103593, 0.06773372005317924, 0.8689096351194123, 0.8155166723355876, 0.9065805704136722, 0.21242082569926402, 0.5107560587774639, 0.27125147227476254, 0.014159571479095634, 0.8773282669685019], [0.3524393185866609, 0.24912221148452085, 0.783923932392308, 0.9828102429482117, 0.6915893306704715, 0.6109100026342048, 0.3809283575544842, 0.41878034547213294, 0.3445517508702226, 0.8847283857518485], [0.4702545152529888, 0.041924290018717, 0.6687822043496733, 0.8731907029629464, 0.8602244813262978, 0.6999041739064683, 0.2686202881032014, 0.7905510640002681, 0.1346650462911988, 0.5858596755910802], [0.550512287137937, 0.31844604436513757, 0.5975686727182248, 0.8864906328444393, 0.0215435747932744, 0.8335933253924596, 0.41848170074288116, 0.052502530908654976, 0.0038933023140970136, 0.8115928625118847]])))-4.979604384498577-3.826805064901178)+2.221397745205276*np.log(abs(7.035366251732899/(np.array(range(1, array_x.shape[1]+1)))))), axis=1)

np.mean(8.204524304141824/np.sqrt(abs((np.dot(array_x, np.array([[0.9145342586962452, 0.9398842954088723, 0.5832600528917484, 0.3825588411153521, 0.3300262522734141, 0.1741151203414597, 0.8371946601680204, 0.7234334369720675, 0.08454696080379598, 0.5981330432211198], [0.17324520852346081, 0.8352877931177412, 0.17119944433649315, 0.46948928464508033, 0.203025928676703, 0.9182391691218398, 0.30924480681098354, 0.9465166697402708, 0.3762973477804884, 0.73982972992403], [0.9958636998149908, 0.878268688585102, 0.7787822532412325, 0.6543510936681364, 0.8315656157156225, 0.06869001765079041, 0.7918523498371002, 0.553349458029251, 0.22754276894210224, 0.9559035580453612], [0.3053802893545562, 0.32897446015371246, 0.9537471530105534, 0.3281085615186228, 0.387907156273273, 0.29839930084126554, 0.8293559633785745, 0.8880107397127077, 0.4482703873946524, 0.07613428971322356], [0.29166078501762926, 0.8285292680319015, 0.6481719744189783, 0.06264178409485799, 0.5987766340599882, 0.15164960455770515, 0.062489460876941405, 0.12164814301801308, 0.3396672198578846, 0.4599876964498142], [0.935443457516521, 0.8271427665915602, 0.6442911453319977, 0.8008054681258823, 0.2717714512530838, 0.3433616432340221, 0.8926427585774263, 0.45282936276689256, 0.5935147715038355, 0.523501629699927], [0.7419083832193518, 0.2922906275481403, 0.014411164745409821, 0.6955750375716387, 0.3869646935501595, 0.5257499657402802, 0.9796043459319829, 0.0011866243967474377, 0.5739684049627587, 0.37335447538475197], [0.4722740473282271, 0.016996363427691397, 0.5543737585857104, 0.2945102011331233, 0.2101434400390796, 0.22534559082487438, 0.4724412250918043, 0.47117140581077155, 0.9920221223632707, 0.8595411011208084], [0.9412442291863725, 0.18594865534616, 0.41807145260352185, 0.39676367244843636, 0.6621134651979877, 0.548056792420527, 0.31375117523601037, 0.903368156649416, 0.681287436869064, 0.17038453773087392], [0.7543794569473794, 0.5642356117818741, 0.6566659055807051, 0.9509089363662725, 0.7625066781616373, 0.0007729972692103182, 0.7472788897601743, 0.8399562447592417, 0.09577857944008128, 0.1870816020182421]])))/np.sin(2*np.pi*(np.dot(array_x, np.array([[0.6364868205602457, 0.35063078151793003, 0.2615847703751787, 0.433430748741065, 0.04913413642669784, 0.1218715915565588, 0.624862749846786, 0.7709714263203817, 0.5889620207890334, 0.30954256788024404], [0.8541405565799215, 0.9792234823063783, 0.9387394428102424, 0.2461390240820941, 0.7318902599504519, 0.3467069383911979, 0.04885147019331193, 0.9515053725044439, 0.8070542465988148, 0.3268269123416253], [0.5827050605196183, 0.18286309292074965, 0.8008458010987922, 0.2981486457423974, 0.43826546453689197, 0.6074698607093364, 0.4073718976411973, 0.28046082779704595, 0.533036060750716, 0.9989418618082501], [0.45238027205521025, 0.28432969857465307, 0.9402823886679142, 0.06490887975542536, 0.9986369268602082, 0.9965277373671891, 0.125332128456076, 0.21197472222760594, 0.1814000854653638, 0.02082503541460734], [0.04816725886035722, 0.40894455464131363, 0.9293153567586304, 0.9255622853052229, 0.8386764063145555, 0.7984280693018345, 0.7388566462499945, 0.5092945212323183, 0.9330136931241035, 0.02721251772968114], [0.3833910275890017, 0.7130748693731133, 0.6392475080946596, 0.8825957047538542, 0.10899539286715876, 0.6957390932635433, 0.050606988261414854, 0.4401988994718372, 0.023154501522602455, 0.8980102008068236], [0.7580181126506458, 0.8362638932182173, 0.21478108936751028, 0.44333964012967086, 0.4855552007822097, 0.23062100970060295, 0.8131328125782397, 0.8344836426044624, 0.7882007936567315, 0.7271467674762243], [0.49851661896513944, 0.4429008914843915, 0.1427288891142373, 0.9028036726318692, 0.5615511411821391, 0.7422214119243575, 0.901199481837103, 0.8930396722574793, 0.22482792927383322, 0.3773357526327432], [0.21234926941274024, 0.9559720480204575, 0.33205490713716135, 0.9141519909071737, 0.36766790909557356, 0.24070923081841067, 0.8566113908681087, 0.04486786183674529, 0.8949157885324345, 0.8047850990561285], [0.8905742761245296, 0.870835780586486, 0.010874806588759278, 0.7311335093719518, 0.48803466435569387, 0.9802379268657824, 0.8092841888631739, 0.26441001201190284, 0.10640454771551722, 0.23812646660426362]])))+7.855479455272653)+2.2188586260348817)), axis=1)

np.mean(np.cumsum(np.round(array_x)*(np.array(range(1, array_x.shape[1]+1))), axis=1)+array_x-np.cos(2*np.pi*1.1964443088457182), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(np.round(array_x)*(np.array(range(1, array_x.shape[1]+1))), axis=1)+array_x-np.cos(2*np.pi*6.74888163613034), axis=1))

