Datasets:

---

BasStein

/

250000-randomfunctions-5d

like 0

np.mean(7.7060435823382525+(np.dot(array_x, np.array([[0.13247985840484644, 0.9519294071175493, 0.34281035421179473, 0.36313608210178494, 0.2627454474886417], [0.07306716430468319, 0.613531980962221, 0.9161675359461328, 0.9894003945408262, 0.8142434158795765], [0.5477005731033988, 0.04123685604281113, 0.35833289393299794, 0.4203208532229493, 0.5876870263104356], [0.198366034895335, 0.9039753350695641, 0.15345734837173075, 0.047728909822084664, 0.011295910667001086], [0.717699024544292, 0.5016921948922098, 0.32608060589614907, 0.039898813395495614, 0.5853344401592364]])))-4.621075214704785+np.sqrt(abs(-((np.dot(array_x, np.array([[0.8165216082952165, 0.6637392824771385, 0.49738283361212354, 0.03668828440653937, 0.8476116558642629], [0.12487567032084002, 0.1882742342602608, 0.11298338739972436, 0.23488164332041028, 0.42027190245243484], [0.3356362443727118, 0.4530249472593073, 0.9842776191255421, 0.9996354471085005, 0.34277160365942183], [0.7009845673845679, 0.04463442126966166, 0.7979815313201492, 0.16269601358112729, 0.5336580618881358], [0.19144345141627128, 0.42234571468646354, 0.3349247271622914, 0.5673384445734714, 0.039401143287904894]]))))))*abs(10*(10*(6.377163009991401))), axis=1)

1/(np.sin(2*np.pi*np.sum(np.square(array_x-np.exp(7.385609609614093)+7.097010506820102), axis=1)))

7.107594958641729-np.mean(array_x*7.910444381418573-3.5954428168405075, axis=1)

np.cos(2*np.pi*9.790061838638115)-np.prod(6.155067945090829+array_x, axis=1)/np.exp(np.log(abs(np.square(9.612231014435725)))-np.sum(np.log(abs(array_x)), axis=1))+10*(np.sin(2*np.pi*np.cos(2*np.pi*2.9847730094761884)-np.prod(6.199261160906662+array_x, axis=1)/np.exp(np.log(abs(np.square(4.384527016400083)))-np.sum(np.log(abs(array_x)), axis=1))))

np.mean(np.square(array_x-3.8622983233586567)+np.cumsum(np.square(np.cos(2*np.pi*array_x/9.013448560208772))+np.log(abs(1.3267481741886942+array_x))/abs(9.867184691852884), axis=1), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(array_x-5.288330427497128)+np.cumsum(np.square(np.cos(2*np.pi*array_x/8.32889567367101))+np.log(abs(5.9872611801608295+array_x))/abs(2.8158086762970242), axis=1), axis=1)))

3.1035531830374605-np.square(np.sum((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1))

np.mean(np.round(array_x-2.735938453392872)-3.2968795186434736-array_x*8.964540369776895, axis=1)

np.square(np.sum(1.4237684059451383+array_x+array_x, axis=1)*1.9909465700967477)

np.mean(np.sqrt(abs(array_x))*5.635869348333478-np.square(3.357026643121026), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(array_x))*7.800946637063856-np.square(5.569410687185462), axis=1)))

np.round(np.mean(8.0851332375683-4.061891630301901-array_x*np.exp(8.354797409169379), axis=1))

np.round(1/(np.exp(2.098644751149868))-8.81364073939557*np.exp(np.sin(2*np.pi*np.square(np.sum(np.sin(2*np.pi*np.exp(array_x)), axis=1)))))

np.sum(10*(np.sqrt(abs(array_x)))+1/(8.44061015496231), axis=1)

np.mean(np.square(7.321292156558861)+array_x*8.023403238764665*-(6.15338113139926), axis=1)

np.mean(np.log(abs(np.square(1.468740301245148-array_x/(np.array(range(1, array_x.shape[1]+1)))+(np.dot(array_x, np.array([[0.4605292387733777, 0.43298150881489716, 0.6331917666089394, 0.24649684027692265, 0.9053554005019101], [0.15386089078889975, 0.44219037038243403, 0.1620953058026382, 0.9082942777528591, 0.6230676000260266], [0.4561312010506905, 0.5829580081022494, 0.33106594918499466, 0.5900811288651866, 0.7318401132134812], [0.14921728951123647, 0.5663295019656313, 0.34864942592650594, 0.8732655531685879, 0.20121384834724687], [0.9110409912843629, 0.1365191599600497, 0.8125692557813727, 0.2004165331814537, 0.23681723373800956]])))*np.round(np.sin(2*np.pi*array_x))+8.813868440810326))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(np.square(6.998264812240078-array_x/(np.array(range(1, array_x.shape[1]+1)))+(np.dot(array_x, np.array([[0.17038632493172545, 0.11086186717877666, 0.3878852723223096, 0.2468583665282823, 0.20010825544945543], [0.12749526756370666, 0.3366123869322796, 0.09258591915681924, 0.23084501975624983, 0.1364881711489161], [0.9295529977237502, 0.13926901229628352, 0.05126198782763247, 0.5577912256932044, 0.6626042280419691], [0.3405970591884482, 0.7732044781890489, 0.45930342425275716, 0.25862333170903584, 0.32779608484363343], [0.31042790636160045, 0.4222889763596972, 0.19849427219756444, 0.8244460754594132, 0.8249270618386498]])))*np.round(np.sin(2*np.pi*array_x))+9.311893878275638))), axis=1)))

