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np.round(np.sum(np.round(array_x)-5.738506254068621, axis=1)-np.mean(abs(array_x*array_x)+3.0680818709938773-array_x, axis=1)) |
np.mean(np.exp(np.exp(np.sin(2*np.pi*array_x)))-1.4380159645977906, axis=1) |
np.mean(8.508540172796494*10*((np.dot(array_x, np.array([[0.1819406674170686, 0.9127116071849223, 0.9569712646146591, 0.5001385184784773, 0.273301852152504, 0.8300001591003277, 0.3862863105183195, 0.14053622640362606, 0.9486641318052779, 0.5697744510880665], [0.21913615222664018, 0.9501915721095062, 0.730904247619455, 0.28487189990089745, 0.5651743592708128, 0.37619811261781344, 0.6498237128617153, 0.7483040824696472, 0.883054209005523, 0.2896678149755253], [0.7791817048878742, 0.8315741699132836, 0.7578387885602531, 0.28603116730618605, 0.8098675394478198, 0.5318094030537031, 0.03032942911944514, 0.8348726135510615, 0.05761479959741378, 0.4158076866580914], [0.020886836277188014, 0.9835340519137838, 0.11605561213515247, 0.18122149687598477, 0.7529595857210221, 0.23258084022093173, 0.8593797689956246, 0.4150366470650215, 0.2832391897268326, 0.2604797223010663], [0.3946401344067638, 0.45352676658055224, 0.6098646242343502, 0.41776701839887265, 0.7390700490284874, 0.8534383852505116, 0.7512156490526787, 0.32301808782204156, 0.30001757169390575, 0.0590422802018612], [0.7527277387140158, 0.34089275865509105, 0.9273738531868657, 0.5401200755495613, 0.6512869083874101, 0.026216791909286385, 0.1364072173873998, 0.5302099719306128, 0.011105465411211046, 0.007354273202144879], [0.4657423796872401, 0.9827972548901707, 0.8174282564243747, 0.17683552167233108, 0.44630474304861356, 0.5583304417522881, 0.3186981146896837, 0.5982349212058587, 0.8068632787700726, 0.7065733130058851], [0.9274034606474443, 0.28559445220308344, 0.11628300674983916, 0.5251923989445415, 0.5085700980076063, 0.8791074623312151, 0.1835470227580589, 0.5027266069940652, 0.6869301850800155, 0.18430269882314954], [0.2748408931339432, 0.11722117851281033, 0.736660121431402, 0.7156358701681516, 0.8197674685469228, 0.37323799453590634, 0.837100994581382, 0.31292232958236754, 0.6889303487220273, 0.21580766575632693], [0.11525867270043888, 0.29796091939628255, 0.8634576661161185, 0.8742099386158623, 0.9719955574435943, 0.5923253867379038, 0.01491409529886678, 0.681220708084918, 0.8283084033188086, 0.36140649876139697]])))+1.4042134285770425+np.sqrt(abs(np.sin(2*np.pi*array_x)))), axis=1) |
np.mean(np.exp(4.544081321122368+np.round(array_x)+np.sin(2*np.pi*7.712552846616512)), axis=1) |
np.mean(np.cos(2*np.pi*np.square(np.cos(2*np.pi*array_x)-array_x)*np.log(abs(7.225458457319316+array_x))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*np.square(np.cos(2*np.pi*array_x)-array_x)*np.log(abs(8.874125063953443+array_x))), axis=1))) |
np.mean(np.square(array_x)*10*(9.768071619776071)/5.223656651239518-np.sqrt(abs(array_x-9.198775730440566)), axis=1) |
np.mean(np.square(np.cos(2*np.pi*array_x-6.98617456612085)+1.5673077037336216)/np.cos(2*np.pi*6.827605655028501+array_x-5.605084765304697/5.140366455065347), axis=1) |
np.mean(np.exp(3.082631565354279+array_x*2.986105668757891)/np.square(3.276742929289316), axis=1) |
np.square(np.square(np.sin(2*np.pi*np.sqrt(abs(10*(np.square(np.sum(np.sqrt(abs(array_x*6.683578130136637)), axis=1)))))))-9.671442733764136) |
np.mean(6.659448968046226+np.square(2.465903458709407-array_x+np.sqrt(abs(7.263513762185721)))*np.square(np.sin(2*np.pi*7.129395875489545)*array_x)-1.3683818621271737-(np.dot(array_x, np.array([[0.021217345782943875, 0.47147910243879443, 0.5632415431249971, 0.07572669511468633, 0.5061983694162961, 0.15605951743135582, 0.3856408673897316, 0.1324246543095503, 0.6886821414561954, 0.06261198442978255], [0.14824977672118056, 0.1624577328532789, 0.5098716281802792, 0.5678601760163371, 0.2939055693179913, 0.7352830904584856, 0.5052883049604373, 0.6236453373391212, 0.11788522302047666, 0.6891103123373827], [0.7877779996400462, 0.45237464132979377, 0.7695024386731677, 0.09639295627452005, 0.13571229109883165, 0.06192246888730446, 0.4136868753565879, 0.44532748415773493, 0.7401379981868883, 0.9133088917886021], [0.5151933510224902, 0.7192468284582199, 0.1948978215454824, 0.22174100471365765, 0.40189561234596294, 0.38507576823922285, 0.23237261845543367, 0.4601311657596414, 0.680181332669727, 0.21848337507077664], [0.3310569803970632, 0.6217113890548438, 0.9153492488817082, 0.8785428980341405, 0.5648920162987728, 0.001471850900872318, 0.6660661286676632, 0.29744933110772864, 0.8132984647534385, 0.11239250254132305], [0.8526054792974543, 0.29379518904042357, 0.5299171883773106, 0.7987975359731581, 0.8823915160489822, 0.9551200985033237, 0.871002988630777, 0.053606606621746766, 0.0015168612348591681, 0.08878407225449825], [0.6645973304119138, 0.5149895446516257, 0.852599215460467, 0.440706710168418, 0.09327543092076673, 0.9580335025940393, 0.12064676990771916, 0.9116956082469219, 0.6353963115293405, 0.40369726116368376], [0.8581987724167395, 0.2221932366269781, 0.2834385680466839, 0.5392909388581838, 0.9123710326321066, 0.8298170952282903, 0.3697222297044015, 0.9246248272634987, 0.182330033365808, 0.6483236072622227], [0.6552801820184838, 0.35619168387219036, 0.4125689562259608, 0.3505409362033083, 0.5264840456486125, 0.12776788560109298, 0.5902782420378335, 0.8990248129613951, 0.25279728876455254, 0.6677533376258429], [0.8290073016585445, 0.697324579207155, 0.5594676457271982, 0.690395264285077, 0.9703827633300672, 0.2637648286023506, 0.8719976783446137, 0.9825750751199028, 0.8581827829713186, 0.9553614596104727]])))-3.697031335638155, axis=1)+10*(np.sin(2*np.pi*np.mean(2.55563658122332+np.square(7.139115158441033-array_x+np.sqrt(abs(5.354996643839179)))*np.square(np.sin(2*np.pi*7.436209588439725)*array_x)-3.0524614389857483-(np.dot(array_x, np.array([[0.45177655622327095, 0.6540855516746784, 0.7702075111711975, 0.12529997763820033, 0.5988836605169348, 0.6496471597550645, 0.8644612950343713, 0.26898151540725324, 0.7753906944399254, 0.3109613492511002], [0.5595211765405644, 0.6939985268509508, 0.1729376278779039, 0.8733413614469063, 0.48686830968581585, 0.6042366987636184, 0.20861415950084894, 0.03455268397028266, 0.8217090685855254, 0.7860880456427898], [0.7773627172391421, 0.3660437214784541, 0.7507884242370764, 0.7587922420846581, 0.6395874259597399, 0.6510097883062137, 0.9476608729187126, 0.621181342124106, 0.04761526387859405, 0.42141074473956264], [0.8589166203640972, 0.8188933142928568, 0.5303078921983619, 0.573146223559166, 0.5860007905338673, 0.7509532296895228, 0.9753282547366365, 0.23136544464569042, 0.35126731972637004, 0.1449014435102921], [0.21270332228237265, 0.3734239207270523, 0.8463767931567624, 0.899790197723318, 0.38491236958646813, 0.019111784765924678, 0.39830374003189073, 0.7089189745018777, 0.4358528682458931, 0.5365800542532253], [0.186523240250973, 0.9080209996289302, 0.7695607180997999, 0.11188279535463597, 0.26788186334919883, 0.041268106542814875, 0.02599135057272861, 0.34944841154873796, 0.3312723613928521, 0.4637595711361985], [0.02147460295121517, 0.08081725297344278, 0.035549503887087996, 0.280047365330925, 0.31704810398450955, 0.24750794809998733, 0.7327919293477834, 0.3636535805660791, 0.6601199656723226, 0.9187227670584662], [0.034108497843037044, 0.7846327532784123, 0.270838817698119, 0.9493158369898389, 0.830516883149804, 0.22225215613345395, 0.26230591260704983, 0.634601163358717, 0.6262173410600981, 0.6206309273122317], [0.8615152390240102, 0.33765852053643974, 0.001287464942395733, 0.3049231258836691, 0.35648180281207054, 0.8908088347977559, 0.4737848109863617, 0.7668501805733782, 0.9483969998997668, 0.19148985788933148], [0.789100770789175, 0.9061877913368074, 0.3686201548759559, 0.48431984684089824, 0.5253060270627521, 0.3201166842187848, 0.1447712190835233, 0.8216244448608816, 0.8401491331697497, 0.8761870293101016]])))-7.608582885123635, axis=1))) |
np.sum(np.cos(2*np.pi*np.square(np.square(np.cos(2*np.pi*np.sqrt(abs(2.3644142173425338))))*np.square(array_x/8.009775499676007+np.exp(8.044587951047294)*array_x))), axis=1) |
abs(np.sum(array_x+10*(2.7898998442673117), axis=1)*np.round(3.5926700297257366)) |
np.mean(1/(np.cos(2*np.pi*9.981489899760927+np.cos(2*np.pi*array_x)+3.8035415458797854)), axis=1) |
np.mean(10*(np.square(array_x-6.12887619062174-array_x+5.443440147035158*np.sin(2*np.pi*array_x)*np.round((np.array(range(1, array_x.shape[1]+1)))))/np.square(6.532866883375319)), axis=1)+np.sin(2*np.pi*np.mean(10*(np.square(array_x-1.6990762790882188-array_x+4.453927951014141*np.sin(2*np.pi*array_x)*np.round((np.array(range(1, array_x.shape[1]+1)))))/np.square(7.247657661805121)), axis=1)) |
np.mean(np.exp(7.03045985172469+(np.dot(array_x, np.array([[0.8602997492741224, 0.7699651420321263, 0.040697533493946114, 0.9963312056616261, 0.9485683919842378, 0.46618682074169093, 0.698518309707324, 0.9886364162027778, 0.33539261203995296, 0.7841255565982268], [0.47599244839877297, 0.6182163032197816, 0.7159806265170063, 0.9492887987999671, 0.21065419784614847, 0.350913527210035, 0.08563283979393, 0.5712300353067553, 0.8845859576573162, 0.14109678480916266], [0.8912738472817668, 0.23474509403975186, 0.9365005794383816, 0.01054884539427181, 0.9103775761653168, 0.5098239288007022, 0.4842540704506543, 0.5584040330577821, 0.3283312629882532, 0.7680197270250021], [0.3996475169411693, 0.2625210625715122, 0.34934136046437103, 0.558968584858877, 0.2817264084723472, 0.2967429117441702, 0.7350836576069892, 0.5796743300883509, 0.6741649534471468, 0.24264224535819745], [0.17399171760901044, 0.773096357850137, 0.28950270197603356, 0.06758136883123922, 0.6224149962065908, 0.4604454665381973, 0.04821619980544767, 0.24396510801497706, 0.6740591199156262, 0.8030215725193511], [0.6728754910974047, 0.8172937721533609, 0.4322377752023461, 0.6155172317504838, 0.5399989106415374, 0.8196810727776873, 0.26273281241541524, 0.47748779302519895, 0.10314278217460437, 0.1865390505213964], [0.6811006078150582, 0.8714328538774802, 0.4521384994020007, 0.35806457799164726, 0.3746782098724388, 0.47527733477393797, 0.5957955685924697, 0.6040548497882213, 0.9283884609820073, 0.4133036034435401], [0.2320189716130464, 0.6827231223135255, 0.7251542226048003, 0.8085389482338868, 0.3528039458286426, 0.10665237631388091, 0.9728689514863345, 0.38537295789905335, 0.049305439094725734, 0.872411680441254], [0.8303063568480068, 0.9668983465961765, 0.290501962936468, 0.5533170424888845, 0.43416518954405137, 0.787150260145743, 0.9046700461969923, 0.0002997112146183589, 0.8752855809581465, 0.48021268550443863], [0.37113861747639454, 0.5276757737162643, 0.9927100338995576, 0.9578515089486106, 0.6807939107281183, 0.2822352480084098, 0.16730161972397883, 0.3757792221752959, 0.6892778572189159, 0.004732750009970865]])))+2.901286631307536), axis=1) |