10*(np.sum(5.46136067417938*array_x+np.square(8.953777048301657)+np.sqrt(abs(np.sqrt(abs(2.0422203497568696)))), axis=1)*2.379936664293977)

np.mean((np.dot(array_x, np.array([[0.42653317744474395, 0.7548648059701246, 0.39938927181364703, 0.10391662366679633, 0.36042113313989144, 0.8657883637326035, 0.9431158705048733, 0.2659893802415181, 0.588217239115122, 0.36972987557523895], [0.7831396085137227, 0.6678146295436529, 0.3845761365573914, 0.35360552883673757, 0.18453626863365835, 0.4051508634003491, 0.6578185111770802, 0.6500144296782202, 0.2710246242044654, 0.6278209218782833], [0.7081871965195108, 0.4923207949599404, 0.49499524135876527, 0.8013746056410782, 0.19216518966349827, 0.8410664452710533, 0.6457902125401744, 0.05627578254460208, 0.2779526590115967, 0.32191690685866114], [0.5598265782531877, 0.9579604507589904, 0.4747601583734544, 0.07347548510201718, 0.9354236067340337, 0.15428587862120147, 0.8677469780233967, 0.34132053931444084, 0.8549444491503345, 0.45585749958098676], [0.7805293746983587, 0.39886740933002585, 0.7090805531115209, 0.787360356147628, 0.4918179620571429, 0.8676482110178465, 0.5284208764788982, 0.11880071846583184, 0.9828845653319094, 0.06607857881334134], [0.31404360745313775, 0.17986372659704186, 0.5487840003793617, 0.6014876381523254, 0.13464269377408578, 0.42654979080968647, 0.11917140935224013, 0.9925966302232577, 0.10466620585199449, 0.46404002552436696], [0.8111556447226101, 0.27421678377349823, 0.7148609554426169, 0.8909341072215865, 0.38884497441605226, 0.12625120175266857, 0.927951514322023, 0.6237275015027898, 0.9881860115451581, 0.7554901618766802], [0.49301248226762884, 0.9930340885933575, 0.20346571472800357, 0.6871467977389133, 0.11579574993522757, 0.6435063340114806, 0.2568024398708799, 0.49634828333616987, 0.001170480727300638, 0.09332208378232953], [0.5383826553977309, 0.3214188644720456, 0.3913621328249588, 0.2455596225395431, 0.29470159179644706, 0.14852032220120726, 0.05987751520528761, 0.9990121979434935, 0.36892944068480904, 0.2935086853400808], [0.7520474182867962, 0.20495021415587433, 0.6917894190690526, 0.8969094391248527, 0.5983492455072036, 0.8386436675218797, 0.7094623616989787, 0.2773212495249848, 0.7260463373453064, 0.9197984044491562]])))+1/(6.960736950632317)+np.sqrt(abs(array_x))+array_x+(np.dot(array_x, np.array([[0.32365180864679877, 0.6577023287869533, 0.7556078824867326, 0.5495413819436898, 0.505558714123755, 0.024381725070621862, 0.8722537281128102, 0.10793849828428059, 0.22930281862506297, 0.672052642253827], [0.06199143274625085, 0.26842610920333354, 0.7100080124904304, 0.3343354987745175, 0.2416494928920121, 0.9395920144224713, 0.36965523110603704, 0.09956474188927422, 0.48434060067687834, 0.004389612746640514], [0.012537989977663422, 0.4050783058730727, 0.7234035996618052, 0.8171855925918371, 0.8590133156309573, 0.7291163592809362, 0.2808916831029956, 0.222870672915505, 0.4919875118949337, 0.6098595275669287], [0.7357442386540267, 0.9861015991807619, 0.45254930621678513, 0.06410598571004944, 0.5864749546402183, 0.7514562405980663, 0.6121800406369169, 0.9502906679694745, 0.5240539211474542, 0.5861828081561677], [0.04754487423988207, 0.8725994625874262, 0.7720359150334022, 0.12128789054847655, 0.10658059121517183, 0.7917147104853606, 0.966261176322927, 0.9723155384067079, 0.20875067948115034, 0.8455644010848984], [0.8324462977718206, 0.10175605541555865, 0.3283331793473542, 0.1012231676402332, 0.5921949588306352, 0.1506893553987385, 0.8331854432487465, 0.29407927610671203, 0.7998508468402139, 0.23679655073228267], [0.039691485147417715, 0.05795848208116661, 0.5679575843712809, 0.7987128092958025, 0.6172160799342412, 0.28289774551900104, 0.8534162311255786, 0.9074457499265868, 0.8588516151799527, 0.5773776473068646], [0.487788458851096, 0.8810795118414129, 0.25950860501742523, 0.36749810753659595, 0.5307077751239788, 0.2603139547083587, 0.6486585208012856, 0.9344059358548165, 0.5492476711685879, 0.8850546868626653], [0.38928900658611687, 0.9133245787051603, 0.21673710300454607, 0.8073615259793043, 0.5184385127192856, 0.9552003648641342, 0.8779873926689885, 0.5920991463689234, 0.6046651297111754, 0.49357501253019787], [0.8978857061716187, 0.792242899844732, 0.8554965355951176, 0.0961221391180681, 0.3443888929087332, 0.86525278446595, 0.6105916196530065, 0.19747911604288693, 0.2723810590103489, 0.27404493755448667]])))-2.869181212471959, axis=1)