np.square(np.mean(np.sqrt(abs(5.787319165320289))*array_x/10*(array_x)-np.square(array_x+8.978726771600993), axis=1)/6.56005241421236)+np.sin(2*np.pi*np.square(np.mean(np.sqrt(abs(6.696210793811207))*array_x/10*(array_x)-np.square(array_x+5.972096310380353), axis=1)/2.6909090475023705))

np.mean(8.2965063698555*np.exp(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.572534460155391, 0.2932765462320227, 0.1666141214407445, 0.12102161454284521, 0.5900996151143004], [0.6545070714068437, 0.19746432831136107, 0.10417781891384481, 0.011634290245876255, 0.5973681338508652], [0.6026530699264834, 0.27735005768621357, 0.47719144741814556, 0.9222505867496811, 0.4169426388412275], [0.9396656337709468, 0.4194407818929652, 0.2868064639431631, 0.18733827605282094, 0.9168915587954299], [0.07665230978800452, 0.6961809704057681, 0.6668692609796015, 0.18042502969254992, 0.054700796612834]]))))), axis=1)

np.mean(7.906340375649517-array_x+6.230949589327284, axis=1)+10*(np.sin(2*np.pi*np.mean(4.317747597471349-array_x+5.366498106895739, axis=1)))

np.round(np.mean(np.round(10*(9.037503773269343+np.round(5.194836733160176+array_x))+7.206211364716372), axis=1))

np.mean(np.square(array_x)/10*(1.5418963910436612)+np.cumsum((np.dot(array_x, np.array([[0.21094966241416513, 0.057559266844030565, 0.7034243159759629, 0.9943859348453858, 0.5811793240992205], [0.354631685900485, 0.7705176938352679, 0.9159428439783862, 0.9680571015873801, 0.1854922852301043], [0.0850601616627431, 0.7331208707075159, 0.02141721728999546, 0.7073987015508617, 0.15702583371829648], [0.9045368276210506, 0.45961437685677253, 0.19625600082567551, 0.34188731802160455, 0.4142905755479246], [0.4178000407822756, 0.6274813672238256, 0.42393585316908455, 0.8753915665586861, 0.5233515046396928]])))+array_x, axis=1)+-(np.cos(2*np.pi*9.530617369104426)+np.square((np.dot(array_x, np.array([[0.9522619471632044, 0.5808197464355784, 0.19009127070611376, 0.9026413194127819, 0.8908766460941422], [0.42217345598564493, 0.39634739130372043, 0.9492380746370612, 0.6983146181866389, 0.5387471023805026], [0.8960923028586182, 0.39553919137532234, 0.5168884648846865, 0.3406791540740982, 0.6470925019876766], [0.47093701063231486, 0.705762662689321, 0.7689358285441682, 0.020184716402783853, 0.4512749281946139], [0.4318284189778292, 0.9340672597399663, 0.9885897073531558, 0.8150271394913935, 0.11824028476934789]])))))*-(6.187939533501098), axis=1)

np.mean(abs(1/(6.722856030648267))-np.round(9.843205868356542)*array_x+array_x, axis=1)

np.mean(9.214106197362144-array_x+np.square(array_x+9.238701118078506-3.703312319305626), axis=1)

np.mean(np.log(abs(array_x-5.486345584116817))-np.exp(1.4154874251544973)-np.sqrt(abs(np.exp(array_x+8.017697006377938)-np.log(abs(1.5200575717827216)))), axis=1)

np.mean(3.3377854851694932-(np.dot(array_x, np.array([[0.4022725600211048, 0.7220379238242398, 0.018057621228039555, 0.8552013216504532, 0.6956448827471462], [0.3910667074116564, 0.2848379629334502, 0.7643583139596425, 0.4335221969718134, 0.9217301269454826], [0.3402886679690059, 0.21261557098903727, 0.6936411414203063, 0.45952278713434513, 0.5473959821971345], [0.945248528566079, 0.30102633426698133, 0.5055717339218161, 0.5705707381501933, 0.6084129419921803], [0.3820705240100972, 0.1034105368048821, 0.33597347779468856, 0.1383206050209287, 0.9092459230374648]])))*abs(np.exp(array_x)/np.sqrt(abs(1.1855880343684944-array_x))), axis=1)

np.mean(10*(array_x*6.282748215043156+7.869939755755984), axis=1)

np.mean(np.cos(2*np.pi*np.log(abs(8.572345898219897+array_x*np.square(3.5433149297324915))))+10*(5.736573342709419-array_x+2.5756955486013853), axis=1)+np.sin(2*np.pi*np.mean(np.cos(2*np.pi*np.log(abs(4.2016567774900215+array_x*np.square(5.26062707621499))))+10*(9.463423597755959-array_x+9.385619068833558), axis=1))