np.mean(np.cumsum(np.sqrt(abs(3.0431243832147676*np.sin(2*np.pi*array_x)-5.191239949061048)), axis=1), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cumsum(np.sqrt(abs(1.045375632159778*np.sin(2*np.pi*array_x)-4.51974401361772)), axis=1), axis=1))) |
np.mean(np.sqrt(abs(6.189760376658481*array_x))+9.695752329446467*np.cos(2*np.pi*6.803741139042334*array_x)/2.064384325936036, axis=1) |
np.mean(10*(np.sqrt(abs(3.699563801039976*array_x))+3.904164521593894)-5.307885454377367-np.square(array_x)*np.cos(2*np.pi*np.log(abs(5.6356212790406195))), axis=1) |
np.mean(np.square(np.exp(4.287586029534185)-np.cos(2*np.pi*8.029177771441471-array_x+np.round(7.9642357419792225))), axis=1) |
np.mean(10*(np.square((np.dot(array_x, np.array([[0.6176609176392275, 0.4721877588496137, 0.30179112929575747, 0.7094905120802433, 0.23858248704768892, 0.00999193140373078, 0.30249888948028525, 0.06727069060365065, 0.5093086295232628, 0.9122872773818739], [0.7780965895334725, 0.047521312132960625, 0.6650712591201828, 0.5019381443457933, 0.8730202903027324, 0.09242923003366244, 0.23473513405810265, 0.7045796884629747, 0.7861871230285433, 0.040487984433374646], [0.039845461284056194, 0.9161178020171716, 0.11311603091857858, 0.5235232285079172, 0.7107419829951156, 0.14370910904202439, 0.6054014580261068, 0.0799471903318636, 0.946842402903268, 0.7556159943732048], [0.8533713892668302, 0.5024711440179023, 0.17386612749031782, 0.7889694085486604, 0.5439478570554946, 0.754621036014484, 0.6805434117697672, 0.6105288684873105, 0.878526337703324, 0.2646629100968001], [0.6268126735523039, 0.525391671537039, 0.6630305310856618, 0.5566896477141883, 0.18990922872409888, 0.6068864842567335, 0.3459558501958827, 0.7807335002129242, 0.9070260154937566, 0.09834271155438434], [0.1262038254757939, 0.5053505310763701, 0.0732479212709316, 0.9150342579804776, 0.04122332705991116, 0.21623374115929284, 0.006888519938189619, 0.5764777202207979, 0.6734435032208662, 0.7300668178062165], [0.8472934164564991, 0.6112847467423638, 0.02193315819982211, 0.6143578658283355, 0.5867371313973857, 0.04815300434939307, 0.3271799770731051, 0.45057382684195824, 0.1875278473724451, 0.32711775018393363], [0.1916644196911218, 0.31360828728307255, 0.5115132543288511, 0.4515295328281551, 0.016259866386613253, 0.8625554621757449, 0.7497296221700785, 0.8338665536825791, 0.41863184908418083, 0.12901681975677914], [0.7503476566423641, 0.7191697556503442, 0.3061808276577802, 0.09418068649925493, 0.8489643813418324, 0.9484940103434534, 0.14077544224623162, 0.8211065401712235, 0.21851960097072587, 0.5957959640917171], [0.7000940514208203, 0.9463509891829489, 0.7537524859925694, 0.7609477600755747, 0.27350733518242176, 0.46042223597293264, 0.6760566493155106, 0.15124253034947888, 0.17701319713210484, 0.35674417485855603]])))))+np.cos(2*np.pi*array_x-array_x)-6.092238361862407/4.163676489087978+array_x, axis=1) |
np.mean(np.square(6.902662603729492)-np.sqrt(abs(1/((np.array(range(1, array_x.shape[1]+1))))))+7.298823385617082-array_x/6.002035227320045-10*(np.square(array_x))+np.square(9.276631122617161), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(7.605113483618027)-np.sqrt(abs(1/((np.array(range(1, array_x.shape[1]+1))))))+5.806645657579975-array_x/5.452412543236006-10*(np.square(array_x))+np.square(5.561719102470855), axis=1))) |
np.mean(np.log(abs(6.542978727082021+4.863025391730911*4.703017633014645-array_x))+1.7488978039406458+2.7869746189276476*1.4280105428922714+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(5.81633549705637+9.58487126079408*9.407320935053297-array_x))+3.528348786459736+3.506496782614136*9.152143141646468+array_x, axis=1))) |
np.mean(1/(-(np.sin(2*np.pi*8.313608433699834/8.85090564798092-np.exp(array_x)))), axis=1)+np.sin(2*np.pi*np.mean(1/(-(np.sin(2*np.pi*3.091320903009414/9.221517873757728-np.exp(array_x)))), axis=1)) |
np.mean(3.0467570298738105*3.487163774450831+10*(array_x)+1.9711849617012034*6.563379253804512-np.square(2.9591218115393545)-(np.dot(array_x, np.array([[0.173858236244529, 0.7771386713575715, 0.3937732785915179, 0.27614482778635874, 0.31909656089052707, 0.6023084598684482, 0.8666123020244719, 0.4673106873518972, 0.9064083678446142, 0.19530317359022387], [0.22071992084273384, 0.6942310990463145, 0.4217020327836506, 0.009082828426048017, 0.3054125418027567, 0.10184290282872899, 0.4563399649287233, 0.20480626845976446, 0.4017421321261835, 0.4681421747501068], [0.10937077127169514, 0.4319728559748073, 0.9792537984605127, 0.3354108375038033, 0.5323992735698836, 0.4221515963027228, 0.4455849302349676, 0.21954418294737266, 0.09868593301676476, 0.8600591659835027], [0.3346442183057904, 0.08914625511816432, 0.8341511253605746, 0.5866051324676661, 0.2595650613429832, 0.16894357769099932, 0.4174493781727544, 0.7244456259187428, 0.056396287156127434, 0.595311400784167], [0.7647105812130519, 0.3399323758805948, 0.4692661238503203, 0.5719662355508559, 0.9952280946622675, 0.8721170546976692, 0.37387203499460553, 0.005971302339528917, 0.6777669776836915, 0.08257082711303432], [0.9081592634323247, 0.16342651860099755, 0.08610516006236935, 0.8524505972423689, 0.8579354760456916, 0.8165307511044626, 0.6626857718033118, 0.6284444882144682, 0.0191031382996546, 0.7823028090320543], [0.7525910454468026, 0.5716785709767924, 0.6945847876914889, 0.530734264969459, 0.9676761321355579, 0.35460372733769796, 0.9494899607494609, 0.9363290491436824, 0.7472805632037262, 0.06906066407767875], [0.7376122877281053, 0.3023363297532944, 0.11289379868139426, 0.2761502472735633, 0.4985555242700155, 0.4678210582371034, 0.4275378311948077, 0.06125450285399758, 0.762605790878531, 0.6919278612742212], [0.9471142789149775, 0.34864195405681475, 0.9151392043475042, 0.20495730119456312, 0.6631523945450698, 0.12160648639635552, 0.32591823846366863, 0.7286650150551318, 0.20041745555127966, 0.16207018568855558], [0.5015623505740361, 0.9809280561132465, 0.3936574621789636, 0.34018729286388094, 0.9692973094060744, 0.6449320261612771, 0.6944717389293662, 0.6158232497450538, 0.12315803959687854, 0.3914364111951313]])))*5.113881360345141, axis=1) |
np.square(np.mean(np.exp(7.887002690590492)+array_x+np.sin(2*np.pi*array_x)-6.411662462240802, axis=1)) |
np.mean(np.round(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1))))))-5.430752538809413*7.163175369076627*array_x, axis=1) |
np.mean(np.sqrt(abs(2.07544806226438))*(np.array(range(1, array_x.shape[1]+1)))*array_x+1.1688307260726498+(np.array(range(1, array_x.shape[1]+1)))*array_x*6.056245246531458, axis=1) |
np.mean(np.cumsum(np.sqrt(abs((np.dot(array_x, np.array([[0.9574869239112923, 0.7766333105132065, 0.4893120559248515, 0.35992762537056966, 0.8993828819855644, 0.8430457906059058, 0.535518675773129, 0.7581870066703428, 0.7169155639839591, 0.12694627792200852], [0.34259080852028145, 0.6851107505608397, 0.21980281560903914, 0.16242674849122984, 0.6182300260057876, 0.8493071249647542, 0.6682633941426308, 0.297778957139041, 0.7816366042203153, 0.8984238112424807], [0.23428925159342406, 0.08324802026958367, 0.3322806379474563, 0.6203024456232871, 0.3602938443090212, 0.773526232418374, 0.7215646329661026, 0.37075586312967435, 0.22136522873695663, 0.4902362407080737], [0.07767774323830745, 0.28663292723560974, 0.8671761795216935, 0.3949978283499638, 0.6260251425660337, 0.6907921894137539, 0.7778659220817624, 0.5249350313149994, 0.019978967528596292, 0.120897270458871], [0.2055979940047833, 0.20925861517363886, 0.08109902081340414, 0.48012481550247155, 0.2223958485360581, 0.7955660629984516, 0.4237383976242185, 0.9618393525055067, 0.9816394041835383, 0.9056010518012708], [0.8489751326409871, 0.5008552550325046, 0.4391396302030648, 0.06470436646690225, 0.8778045871613251, 0.8501483274446794, 0.9802895203761984, 0.16080774681594845, 0.9495778194344678, 0.7824948085459494], [0.10998367164877676, 0.5202321297302336, 0.7076309036957248, 0.03701628076112151, 0.6676228526960294, 0.5799385312558434, 0.03039093022534234, 0.269880347914333, 0.5337961069545096, 0.21601845377206397], [0.6803041178807432, 0.6762896207377305, 0.7486031807482617, 0.0669330538340106, 0.5606356715303217, 0.9673942529502572, 0.12327941621174077, 0.33919354708779836, 0.3348267091248759, 0.7074788866244278], [0.5169505640296684, 0.8173807956103889, 0.7074171102645508, 0.9910317151343307, 0.17949485156452538, 0.6780257221314419, 0.2006111674488238, 0.01961273203910363, 0.674556369101423, 0.3930245243552125], [0.6732464794357288, 0.0347394431110819, 0.9790618254160566, 0.7409760810865555, 0.459915234252799, 0.05367509522291025, 0.24483951255944747, 0.5230974248191381, 0.5727937272370177, 0.8704324225268926]])))-(np.dot(array_x, np.array([[0.10605524656169274, 0.914698119888526, 0.9495771891029914, 0.8582869241747774, 0.31721988455704553, 0.2909366572172407, 0.6614845898720284, 0.042161441905461006, 0.44243799789279503, 0.16022651145561495], [0.9680364503493146, 0.460139482184159, 0.2843672951046694, 0.347129031676755, 0.2619125238795119, 0.9416450774026199, 0.9098481082382675, 0.4279503079721817, 0.9160882528155712, 0.08506349555459636], [0.376194295631733, 0.9815444333630151, 0.661185869658407, 0.7270913434434808, 0.7051622192306327, 0.4804063327209849, 0.5224841739240467, 0.8770043522639431, 0.3614335956665464, 0.09088690617538908], [0.6717278523058015, 0.2069174217792128, 0.8149137925989324, 0.7534627109025533, 0.4749660237774731, 0.23943684688545996, 0.1635534941030493, 0.4859255304694826, 0.8987525469037682, 0.010783712173300875], [0.3880407771172695, 0.80949580471264, 0.657870735677521, 0.880894546332336, 0.1969934604314334, 0.8954498581285275, 0.5609234733980286, 0.9707240717137027, 0.20653131029485472, 0.39530798184341476], [0.33651505296599815, 0.6146702761453107, 0.5992293501353708, 0.040445151565962734, 0.4660455614251202, 0.2702623212863877, 0.9840670966885837, 0.6400912294448105, 0.12378106471988226, 0.9863164984157872], [0.5093156033513923, 0.2765440646952543, 0.007647261145295681, 0.6560161166117319, 0.2800929682736901, 0.1413958677246533, 0.5556573390336307, 0.5560878869958998, 0.13668507527066143, 0.7932094320456519], [0.8648968088032778, 0.1473241085631376, 0.021244041407992298, 0.3855112152072817, 0.571762423030557, 0.8615196428482315, 0.8750067575931494, 0.5317732130557534, 0.21303429930462903, 0.9141207024641952], [0.8351209849027357, 0.8813860351354165, 0.5610711041921993, 0.41964759528420703, 0.11058915042613482, 0.5712394246130663, 0.44739265904600056, 0.7007367612710905, 0.3730927755793728, 0.15330675547974515], [0.8527650782040125, 0.8792343919993654, 0.007092236636124349, 0.27938423543854596, 0.11329768832514453, 0.25934892026524536, 0.3893619000994293, 0.521353193467124, 0.5740153910445918, 0.7170407183819097]])))*5.722334143842693-4.846891229910595-9.742804261707745)), axis=1), axis=1) |
10*(np.sum(np.sqrt(abs(np.round(array_x*7.510056301017167+2.0066752681835256)))-array_x, axis=1))+10*(np.sin(2*np.pi*10*(np.sum(np.sqrt(abs(np.round(array_x*2.6310657761282386+3.3517946951580107)))-array_x, axis=1)))) |