np.mean(np.round(array_x*-(1.4192691994251914)+array_x)-10*(2.9942661495827982+array_x), axis=1)+np.sin(2*np.pi*np.mean(np.round(array_x*-(3.739797349518387)+array_x)-10*(8.602237159212727+array_x), axis=1))

np.mean(7.97648429578856-array_x+np.round(abs((np.dot(array_x, np.array([[0.9763091106702985, 0.16296098624764, 0.6232754524007917, 0.6607775306168817, 0.5501232037839353, 0.9842079087445789, 0.5086437558626992, 0.807503830025683, 0.5178323194505706, 0.5719726674928202], [0.7014808613216017, 0.7862657410841106, 0.9067368316787242, 0.7040734047200291, 0.6800928127889504, 0.2906860923550092, 0.42815506834257966, 0.32684213709514465, 0.20804096259795468, 0.16565960092960152], [0.9387101571996257, 0.851109729921663, 0.7219807043425964, 0.5935809334882615, 0.7886606169060263, 0.9756661945982067, 0.32970563468011327, 0.05124305025749443, 0.9548826941900519, 0.9269367770693487], [0.6783384863244706, 0.9722431477902693, 0.9020426659875233, 0.24049080673853396, 0.3805223224728487, 0.2460321751755562, 0.7428260205408419, 0.678617715204582, 0.4061676395917445, 0.5568705487921413], [0.912714165573331, 0.9406811921198533, 0.9758868999779882, 0.22813019459930672, 0.30889803222087153, 0.8318186619463487, 0.2773407304240927, 0.4904897888904437, 0.7233822578854496, 0.9590956847158942], [0.12308522220430507, 0.26272927263110035, 0.3211029259777127, 0.2115064952314657, 0.4103548775140803, 0.7267684929447468, 0.7253263640371237, 0.26166868345066474, 0.7839245746357799, 0.8263699300163769], [0.24189001539398158, 0.08417082407380672, 0.04574223298622215, 0.14101008227584788, 0.6585908475626657, 0.7238330244864891, 0.24610346496104396, 0.26594668064176885, 0.5330984785995263, 0.20072501437552337], [0.29675520423897495, 0.5861465085231438, 0.6080851913285784, 0.4990747193622679, 0.3796190351428753, 0.2713878345009002, 0.4221280013314116, 0.6875021600788352, 0.5040332076776606, 0.5569221097839745], [0.13234014455910714, 0.35602037551234544, 0.17047949011237307, 0.646082269689413, 0.7054098591315274, 0.282152271464705, 0.444032861286319, 0.9099807615713852, 0.2091567405896596, 0.2864130842733704], [0.28243043487087804, 0.885461126495166, 0.5859395762641383, 0.7048234712399762, 0.3726958873741102, 0.23029310807997716, 0.23394373505274646, 0.7872622297676692, 0.3473709217255774, 0.8195846598735956]])))*8.171932193130218-np.exp(array_x))-3.9616767274327898), axis=1)

np.exp(np.amax(8.440407232991852-array_x, axis=1))/5.065001086913273*3.3232358666394535

np.mean(6.184302807162522+5.724290363474756-(np.array(range(1, array_x.shape[1]+1)))*array_x*4.943257572575133, axis=1)

np.mean(np.square(np.square(np.cumsum(6.656101994514691+(np.dot(array_x, np.array([[0.2944437583554518, 0.14303495309124237, 0.569355147201934, 0.7062964631034565, 0.40346778998898514, 0.14752766322121214, 0.9084855331855082, 0.9493044681613553, 0.5023672166036562, 0.49711384909208034], [0.29055116388307145, 0.24655137330369858, 0.439997247049116, 0.21754559174863353, 0.5445367436299607, 0.4718530681710795, 0.7982090160261218, 0.23052772513820685, 0.6511013531907939, 0.459496637943421], [0.9046378786444138, 0.7044327340646086, 0.48366381398190195, 0.21200771608878088, 0.2206337680391749, 0.233927861288295, 0.008371789454345357, 0.8357112961433852, 0.5932325380826835, 0.14356065344169855], [0.8922170313491578, 0.3576094661796938, 0.5575063283297154, 0.866547603694003, 0.41940416428878424, 0.8580559048507804, 0.09229455118561614, 0.6058362763679206, 0.5675522251988442, 0.4932557883940234], [0.8808268027893691, 0.9849769689320294, 0.7869329009714642, 0.6932727329101517, 0.350410863024159, 0.3275545777562455, 0.6286938311550595, 0.3974672661651323, 0.7727811760625842, 0.3024905782849503], [0.8700882384419721, 0.939120374948751, 0.3155435248615288, 0.5390617636479144, 0.33559566455111567, 0.7176716046123374, 0.09238336258513757, 0.4124453842635287, 0.03582686818460412, 0.6047184136345686], [0.08910405520030351, 0.8661163938787042, 0.2316230369988167, 0.7066074823663376, 0.4097941677840313, 0.4700772042437499, 0.5367304781967134, 0.3884553892555006, 0.9355119202344322, 0.9380641109062847], [0.21200146021374378, 0.4214321010206594, 0.12856934977555612, 0.6037539678862526, 0.08448999484761954, 0.5676840122565612, 0.038369890575529864, 0.45910019537340807, 0.7340551801583306, 0.5027257419793044], [0.39516069076781213, 0.23952011266250717, 0.029082085246917355, 0.024662460184514345, 0.02646156804444222, 0.463152139195975, 0.4448365310997102, 0.11216066801918323, 0.0885869520901994, 0.8231488210154667], [0.8902863985025861, 0.9112653357147282, 0.3040557507563387, 0.7403456913509214, 0.5603099867930572, 0.7331920541692933, 0.03829591089303375, 0.23775501794808007, 0.6909920788524466, 0.9633795606484021]]))), axis=1)))+4.800129460565888+(np.dot(array_x, np.array([[0.9807292201813523, 0.6153368753104032, 0.6848308718118007, 0.8887927365945758, 0.6907878774098019, 0.21740517501451118, 0.6726446813792343, 0.6484330375404238, 0.7170458598169883, 0.8432645580807783], [0.2708576591807943, 0.19497256054320433, 0.7160890485335629, 0.5939981823673627, 0.8848404830950182, 0.8623496507410532, 0.49912773111406816, 0.20126334077635333, 0.7376174843885218, 0.9541553993789014], [0.27674012580062524, 0.7967465556309551, 0.3513164580912431, 0.19470867906577882, 0.5076131582014717, 0.1337543162881667, 0.27017385718498077, 0.6139877445050131, 0.09955322628558538, 0.9223017595786722], [0.43241109376366105, 0.5742067044465093, 0.5265310352912208, 0.9481945586822376, 0.7698252357772275, 0.29415376299910057, 0.5104337268569381, 0.3699413776353353, 0.48198114449004703, 0.8011562029525033], [0.39297574102571975, 0.6882902329344465, 0.9762073011740606, 0.030452580978240218, 0.20198697239948393, 0.4664016514305557, 0.0034709356874981223, 0.6289539872788169, 0.5872327820110915, 0.6579466073336738], [0.6085044208566169, 0.04894440299059244, 0.16745077873790937, 0.272130841703739, 0.16057473329717886, 0.9649192340463079, 0.8944151638126592, 0.6047526834893339, 0.5402915405019115, 0.5728870128203365], [0.12041660265483967, 0.48805335700837027, 0.5128619093690553, 0.9013458619891934, 0.6740429175483735, 0.3061478011210911, 0.6888003762063633, 0.7451478410700483, 0.9626176168679104, 0.12246114327528357], [0.12130592690113218, 0.48908200414115466, 0.7126353258543001, 0.6707453064511505, 0.22208695046272553, 0.2419293658581605, 0.08861034787075472, 0.26217192623506935, 0.6478102806266747, 0.23391126187459554], [0.5647498032294327, 0.2874518030046854, 0.5778526974379168, 0.9389504997322717, 0.656812923057761, 0.12464529245830291, 0.8398531021059731, 0.7366509979193576, 0.21417673330693277, 0.7241438044965031], [0.18951121643864555, 0.005606000020345836, 0.3265410636759333, 0.4478190755290008, 0.4508540982560324, 0.6273323381812277, 0.40053131124845576, 0.9703744861855387, 0.5047041621299541, 0.8323539684034625]]))), axis=1)