np.mean(8.749542672812304*(np.dot(array_x, np.array([[0.7158389980347964, 0.34526169594166534, 0.7884348869545039, 0.5037268135203153, 0.8138396754204393], [0.40001474200233267, 0.9230037805280428, 0.8471889126595785, 0.11590242185762634, 0.48604770170661815], [0.5597788671960362, 0.3201643818703165, 0.4258016151616034, 0.5656431730736945, 0.04707953449576929], [0.753632075002809, 0.5502721619407747, 0.06980075076485603, 0.828551364182313, 0.048956274996548754], [0.4050530227128104, 0.7448442478338054, 0.5009237170936444, 0.5879705390036322, 0.4017480868526222]])))+np.square((np.dot(array_x, np.array([[0.7077064802697639, 0.4072758456448403, 0.6954405800668625, 0.9899789443245964, 0.8756649937029638], [0.7303492576474088, 0.47724885127315686, 0.5941072341763354, 0.5256382156860021, 0.36382864591065134], [0.014000545341681492, 0.5780208956872482, 0.07445156614292825, 0.4692433239871098, 0.9980562732068167], [0.3520916793523077, 0.3824444560133785, 0.26690715006682497, 0.9954687760602631, 0.6191509669262945], [0.658935624519384, 0.11512125968869502, 0.03837085139583629, 0.5858078337147359, 0.5840199540566463]])))/9.10059745078862)-np.square(4.601138487226365), axis=1)

np.mean(np.round(np.cumsum(np.cos(2*np.pi*array_x)-1.7134416994954629, axis=1))-np.square(3.609200995183128), axis=1)

np.mean(array_x-5.016600471311162-9.817434012978083*array_x+np.log(abs(1.6223615135543494+array_x-9.343346676924865+5.538634729943942/(np.array(range(1, array_x.shape[1]+1))))), axis=1)

np.sum(3.2436201715285407*array_x+8.743543221090338-np.square(4.330695949184993), axis=1)

8.472606891793603+1/(np.cos(2*np.pi*np.sum(array_x, axis=1)+9.75006959759271))+np.sin(2*np.pi*6.779756501775825+1/(np.cos(2*np.pi*np.sum(array_x, axis=1)+3.739087745238868)))

np.mean(7.8855407375039634+9.925626999392252*array_x, axis=1)

np.mean(8.94545938850057+np.square(4.421542498496807+array_x+9.030039665769625*array_x), axis=1)

np.square(np.sin(2*np.pi*np.sum(array_x*array_x-5.076027847418299, axis=1))-1.5265926369426028)

np.mean(1.527222317414516*array_x*np.square(6.768501815657405)-np.exp(np.sin(2*np.pi*np.square(4.240868939683705-(np.dot(array_x, np.array([[0.635689205783304, 0.09074788272058942, 0.9865704540045016, 0.05555194543708131, 0.3542827929314736], [0.9333889644614668, 0.0013418447268009892, 0.27025258728842705, 0.42008771618584484, 0.978307772252385], [0.7555728799981056, 0.9552716265530844, 0.8129293788611484, 0.27986435776277285, 0.7515928983623594], [0.42816059095413783, 0.4880599489738242, 0.051093598157500475, 0.832549145187068, 0.5081919908473123], [0.12758280809091305, 0.3724151741242705, 0.5162712120592642, 0.5208172337902917, 0.0707160511219489]]))))))+3.7281407282445445*10*((np.array(range(1, array_x.shape[1]+1))))-np.cos(2*np.pi*8.927384186113287), axis=1)+10*(np.sin(2*np.pi*np.mean(7.634804443570106*array_x*np.square(8.458167865618663)-np.exp(np.sin(2*np.pi*np.square(8.129156139398663-(np.dot(array_x, np.array([[0.9389817978958368, 0.35752788917737455, 0.3831842710337827, 0.2577584236301377, 0.10965837604452777], [0.5633736177470857, 0.43925510389128153, 0.6802405054291629, 0.48828183290411065, 0.1756574867688726], [0.3530810057408036, 0.4105786909604845, 0.18850220196838863, 0.720948401870374, 0.2000130159755874], [0.2553461275623379, 0.6760779857813497, 0.032131879188606605, 0.6516786444613119, 0.21179151787005845], [0.08608579638591507, 0.23975730874750079, 0.5972628970078769, 0.6371979344512622, 0.6079869591150704]]))))))+2.2468724488776486*10*((np.array(range(1, array_x.shape[1]+1))))-np.cos(2*np.pi*3.2657479787940606), axis=1)))

np.mean(6.729646273115175-np.round(5.993619706017759+array_x)*5.404985265368452, axis=1)

np.mean(10*(np.sqrt(abs(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x)))))+3.1409179746497173, axis=1)