np.amax(6.293941191015361-array_x*7.7328138018698365-8.016928679928231-np.sqrt(abs(array_x))*4.804120443561385*(np.array(range(1, array_x.shape[1]+1)))-abs(9.939345278421259*array_x/4.175183268797005), axis=1) |
np.mean(np.round(array_x)*8.011175074659123+(np.array(range(1, array_x.shape[1]+1)))/7.277595000148159+np.sin(2*np.pi*array_x+(np.array(range(1, array_x.shape[1]+1))))*10*(5.255734169464328+array_x)-abs(np.square(4.705712222037043)), axis=1)+np.sin(2*np.pi*np.mean(np.round(array_x)*4.728893381217226+(np.array(range(1, array_x.shape[1]+1)))/5.88676194847027+np.sin(2*np.pi*array_x+(np.array(range(1, array_x.shape[1]+1))))*10*(9.14141388739854+array_x)-abs(np.square(8.785529696744337)), axis=1)) |
np.mean(abs(1.9895909436706718*8.262526822037838-(np.dot(array_x, np.array([[0.1397549592919154, 0.21788314632447092, 0.5904281878494633, 0.7495661078556397, 0.21253384719518287, 0.03999788374147273, 0.3361345389689032, 0.7682658794932773, 0.9465572411959383, 0.5332790612105404], [0.4271121341135914, 0.07446090153483498, 0.02991176746123103, 0.00024019314295065097, 0.8483183567386812, 0.38392380528802394, 0.4155534192607383, 0.8357688291734842, 0.06808648879916657, 0.6396329180656675], [0.2098013908834463, 0.1830097316873618, 0.09145644582951284, 0.49868877692187497, 0.603627129493504, 0.25737788389483973, 0.6189004280177677, 0.8109747432584979, 0.2925719989672477, 0.2674675295502882], [0.3555402890599517, 0.927447924735946, 0.2491550690260651, 0.6815154113604776, 0.050476922301079474, 0.27829726570641966, 0.7994135201616602, 0.04209470171389229, 0.639186918087782, 0.7558073852522575], [0.24443087607517255, 0.3267071995307701, 0.8317987727712266, 0.37927159047974246, 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0.40067340184547817, 0.9488734538877596, 0.5753793078310511, 0.8993007421445742, 0.08901057220774511, 0.28017491142209694, 0.5316945826843114, 0.6601114727844754]])))*8.223194527097714-1.1020138853474455)+(np.dot(array_x, np.array([[0.8778316124199393, 0.1194508429645812, 0.3906241087661533, 0.39767262345398713, 0.8817147110012872, 0.3688036752128756, 0.3352816246268645, 0.14952913947339286, 0.8273239900245197, 0.5506835453699895], [0.9592708947679228, 0.9484926177302114, 0.6372881484546317, 0.9059681356360422, 0.9283560296854015, 0.5974080987617171, 0.07460597272332381, 0.2124322474934549, 0.7471944006201267, 0.5878724730334411], [0.42459705096111744, 0.6825767066675887, 0.21782367483090803, 0.2217219464940392, 0.3909872062491627, 0.8707920788938875, 0.19522019682700398, 0.6961376504244425, 0.09966032499146726, 0.36438278775836663], [0.3537411719065511, 0.5561716975833289, 0.4562766209784247, 0.8720719488491263, 0.0744967058428112, 0.8533560727400319, 0.9030418383689658, 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np.sum(9.78332218205935*(np.dot(array_x, np.array([[0.7403400125115223, 0.563976691268031, 0.09465332736394116, 0.5745976220054608, 0.7826822563324739, 0.19145482695984328, 0.8597289826730844, 0.5152231505319251, 0.03263410968881686, 0.7181764976994661], [0.5396274049477149, 0.6231746549304137, 0.0791146773677206, 0.18214581879269087, 0.09377634952105096, 0.16597997219986116, 0.8532567876949329, 0.4942644188929861, 0.5306394733779224, 0.20368840658223009], [0.1551098626269749, 0.6081224055274751, 0.6339525457136396, 0.173095878093402, 0.9024396233135908, 0.02867234521479789, 0.20519086168356204, 0.38111345363736315, 0.20431756701005144, 0.01924326835242307], [0.07443695564003883, 0.7014932316147355, 0.7508221135718499, 0.04803015423538237, 0.8363665830229626, 0.2944712832512103, 0.8426103158254897, 0.10911277938647657, 0.5530493708938705, 0.6005541092341012], [0.1394860930658629, 0.7934739912186511, 0.7824505682324523, 0.6053964349615661, 0.4373644830087642, 0.7967118753564368, 0.3329401590473763, 0.33460559790129696, 0.9542728637602933, 0.085295891838434], [0.5689758752632567, 0.45779729493129784, 0.06024304441549966, 0.5698151063421742, 0.5694121199983648, 0.23829389429853998, 0.21700267253532501, 0.5173651620181744, 0.403335947260525, 0.48878113615443264], [0.3916653302192834, 0.0714288895381523, 0.8212046662399964, 0.37015695551609673, 0.9312170960453838, 0.6015305996168735, 0.2924279692317757, 0.2765976707133725, 0.16536138836432834, 0.8653250788993454], [0.10946091727559204, 0.050754708439473495, 0.5264185946417237, 0.6317430570130776, 0.7962202228717793, 0.48217532160113263, 0.548432058900343, 0.6895331963625536, 0.09045556735126592, 0.17139864315020636], [0.5365894700323667, 0.17797727581858414, 0.7644581230930051, 0.8894818718775106, 0.924774568404918, 0.5131679414868406, 0.6281526175208879, 0.017250623770409312, 0.8691899260987777, 0.8740602219817298], [0.7634162763393881, 0.6361710673499119, 0.3892795627211578, 0.8903705467509073, 0.47604760551533853, 0.9483766759717945, 0.7014700108033617, 0.3275402834258856, 0.3986905777884918, 0.11012015242611606]])))-3.1269888953197444+np.square(2.4273483863065226-array_x)*3.6597053827393853, axis=1) |
np.mean(np.cumsum(3.6782230454191196-np.sqrt(abs(1.926604751064661))*10*(array_x), axis=1), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cumsum(5.3490247289836255-np.sqrt(abs(2.9841678942198278))*10*(array_x), axis=1), axis=1))) |
np.mean(1/((np.dot(array_x, np.array([[0.1289417094996239, 0.7445833012650602, 0.4920824405613563, 0.3426124153093367, 0.6267280592194594, 0.31493930499912026, 0.8939404348944499, 0.7611246860786345, 0.6165302005130971, 0.04468944957139498], [0.06277591608036293, 0.21701173013109476, 0.8729231347871502, 0.5624376288478817, 0.39844770456006384, 0.22036770970307495, 0.8502962124753948, 0.571599200133111, 0.4357926096903234, 0.5120723037448154], [0.7029311676037269, 0.6233633173658322, 0.23914263369682132, 0.7711798417650881, 0.21400348404438774, 0.36881153712548953, 0.957299943383425, 0.9058865003989691, 0.2890323969876958, 0.7386598021125224], [0.3931800882469213, 0.04387739507793409, 0.7987147288363298, 0.39490092796908416, 0.5395520176652036, 0.8447428568449429, 0.33111458601775434, 0.8077600188060376, 0.9328864042359352, 0.18944856399883958], [0.7013474999616264, 0.6727184995337264, 0.5935730445111547, 0.004650957010523649, 0.6282086097349951, 0.009189302884362371, 0.6876368081686944, 0.7165392093737741, 0.26756328862407186, 0.3831262277067493], [0.6969709443113205, 0.37424518717123834, 0.9510720772507526, 0.5494089945934939, 0.02647398424162528, 0.9990932399304588, 0.7035445109342857, 0.6890995971434891, 0.3140169351218519, 0.37134496256127714], [0.8678305041748999, 0.9656981452081588, 0.18601783718853515, 0.06258648945037693, 0.8154538390502952, 0.6412338388223969, 0.7966513317654931, 0.5500683903480356, 0.6385183292692079, 0.5483801174192207], [0.5722214260364468, 0.46502862439012527, 0.5317003707699661, 0.06360962584579666, 0.7758029761228472, 0.5591910926977266, 0.8509456316533812, 0.9611960118135522, 0.08180881313286914, 0.5412678032694535], [0.8833998699455562, 0.4131909033873954, 0.7256308594449689, 0.8061967624585138, 0.04078982708044632, 0.174522718772967, 0.6018467953261843, 0.337500266226593, 0.547535369171186, 0.5109613215723807], [0.06137631337762661, 0.7744812128950013, 0.09835168623491275, 0.7569561877025136, 0.6968489173232066, 0.5942807434060406, 0.5745705269271763, 0.5641752557200084, 0.513021014072737, 0.24972048374231537]])))+8.617370249552065/(np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))*array_x-np.log(abs(7.037829820514285))), axis=1) |
np.mean(10*(np.cos(2*np.pi*5.421441422428268)-(np.array(range(1, array_x.shape[1]+1)))*array_x+2.881426881801703)+np.log(abs(1.7422757794967059-(np.array(range(1, array_x.shape[1]+1)))*array_x*4.450263193611963)), axis=1) |
np.mean(np.square((np.dot(array_x, np.array([[0.8574807850022216, 0.8652374145780776, 0.20428041491743731, 0.5945162638308957, 0.2500974612427629, 0.8756502371981254, 0.616732369922778, 0.5475201777558771, 0.4658071680921162, 0.9402661387168557], [0.9589960958028518, 0.38841149420473964, 0.4178273768203208, 0.0025087977994551203, 0.5550672901104646, 0.4283238319544268, 0.9622566695787947, 0.8322094325631886, 0.46554260492296684, 0.9322056664601591], [0.3599540598603922, 0.9029423035130842, 0.6093940974727156, 0.7359544374300226, 0.33154427608317827, 0.5663805461174823, 0.3994761045761699, 0.7729143776081416, 0.8650921491127562, 0.5368970912978795], [0.7484646270019549, 0.12929072656677976, 0.9765127227133734, 0.21303091683547637, 0.3439845663529768, 0.4351421819817666, 0.8683572520621118, 0.17386355297457745, 0.7466664790126328, 0.23299135981299968], [0.731303234008366, 0.9190087890460334, 0.7901893635728514, 0.2557796269224817, 0.8307919418043552, 0.22802608336798558, 0.8983312263824526, 0.33856785857788496, 0.48443712501089353, 0.8806751965193521], [0.12209146788021619, 0.07491563486910302, 0.22138937589223429, 0.05410317550448329, 0.4702179052930954, 0.7097982690240864, 0.010608836372500763, 0.22844488665997997, 0.16697245421245555, 0.7603158647683852], [0.9610598622600613, 0.8211594364666147, 0.003590618485276975, 0.8941065835482029, 0.8208696860310439, 0.5278083406783197, 0.340865922584628, 0.9025802121624391, 0.8234454003908317, 0.3477267739281418], [0.20964565800759793, 0.7600702274549876, 0.05447425138926276, 0.3731832861365778, 0.9529692598283587, 0.5568302851482356, 0.567746630020482, 0.5366284015542662, 0.9921149500236713, 0.1724310682097029], [0.867762115785772, 0.8841872962226207, 0.011951998769127448, 0.16017137885377397, 0.4832415556866275, 0.2534635204221366, 0.30657572702688807, 0.7637682879654797, 0.6055654255177632, 0.9819283208667507], [0.5777759127692473, 0.7317621411401931, 0.8681278880851432, 0.9247582615932954, 0.25869146960126244, 0.19703605770837085, 0.11112902761385235, 0.4389580571041578, 0.32769029077151646, 0.038015802814778454]])))-8.312098952844002)-1.2176877828085777+10*(-(np.square(array_x))), axis=1) |