np.mean(np.square(array_x+9.830836645618794)-3.662338459408784+1/(3.830410768379763), axis=1)

np.square(np.mean(np.square(np.round((np.array(range(1, array_x.shape[1]+1)))*array_x/3.3202660890207003)-np.sqrt(abs(5.933553921795441)))+np.cos(2*np.pi*2.502902674820264*(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1))

10*(np.sum(3.8950256362128894+array_x+array_x/np.exp(3.831281876006562), axis=1))

np.sqrt(abs(1.914653753228528))-np.sqrt(abs(np.exp(np.sum(array_x, axis=1))))

np.mean(10*((np.array(range(1, array_x.shape[1]+1)))*array_x-8.642290179185242-np.round(np.log(abs(np.square(np.round(7.663420811803513))))+(np.array(range(1, array_x.shape[1]+1)))-np.log(abs(array_x+8.22436579316281)))), axis=1)

np.mean(array_x/7.838247851600014/7.150574223965099-array_x/np.square(np.cos(2*np.pi*np.sin(2*np.pi*8.096385443086092+array_x)-8.370031749872016))+np.exp((np.array(range(1, array_x.shape[1]+1)))-array_x), axis=1)

np.mean(array_x+3.611642524927368+4.344007269028294/9.326989453838838, axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+6.634534796070216+9.460817787553404/6.2268870137858645, axis=1)))

np.sin(2*np.pi*np.mean(np.sin(2*np.pi*6.15626258140878)+np.sin(2*np.pi*array_x), axis=1))*np.exp(np.cos(2*np.pi*1.925639579664066))+np.sin(2*np.pi*np.sin(2*np.pi*np.mean(np.sin(2*np.pi*9.415127841097558)+np.sin(2*np.pi*array_x), axis=1))*np.exp(np.cos(2*np.pi*8.799754094022548)))

np.mean(abs(9.749308106923236)/np.log(abs(np.cumsum(array_x, axis=1)+array_x/3.5619939922679595-8.173433797049128)), axis=1)

np.sum(-(abs(np.square(7.030194644962757)*(np.dot(array_x, np.array([[0.83813461351848, 0.8259949250033584, 0.6795375537479094, 0.4154717057154157, 0.8675975398518426, 0.7849185136635433, 0.8388087805054995, 0.5143167462432955, 0.12588360614146588, 0.9096114534563142], [0.28044574327462957, 0.751855414218665, 0.47512121450821065, 0.7489266238048852, 0.8668407971955376, 0.4093764379972674, 0.9819465378681193, 0.27876567896808724, 0.6939287211367093, 0.052711078596657024], [0.9040899378252516, 0.8958998714096102, 0.6785073975004423, 0.08327236676761907, 0.7077131091266016, 0.04914064014026731, 0.7675383690174165, 0.7388500464595176, 0.7426333654810033, 0.845351386548044], [0.9985185173464063, 0.8976825707806831, 0.02473652118052072, 0.6367567441492318, 0.5672103229983868, 0.47572940250788254, 0.9392151062016143, 0.6022287032080818, 0.6254313649964414, 0.22490466397824238], [0.04620727936832891, 0.15655883876970556, 0.25653908026235395, 0.6207071563918952, 0.7734867522440347, 0.9224541933963183, 0.07466492691034243, 0.10359549894393871, 0.5810381827172171, 0.7539531734463796], [0.4952585232593313, 0.3704602617262599, 0.4631322075990989, 0.4060060804722232, 0.4478773213495596, 0.16458819415693182, 0.441503719713201, 0.3391552638680889, 0.5763542175472062, 0.5786762765511573], [0.08561122291300571, 0.2181782473616084, 0.8537427371295775, 0.04428957096880004, 0.4250340167264829, 0.9363865473549119, 0.5615688866776933, 0.2106360557390221, 0.04842923401265464, 0.281821477210773], [0.07454993650874997, 0.024229685400981027, 0.37020303782261377, 0.28524175504368177, 0.23811017414199287, 0.6937702799502913, 0.0059073037860637445, 0.6952347430960606, 0.9146091065546176, 0.8552874564432823], [0.6127158217548835, 0.4113303409910358, 0.8214159885170091, 0.670748074907345, 0.7436549150365604, 0.1173787798871897, 0.9030707087911647, 0.9790451592774897, 0.9564206481419926, 0.8968389034242075], [0.5214660867856052, 0.8796319968927945, 0.9088995133826226, 0.8339059396916225, 0.7741486171979713, 0.7818491104646215, 0.9861906895321954, 0.11199100872610801, 0.6924054689162589, 0.9545837130566203]])))-array_x+5.48919145058311))*np.sqrt(abs(array_x*array_x-4.812132261860164))-4.127090441876516, axis=1)