np.mean(10*(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.12543743492238313, 0.6639906897687784, 0.9146090625673918, 0.3978997535053289, 0.7500093573096509], [0.07447751110549161, 0.7305366926704471, 0.2505251310615838, 0.04753507757705078, 0.43217277972775103], [0.7243545375837706, 0.3090925762145489, 0.0827398215674855, 0.7749692396145813, 0.463752913580591], [0.05414921615122936, 0.10307423379325953, 0.46792133624319887, 0.036774713008119075, 0.05799278075763625], [0.6501940107491305, 0.299430352244925, 0.7541114699269613, 0.8977113726329332, 0.8715439355495662]]))))*np.exp(array_x)-2.286229279484142), axis=1)

np.mean(np.round(np.sqrt(abs(array_x))+4.3112361206962975)/9.236878747716817, axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(np.sqrt(abs(array_x))+8.625545878498428)/6.494858323955951, axis=1)))

np.sum(array_x, axis=1)*np.round(5.475162616696059)+4.36294580563875

np.mean(array_x/1.0293947079303543+array_x+np.exp((np.dot(array_x, np.array([[0.7462501519496003, 0.6284925985963863, 0.12874982100555676, 0.11669118515955601, 0.2144725126331407], [0.4228764868451327, 0.32184756367612666, 0.5975497010763092, 0.044561926369752536, 0.3291247408611685], [0.9161916023696686, 0.5694031166429808, 0.4816155659329996, 0.6022425235445448, 0.8540833851527105], [0.7757113697707296, 0.20979038420563245, 0.4246448082683921, 0.30874921192995775, 0.07881666558429767], [0.24541619406322124, 0.9610633131351874, 0.7483393738389892, 0.6507200459267218, 0.032584428963978396]])))+5.6217907698956635-2.8062674134712826), axis=1)

np.mean(np.exp(np.sin(2*np.pi*1/(np.cos(2*np.pi*np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x)))))+9.055641446712384-np.square(4.020841399794328/(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)

np.prod(np.cumsum(array_x-6.379063496316261, axis=1)+np.log(abs(9.173111414656116)), axis=1)

np.mean(np.cos(2*np.pi*abs(5.342235501354208*array_x-(np.dot(array_x, np.array([[0.17109089097933083, 0.32022635877083516, 0.4894365108007116, 0.17665483494447665, 0.21043498554278262], [0.39380775374585786, 0.5746610982799067, 0.5626870031953437, 0.25830808589462617, 0.08967835831528859], [0.9193109364285401, 0.23018939299815766, 0.22959238957832306, 0.19765723572035065, 0.7602638347989433], [0.43477784561161803, 0.1754821387462372, 0.5496813135323283, 0.01351757165693912, 0.25061884529982925], [0.7370044855650285, 0.9425798018382755, 0.24350088687205307, 0.8287044280955691, 0.9118949390429077]])))/4.326600004912065+np.square(6.592614394528731)*np.exp(np.square(8.901147470682744+array_x/7.0612670452478365)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*abs(8.569411661646198*array_x-(np.dot(array_x, np.array([[0.24293367606188088, 0.08599379935510254, 0.720108259315126, 0.15350769176386836, 0.4995332043660424], [0.6825301596021175, 0.8669713188929137, 0.8206532158730074, 0.23951900644569923, 0.37235405909429853], [0.39602075285923033, 0.32671636202150234, 0.9282840427855299, 0.14300488118536092, 0.970149128353582], [0.9845111807139297, 0.5888595846943147, 0.8252993822098617, 0.4107926158369746, 0.4381105196225906], [0.022871161723981626, 0.5545456126203723, 0.4648871473542141, 0.8217436815452065, 0.025008108812235696]])))/4.561291505701989+np.square(9.174694440586505)*np.exp(np.square(5.93541537187536+array_x/4.677390494726334)))), axis=1)))

np.mean(array_x*7.189895566869311+9.180768627905747+array_x/3.869156739588173+np.sqrt(abs(1.1816744427430386))*4.701773793389288, axis=1)+np.sin(2*np.pi*np.mean(array_x*2.0442513932690387+3.958716348539173+array_x/2.1646413959899244+np.sqrt(abs(2.2603166283569576))*4.363045564194562, axis=1))

array_x[:,0]+np.sqrt(abs(6.967062958936856))*5.4722385755358784-np.amax(array_x, axis=1)+10*(np.sin(2*np.pi*array_x[:,0]+np.sqrt(abs(6.789995710281027))*6.359759272710406-np.amax(array_x, axis=1)))

np.sum(1.8623772151552025+array_x*5.614362651972469, axis=1)

np.sum(2.419295304163202+array_x*2.843948186011696, axis=1)*3.7594852980357545

np.sum(array_x+array_x/1.8375457876711465*array_x+9.369031572813542-array_x*3.3410684616402264, axis=1)

np.mean(6.156078156683185/1/(-(1.7453321786967737))-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(5.690223615975204/1/(-(3.0579738322884635))-array_x, axis=1)))