np.mean(np.sqrt(abs(np.exp(3.6045609413725064-(np.dot(array_x, np.array([[0.035607709258713593, 0.5255085836857081, 0.7609117783530998, 0.9600211873180491, 0.7036463243555666, 0.7714217653449515, 0.5635436429262277, 0.8098306964645252, 0.276892849872882, 0.9544618510223758], [0.667842642990073, 0.6786524052291918, 0.241919572256314, 0.44594267902847584, 0.9853085094013316, 0.03711598904009428, 0.5720715018116478, 0.6125423625437004, 0.19795139194776812, 0.48875500064210664], [0.33300720253024285, 0.041743301584574066, 0.05372873501220754, 0.6919081144430408, 0.009638833279301484, 0.7825752762293718, 0.16140678326135283, 0.6569366581046516, 0.9571976526684486, 0.6264341888573486], [0.6662804733223416, 0.591328425543116, 0.5093994447858299, 0.8281347331378454, 0.9644358903695517, 0.6353726387998282, 0.14555965561533457, 0.8169406149176561, 0.6552252837068551, 0.30112427315339074], [0.038906311456736176, 0.7520742932500035, 0.2793276286984905, 0.8663417030001795, 0.9934665543562071, 0.3281064732270015, 0.5772426721726238, 0.7470561196638222, 0.430459494680986, 0.92107715549129], [0.026946007025694607, 0.1670925744585452, 0.6864317253512002, 0.11567814377609054, 0.21541441003896888, 0.3876010435983275, 0.05665458046529104, 0.6385141629903418, 0.1255747477816791, 0.7535413678125295], [0.219775280764397, 0.15844819352195472, 0.9519155845485308, 0.011600759299200014, 0.43808892631802576, 0.9720886486162378, 0.9950177470372281, 0.27947127926867465, 0.42471672232281865, 0.7533480168984413], [0.20658350383051516, 0.5819813108237281, 0.5918532984761427, 0.258644969815933, 0.9034320858500702, 0.15591613719995434, 0.16038700685119478, 0.4981773428044243, 0.4363686080184267, 0.6988444997991039], [0.9569625208643865, 0.09035091748289237, 0.5785446554131041, 0.3434685748628451, 0.5807135336386504, 0.2399842163342979, 0.07333005296172057, 0.6005125060843282, 0.12320553819264601, 0.8051297924253517], [0.9826673766267306, 0.5086804528980483, 0.9827256412234422, 0.699775479500408, 0.3882414522240679, 0.69728858787229, 0.786159675836629, 0.9480341188335535, 0.569695908721271, 0.08390891866555916]])))+np.cos(2*np.pi*5.860472650693974)*1.7053150578700662+(np.array(range(1, array_x.shape[1]+1)))+np.sin(2*np.pi*5.95303902904599))*2.8863403787755746/6.599041077724223+array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(np.exp(8.237439650264411-(np.dot(array_x, np.array([[0.3178911279688845, 0.718572268194762, 0.12765280344789498, 0.7238046354497195, 0.07868453785192231, 0.009915852442502149, 0.9625657215351684, 0.9186954949344056, 0.012797512646159359, 0.06243536292074092], [0.24720041015278804, 0.9920162965319805, 0.4447828958302532, 0.2633311005606974, 0.080711939316922, 0.8423745427947739, 0.475913829868279, 0.7066917567422921, 0.5484113082199316, 0.8743349335933481], [0.7282477716554796, 0.2079967519433925, 0.7760654302117034, 0.7196079133086188, 0.884167617070989, 0.6849676714773427, 0.5185612002830431, 0.6322870357083411, 0.14401634481024428, 0.35712470649474737], [0.0967865214338739, 0.8504188705031402, 0.6449150747366867, 0.9773057101729637, 0.26780604319621526, 0.6452963131902681, 0.966542548768608, 0.7335251057562133, 0.7742896346286126, 0.7476851076808742], [0.2352323221774978, 0.4561516884749348, 0.5423974601620416, 0.12194408565547832, 0.38855254381913507, 0.7969350513415133, 0.08154549342833195, 0.2595108508990903, 0.7738774507528685, 0.14228490150818007], [0.8690805218164014, 0.5295665495651706, 0.6759569025506382, 0.1247255385053675, 0.569416076502245, 0.9322420194332116, 0.38168655878429847, 0.4622136193952252, 0.9469513298961284, 0.4169999542852594], [0.5873691768141923, 0.9402324467480745, 0.3564752565619409, 0.515137677925113, 0.07387548991662862, 0.5766229524284295, 0.889936971258411, 0.5867656241046492, 0.5540849909037074, 0.001804657384038011], [0.5797212251966992, 0.21182103022803433, 0.5390595035709962, 0.6467537736018856, 0.3028193897918592, 0.1540797610085387, 0.8764560044813243, 0.29637586797474813, 0.4401684750719631, 0.2899623554930172], [0.10363456487784828, 0.17856820717356392, 0.5072780033513945, 0.925392515433095, 0.41176304416670206, 0.7817791333934235, 0.3367354175946926, 0.1604550004213584, 0.5088442290471109, 0.5372214478221701], [0.7538217167953546, 0.7232542819968004, 0.8342642870312125, 0.17432124581050523, 0.6166840506288055, 0.7866836850839776, 0.23511845734979198, 0.07002048861582844, 0.8203907311721412, 0.5645818070352225]])))+np.cos(2*np.pi*1.2364010918370498)*5.890319646359761+(np.array(range(1, array_x.shape[1]+1)))+np.sin(2*np.pi*4.4018012069546675))*4.795139482990784/3.2413981671592134+array_x)), axis=1))) |
np.mean(np.cumsum(1.8616624329160052*array_x+np.cos(2*np.pi*array_x)*3.653485849200208+array_x, axis=1), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(3.5299251169520893*array_x+np.cos(2*np.pi*array_x)*2.0677275635965584+array_x, axis=1), axis=1)) |
np.mean(np.square(np.exp(abs(array_x-2.801583724577185)+5.713066185301558-np.sqrt(abs(4.021048323608568/(np.array(range(1, array_x.shape[1]+1))))))), axis=1) |
np.mean(1/(6.053319611497562-10*(array_x)), axis=1) |
np.mean(6.453260285039264-abs(np.round(5.148681472625807))*array_x*np.square((np.dot(array_x, np.array([[0.28870968817608234, 0.28426578536072067, 0.8219636770899362, 0.6103850113412903, 0.3903002343693055, 0.5141580865558327, 0.86083923644248, 0.32017858005389577, 0.2130954043436225, 0.6068198227106274], [0.8550301241133851, 0.978072047844415, 0.4242397510769086, 0.3201407487768644, 0.3444403748518513, 0.11482998082804985, 0.3417331742092393, 0.6607887139028836, 0.3226648219332544, 0.27184388717135877], [0.18612485571955495, 0.5431921261762289, 0.10954393747120728, 0.8271835640487472, 0.1147139308772287, 0.8030258655295649, 0.25939302297784406, 0.9475475274261079, 0.5905369001586194, 0.45528241297175476], [0.8338303830386692, 0.4878902395913862, 0.4826539298732151, 0.8539869095158129, 0.7743973753991861, 0.9818233578637127, 0.23152407853310542, 0.8854613720634317, 0.7190144937389891, 0.17255549302940965], [0.295903082054756, 0.30144584125723284, 0.802512314422765, 0.10899705421886063, 0.4188222040673525, 0.14211095781565708, 0.5775356937677335, 0.722681880086132, 0.5101548518263708, 0.47253511726029873], [0.8651332029647282, 0.47895420014944035, 0.40051639664819316, 0.8244866488828346, 0.0668167949064905, 0.9965226359577826, 0.5863817694068353, 0.9958255053793168, 0.001714032853968872, 0.5040722104434824], [0.5111130457502168, 0.45757418085636437, 0.7485300185735757, 0.22819502315800722, 0.8155934468751739, 0.767498765374281, 0.11167965307098426, 0.5310782980037401, 0.8821830588237761, 0.2052409371719165], [0.6289584363675604, 0.3313252777169604, 0.04748778619072036, 0.3493497079077714, 0.667808364928364, 0.041466373344059226, 0.15205040285126747, 0.9226655902152693, 0.30594600732845356, 0.3470469761987912], [0.9919349383792891, 0.5526311268337232, 0.0438435008323228, 0.8417721522622118, 0.10468873103011767, 0.9323027149875254, 0.4130050146937446, 0.8184091975312415, 0.9516854667903712, 0.4669012455618957], [0.8265000294316234, 0.7709429394811378, 0.1616231211059338, 0.0514876903015401, 0.6731497728554445, 0.4541794185152177, 0.11791679008022493, 0.40850218557561846, 0.059806177405278604, 0.2512359468090879]])))), axis=1) |
np.mean(4.085292458560552+array_x*5.126709014421625-np.sin(2*np.pi*np.sqrt(abs((np.dot(array_x, np.array([[0.1094075124728886, 0.5879883508652806, 0.4131673321646997, 0.17021014463907513, 0.8795959362120069, 0.7009542943935656, 0.22191102377722738, 0.7457184154058425, 0.9819595663760513, 0.888034227926948], [0.284040489201107, 0.31676002658472635, 0.5679891440744186, 0.6817766671099421, 0.9820276916811055, 0.11157315775561671, 0.8768354825398516, 0.6569958990447834, 0.6204494382830305, 0.9887634040682415], [0.7212149945086792, 0.4448452258276012, 0.4301227506753116, 0.6553869150030291, 0.3808010660559198, 0.9860781474476557, 0.6904115125337816, 0.9408058771541644, 0.02464589438588094, 0.09254181775923431], [0.6337872907047342, 0.6546620916951702, 0.4179646235671628, 0.8635500395088256, 0.16634458122617113, 0.03877867854962924, 0.35531225276943623, 0.9538854069310777, 0.18180593604361284, 0.9712665631362501], [0.7573244526971157, 0.7236710708539136, 0.5720082723047698, 0.5077307688817786, 0.6780732716435539, 0.9209316647355423, 0.30335428446063906, 0.9075933730185859, 0.41051544218130953, 0.9269133721739521], [0.3517560579426037, 0.48491315276107383, 0.00792031684237382, 0.3571735449941077, 0.019713839624239893, 0.15995148679727922, 0.21354736019942266, 0.5771554335399599, 0.20001177235518197, 0.6309267134586753], [0.13059204802148494, 0.4613198874520058, 0.9997998988695924, 0.46514349222244256, 0.7511049397828317, 0.24131563312402415, 0.6484649717642612, 0.40438898178298355, 0.11067140460995928, 0.23573076293154305], [0.6282819973985126, 0.3552737811839688, 0.9774377782365008, 0.3880446055139196, 0.6255827406256741, 0.98918459197733, 0.1505571392456122, 0.3536772692095288, 0.2397561481629118, 0.8195439816626728], [0.5099474953183685, 0.1399154043878975, 0.016739408946350065, 0.34309737983292066, 0.7276194023309, 0.17730880244576042, 0.13975559203231103, 0.48951206240408607, 0.5863262721119764, 0.9440071011005345], [0.7762377663888803, 0.7829186244669075, 0.3006584114347993, 0.6479942444099853, 0.8127011668787891, 0.5240949738749104, 0.9233966993668877, 0.897558546352746, 0.12412444763973285, 0.9760382195651788]]))))))/np.cos(2*np.pi*1.8719749908094048)+3.026633685341143, axis=1)+np.sin(2*np.pi*np.mean(2.1656066134842575+array_x*7.302028827397295-np.sin(2*np.pi*np.sqrt(abs((np.dot(array_x, np.array([[0.767674756349433, 0.9143174441611771, 0.967086472427037, 0.12940496686223812, 0.21856781188128815, 0.7686337557091704, 0.6206552054895408, 0.4855922270450428, 0.07057425697793607, 0.7219723243588049], [0.30813433212357566, 0.9801127901853856, 0.5478800019588693, 0.16077173340622752, 0.6495598326643718, 0.5769935469932544, 0.08529392249654233, 0.26582096938500377, 0.9708499567060809, 0.10157854286371792], [0.21155039830199107, 0.7585498628246018, 0.497374446658881, 0.6937078462111005, 0.6569122719163577, 0.3306346133047091, 0.534944606346057, 0.7080884873244161, 0.8004295878610415, 0.552188228910471], [0.1548617085763766, 0.08978697701007976, 0.07271785723339774, 0.12379327312013366, 0.9797594127654654, 0.5099418383635773, 0.404691231155469, 0.18979710248698445, 0.1122404246554024, 0.008323013521847278], [0.07861523794749681, 0.8667892941231584, 0.5987315888902534, 0.38360322962467575, 0.1276155400098009, 0.7913207704090823, 0.7620316474571899, 0.6392946847276438, 0.6457937915655151, 0.7287934296534281], [0.17743974277710206, 0.8834909001223329, 0.41993933124944927, 0.9047292023199367, 0.8736255974538789, 0.7758134972625402, 0.99447591059602, 0.7796418618049556, 0.5681648680891759, 0.2626452943116806], [0.8065653186602666, 0.7820738501349718, 0.8675993360752211, 0.6707480562515808, 0.8815309660006236, 0.13451760345399666, 0.42257884446481775, 0.9792201406913411, 0.8605795273191825, 0.17555202468056663], [0.1612103434446457, 0.6490992667764488, 0.7035183167682908, 0.2322612365247363, 0.8589257015604502, 0.015042211342100487, 0.6456872499167798, 0.0715004086735106, 0.6113172953913084, 0.496257938668481], [0.8456455908383819, 0.5307234981427159, 0.3488659088235141, 0.889592664889766, 0.9646486902960503, 0.05502874309327144, 0.16310118906516669, 0.16948076665832545, 0.0019658938902993306, 0.0600087605154741], [0.7133335014282718, 0.9697973505730768, 0.3002605673633405, 0.6309103774919115, 0.3903948770604918, 0.403136972509045, 0.54020422901203, 0.600741714308477, 0.28498932106161723, 0.6539589831941289]]))))))/np.cos(2*np.pi*7.3840306853532125)+7.449453360956457, axis=1)) |
np.mean(np.sqrt(abs(3.008481458770134*(np.array(range(1, array_x.shape[1]+1)))*array_x-1.8919112222680126))*9.052965802407844, axis=1) |