np.mean(10*(1/(np.sqrt(abs(np.cumsum(np.log(abs(array_x)), axis=1)))))-3.7290611701895564, axis=1)

np.mean(np.exp(5.3746195731239546)+array_x*array_x+10*(8.3367627414613*array_x), axis=1)+np.sin(2*np.pi*np.mean(np.exp(2.761436113713441)+array_x*array_x+10*(9.65632669579241*array_x), axis=1))

np.amax(array_x+9.968163619121578/1.3586557430047073-1.937353071283948, axis=1)+10*(np.sin(2*np.pi*np.amax(array_x+6.433156251463865/1.6105031864073927-7.388186486988398, axis=1)))

np.round(np.mean(np.sin(2*np.pi*array_x+9.09448599234052+8.612601501331394)-np.cumsum(3.249034124408833-np.round(array_x), axis=1)*2.0529089387527524*np.exp(np.square(array_x/2.434917366472094)), axis=1))

np.mean(2.7011185748932607-6.218140465258095*array_x-(np.dot(array_x, np.array([[0.3483814196040781, 0.43832865139977084, 0.21048640438301136, 0.6745731676116551, 0.8411336813150019, 0.47034443836473105, 0.8173333984140699, 0.5090874202987411, 0.2561426485402827, 0.2517653297552487], [0.10532648804784339, 0.9092928928465547, 0.5580999744819413, 0.3349902192482176, 0.8604219712266915, 0.3066104496131069, 0.8009338654200168, 0.44056772360324714, 0.9081047816707393, 0.3935404204223222], [0.8774173044998501, 0.5578674824464098, 0.41860119598097834, 0.3808254813475985, 0.000408302069142219, 0.23400627810604802, 0.3289856647049695, 0.7235464110223195, 0.9873426664526062, 0.7961722986318873], [0.5626941123964323, 0.9937657372644664, 0.3741511466732138, 0.8533186926657853, 0.8710689880530098, 0.1881464884034918, 0.7049083444532074, 0.15049473898652022, 0.041082536577892625, 0.8489213766077229], [0.60255395022596, 0.8654099827543381, 0.13273232703747073, 0.09120479004378834, 0.0616703402621851, 0.9345439808652335, 0.38495011263993006, 0.5973731965953047, 0.4216674165687311, 0.5610324491979956], [0.4145470344683896, 0.7150079447360449, 0.011887564459728828, 0.3995661913273314, 0.5642953185675883, 0.7721380554696569, 0.272158584884865, 0.6608974726329753, 0.8239174644502223, 0.7846310914344737], [0.3368270787391717, 0.38489561412591555, 0.9332166746851673, 0.27310854830398357, 0.8101543856191733, 0.7938613954142982, 0.25544427960510774, 0.026348120455764512, 0.6187582944454564, 0.1881537196753571], [0.1877255111933459, 0.20900150619457525, 0.8327529226076361, 0.5849329263700591, 0.6068597840147743, 0.5985769107098349, 0.1989774603212665, 0.3704129648163199, 0.8497582102419527, 0.003920879276871347], [0.6635428330991782, 0.5205366486071166, 0.6082697122935912, 0.49946033491489983, 0.023166005745101192, 0.5529849102907478, 0.04462105744406186, 0.1814609842450554, 0.1285888611756537, 0.03556848363183984], [0.9316919957192267, 0.83260872578374, 0.8530623887185123, 0.15536396269724617, 0.9061534454260675, 0.6885121622404574, 0.24732672074036166, 0.6346933499879843, 0.7207352413745841, 0.06541107360031717]])))-array_x, axis=1)