np.sum(np.square(7.4353007707548615)+4.1644154748350735-array_x+3.3574849099847506-np.exp(8.49681131519575*(np.dot(array_x, np.array([[0.4263942151003368, 0.4465418863963868, 0.6203503935651978, 0.06071787685051899, 0.4481733574467126], [0.19197081736453359, 0.46337069600153624, 0.9557701877905845, 0.5102336726809732, 0.7820372617992575], [0.6160304589969987, 0.2304541111853171, 0.7979757580797572, 0.5974684043052916, 0.6996387451959228], [0.48609655849581423, 0.7610716440699227, 0.7277797899603154, 0.2655367718814531, 0.6236634492736375], [0.8099719216690626, 0.4688115735738224, 0.5653443688976724, 0.3514834196598805, 0.4586969837700109]])))/(np.array(range(1, array_x.shape[1]+1)))), axis=1)

np.mean(abs(np.square(np.log(abs(4.953106555246664))-np.cumsum(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1))), axis=1)

np.mean(3.4493302229674723+np.square(array_x+2.975485896968112)*2.1683774661596447, axis=1)

np.sum(np.sin(2*np.pi*3.450695732614579/np.exp(array_x)), axis=1)-1.1348888542689464

np.mean(np.square(np.square(1.1272777553261206)+abs(abs(-(array_x))*5.159257507672575)), axis=1)

np.mean(np.log(abs(5.76711591864289*np.exp(abs(5.635598045386647)+10*(array_x)))), axis=1)

np.mean(np.square(7.050283296524037+np.exp(np.round(array_x))-np.square(7.402348204398221)), axis=1)

10*(np.amax(array_x*array_x, axis=1))*np.sin(2*np.pi*np.log(abs(1.7202888996475605)))-np.square(np.prod(5.236268518263399/1.2217278911655767+array_x, axis=1))+10*(np.sin(2*np.pi*10*(np.amax(array_x*array_x, axis=1))*np.sin(2*np.pi*np.log(abs(9.196642257711611)))-np.square(np.prod(6.791336570997036/9.672170426148938+array_x, axis=1))))

np.mean(10*(np.sin(2*np.pi*9.177437513397312+array_x))*np.sqrt(abs(1.0131425523078863))*7.9678772895870775-array_x-np.sin(2*np.pi*array_x*2.8088667040854074), axis=1)

np.sum(abs(array_x/np.log(abs(4.8453554494877125))+6.313155136470217-np.sin(2*np.pi*-(np.exp(array_x)))), axis=1)

np.mean(np.log(abs(8.63635299229446))-array_x+(np.dot(array_x, np.array([[0.12324454565945853, 0.0943204838808479, 0.38545669805769056, 0.22416649260589772, 0.4557795044415307], [0.11823642495617348, 0.10846871307829564, 0.7371273641164545, 0.3251875606360264, 0.45325910989375795], [0.5661562140211438, 0.6729219841109847, 0.7322619013863286, 0.609417333144058, 0.3335782433180333], [0.26393544926054935, 0.716731292361471, 0.21926739014143304, 0.48839199667365363, 0.8644596125557695], [0.1901407412494578, 0.5432732022215673, 0.24811563264903913, 0.2507611175965838, 0.7380353124870561]])))-10*(array_x)+np.log(abs(np.log(abs(9.798511468419417))))+7.900878677428191, axis=1)+np.sin(2*np.pi*np.mean(np.log(abs(1.191378635037202))-array_x+(np.dot(array_x, np.array([[0.970167868294105, 0.7916333749402299, 0.9093653099532084, 0.9544350329267847, 0.21487520758280154], [0.46940181053914476, 0.6452629494420867, 0.048125447343543515, 0.657486577761108, 0.8573975734185514], [0.14892781234206265, 0.3403986076818041, 0.4850000570355357, 0.9855302480556881, 0.7385351054403455], [0.23462149066752946, 0.7084563604089651, 0.25428046208792177, 0.2697447628149786, 0.5329732307784689], [0.8884342277570644, 0.5946558984778864, 0.04800825808813047, 0.9764301350344078, 0.5586694319202553]])))-10*(array_x)+np.log(abs(np.log(abs(8.669478866924416))))+5.764421075708721, axis=1))

np.exp(np.sqrt(abs(np.round(np.mean(abs(array_x), axis=1))-7.699961434312686)))

np.mean(np.square(np.square(7.449234016557595*np.sqrt(abs((np.dot(array_x, np.array([[0.7696997864608333, 0.4161242368721766, 0.8455580257813381, 0.47614571769823677, 0.2326901774702863], [0.23501801066518557, 0.48222752468230823, 0.6835944227641938, 0.2276233183182813, 0.03020425531804871], [0.3975956763857962, 0.051639708544709007, 0.21450851966278073, 0.09057988802048944, 0.7727889486444229], [0.25488160016803585, 0.7382281289001847, 0.47892617464685117, 0.8570287566351183, 0.5322245822594767], [0.5762750108952702, 0.29660889398987667, 0.3853471118917734, 0.278762284355794, 0.6858719474609574]])))-1.6064362046741536-array_x)))), axis=1)