np.mean(3.239209597314984*(np.dot(array_x, np.array([[0.689994078576638, 0.04745348633001134, 0.0073012997736064955, 0.7513306735131077, 0.25356982043572285, 0.1350810025591106, 0.5108856870092753, 0.054440952345778504, 0.18279423492656754, 0.45786095591484366], [0.33130387381793447, 0.6348344582694304, 0.9954496385317217, 0.8474314131940993, 0.47678495666514364, 0.3196057316491664, 0.22543726677954767, 0.23213478893314776, 0.02829893056391597, 0.6771943486519562], [0.12497287631964327, 0.5892777827256862, 0.5913987350840376, 0.9780591908085217, 0.582537777437417, 0.9882893265381282, 0.925504187640607, 0.35879537197648836, 0.016540642896358815, 0.9786741884171107], [0.5265357937959939, 0.7487246140800019, 0.7871075786517449, 0.680872357751274, 0.00208345282357969, 0.27913724571725684, 0.6922853702069814, 0.694381571855512, 0.6094354113963495, 0.913344945193988], [0.9129865215216971, 0.39063429159436214, 0.10862941774603352, 0.8248700912145391, 0.7800097072108908, 0.6623461434096187, 0.7361915983340809, 0.9211946247304917, 0.5773659410276714, 0.04456401283289768], [0.32661958507418154, 0.054564473192982965, 0.2977588491102945, 0.8824269371467383, 0.7231512515273781, 0.063380041934603, 0.9918940918363173, 0.01795197431142792, 0.34881583826771045, 0.6497550650462259], [0.12530778823149025, 0.5676345646365417, 0.3538067253878634, 0.8171736293594413, 0.3472361526710652, 0.04804776272902278, 0.42678474404497624, 0.07420759265035648, 0.9845641097099048, 0.6764064718874464], [0.6023038141613716, 0.42910659991577194, 0.9617377364460046, 0.7339584500591696, 0.5648998468676689, 0.3651414260777348, 0.9941087820691864, 0.5370459130614844, 0.9722046324040406, 0.02614954906425715], [0.4719006279562664, 0.10619576925940133, 0.3849711176009639, 0.5815201937306382, 0.8782308150774741, 0.13397230353831435, 0.5260505609827866, 0.3354558804410497, 0.32497743768073806, 0.4498226898446621], [0.5249745978414171, 0.06605804096554058, 0.2751371680355129, 0.45030901375450716, 0.5382307099874818, 0.6193058356996703, 0.8426197398817112, 0.2662815765353632, 0.4709437453962604, 0.9050829938234394]])))*2.9502452005384314+10*(np.sqrt(abs(1.7203038871564613)))+np.sqrt(abs(np.log(abs(7.144034982339225))))*(np.dot(array_x, np.array([[0.8148461328374274, 0.7798244233377595, 0.10105870390567717, 0.9221131617544991, 0.3233854987580562, 0.02435796830192538, 0.3964467841253472, 0.7742604886375376, 0.42462428528732743, 0.8328838610964159], [0.06690135524048557, 0.15341874193744043, 0.0007873179053132784, 0.6630295815104226, 0.20099653061793266, 0.8858813021103442, 0.10887793341185903, 0.9856836825808883, 0.46286250033013643, 0.7998394780843215], [0.7329356293756744, 0.022945068988283746, 0.8717124588192341, 0.14140402276499087, 0.4454702053801245, 0.5481371030546015, 0.3999353037881712, 0.7181112836898712, 0.28252667699099754, 0.7556576079761933], [0.2596250417636934, 0.36340140842856816, 0.9012778057132079, 0.38633630553897313, 0.24671731500571437, 0.7877056113249242, 0.8392456786788866, 0.14839319163875897, 0.2564794758364358, 0.7567260513880749], [0.9667766727241361, 0.4161747740255295, 0.7770287903870735, 0.5079073838654965, 0.9119855371021208, 0.9742576914815333, 0.7482549016200247, 0.19199418785096678, 0.5486718095610738, 0.2607600042956686], [0.47509971076358537, 0.10055320945033686, 0.8898396129730103, 0.5429248935097202, 0.5494747415398206, 0.8171432340675501, 0.9921103548902888, 0.6838348635242605, 0.3434906231412437, 0.6720366292090403], [0.03884693868455935, 0.169016122419807, 0.8886593424269342, 0.7524683781115127, 0.5683916850146332, 0.25531949229559736, 0.45696371827560245, 0.6229181841517252, 0.18475817563685693, 0.8907210898401151], [0.7262298375623214, 0.9847899596095656, 0.3385025711166796, 0.3180229254596516, 0.18675500881831353, 0.05779363038940133, 0.3097408764402053, 0.9574972951185267, 0.9579792705021459, 0.884230021111439], [0.1982480814788331, 0.06142177127310944, 0.596750722626175, 0.944144612381, 0.3259461668547319, 0.6392202990206777, 0.2415538867297864, 0.06579727606445784, 0.38380899914339595, 0.5055550095745275], [0.010232403608841811, 0.3185975314820503, 0.266925104867044, 0.7548178218281312, 0.9208027773498346, 0.21328206091065594, 0.8517613821230791, 0.8951356506061479, 0.45912809589056813, 0.04894769769215146]]))), axis=1) |
np.mean(array_x-10*(abs(np.sin(2*np.pi*array_x)))-array_x-7.887984254218535*4.753384254153515, axis=1) |
np.mean(10*(5.998715735754143)/6.896412858364903*array_x*7.61667643075731*array_x+6.342901839926179, axis=1) |
np.mean(10*(np.exp(np.cos(2*np.pi*np.exp(array_x-5.854018229567923+8.545677434401778)))), axis=1) |
np.mean(np.square(array_x*6.075282364664634-10*(np.log(abs(9.725740643930072)))), axis=1) |
np.mean(np.square(5.395127679616672*array_x)-5.6948592858901135-array_x-np.sin(2*np.pi*array_x), axis=1) |
8.405632402883816-10*(np.sum(np.cumsum(array_x-9.225892340525276, axis=1), axis=1)) |
np.sum(np.log(abs(np.square(1.773887460044588-array_x))), axis=1)-array_x[:,0]/3.229149258490138-np.mean(abs(1.0022691492558538-array_x)*np.sqrt(abs(8.917454795088304)), axis=1) |
np.mean(abs(2.7005677829782773)*(np.dot(array_x, np.array([[0.47615188278803977, 0.9732597736858497, 0.7845643913360046, 0.25893960098562074, 0.3343458972239599, 0.032479837584589055, 0.8437131195376681, 0.7136420365072718, 0.8387210931420306, 0.8208357693688055], [0.0034556869728706863, 0.6234476202990703, 0.41282743910784436, 0.16718148403366984, 0.5343062734363124, 0.5830201864665734, 0.9500957568304107, 0.35380906512788357, 0.7229427949719381, 0.5498177415494064], [0.9250233887631174, 0.052362125277218285, 0.16504253651791023, 0.6505310679396311, 0.087164319720064, 0.40234509419503095, 0.8080749717935068, 0.8091800985614046, 0.6965684445783119, 0.18465120603828755], [0.28756877362910604, 0.24054440586978532, 0.4955533701234638, 0.04132993757293646, 0.7827029443598341, 0.2290084032749855, 0.7784168506136548, 0.6739225772013949, 0.6609469104368471, 0.6126423774098925], [0.06193651953619905, 0.686262032901161, 0.6618492475363873, 0.44940236498176, 0.2774814235484122, 0.6688866957750138, 0.5500310331742487, 0.9978502874991895, 0.32077129207517907, 0.09839668885968367], [0.6032550005087617, 0.8168933896162558, 0.3541326860384203, 0.36778996022590127, 0.6434395622514619, 0.6910752260033126, 0.5578276263265892, 0.22188490842626907, 0.42144678341207376, 0.1880852261993584], [0.568101989860506, 0.7606241882058753, 0.3616882174382682, 0.22714272518082912, 0.9579551906572235, 0.7685363395981385, 0.8268363246040081, 0.6464984051359725, 0.9032911219989683, 0.5714033347008777], [0.771438544954467, 0.9721702449953453, 0.5571661592189878, 0.3919957578159381, 0.49178890981343504, 0.49270199250785107, 0.6615250741368788, 0.7196638148030433, 0.3555374357380551, 0.20647435976301654], [0.9484628282450205, 0.22013871933339602, 0.6174600363731914, 0.9338738800266363, 0.7468654539930953, 0.8605602166244054, 0.5930737784575627, 0.7890525717430023, 0.7470897969020681, 0.6254446776120659], [0.6273705025580704, 0.6629230949535204, 0.111745254835107, 0.8017133211729869, 0.3751693621775197, 0.5711981804056417, 0.43208152829627566, 0.8937670537882704, 0.5497787064048016, 0.7732999335557944]])))-5.030275824512638+np.log(abs((np.array(range(1, array_x.shape[1]+1)))))-array_x, axis=1) |
np.mean(np.sqrt(abs(np.sqrt(abs(array_x*5.860078871343903+np.log(abs(array_x-2.3575898669819595-9.350967579614757))))*10*(3.4693566622801635-array_x+np.square(9.647743552181515*(np.dot(array_x, np.array([[0.9232906325884443, 0.477300146039521, 0.13530322864349986, 0.335347659128836, 0.7298931561005075, 0.16286706960011077, 0.6331606570293852, 0.11703639105429176, 0.9981367131717692, 0.1639945919269301], [0.870949557565714, 0.6949889050016766, 0.14727534271006548, 0.9588110925874489, 0.5486446965975376, 0.20102314426912893, 0.6255273272918521, 0.643381501848236, 0.009413355908924559, 0.47547077563390094], [0.9909943097226416, 0.0769621167045722, 0.24949350454002084, 0.0899666046633053, 0.6362728284033559, 0.4905544795876209, 0.18866977348708636, 0.3276436814448461, 0.08101767397232518, 0.33471932485819456], [0.5100071005749198, 0.8765598427567082, 0.5532162062493111, 0.16517247246899525, 0.1764839935340633, 0.9135846930643946, 0.39715267412127175, 0.06285256336267597, 0.1155500424933964, 0.35035135677233664], [0.2574627996560611, 0.41755892094489466, 0.6914224440603295, 0.43843368783175196, 0.5557214566437984, 0.049157663916660765, 0.48505352054943063, 0.7160342048858156, 0.8799770145681002, 0.11161941520707841], [0.3664659297366444, 0.594945122636083, 0.7157335296204838, 0.8096050284915526, 0.48033918276779985, 0.8179446266587, 0.3263801437283609, 0.4925595009728436, 0.3564900824243691, 0.717745904373281], [0.09752470089572485, 0.9881229385997535, 0.5515491226488486, 0.7257156161358039, 0.7474156191043108, 0.03791951088374468, 0.5824693099837523, 0.8107142946106001, 0.30756613860935067, 0.9892310738419253], [0.4786476446278455, 0.5819117345550032, 0.579691021904215, 0.8098523889558912, 0.1040606710676617, 0.2532122497228084, 0.035264903676353154, 0.973296210433184, 0.9194145247348576, 0.2897464984837027], [0.7001998952695627, 0.18962062260890578, 0.2099636625873691, 0.04067799484590329, 0.40127924465559617, 0.2505968906134358, 0.10979887960214485, 0.5780793846821222, 0.11217576325743783, 0.8674879939930029], [0.7916049521980152, 0.6855254872701869, 0.9100024727813083, 0.0325201220704211, 0.1369273791752771, 0.6085283617796912, 0.05909522372352205, 0.01807586410791573, 0.7055627034194124, 0.858422119742536]]))))))), axis=1) |
np.mean(np.cos(2*np.pi*array_x+6.23559960691932+array_x-2.1001143438895546-2.3212748685742897), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*array_x+7.484279926526212+array_x-9.766808867390512-9.075737707057149), axis=1))) |
np.mean(np.square(4.210955024116997/np.sin(2*np.pi*array_x+4.608397070689439+np.log(abs(2.072015214675642)))), axis=1) |
np.sqrt(abs(np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)/np.log(abs(3.9553058208737464))+np.amax(1/(np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1)/3.1779868688179604)) |
np.mean(10*(1.2665641164565145+array_x-2.5109467962471443+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(2.504389714865934+array_x-6.249038632372726+array_x), axis=1))) |
3.0497601876788734*np.sum(np.cos(2*np.pi*3.764718569810343+array_x), axis=1) |
np.mean(8.683986252741809+np.round(1/(1.5397907743564732)+np.log(abs(1.1692121512290115+array_x))), axis=1)+10*(np.sin(2*np.pi*np.mean(3.8757066371756803+np.round(1/(6.961354657705271)+np.log(abs(9.809735502531632+array_x))), axis=1))) |
np.mean(abs(10*(-(array_x-6.659938948182558/np.cos(2*np.pi*array_x*8.349688901004154-8.279318276177225)))), axis=1) |