np.mean(np.square(array_x-6.133516149310851*array_x+np.exp(1.033411462645045-(np.dot(array_x, np.array([[0.43757108654633026, 0.3173826660161795, 0.6587113329826257, 0.3874499791627891, 0.06253888588368539, 0.2095435124224967, 0.8598285834206606, 0.9468901071621746, 0.5075325285066686, 0.1124891573259541], [0.08890220297539464, 0.5735106196347061, 0.6552717593387195, 0.6071553165273205, 0.2297501147717076, 0.14588185829228828, 0.4984049301476965, 0.7310578134758142, 0.2983525543105391, 0.1811966821955785], [0.9144035951978585, 0.9251426366941482, 0.5508703916169118, 0.05150473263053401, 0.10419263989199823, 0.16096209925519833, 0.2823157465330929, 0.46081951121501785, 0.9575239807756702, 0.4584202399221513], [0.6579600314863835, 0.7230671291887435, 0.585265740467184, 0.06354881615545438, 0.9332175533178421, 0.8458486364214338, 0.5931067950109608, 0.8892948275136942, 0.30271731267662705, 0.8099172708564026], [0.7188324986377896, 0.37450135796822737, 0.25515979033449976, 0.5501186666980818, 0.17859228160284824, 0.45704172772622165, 0.9287326574403137, 0.6123010105299656, 0.8906523855283736, 0.8824289774128181], [0.3208562321610007, 0.013406221329828383, 0.20800159563375709, 0.8063940043921035, 0.3827413082382668, 0.9887278162923531, 0.8386206811381156, 0.5389291295157552, 0.5510989145009036, 0.8297956234269754], [0.018487186611012008, 0.4432479614714365, 0.34844150580482003, 0.8183369069158369, 0.5180193253026698, 0.026880387020274066, 0.7970670319839379, 0.1652898357256799, 0.6136133250994142, 0.3490990891876893], [0.9437889837100216, 0.6137373481599828, 0.5400322055099261, 0.7544119864677288, 0.8510159426102928, 0.1852187600749503, 0.37999230977604515, 0.8542696576532749, 0.6565612685358032, 0.04579021437101949], [0.7154347636726687, 0.6469770716664873, 0.7700092855646611, 0.5184397526785817, 0.08788180640822418, 0.28702528676185046, 0.7044681012037001, 0.6810267074304315, 0.5908557840994436, 0.9833763663213758], [0.22635235908860762, 0.7316311041991229, 0.7891841076931612, 0.3129272218768716, 0.22841248966282202, 0.6147866292856974, 0.6169289539708546, 0.3181499063580949, 0.347331579383084, 0.26210600500416736]]))))), axis=1)

np.mean(np.square(9.111388669652118)+10*((np.dot(array_x, np.array([[0.0414040680320964, 0.5693739813347916, 0.6397564364357592, 0.8614613221843758, 0.7578366387014035, 0.894010623833306, 0.11974457400626082, 0.3384974771431156, 0.16683468365714027, 0.1311001389791573], [0.2847317476917428, 0.3509505284962413, 0.2913531486031361, 0.20416052838625998, 0.7768286615119734, 0.060210936497739675, 0.16109801431493265, 0.7431385759701757, 0.40624724682794333, 0.31025663166565953], [0.8833828358914204, 0.7810152759675475, 0.8711716955301891, 0.18588063075398398, 0.5940527638546169, 0.4912857925786598, 0.9608549161253591, 0.9935486300875714, 0.290865631230106, 0.7154695160307128], [0.9353638980807418, 0.17269137601540896, 0.0006077495269097843, 0.7348305535830174, 0.4368035006681944, 0.7793883783991062, 0.01908762241840689, 0.06467673969041299, 0.09665457807956768, 0.4543009179074978], [0.5266163558829396, 0.3968634142774242, 0.733333146241073, 0.5742189313380356, 0.9077213290531989, 0.15699614862463118, 0.04769488567298452, 0.745245183361118, 0.8618596995344793, 0.994666341598671], [0.2406593571629171, 0.7388458129779135, 0.04757029797268797, 0.9079677602398226, 0.7677787607387165, 0.632541975631017, 0.4461232431474559, 0.4525521675791203, 0.4248287706066334, 0.36199098979208655], [0.2913785848837782, 0.36776149049811757, 0.5386279442621649, 0.20075960179912022, 0.6468633366927781, 0.9464731429647002, 0.50888148255457, 0.10114345597470065, 0.14028459864245935, 0.07224728554491644], [0.11581038036560065, 0.33105506213578706, 0.2007685819832653, 0.5832307258793593, 0.3371395084236405, 0.3234816597384823, 0.05739478545108423, 0.2035701168726377, 0.8321626700730802, 0.8404323653300863], [0.5972378573014326, 0.04920500549920337, 0.7862712648803813, 0.5568723586455521, 0.8739249275051638, 0.7672916057613735, 0.8393229615204952, 0.2317775632729424, 0.40157054469048226, 0.24383094563419017], [0.22253889141779015, 0.5772177762079237, 0.49448028904748054, 0.006041375992845821, 0.06746412380895861, 0.2222107155356281, 0.6368393387496002, 0.9409926767978265, 0.12950266576360425, 0.8511935132402529]])))-8.84884215221034), axis=1)