np.sum(1.1071962181345611+(np.dot(array_x, np.array([[0.963799939897548, 0.885200531927766, 0.8327488546746852, 0.9067728511899541, 0.5337467858894348], [0.5293267076342614, 0.8817354237327367, 0.6047684636560263, 0.07762915398637726, 0.28478915619330525], [0.38603171603475095, 0.8814167467576534, 0.17082647905038706, 0.05570475292455013, 0.6420206095023879], [0.9636320831320707, 0.7005122833368229, 0.7312604712730351, 0.6552714200741924, 0.3315846165773687], [0.9031473035739691, 0.3978947478894781, 0.9583931590681125, 0.8711554278343121, 0.6886034267010688]])))-1/(abs(7.853289258911898)), axis=1)

np.round(np.mean(np.square(4.693305668670053+array_x+1.8212904241519003-np.sqrt(abs(6.654103444794424))), axis=1))

abs(-(np.exp(np.sum(array_x, axis=1)+8.978907378688891)))

np.mean(10*(1/(8.07684430594741)-abs(2.5795181576767323-array_x*6.842542083690864)), axis=1)

np.mean(np.sin(2*np.pi*np.cos(2*np.pi*3.2840330556395037)*5.964937073820383+np.cos(2*np.pi*array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*np.cos(2*np.pi*9.38467154161669)*9.369566214626701+np.cos(2*np.pi*array_x)), axis=1)))

np.mean(2.0785931971531717/np.log(abs(np.cos(2*np.pi*6.941306715140906)))-(np.array(range(1, array_x.shape[1]+1)))-10*(abs(array_x)), axis=1)

np.mean(-(np.cos(2*np.pi*abs(np.exp(array_x*6.243951078955949+8.567449744390814)))), axis=1)+10*(np.sin(2*np.pi*np.mean(-(np.cos(2*np.pi*abs(np.exp(array_x*9.901128792577584+6.02250547247726)))), axis=1)))

np.mean(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.553634900578723, 0.9942904717087254, 0.3418252943379366, 0.4828549239537365, 0.28306860517469357], [0.380318865912601, 0.7485337144726415, 0.2031266641765055, 0.9146266677314371, 0.5709070461446737], [0.5625175011242806, 0.49340701943986986, 0.1879793639059223, 0.8263212945593362, 0.8199707971542628], [0.4335146142277493, 0.5661136239554658, 0.9687912495807446, 0.23871729173389478, 0.5257498946017461], [0.057321867816525485, 0.8339250203085923, 0.8744907880278752, 0.005552344317029112, 0.06763035498588932]])))-2.8376168485757707)*3.6201840756276793, axis=1)

np.round(np.mean(10*(np.sqrt(abs(np.sin(2*np.pi*4.80124465020794)-np.square((np.dot(array_x, np.array([[0.9339236211308566, 0.7499176877968052, 0.7891735234560363, 0.1869782804973681, 0.8534426718293598], [0.946101077701981, 0.9748518097527087, 0.3696289950023506, 0.9865329921258258, 0.595721446609046], [0.5083885692509297, 0.9481484256081101, 0.698387304709799, 0.8058507649131262, 0.35207379590351395], [0.8398530463254237, 0.314981475475977, 0.039962093649006825, 0.7883196982290861, 0.7356308253246369], [0.7709282331246715, 0.5327756766994074, 0.9869396440352856, 0.5173984072822339, 0.48501997065531044]]))))))/-(np.round(-(8.879436251699769*array_x)-9.402135849889495)/9.806562079186975)), axis=1))

3.48476836352598*np.sum(7.299178058846006+array_x, axis=1)

np.mean(10*(-(2.8420198560973002-array_x-3.5481363519679334+np.round(np.square(5.705078833706213)*array_x*array_x))), axis=1)

np.mean(7.3080615235013+array_x+8.626728848175514*(np.dot(array_x, np.array([[0.6553247516975083, 0.16297679976242563, 0.5312623136757942, 0.24973529202869027, 0.8885229120365934], [0.9227802751979324, 0.9403701099120202, 0.7317664629629499, 0.9489769682583712, 0.16578713820542545], [0.8386168298424355, 0.9134818837431898, 0.5154065373449356, 0.20342538809551047, 0.5111088005810556], [0.6840779001169206, 0.8367051103162305, 0.927326629866635, 0.29777564014824587, 0.11497890547586698], [0.5086295077563315, 0.7072679220271827, 0.1280777625773325, 0.4191853154672902, 0.9639338608063206]])))+6.860047088045426, axis=1)

np.mean(array_x+np.log(abs(9.497442080049872))*5.667926766080174, axis=1)-np.sum(np.round(array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+np.log(abs(2.841674469552108))*1.212426519575783, axis=1)-np.sum(np.round(array_x), axis=1)))

np.mean(10*(abs(8.365312274129678)+array_x*4.919457866988968), axis=1)

np.mean(np.square(np.sin(2*np.pi*np.cos(2*np.pi*array_x+np.square(array_x)))*abs(array_x)-3.1529584347371005*10*(np.exp(1.9516562677051423))), axis=1)

np.mean(10*(np.cumsum(1.188234520271691+array_x*array_x, axis=1))*3.3765464046932694, axis=1)