np.square(np.amax(np.round(np.square(array_x)+8.872308858794433+(np.dot(array_x, np.array([[0.20722845490414088, 0.2619760976766, 0.3281262087596122, 0.7648325719824731, 0.7512531027549543, 0.45354122021564736, 0.6167829235550454, 0.8518514061724438, 0.5540613526593734, 0.34924076910395885], [0.9330009166687675, 0.5583510009253224, 0.8830980278282062, 0.9797190031207689, 0.03057655012500049, 0.9449382524753438, 0.10087232700565063, 0.12788710041219775, 0.31909859185247336, 0.33931164580365303], [0.3460569137425422, 0.19881113603413592, 0.8401343280493556, 0.9457215341509779, 0.6086063873027989, 0.2806313326613523, 0.21685921317664558, 0.12840129497125718, 0.1401405550981668, 0.4852452847900962], [0.9645657159848264, 0.5104363184329944, 0.8798734067514378, 0.00806951353633012, 0.19584451223929222, 0.582525680017198, 0.7043380261876149, 0.4764544806479667, 0.5546389142029022, 0.44934334931641273], [0.19533227031168687, 0.3054306598356049, 0.7830834025386061, 0.5974030359473708, 0.6021711262267027, 0.989608162455769, 0.18025577491302214, 0.10489812800566489, 0.5250985074360704, 0.36095054102708957], [0.8707973973051975, 0.9351092728979665, 0.1580940390075516, 0.0016237598002838416, 0.6152201043120792, 0.8433096473525924, 0.30523593221855305, 0.6333782847298061, 0.3760128526579597, 0.2828934747228603], [0.36071500582350546, 0.8901872783959462, 0.7669373384973052, 0.5889722866796545, 0.4525704168313155, 0.12666118321601694, 0.24897955795453808, 0.8424121549291707, 0.3629326752206249, 0.06437027950798135], [0.7470435947935481, 0.39231984470524606, 0.6239138489251245, 0.05157847227569956, 0.5413452188138022, 0.45167748328195656, 0.02870082413315933, 0.34971497841770327, 0.47267631363651186, 0.7976815490265873], [0.6055429053767757, 0.7806413352741675, 0.1379651440001597, 0.9069242021847981, 0.8426275209062627, 0.6491640312557703, 0.854975858102228, 0.37167002680345573, 0.6632100384496826, 0.7571767851127948], [0.6382473181705024, 0.9651235164769995, 0.9962222189343284, 0.8324065708456665, 0.798607240586857, 0.7717084263693258, 0.029902926928568774, 0.5999768320500873, 0.5426176427654198, 0.05628524019478587]])))+4.642446909959505), axis=1)) |
np.mean(9.025643325781434+np.sqrt(abs(array_x-np.sin(2*np.pi*3.561243282931583))), axis=1)+10*(np.sin(2*np.pi*np.mean(3.663983371717788+np.sqrt(abs(array_x-np.sin(2*np.pi*5.3118244215762385))), axis=1))) |
np.square(np.mean(np.cumsum(np.square(array_x+3.0286213813289056-7.237871046710056*np.square(array_x)), axis=1)*8.853807094500382, axis=1))+10*(np.sin(2*np.pi*np.square(np.mean(np.cumsum(np.square(array_x+7.166771238762689-3.2276237635141865*np.square(array_x)), axis=1)*8.759007045861283, axis=1)))) |
np.mean(10*((np.dot(array_x, np.array([[0.6999133522677408, 0.7122405553726294, 0.4561221085359365, 0.4334602909587494, 0.24595453560091196, 0.016962609146439434, 0.12185538029823595, 0.18221556279764062, 0.2728497251942841, 0.03520958649216799], [0.21377555104951318, 0.08688364342044086, 0.9359673287318537, 0.49057814611735995, 0.361553586529216, 0.588819718806881, 0.7305751623579952, 0.20804737420328856, 0.028077550608528345, 0.5452225822793211], [0.6340727764057665, 0.10085226992679086, 0.38525140716520234, 0.3033337691204747, 0.5963039579284016, 0.8592066823604566, 0.6594114114014891, 0.980660789014993, 0.5482375615466983, 0.8643290567774532], [0.05803460857514964, 0.539281180289064, 0.1643489801766882, 0.6212965367463761, 0.6628089726970088, 0.40510866655457745, 0.2879739495687611, 0.6984505461658804, 0.43833761025713647, 0.956427200750649], [0.02234303887489042, 0.9219012114044273, 0.07389617187106667, 0.8688900998509996, 0.7214409103312786, 0.42408797120444053, 0.7236346670887335, 0.10860639734124877, 0.927992325564527, 0.02760886202274293], [0.28794039760552925, 0.9468835839175451, 0.16677992675399567, 0.4379878156456216, 0.8985459542942563, 0.7927517298039394, 0.7168488922518854, 0.22163763898767785, 0.45810170681655094, 0.13521876038776726], [0.7524360804202193, 0.99349692057001, 0.42892158350085585, 0.22564314477078806, 0.017215251997632075, 0.9928793857658909, 0.9690684559036415, 0.35756724714955523, 0.03613435086104866, 0.34697461588876455], [0.37456362880850247, 0.43692145321508313, 0.8548709905414922, 0.8455353907495451, 0.48483091841180903, 0.3826998283035228, 0.7330119113726802, 0.534563353793505, 0.8286045188999056, 0.9097499285475065], [0.4269390486244812, 0.48374712548400756, 0.7151790640646761, 0.6629999041678324, 0.9576475598505444, 0.45626042856159255, 0.7681297190309626, 0.4104418029861324, 0.031430404076717156, 0.5959248571081892], [0.6262151436869714, 0.12267290348598492, 0.18621958175947295, 0.36455916152334, 0.6355038284912259, 0.9236378713116856, 0.8306905214637454, 0.11732644513318735, 0.006527755003861158, 0.29426686657831214]]))))-1/(np.sqrt(abs(6.039738040439799)))*8.569439757161081, axis=1) |
np.exp(np.mean(1/(np.cos(2*np.pi*array_x)+5.534951110966325)+5.397850009278805, axis=1)) |
np.mean(np.sin(2*np.pi*3.036610429518066-np.exp(array_x-np.round((np.dot(array_x, np.array([[0.79864032829351, 0.18352824360757325, 0.6700786215085007, 0.13419402280637316, 0.817856465849986, 0.007991348813255028, 0.15047416354463206, 0.10158613525000049, 0.92746326279978, 0.8324456441583783], [0.9697579009176551, 0.27093294916721966, 0.9327191955148928, 0.02324487420167043, 0.13195680872605087, 0.5874532108630605, 0.39208670231731835, 0.2715567785643541, 0.3460860923192387, 0.8906006813056467], [0.3276110672849797, 0.6157681103751111, 0.9398717229472658, 0.21794591299467136, 0.5875287677032167, 0.6236650954201857, 0.29220631898459337, 0.4779978095469659, 0.7610703916760679, 0.694845284704495], [0.5906559256294326, 0.46624040994486426, 0.3211024442057365, 0.8583888130981413, 0.13831420871190936, 0.8264760314260983, 0.6926005751657034, 0.7104609594775441, 0.30042659331982935, 0.06427420995972244], [0.4035380392748814, 0.26283387665981583, 0.30715223235861255, 0.7838277201433488, 0.02109474179900983, 0.5758101910217296, 0.47740550584898256, 0.8786216760413125, 0.3116499642267978, 0.1301159339428033], [0.055666833912230596, 0.9221189434027399, 0.6399567864275063, 0.6344706378132963, 0.08548397259089546, 0.5088365330642625, 0.9168516803239571, 0.4138415160691582, 0.25627017380190065, 0.6858501515856907], [0.18661328539562594, 0.8045354614992609, 0.7607461609405793, 0.9475918616412267, 0.6443091196890764, 0.6905596588259366, 0.3175274507385265, 0.8196087013531522, 0.6723620720467917, 0.5653198312178926], [0.2792532994686472, 0.26668508774860333, 0.7221934008521573, 0.5217830045886075, 0.5202119169199886, 0.4769229757976141, 0.3297106021031855, 0.4498018181229151, 0.4520640698655257, 0.48898646543659885], [0.6237902805294113, 0.5805341826350239, 0.9517624272032855, 0.2747571981219793, 0.46233921634053265, 0.7668432949049631, 0.671774770500699, 0.8011024902603363, 0.3097412628334498, 0.8819413759034838], [0.10525289836830853, 0.8198855345858894, 0.8958211488629418, 0.745407940236496, 0.34819954857369384, 0.38416829405888875, 0.9718437201217676, 0.3772370258408363, 0.2682151943058534, 0.09250327894834176]]))))))+np.square(np.round(5.336373672208684))*np.cumsum((np.dot(array_x, np.array([[0.20626436869268727, 0.3412374217966786, 0.4912580487667947, 0.49396291596411557, 0.7729049359921504, 0.293463101616556, 0.45965158439689546, 0.00841373023930414, 0.44001726398608587, 0.9851984296550724], [0.7434909594919402, 0.9995285563205151, 0.10476431878601011, 0.30486108153400004, 0.6625523329460905, 0.4425111965333758, 0.0803880276868687, 0.09932389999856739, 0.41316660548229345, 0.33131351241614426], [0.046039316049510504, 0.11141774959841666, 0.3303150612475736, 0.4301978187341917, 0.2904948640484625, 0.6766294222916163, 0.8092588286012302, 0.24017256248323737, 0.8636203622028111, 0.22849155362736262], [0.9620400719128027, 0.16421658976881826, 0.9061247850542077, 0.19067532778966778, 0.26406913784008346, 0.8447871219079742, 0.369272379355124, 0.14289558392361612, 0.015978957693208673, 0.283369827522191], [0.5934715213255469, 0.1660729783028181, 0.23387547276248655, 0.7189577139877338, 0.2566841831723038, 0.6970981789471774, 0.5738476548513919, 0.11594046713681294, 0.8204855866967528, 0.4018100880923676], [0.6774347176249128, 0.022371603813612362, 0.3711467338707136, 0.5465141333157926, 0.4274960198586024, 0.09718649887901276, 0.642244727335136, 0.4289918679325322, 0.2858160843536881, 0.6293811671804653], [0.4646225030105444, 0.9946850272552737, 0.003564475214092644, 0.19042241462249032, 0.01299447189152736, 0.3185477148340923, 0.7580467703554472, 0.46104337937004336, 0.8518363934978809, 0.13404485389495924], [0.6190395532349255, 0.6493342375335552, 0.2832696999757033, 0.07930307014897553, 0.166274438340847, 0.2571569682759257, 0.9327871303650389, 0.1230765915499773, 0.036173251622698355, 0.3583804386574575], [0.9221079195317771, 0.025154421264943383, 0.9737832914036799, 0.9911387251826759, 0.04754094289623856, 0.06927279228759953, 0.5878127751903676, 0.7780360602043989, 0.29743376575425995, 0.9281337018161936], [0.36885828627852235, 0.6155816603212739, 0.0038917737527573992, 0.566617073299359, 0.18598268335140977, 0.44238710874584297, 0.6135099266883839, 0.6587702386298679, 0.9668625385453246, 0.1637155321512095]])))+7.28398637849866, axis=1), axis=1) |
np.mean(np.cumsum(6.990220975446917*array_x+6.334196564079829/-(9.989147692774173), axis=1), axis=1) |
8.668293888538859/9.596018535740075-np.sum(np.exp(array_x), axis=1)+np.sin(2*np.pi*7.155596851304091/9.98300126307949-np.sum(np.exp(array_x), axis=1)) |
abs(1.5445827445629705)-np.square(7.978694615883826)-np.sum(array_x, axis=1)+np.exp(8.218871752729015)+np.sin(2*np.pi*abs(5.858412832141331)-np.square(3.4161884888111715)-np.sum(array_x, axis=1)+np.exp(4.145334004262828)) |
np.square(10*(3.3810670037083996)-10*(np.sqrt(abs(np.amax((np.array(range(1, array_x.shape[1]+1)))*array_x-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1))))*9.751665284841538+array_x[:,0]) |
np.mean(5.985995105946921-(np.array(range(1, array_x.shape[1]+1)))/3.8148388509397644+array_x-4.322516433999002*10*(np.sqrt(abs(5.190215542051596-(np.dot(array_x, np.array([[0.05915583389919188, 0.6016788318288958, 0.4352914468221879, 0.22941508511329867, 0.6414549169733484, 0.45651829382780085, 0.34296153475746793, 0.7307614549088617, 0.34761210433668555, 0.6746600871830186], [0.9193698589629092, 0.6018564669386801, 0.2054986431583733, 0.09454697907565945, 0.6623589991083515, 0.0038459734948819158, 0.11752834782758936, 0.005064276796741596, 0.4497705372553067, 0.1998649991315431], [0.5398323065545236, 0.49367055186376996, 0.7151103032952316, 0.6773309375580958, 0.07451547144019344, 0.4313521336559579, 0.5102672346792568, 0.22473025559053084, 0.7090052160528914, 0.8512713464740844], [0.5963323630091311, 0.28038651723356556, 0.3884304306218832, 0.32143123739340296, 0.5737643154802666, 0.23109414726937094, 0.3546676190436737, 0.2513833677732865, 0.9384516916805978, 0.4341245978042044], [0.2668669947507456, 0.624773104796389, 0.5945346575778918, 0.7596617212023367, 0.06312869807566901, 0.6693216481500481, 0.9887834062660695, 0.7505956465753973, 0.7293436435362505, 0.07161088638280433], [0.4460141455120805, 0.6849040339045391, 0.062021462686665974, 0.309657609773475, 0.24241030081896342, 0.4802917014189835, 0.480559670828132, 0.6024366355836535, 0.2831558060811873, 0.05633861909680227], [0.6473284275665379, 0.6526694071577981, 0.23005575318027838, 0.3058299110857299, 0.2464587592582762, 0.5350392332399866, 0.8000016439270343, 0.8683321517374911, 0.31631912316573707, 0.9420351439188255], [0.4054660300700359, 0.15448708548220846, 0.11235523397874236, 0.010753798283862248, 0.3068406373215863, 0.4756977994495497, 0.25056645028648383, 0.9509617050299579, 0.9810887250066644, 0.5521279199919459], [0.1073127892656085, 0.9557260451011863, 0.6695727819200308, 0.8313907585377224, 0.7700905724559634, 0.7969032878269362, 0.2495778490617706, 0.016023571431719397, 0.6723721560724429, 0.033619949889946765], [0.9092896550619375, 0.48455039131706923, 0.7584751485813263, 0.7276322024728414, 0.3060570773132941, 0.9132840980627914, 0.7863928440810177, 0.41856388033931335, 0.0742482894304235, 0.15380523455193273]])))*6.030887798048005))), axis=1) |