np.mean(np.square(array_x-1.3803036101720076*1.3061492915305721+(np.dot(array_x, np.array([[0.9186566756832363, 0.3338669148788368, 0.21963690839474914, 0.805182564914388, 0.2842086383539738, 0.42952448674754984, 0.27976664580488175, 0.9199934187734918, 0.6271833360735867, 0.21231514745430058], [0.6196764763472361, 0.5922315049495599, 0.21754654951252828, 0.24691563632344427, 0.38899987148247317, 0.09454265051128519, 0.865145825415697, 0.5663100676846392, 0.5151882198731923, 0.735389116138001], [0.939411375552211, 0.9853635465204175, 0.08971370215112962, 0.9111421829361577, 0.8879048412491238, 0.6368501488201136, 0.9435424914462501, 0.9973731584858894, 0.09914032657831073, 0.6865851469036253], [0.7551585211143251, 0.8981370723567208, 0.4266590275920963, 0.5252186561538159, 0.012172897695546081, 0.28168632434911356, 0.1448803762136489, 0.42933380047090597, 0.10241334509407196, 0.78082629879266], [0.5755677414873588, 0.994979658591277, 0.974918436556909, 0.10015682828173822, 0.39209691103113153, 0.5699610956532417, 0.8934291154539817, 0.24232408726311538, 0.7434138237201582, 0.9149576660551216], [0.054665037330955624, 0.6458624580883773, 0.2809242216434613, 0.6257469460100406, 0.8023227844978646, 0.050281657179544914, 0.0860474667483162, 0.8370882154873888, 0.7091293392011369, 0.455809380771499], [0.3305227537347185, 0.49333576069076024, 0.23449297673646252, 0.10495281512548271, 0.29536321769576745, 0.6315989785279638, 0.25516281755582204, 0.05078058995436874, 0.06741254224213689, 0.7233824492623996], [0.038725043785447055, 0.5850497406022379, 0.8951272956783412, 0.8468845787186527, 0.9349428268578167, 0.4249378087684652, 0.472570167868715, 0.3760315052926667, 0.7250744482314556, 0.816215071363735], [0.7936121253645306, 0.6952132155795071, 0.18243322186610877, 0.6568908855511173, 0.686140732512444, 0.28145519472382896, 0.524409480815571, 0.7652502563401503, 0.7060897066773579, 0.09535399200265327], [0.4845078882758417, 0.3152295358991032, 0.5164967774845394, 0.5815459113860786, 0.6923480374925054, 0.9414348404733441, 0.5947675768417424, 0.9895601468302717, 0.6887525878682257, 0.4232912063863301]])))+5.959954308545158/6.136304421637644), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(array_x-8.136686902525462*9.96679531001908+(np.dot(array_x, np.array([[0.0040068033147968585, 0.6953269019320695, 0.3581894139734636, 0.3571666147856781, 0.3751074218678303, 0.2065687559284145, 0.6936661880679675, 0.930339429150875, 0.4749926375706043, 0.14218286729508478], [0.33274111063340217, 0.9843880961102044, 0.7885973802242149, 0.631047494080204, 0.9440566403318362, 0.3122197967040463, 0.23502589715283995, 0.6073101627876047, 0.6237886268813001, 0.1916899122438841], [0.9751022808514093, 0.9763032332007028, 0.7566381824516103, 0.9636478304561378, 0.6664801604592135, 0.7465259609605336, 0.7942431477782013, 0.9941148432684769, 0.6184989274198205, 0.7097692379577003], [0.6729361005113874, 0.060377982152508336, 0.916592642855256, 0.800386504763037, 0.16123435051347212, 0.3748638263439702, 0.8757394485235819, 0.7432338423272631, 0.33817994014405495, 0.5906161976854848], [0.8949861631829831, 0.8380420180685847, 0.5838713293106933, 0.8935518437111559, 0.434434131235756, 0.8170325192234347, 0.5321252350129533, 0.03969917730926531, 0.8978656518314285, 0.06442039699103608], [0.216008357612961, 0.5215401346703941, 0.40424574100735433, 0.8735979813873702, 0.6204794418527523, 0.8774675234537269, 0.3300059067529777, 0.7696933953568986, 0.7592016024944412, 0.7818870151315627], [0.18311221154647506, 0.1563248298265687, 0.6708832031024177, 0.22251186551039392, 0.45428456886676283, 0.9102591932729048, 0.31213748026459787, 0.020151302384504466, 0.13803369732831594, 0.9417591827760143], [0.2131877881528813, 0.2307827016288322, 0.6333219297905003, 0.9338855363490342, 0.41198494916292316, 0.9014309472664055, 0.026724032765140793, 0.9223356505862794, 0.19491705310032237, 0.7932442823559633], [0.35632902668517485, 0.42112024559090333, 0.7735604771405425, 0.7633727523652721, 0.9294244544288013, 0.8877006759734084, 0.8238680669119697, 0.029704001999058427, 0.9801948977206908, 0.6797528618761113], [0.1477806710415619, 0.556297601460417, 0.4223312358793806, 0.9184752281354248, 0.001996170426693422, 0.8702653582941994, 0.38252666466172547, 0.8373976770904938, 0.6644708113519812, 0.4073454115929569]])))+7.01749471681138/2.2314478791644685), axis=1)))

np.exp(np.mean(np.sqrt(abs(array_x+9.466910699739538)), axis=1)*np.mean(np.exp(np.cos(2*np.pi*np.sqrt(abs(np.sqrt(abs(10*(2.0588864530605773-array_x))))))), axis=1))

np.mean(np.exp(np.sqrt(abs((np.dot(array_x, np.array([[0.24588901035943367, 0.5063989514764835, 0.8516070300832479, 0.18755825654456404, 0.03604945762493472, 0.9940496339205371, 0.5701035799570745, 0.8611461039195957, 0.19986018800783156, 0.9048102956560311], [0.060344148082764115, 0.39854721403812055, 0.3333096908652029, 0.648021022734555, 0.9292215715134081, 0.09949928785371431, 0.08412073790423036, 0.8330766533695568, 0.13866247864369052, 0.2857067020401095], [0.764142648057324, 0.1868894110032876, 0.8319767918351141, 0.312367028175652, 0.6841664114211352, 0.5557211616459841, 0.16224413290454454, 0.2200438428414021, 0.6661686191437269, 0.9700681792130845], [0.7438290746508804, 0.3525532151478492, 0.7872558045895901, 0.33239035748856516, 0.18385184115544217, 0.30318075840673875, 0.01489382154576191, 0.335598891755611, 0.13479312257528697, 0.1653506348969812], [0.2974439481995348, 0.11331307047980488, 0.38937209553749963, 0.9367363011797198, 0.19721980799182082, 0.11065642681882482, 0.29619498214596895, 0.4249906758789166, 0.6669212372589587, 0.8374880252014731], [0.1678022776438507, 0.5652598166548485, 0.20710708117216203, 0.07245601388728262, 0.189633456640383, 0.4932978205360431, 0.8589570589115653, 0.280149871016548, 0.5059391086070765, 0.748880678223193], [0.6594919618880921, 0.4274799253559186, 0.810972144610559, 0.3161742293855422, 0.07486124040982689, 0.6770908393163222, 0.003564556245442052, 0.7206700293922514, 0.06973934762417966, 0.9270818838213473], [0.09993642636400069, 0.7219445173786943, 0.673461915064669, 0.21625287498421786, 0.9141524108895336, 0.24912542533705084, 0.5320211478124038, 0.047601032143594124, 0.7016640842743188, 0.730385279032157], [0.7483343925715328, 0.06165224976675465, 0.5483622324206264, 0.3613936339502113, 0.8996320161176613, 0.8757193234372472, 0.18730131527477367, 0.7181426295399091, 0.059586253708317605, 0.9460494730066418], [0.02858702816193337, 0.8784430800357709, 0.1682940036346322, 0.1607865927584886, 0.23935635968822844, 0.3042879520323202, 0.44744003284080514, 0.13574136388922042, 0.7715894066887572, 0.29421707367855754]])))-9.25337807952429)))-10*(np.exp(2.6299835572222476)), axis=1)