np.log(abs(np.log(abs(3.8775708084430023))))-np.exp(np.sum(np.sqrt(abs(np.sqrt(abs(array_x)))), axis=1))+np.sin(2*np.pi*np.log(abs(np.log(abs(9.296082839238505))))-np.exp(np.sum(np.sqrt(abs(np.sqrt(abs(array_x)))), axis=1)))

np.square(np.round(np.sum(8.734327509689244+array_x, axis=1)))

np.mean(array_x*4.466918943169763*2.887719295109032+array_x, axis=1)+3.9026481806834186

np.mean(10*(2.7565024688394093+array_x+8.499129789011832)-(np.array(range(1, array_x.shape[1]+1)))+9.520278648893367-np.log(abs(2.9771044740807286-array_x))/6.8466558590957085, axis=1)

np.mean(np.sin(2*np.pi*np.sin(2*np.pi*3.43498284587798*2.0175448533806937+array_x))*abs(6.617809646910724-array_x*1.123275888832263*5.3616828287993545*np.cos(2*np.pi*np.sin(2*np.pi*6.527039039527048)-array_x)), axis=1)

np.sum((np.array(range(1, array_x.shape[1]+1)))*array_x*(np.array(range(1, array_x.shape[1]+1)))*array_x-abs(5.4530742543711685)+8.088943332437607*(np.array(range(1, array_x.shape[1]+1)))-3.903680860817591/(np.array(range(1, array_x.shape[1]+1)))*array_x-3.5416843559710305, axis=1)

np.sqrt(abs(np.prod(np.sqrt(abs(7.237673300450883*np.square(np.square((np.dot(array_x, np.array([[0.6418965852230986, 0.770204646554494, 0.6263312383817073, 0.6270771740955146, 0.5246662631641898], [0.3234609001073736, 0.42556419407285684, 0.8480330339559553, 0.005267143969573618, 0.8949023712955454], [0.8431056022909281, 0.10598251186536412, 0.6628479790084945, 0.9322373125099098, 0.7947882390296568], [0.6138800518022559, 0.1937456065698, 0.44680964425888725, 0.2945643667153015, 0.15182362273697447], [0.05555424920380447, 0.526715301688818, 0.4733723831068145, 0.018678271091512988, 0.32675964646061495]])))))+(np.dot(array_x, np.array([[0.6504663873072876, 0.07671624223102425, 0.7059596986853626, 0.012839825106514269, 0.28306660056737765], [0.3693832716037505, 0.36434652653528354, 0.3989984827813676, 0.360269788930238, 0.48841496399178197], [0.20392227221768677, 0.996860670999145, 0.48002370208229417, 0.47101525920547904, 0.15413097537571707], [0.10781867675304069, 0.3303981842436723, 0.534243227013473, 0.6125312005191051, 0.45732602935930944], [0.8931122012446306, 0.11570870060670524, 0.7905237512025264, 0.38163545003242394, 0.33644540998702566]])))-abs(np.sin(2*np.pi*7.933683933057977)))), axis=1)))

np.mean(np.log(abs(4.217086247570867))-np.square(abs(array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(7.020421828787737))-np.square(abs(array_x)), axis=1)))

np.mean(10*(2.592576046224374)*np.sin(2*np.pi*array_x)+5.890789024073214/3.4147283723206887-array_x, axis=1)

np.mean(array_x*6.341956470291795+5.8942172199310185-array_x, axis=1)+np.sin(2*np.pi*np.mean(array_x*5.710900835892307+1.3199227447560031-array_x, axis=1))

np.mean(5.325826323808812-(np.dot(array_x, np.array([[0.6550970704010184, 0.44171841781075905, 0.13899177685182373, 0.5204958186428591, 0.1264813238606347], [0.8629305981735237, 0.7963832411522827, 0.43501407707293505, 0.10666367598088444, 0.006292013358637383], [0.48562097874549015, 0.9179437880314021, 0.9986751592575517, 0.12658915877932087, 0.02744374257806803], [0.06554466382517288, 0.5990907878520131, 0.7397483707479542, 0.9499039733352729, 0.3834746835596735], [0.33037073922349447, 0.28740573519310075, 0.7816610022904963, 0.8695516847706575, 0.18048412645459844]])))*(np.dot(array_x, np.array([[0.195595765393076, 0.7375373785739626, 0.617540885530511, 0.5517416429793075, 0.6217369913080574], [0.0035876464268368435, 0.24568531817130335, 0.859524587485563, 0.47445946744305745, 0.35013555117966644], [0.1194583915468852, 0.7380060185945457, 0.6047409378345981, 0.0680682990434528, 0.3837514888495841], [0.7647224896641551, 0.7489616604579687, 0.8561934841453422, 0.4519342600208307, 0.6186175487137169], [0.7932729308216343, 0.5646033869825607, 0.6915345283766896, 0.766693793102058, 0.5516410102565934]])))+np.round(np.exp(1.9594852403605543))-np.square(np.log(abs(8.669599898535399)))/np.cos(2*np.pi*(np.dot(array_x, np.array([[0.465925099920515, 0.6380645880474177, 0.21296024352122345, 0.990893688854182, 0.5778260245558737], [0.47230200117193133, 0.5555292478226127, 0.9243402393370423, 0.6648891311121526, 0.7330271665965499], [0.04758366122248259, 0.7860994557834039, 0.4347043151125608, 0.6324203282826699, 0.6065705669575256], [0.2692103071554601, 0.8352675712008565, 0.1947250419815526, 0.25883962052100584, 0.6728932588981188], [0.983731202418652, 0.29199387047711445, 0.876912846671085, 0.8350147053199435, 0.14648044799515303]])))*8.746153926000815), axis=1)

np.mean(np.exp(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x))*6.370129698454004), axis=1)