np.mean(2.29664890159459/np.cos(2*np.pi*np.cos(2*np.pi*np.square(np.exp(array_x)/(np.array(range(1, array_x.shape[1]+1)))+9.079565474429286))), axis=1) |
np.mean(8.291931466439229*array_x-5.153293823239166, axis=1)+np.sin(2*np.pi*np.mean(5.224677196815278*array_x-3.225456450048834, axis=1)) |
np.mean(10*((np.array(range(1, array_x.shape[1]+1))))*array_x+7.286989337022205, axis=1) |
np.mean(np.sin(2*np.pi*np.exp(10*(array_x*5.410407426970414/3.215604423567122))/np.square(7.133307251902633)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*np.exp(10*(array_x*9.689657492559084/6.437662479867772))/np.square(3.7242412374176617)), axis=1))) |
np.mean(10*(np.cos(2*np.pi*9.617240103676266))*np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x-3.147888682834224))+8.017406299004739, axis=1) |
np.square(np.exp(8.49306438177449)+np.sum(array_x-array_x*np.sin(2*np.pi*8.566226536222715)+array_x, axis=1))+np.sin(2*np.pi*np.square(np.exp(3.4611015250147634)+np.sum(array_x-array_x*np.sin(2*np.pi*1.1452727794638315)+array_x, axis=1))) |
np.mean(8.784283109481459*abs(array_x-1.8030865177229087*4.918099676856581), axis=1)+np.sin(2*np.pi*np.mean(4.46982868042342*abs(array_x-9.218885558126866*6.486101246564916), axis=1)) |
np.mean(np.log(abs(2.3754400736432704))-np.square((np.dot(array_x, np.array([[0.6352619291714897, 0.20916511637407098, 0.493483543589183, 0.0910713408306878, 0.08196345861340915, 0.1382255834832551, 0.6602637330068759, 0.5661607424149073, 0.41964682401542974, 0.5021474771834461], [0.718862248970888, 0.1069844280637956, 0.9196197295868972, 0.6089285086123376, 0.8711840593477714, 0.028283792564304355, 0.3599265584450744, 0.8753113440500239, 0.16244951871271385, 0.5253412812251018], [0.9904203541840232, 0.738616974251363, 0.20613069073585677, 0.7502849894365151, 0.3711980060044956, 0.18234696745067014, 0.8780025597214621, 0.5093564021163547, 0.0021809293898453763, 0.5485935740826529], [0.14964759262069505, 0.8490855883949536, 0.7320418371621601, 0.4028588526366754, 0.5793910256018858, 0.763336827485714, 0.7632823980430756, 0.5767934939374691, 0.5140027835437299, 0.6178389523738302], [0.8689251804211596, 0.9968119819316824, 0.7685945664383902, 0.3304443126033164, 0.6461234570606609, 0.5334254539170047, 0.7195716605093129, 0.5078895883028116, 0.25792587760279395, 0.7467531587371107], [0.004065235915173604, 0.09159895212334335, 0.6673220203342446, 0.07158571364522825, 0.787018800223315, 0.7892699945887198, 0.31120229622619966, 0.798375352413445, 0.2626848922368552, 0.18204714860433513], [0.7075093732249694, 0.019510247170246187, 0.6885262411616692, 0.7582276393841385, 0.1193555881000734, 0.4894910345165562, 0.3753257811768218, 0.6315198635591176, 0.10657189812520507, 0.4825761150934311], [0.6838490543577036, 0.3051065669743319, 0.9734503530533269, 0.15281686091938917, 0.20371941218643908, 0.23526625644442367, 0.4167051835421882, 0.171042233900507, 0.9409200607479313, 0.6090095173502662], [0.20380068718246125, 0.6335544237909605, 0.13626808245320787, 0.8333604217201007, 0.4787988751311989, 0.982516760020777, 0.142271241973422, 0.3033779426459007, 0.998786273907439, 0.45750470197841453], [0.1261875906158052, 0.2723205914756004, 0.13396145376319213, 0.08669942295167188, 0.1668103560980888, 0.12114515429339057, 0.6557809203052628, 0.04647056989120679, 0.4950857624296471, 0.284729295326167]])))), axis=1) |
np.mean(np.square((np.dot(array_x, np.array([[0.8294251059378008, 0.6994911503679554, 0.15438611789667955, 0.6866207913658017, 0.5142392694663851, 0.507078700958264, 0.7746654140690695, 0.47594016272291206, 0.5692668978931739, 0.5029942395125386], [0.6301282525405164, 0.2848837651688729, 0.16113890542148734, 0.9271421974944393, 0.9822036329616548, 0.36200151927418733, 0.18939059644702483, 0.6667973184055515, 0.060881460638920526, 0.6339269718549011], [0.8842228853174013, 0.2215564829670914, 0.7194012753236766, 0.38825375197361534, 0.03207119391820068, 0.9258953131396547, 0.1443213458704622, 0.6131129963970242, 0.6396313036890763, 0.7599220404509667], [0.4687915422383754, 0.6331723268558993, 0.6703705202580228, 0.7320041847940948, 0.5688334134119213, 0.15391180059890475, 0.6580333363354958, 0.9910875772161919, 0.2960113580645395, 0.4009123073189532], [0.736128671251485, 0.5067026617551, 0.15086130244603435, 0.8187868908907193, 0.5003879554414896, 0.691408572718691, 0.9104893311434986, 0.9797410596838295, 0.23231883232945083, 0.5021680974204691], [0.6681449761984084, 0.1597035145882697, 0.27199312243524687, 0.9224589881064057, 0.8168045003531839, 0.8842014353081648, 0.3455894017427382, 0.9820451048952886, 0.11117078005092573, 0.8255392464817684], [0.6252412485967367, 0.5018490311052293, 0.5346748171825007, 0.2607151039172978, 0.614451329923073, 0.45966277205901984, 0.021290827082613206, 0.9397308928852988, 0.7414256392333424, 0.7801039294789397], [0.8479369744570987, 0.6482917749011564, 0.25059217234360376, 0.5215464007649139, 0.7637607489760829, 0.24268386628680583, 0.058853166843150895, 0.00033776883480352016, 0.0259453533432491, 0.3294823867441077], [0.22692751879667172, 0.9547380047897456, 0.8062432678991364, 0.36462339333031923, 0.9568932742131667, 0.21603481991195084, 0.8360740171520044, 0.15386425841103446, 0.2211863934165349, 0.4179031207461479], [0.17708119113852527, 0.9443675508154038, 0.5845694898848821, 0.3940434089722019, 0.960380812316935, 0.024178749242672937, 0.9662240598944007, 0.18185471793348895, 0.06915242383187137, 0.6749045823261148]])))-(np.dot(array_x, np.array([[0.4572086357799455, 0.5537644334033798, 0.15776358046052197, 0.7288321099577822, 0.14045602319075057, 0.15009911421874567, 0.07560950430605562, 0.034504270142882154, 0.29333123755019297, 0.36910127061487374], [0.5369717935182646, 0.5501226967564163, 0.2071309168839247, 0.10582973882924929, 0.776302929526688, 0.9688703190086324, 0.20440531150569385, 0.7490375732532875, 0.6933068107006007, 0.06852862892945344], [0.9699871620346244, 0.38843838162808486, 0.6653095869188127, 0.026667771230621806, 0.49612087345570166, 0.5734778789783429, 0.13585904101718882, 0.9586389516158613, 0.27990814481806714, 0.2127400920646726], [0.42750256148387167, 0.9142707654746797, 0.9554809303170152, 0.4727275415746751, 0.9977348337863844, 0.9620654425507654, 0.6006814836628029, 0.6524228300767557, 0.37578735583858647, 0.19404937538204214], [0.13493575289114923, 0.7068493196608072, 0.8454723936093014, 0.6168436233757564, 0.5708940471768399, 0.9817892686108053, 0.7265988054470196, 0.8083626242008585, 0.3768883476720015, 0.03617475856681529], [0.06294531631623002, 0.9830248159378036, 0.3882881819647396, 0.42965652143339916, 0.3451664698850785, 0.6751248709627149, 0.5488501874112013, 0.8940266252573389, 0.8446687829268663, 0.8307084833172301], [0.010657585799845437, 0.3068720331355431, 0.034074221133960414, 0.010156964314259831, 0.3026730945361942, 0.9384925396862058, 0.5820036437728213, 0.3715220571096386, 0.6725636462507039, 0.29383577852565756], [0.8215207792096825, 0.5957744083397736, 0.48106625257138036, 0.06494093556476721, 0.4848882916481273, 0.45710230640678984, 0.022419516692125163, 0.05971950215832744, 0.6078358273132918, 0.8930838201122125], [0.4694885348335407, 0.6841883571644781, 0.9764613809790981, 0.8963429007863771, 0.9756864111132929, 0.35953942912826764, 0.5586971819606391, 0.03788694251361591, 0.45258166589850735, 0.5125548944547246], [0.2543833446453594, 0.9819668088635447, 0.556741123663313, 0.12269038398606535, 0.36520281688558476, 0.8380539335782048, 0.5739157236982507, 0.08905129727131922, 0.33671220606517616, 0.10610745918232545]])))-7.043371629543681-7.677025870510918-abs(5.6875252834295535)), axis=1) |
np.mean(10*(np.cos(2*np.pi*5.30081988900848+array_x))*4.698696337582951, axis=1)+9.350017958911753-array_x[:,0]-1.1500537837978368/np.cos(2*np.pi*np.sum(np.exp(array_x), axis=1)) |
np.mean(10*(np.log(abs(np.sin(2*np.pi*array_x-5.989748234226031)+np.log(abs(np.sqrt(abs(1.0436702068769148))-np.sin(2*np.pi*-((np.dot(array_x, np.array([[0.20130853489622336, 0.7997531891088312, 0.25616767024508935, 0.0961690553179746, 0.8193913772792354, 0.060113796858636825, 0.10613769030743858, 0.7139181123956944, 0.5812157462671071, 0.06531754117388999], [0.05243236090261427, 0.4727971738143858, 0.27532535039759964, 0.9511437469277881, 0.4335115900570762, 0.7356972261389727, 0.9905363578535947, 0.9447267673723823, 0.5281346209675384, 0.6700029939220408], [0.8084409511524641, 0.5794766269959556, 0.12000821547657514, 0.3950567902997387, 0.7307954032223555, 0.1734281079497254, 0.6555874439176731, 0.16051054128198783, 0.13123420475029568, 0.555749900637748], [0.32812251376696755, 0.1060843891102442, 0.5030990447048893, 0.2172989002001139, 0.8436691726514365, 0.015841373992892427, 0.010712178931392424, 0.7154234864325869, 0.4999721786926016, 0.047231012053953525], [0.9059902402432641, 0.4261763413267704, 0.5491870784952331, 0.17516358560663314, 0.5454802118158583, 0.6381949285561241, 0.43284914226623816, 0.193941986042379, 0.7355829703969284, 0.11092366560034261], [0.5722202085465088, 0.8686282747770442, 0.9304801675187553, 0.16533051166289536, 0.972632041271534, 0.6314021973019529, 0.8107231769157005, 0.46155255522909844, 0.7644500853179429, 0.060361712496874165], [0.9555435580786182, 0.5472953329713207, 0.3593782530917321, 0.8727416702960873, 0.42038815206762326, 0.9532265388715582, 0.7691997351580109, 0.10758629853339885, 0.8534708418699125, 0.4156888372573676], [0.21739226340524298, 0.5467025398724428, 0.06789863517478845, 0.6928167623428839, 0.05919126653962359, 0.12264308868702889, 0.005580407657080899, 0.2623322210276563, 0.09902335737013679, 0.6632850994467766], [0.6242153527365137, 0.03335122707404148, 0.006838417509875572, 0.9034225824061527, 0.7743543303522672, 0.92443340750472, 0.7757219496177145, 0.28255501738688205, 0.21755673254217844, 0.2864175928731604], [0.4734577941064412, 0.12834481119585994, 0.40457489157834403, 0.9754486642482093, 0.7538300937076193, 0.8508559676022961, 0.9043987375675409, 0.12017736689130787, 0.42359147718722356, 0.9572574664252944]]))))-np.exp(6.768802395776423-array_x))))))), axis=1) |