np.mean(10*(5.051935842029822+(np.array(range(1, array_x.shape[1]+1)))*array_x*(np.array(range(1, array_x.shape[1]+1)))*array_x)-2.5482205610327453, axis=1)

np.mean(np.square(np.square(array_x-np.log(abs(4.388602005249355)))*10*(5.407411111744844+(np.array(range(1, array_x.shape[1]+1)))))+array_x*np.square(array_x*3.389040516902088), axis=1)

np.amax(np.square(array_x*5.6297480602541095)-9.329467071847953, axis=1)+np.sin(2*np.pi*np.amax(np.square(array_x*6.060362318390299)-7.566206824054333, axis=1))

np.mean(5.020834455345093*array_x*np.round(array_x)+np.cumsum(np.round(np.cos(2*np.pi*8.6731155107685*array_x)), axis=1), axis=1)

np.mean(8.544208184053694-np.square(np.sqrt(abs(6.958119946868746))+array_x)+np.round(7.56204883292356), axis=1)+10*(np.sin(2*np.pi*np.mean(2.697969314685932-np.square(np.sqrt(abs(3.9965233978540478))+array_x)+np.round(1.1932821565502016), axis=1)))

np.mean(1.6435466105693544*np.exp(4.324862383296302+array_x), axis=1)

np.mean(np.sqrt(abs(4.201814196262943))+array_x*np.round(4.630665426547795)+1.046037533070777*10*((np.dot(array_x, np.array([[0.7428052310300921, 0.7265137105787569, 0.742103560183901, 0.9950531604962793, 0.48436053240744714, 0.42881634131227087, 0.6286474784543814, 0.6616078921496122, 0.9176251475920657, 0.005249209442475311], [0.8011026040212438, 0.6795195515185486, 0.15585273208774308, 0.3239146535431848, 0.27921543326165055, 0.9980867604393985, 0.6085209620475986, 0.5860890294869473, 0.7343403363408569, 0.1273393934188417], [0.2511767388178182, 0.1425182841960151, 0.5536862624904163, 0.47758849990109153, 0.5498039363092552, 0.1831395922292769, 0.30005518880861115, 0.7411850391183358, 0.09927176550880101, 0.45469599800996796], [0.19918301917664827, 0.05184472464483103, 0.17251531551072596, 0.7476719972298032, 0.44079351747260664, 0.860933931194458, 0.5652254207195325, 0.9562505542345363, 0.15779341020954407, 0.53808423897923], [0.000831797889583985, 0.7063676348468709, 0.9948708659607888, 0.9192567554821515, 0.9395391327263894, 0.5735752695405364, 0.8929203360491753, 0.4152423850411143, 0.11819898791933514, 0.6057243654222857], [0.30811372417913907, 0.3311206298965672, 0.5245265487355172, 0.5151941391027552, 0.810427131026902, 0.7223359660623374, 0.8890149872382904, 0.24751686776520943, 0.12200680631888772, 0.08578420940344023], [0.7138063754102374, 0.37973008712838574, 0.4515417573234012, 0.5297416559073354, 0.4888504374871362, 0.9834876655090316, 0.8889447944128208, 0.4486864002192319, 0.3699363372078174, 0.42221240807084937], [0.7773453841734531, 0.021126271854225998, 0.9023016841287268, 0.1475681510278658, 0.597628024568332, 0.17681921269451106, 0.5726166652255226, 0.7233543795159816, 0.5087915746861997, 0.184709441890342], [0.43014420160341105, 0.9790835080985885, 0.7603400576021306, 0.784357758920654, 0.27620252918780097, 0.24927507982502795, 0.9507780195178134, 0.9297794273997428, 0.13893511743170406, 0.04540444514377384], [0.6728866785650137, 0.5509159029844168, 0.4180506245087786, 0.2675787831221135, 0.5626944711144081, 0.38482498975630075, 0.8856345165171732, 0.8874858348641724, 0.8452077894174729, 0.01793543711318757]])))+7.0074526734808105+(np.array(range(1, array_x.shape[1]+1)))), axis=1)

np.mean(9.702633254139199*np.exp(array_x)+array_x+1.9147014287841762, axis=1)

np.mean(9.78594735506172*5.464576285760431+array_x*-(np.square(np.log(abs(6.121125577431189)))), axis=1)+10*(np.sin(2*np.pi*np.mean(3.8537636261941257*7.097406756974075+array_x*-(np.square(np.log(abs(5.741222704651921)))), axis=1)))

np.mean(1.7584811651111556-np.exp(array_x)*2.4019629501924142, axis=1)+10*(np.sin(2*np.pi*np.mean(7.790864501726169-np.exp(array_x)*2.58374876393546, axis=1)))

np.mean(abs(np.sin(2*np.pi*np.square(1.9114743091898458)))*np.cos(2*np.pi*2.8129551631478567*array_x)+np.exp(1.9105852590864085)+array_x*9.327059820773776+np.square(array_x), axis=1)+np.sin(2*np.pi*np.mean(abs(np.sin(2*np.pi*np.square(2.2954032143100864)))*np.cos(2*np.pi*5.884538660203718*array_x)+np.exp(9.504186629865847)+array_x*7.429137981924501+np.square(array_x), axis=1))

np.amax(10*(abs(2.7608304480957004+array_x*5.11218176854922/1.9351784735437652-array_x)), axis=1)+np.sin(2*np.pi*np.amax(10*(abs(8.278795925832672+array_x*2.33444734600673/6.99040248200507-array_x)), axis=1))

np.sum(np.cos(2*np.pi*np.log(abs(np.sin(2*np.pi*np.sqrt(abs(8.237795029099651+array_x)))))), axis=1)*4.860484744714048

np.mean(10*(np.sqrt(abs(np.round(array_x*6.045305621414574))))+8.093035817080565, axis=1)

np.mean(-(np.square(10*(array_x))+array_x-3.320523905143367), axis=1)