8.925742864618204*np.mean(array_x, axis=1)+2.431849531577272

np.mean((np.dot(array_x, np.array([[0.0015895392311342516, 0.6288794124479371, 0.7604798029062119, 0.3740756454824741, 0.4719763004569316], [0.7372314223996944, 0.034993454406856306, 0.17039334403851347, 0.9691793814711527, 0.3401880922931835], [0.10850751587420149, 0.949459284258469, 0.14588972128804767, 0.7061068176609906, 0.9640583216371986], [0.8969396272463983, 0.0611409383815017, 0.6893054378955742, 0.013588071768836696, 0.8097299651619018], [0.23387854407227882, 0.38341215630484016, 0.4078321131285937, 0.47576776652831165, 0.07321636424227607]])))*2.7805312204543045+array_x+3.315075870487445/9.554072588546958, axis=1)+np.sin(2*np.pi*np.mean((np.dot(array_x, np.array([[0.707539763708424, 0.1631987766979892, 0.40480098529706376, 0.8325399194117603, 0.7246998672290711], [0.6583172922204965, 0.7441498443092862, 0.28537920845603004, 0.06301562945237726, 0.7910126984892862], [0.3664782239786194, 0.4626920251482367, 0.7046903625408718, 0.49819272235249024, 0.916914094744013], [0.8828116843412614, 0.445490920177422, 0.4001165491834423, 0.12939848981700586, 0.3210430615993828], [0.37649717550441686, 0.9472951569547723, 0.6447092529124505, 0.04893005778491477, 0.032384914400439224]])))*1.0943401029232005+array_x+3.9476626062160123/5.4525268258180395, axis=1))

-(10*(np.sqrt(abs(np.mean(np.exp((np.dot(array_x, np.array([[0.8679664950184425, 0.5984195331085225, 0.7421927125883365, 0.5598059035047557, 0.43451862360172755], [0.8252383965784851, 0.13520441639824177, 0.13010631997151367, 0.39039426358609763, 0.2970694983328346], [0.7687218268044927, 0.25964544671780576, 0.8788346070205851, 0.38072862938809837, 0.819378532195723], [0.6033590217480878, 0.9824264708817682, 0.03626774654245002, 0.044277573926489566, 0.6121277135303012], [0.5417494350022484, 0.939392177082587, 0.2662665492346379, 0.5999915858919753, 0.2633828905045098]]))))-5.573915948828812/(np.array(range(1, array_x.shape[1]+1)))+8.32825493962617-np.sin(2*np.pi*np.exp(5.140598164877508))*np.round(np.sin(2*np.pi*array_x)-3.7513720289388353), axis=1)))))

np.square(np.mean(7.50085969361936-array_x, axis=1))-3.6309175251089396

np.round(np.mean(10*(1.3596213461237303*np.square(1.4656243486502887*array_x+6.131820109675818)), axis=1))

np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x+4.2496821759774726/-((np.array(range(1, array_x.shape[1]+1)))*array_x*6.58420832002627+(np.dot(array_x, np.array([[0.21342256610692956, 0.48358009668654356, 0.12621597497783665, 0.733047813270686, 0.05397085577420935], [0.44792024570478994, 0.9680413151464597, 0.5270918553141222, 0.8450509441762891, 0.6585999414725482], [0.04275326161127524, 0.27631237586250934, 0.7266545468315491, 0.4413936142858448, 0.6267044857432406], [0.1908794522493884, 0.4319658652461984, 0.05526753269851081, 0.1270732772392973, 0.30013216757739103], [0.06328530909475993, 0.5508433691399478, 0.689903499378819, 0.6270333291639981, 0.7841784413877577]])))-8.227593288055353), axis=1)

np.mean(np.square(2.7801335353641017)+6.286212861525022*array_x, axis=1)+np.sin(2*np.pi*np.mean(np.square(5.87460661456062)+3.264794231908091*array_x, axis=1))

np.mean(np.sin(2*np.pi*1.3958300924743214/np.exp(4.220500294683983-(np.array(range(1, array_x.shape[1]+1)))*array_x))*6.481222969782309, axis=1)

np.mean(np.cumsum(6.80924360441048-array_x*np.square(5.831416632196266)+np.cumsum(array_x, axis=1), axis=1), axis=1)

np.mean(np.cos(2*np.pi*5.57984019716993)+np.cumsum(array_x, axis=1)+10*((np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)