np.mean(np.exp(np.sqrt(abs(array_x*array_x))*4.627159619025267-np.cos(2*np.pi*np.sqrt(abs(array_x-np.exp(np.sqrt(abs(1.7374322641585895))))))), axis=1) |
np.exp(np.amax(8.392012209171227+array_x/7.495710128768022-7.397028091430106, axis=1)+5.355579596668151) |
np.exp(2.4706020064536762+np.mean(array_x, axis=1)/np.sqrt(abs(2.6965581363096716))+np.sum(np.square(array_x), axis=1)) |
np.mean(np.square(np.square(abs(array_x-5.839296725347465/10*(array_x/7.713949775571624)+3.770280519429808))), axis=1) |
np.mean(1/(-(8.677912090874608))-np.exp(5.495997233095617)*np.exp(np.sqrt(abs(np.sqrt(abs((np.dot(array_x, np.array([[0.053211857884639, 0.174113074440721, 0.3798496172223963, 0.7701447824966062, 0.0036206312135240726, 0.5449958857274919, 0.427865813500795, 0.792196002527976, 0.45844237836127644, 0.6974405558462355], [0.3716807349240039, 0.8695107620622872, 0.8980841777766341, 0.022449015067527944, 0.7141378155780004, 0.04117821619086681, 0.6008337866494023, 0.23927836086697607, 0.9006977611525088, 0.7632017547803934], [0.6788555431103269, 0.8655994081987115, 0.5189817758636344, 0.4004749265748929, 0.7368602028974541, 0.3227060467161069, 0.3899402956743725, 0.8294063328562167, 0.20609700116359986, 0.606958407121858], [0.2240242659394588, 0.14661956781140006, 0.7167775330786602, 0.7187824701011417, 0.6801157173115403, 0.9958379997353327, 0.2356627264700698, 0.6565008019015877, 0.8055480332264999, 0.5195903257695516], [0.10064938777565435, 0.05773245758300938, 0.48734736219496266, 0.8567993732482484, 0.20534650351193584, 0.09125791074428957, 0.9318931401751707, 0.3586331390364169, 0.10596256211633526, 0.5120734523832412], [0.4675712001298864, 0.7010434055439304, 0.9884610306777286, 0.9374406791683717, 0.3264496683506867, 0.20456248428033597, 0.899319975270803, 0.4743191929521341, 0.6919319210289245, 0.7911259648820332], [0.27275434548565247, 0.6400490935023486, 0.8691604940026063, 0.3282980537861765, 0.6502732732587816, 0.5194666613821396, 0.7344947942090754, 0.290309040659134, 0.018687893763080776, 0.47352168756520374], [0.06570910880780934, 0.2397605158583721, 0.4509469930092903, 0.5656484321620573, 0.5488246629285847, 0.6629883372950721, 0.19415471836965825, 0.6009891661790207, 0.6992123875291577, 0.3813801248736074], [0.964769393858442, 0.850715843781192, 0.008448971800630534, 0.9258172113814525, 0.6935851159105083, 0.82333432214597, 0.7185433338281682, 0.6371103523616946, 0.5160052390783855, 0.470650241579571], [0.14327387297996885, 0.7226090321955825, 0.9551043814463726, 0.7411735414152087, 0.9724766054006488, 0.7704812368583042, 0.35949415280448327, 0.9009419087167814, 0.5286797670318341, 0.1367008626421341]])))))))), axis=1) |
np.mean(np.sin(2*np.pi*9.068182703925537)-(np.array(range(1, array_x.shape[1]+1)))*array_x*9.983852135948055+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.mean(4.952891497060828/np.sin(2*np.pi*array_x-1.8277373574281957), axis=1) |
np.mean(np.log(abs(2.0197033499245505-np.exp(array_x)))+array_x*array_x*9.276107779583246, axis=1)+np.sin(2*np.pi*np.mean(np.log(abs(1.74828499781117-np.exp(array_x)))+array_x*array_x*5.119687044550036, axis=1)) |
np.mean(np.exp(3.572625295023-array_x)*np.exp(9.87794551547467), axis=1) |
np.mean(np.exp(np.round(array_x*(np.dot(array_x, np.array([[0.6486600114002498, 0.6007177360822724, 0.7131210301486721, 0.030228231631878377, 0.005456290563337851, 0.15104812636274567, 0.32337058934565344, 0.6923482427679566, 0.07751014809135182, 0.2720930923807653], [0.6002132409216194, 0.7189565327276706, 0.42757839675079334, 0.9663779650186317, 0.9913880559113389, 0.634985764443404, 0.17939131750007398, 0.4799069366104216, 0.855383608090008, 0.49266377936720174], [0.767449880439051, 0.7411279886508587, 0.18114890411868878, 0.4943794845932634, 0.3004072433838474, 0.6829601227843268, 0.7624808761170748, 0.9728230599251996, 0.5772932333242206, 0.031423741811709816], [0.4235048435667357, 0.5839614531231325, 0.5309397876358837, 0.7143854461202728, 0.08359001526356513, 0.17599676426314836, 0.6398962896837083, 0.3988548204070219, 0.09042633489576257, 0.5808301449795603], [0.9028428881977535, 0.661559830729246, 0.27501641692570566, 0.7736784453441343, 0.29967295538663485, 0.954964337337602, 0.5019878919558637, 0.6956072588554746, 0.4011906934995182, 0.6452068302249628], [0.9617120951104696, 0.3776092479878962, 0.10336427690834382, 0.11278471032789772, 0.08831471296838544, 0.7184446589493723, 0.4025381887486592, 0.5035632471517009, 0.9279264476860277, 0.7104468363286], [0.5564310275052169, 0.8532841350101312, 0.07967286793794492, 0.9558190843089945, 0.7276104706209453, 0.9837954646756817, 0.18526327430654665, 0.5392228377099221, 0.6330922158094934, 0.26309962378411245], [0.424182596492778, 0.9574915559570856, 0.24608914920652525, 0.9153071823235172, 0.2617586828034615, 0.051080526924122904, 0.027515977704866357, 0.10108860372743422, 0.1994610932027162, 0.5364071220846441], [0.23848148927518165, 0.4315276249676422, 0.7961102545019715, 0.28578760149787474, 0.17419419423041427, 0.9737445031469419, 0.30562096187579635, 0.20247160910229278, 0.03982922034183878, 0.001755929647405452], [0.9321406133841205, 0.2590947926154016, 0.22791853892874858, 0.020265175161156446, 0.9123100458459005, 0.8504421112299619, 0.405974782309763, 0.4832660049483113, 0.9717407384825991, 0.7971347535281963]]))))-array_x+1.6921882622094828)*9.254202935071365, axis=1)+np.sin(2*np.pi*np.mean(np.exp(np.round(array_x*(np.dot(array_x, np.array([[0.04100285910898582, 0.12908656219790704, 0.8829856273297584, 0.4233700413860291, 0.7187593333223842, 0.7981169494728487, 0.9094793606089807, 0.5743547234199258, 0.7463342581372039, 0.4879040322075052], [0.026727810446807165, 0.5396613660004872, 0.8400761539896067, 0.6828364051101001, 0.7228424316736448, 0.6085098485611143, 0.4943581953403552, 0.5773070985916865, 0.2414695811588622, 0.28863927876866713], [0.20768895414299005, 0.7294394764800104, 0.10380386185552681, 0.31658950608481995, 0.28122155206390076, 0.24773529269278804, 0.485517957146407, 0.8968425664945733, 0.10823748520356025, 0.7167535858831627], [0.40871996026476587, 0.458831905929603, 0.7050421400616972, 0.6242008053505692, 0.5586946243236299, 0.7483452751110186, 0.15771327471508845, 0.21110774436381108, 0.8944891620373052, 0.6874292713502188], [0.48064639262067377, 0.9254269776479227, 0.7694242289992762, 0.05213180213572943, 0.18861760469056554, 0.317667478281248, 0.7327390851310581, 0.09765412599413992, 0.3995487124504101, 0.3610795163845142], [0.12131887790299378, 0.600771270240158, 0.7648425784502899, 0.1594291248355929, 0.22872522918945226, 0.04369477752157247, 0.18954629797870637, 0.9372029498073057, 0.8201867391383448, 0.05439034577440183], [0.7436152353778126, 0.08568665906524997, 0.6070330569233687, 0.666014893989517, 0.9448564969636507, 0.5009096299613895, 0.2919423000038852, 0.07146906983459733, 0.705193361763974, 0.10550330921710704], [0.2949909580230028, 0.6392257160623243, 0.8857616612290871, 0.34431945252993146, 0.806725888146061, 0.36961241136730727, 0.03132992723637662, 0.8291018362199567, 0.6393859221728769, 0.02565790750501773], [0.23827494190474718, 0.6900011749926835, 0.9233508634087225, 0.047626568299652616, 0.5018212167257513, 0.759654573575767, 0.06381007693317342, 0.6024005147337, 0.4293296853926859, 0.5222368449210512], [0.6832998292262804, 0.6209798472203691, 0.019178793438027086, 0.0712411878578133, 0.5428394552996015, 0.6972170834011467, 0.7371675497238342, 0.3564455409726349, 0.7927278722995211, 0.8684189250286134]]))))-array_x+5.1893587320571495)*4.317656796517857, axis=1)) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*1.0821229894529227+1.388003464424906*10*(np.round(7.095832847036719)*array_x), axis=1) |
np.mean(np.square(9.853981267271823+array_x/6.607851700965158+2.189451696396171*np.sqrt(abs(-(6.957783717667572)-array_x*2.43936766140646-array_x-7.60872003413476))), axis=1) |
np.mean(7.826624385644185-np.round(np.exp(abs(array_x)))+np.sqrt(abs(7.066589482209034))-np.exp(3.907270087527195+(np.dot(array_x, np.array([[0.998827193433379, 0.1849219532694869, 0.9849255303296383, 0.16302916561055814, 0.8934385739805154, 0.493957801503739, 0.5339042901475854, 0.6180619246110585, 0.9093328332262094, 0.32764009001759575], [0.09083880867123251, 0.9229586890132823, 0.4352346225148791, 0.22427308854574246, 0.25403314549130207, 0.27363803697583877, 0.05293301402877826, 0.2142206139059496, 0.6407921874938678, 0.14732462148061387], [0.2036743645132888, 0.5549373300790813, 0.8531713445522539, 0.3838701482696829, 0.2486383619985969, 0.020629882553103163, 0.6378756689976748, 0.9161822694267096, 0.937530570409726, 0.8374359665464682], [0.5370011652739343, 0.5607124891682475, 0.8462919462531479, 0.19178501411242488, 0.384050105091738, 0.24596554839941942, 0.6412187738201216, 0.3087477349315684, 0.30143200826473227, 0.3085977397178393], [0.14433628378983787, 0.13141835343385755, 0.484836126160062, 0.43341495691771514, 0.8469219718721961, 0.479088774928887, 0.453532012998914, 0.6782275836108044, 0.8524752105623741, 0.5898656655144123], [0.0834866806188218, 0.253357733657675, 0.6760332960472498, 0.7535738085933865, 0.1440341245024177, 0.839631651139175, 0.6615549476576197, 0.560168184882616, 0.004768150397852167, 0.7357923492021511], [0.026536065232141914, 0.7090260414046003, 0.19487440246150323, 0.23391433723048205, 0.45680235940663594, 0.19171441791894506, 0.24398458592684868, 0.1657410019263338, 0.19154172646164702, 0.4187439398901174], [0.9977998198772023, 0.016619165757455034, 0.07603099538843106, 0.7073015076541314, 0.7965329710978541, 0.08711233977741972, 0.8152436657492856, 0.8929403695949971, 0.49234948324313355, 0.3765490116499536], [0.02149571047346499, 0.6986191899570756, 0.098651165366621, 0.6857071130101657, 0.20941669593162127, 0.6535873319083699, 0.44026362147225506, 0.8477024017913477, 0.7437855747535191, 0.7221474895260339], [0.03616962339162055, 0.8996164788246594, 0.7284325502082449, 0.5763457749328472, 0.8232063210053517, 0.8443027367111118, 0.22755967884429662, 0.16124837899647948, 0.4947231650103934, 0.6171521765669443]])))-array_x), axis=1) |
np.mean(2.8192725351167924-array_x*3.7614404434754647+4.240165356372362, axis=1)+10*(np.sin(2*np.pi*np.mean(5.166058679606232-array_x*3.384844504445619+5.975357462648327, axis=1))) |
abs(np.sum((np.array(range(1, array_x.shape[1]+1)))-8.984136691798353-10*(array_x+3.8264163502079627), axis=1))+6.450236962017114 |
np.sqrt(abs(np.sum(array_x, axis=1)))-7.732624080712428/np.exp(5.487331311552091)+10*(np.sin(2*np.pi*np.sqrt(abs(np.sum(array_x, axis=1)))-3.5284741650130984/np.exp(7.285529071406266))) |
np.mean(np.sin(2*np.pi*np.log(abs(9.950644593480087)))-np.round((np.array(range(1, array_x.shape[1]+1)))*array_x)*np.exp(9.85100161231807), axis=1) |