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BasStein

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250000-randomfunctions-5d

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np.mean(np.square(array_x)*array_x+5.213018640474529-np.exp(3.681392025128144)*np.cos(2*np.pi*np.cos(2*np.pi*np.log(abs(1.5593875136046629))-array_x)), axis=1)

np.mean(abs(np.sqrt(abs(np.cos(2*np.pi*2.7012004841709403)-array_x+7.575803498548926))-6.600113120228784)+10*(8.355457876246476*np.sqrt(abs(array_x)))-7.025720463608698, axis=1)+10*(np.sin(2*np.pi*np.mean(abs(np.sqrt(abs(np.cos(2*np.pi*7.810551405288399)-array_x+7.214026778376109))-3.7258163153653214)+10*(2.9882472698325806*np.sqrt(abs(array_x)))-4.7682967698399725, axis=1)))

np.square(np.cos(2*np.pi*np.cos(2*np.pi*7.858894210748697)))-np.prod((np.dot(array_x, np.array([[0.29279403144051885, 0.15235470568773046, 0.4174863747960118, 0.13128932847325603, 0.604117804020882], [0.3828080591578541, 0.89538588428821, 0.9677946717985019, 0.5468849016694222, 0.2748235698675966], [0.5922304187618368, 0.8967611582244098, 0.4067333458357483, 0.5520782766919708, 0.2716527676061459], [0.455444149450027, 0.40171353537959864, 0.24841346508297102, 0.5058663838253084, 0.3103808259798114], [0.37303486388074747, 0.5249704422542643, 0.7505950229289875, 0.3335074657912753, 0.9241587666207636]])))-7.262320018697254/6.819400926631894/6.073955846307857, axis=1)

np.exp(7.715072702709154)+np.sqrt(abs(np.mean(np.log(abs(np.cos(2*np.pi*np.square(array_x)))), axis=1)))+10*(np.sin(2*np.pi*np.exp(9.321327445620218)+np.sqrt(abs(np.mean(np.log(abs(np.cos(2*np.pi*np.square(array_x)))), axis=1)))))

np.mean(1.9022287191387055-array_x/2.738873973786818*10*(8.153775588049156)+array_x, axis=1)

np.prod(np.log(abs(array_x*array_x+3.439312564451593))-5.985313295268398-array_x/9.055559184162185, axis=1)

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

np.mean(4.811279541227209*np.round(array_x+np.round(2.696336270671564)), axis=1)

np.mean(np.sin(2*np.pi*array_x)+4.730436080498922*np.round(np.exp(array_x+1.3046609544251773*5.599845201338839))+np.cos(2*np.pi*array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*array_x)+1.4210858570589247*np.round(np.exp(array_x+8.060820661536468*8.067876859357586))+np.cos(2*np.pi*array_x), axis=1)))

np.cos(2*np.pi*np.prod(3.901174129043973*array_x+6.250670900667211, axis=1)+5.4650218463261595)+np.sin(2*np.pi*np.cos(2*np.pi*np.prod(9.975001120862824*array_x+1.9924896864217487, axis=1)+9.683914085184577))

10*(4.6187567974421295+np.sum(9.816801342704983*array_x*(np.dot(array_x, np.array([[0.35798393686119634, 0.4351419865163296, 0.5909267255335692, 0.7223915186982364, 0.31763187327394615], [0.3289537599189831, 0.019691642723703717, 0.040874860094018306, 0.2578216943085364, 0.7402449976749567], [0.6283138303739122, 0.7697890206778347, 0.7689194362148217, 0.8565674676693013, 0.7203192659868836], [0.9790109190008228, 0.8988252193018174, 0.5867171662232342, 0.5881576704911717, 0.034267040352229494], [0.9985265777083543, 0.13157599736614178, 0.740347196631592, 0.8210151951243089, 0.3730545293052032]])))/9.78928074287094, axis=1))+np.sin(2*np.pi*10*(7.611751717988082+np.sum(7.728713058503994*array_x*(np.dot(array_x, np.array([[0.19685205466531375, 0.09875988679055503, 0.7486060058295778, 0.4526535292056957, 0.7137177590011357], [0.9154076488518006, 0.1465837361510567, 0.9191710007237996, 0.4116264595084367, 0.30526700989728905], [0.9430622606027791, 0.9906516926063994, 0.19889221776744814, 0.6568383469519833, 0.10649531377106036], [0.6509140038575058, 0.8273132277758497, 0.6844985465240676, 0.41733314206259575, 0.38306635956376955], [0.39312241522341707, 0.5897118179929232, 0.8815672700724956, 0.9290661572687678, 0.05352962020731811]])))/9.187058798462775, axis=1)))

np.mean(5.445933947041055-np.square(array_x+2.893636977469278)*np.round(-(6.785196559175057)), axis=1)

np.mean(4.512884601263547-array_x*5.861204340052087, axis=1)+10*(np.sin(2*np.pi*np.mean(1.3768642036769885-array_x*1.1625842179777202, axis=1)))

np.mean(abs(5.461424930313463)--(np.square(array_x+6.42235948600018/9.583297261547667))*9.908698803717591, axis=1)

np.mean(7.292877381001127-10*(array_x)*2.2266760648270214, axis=1)

np.mean(np.sin(2*np.pi*3.32573721276842)+1.5734053544988647*array_x-np.exp(9.186721519021622*array_x-6.142080772455571), axis=1)

np.mean(np.log(abs(np.round(2.508198347029168)+array_x+7.266580617209066-2.1644001895806295/-(np.cos(2*np.pi*array_x)))), axis=1)

np.mean(np.cumsum(7.732188797464891*(np.dot(array_x, np.array([[0.9999489702149168, 0.2698888205643851, 0.301768552165938, 0.1649889392381687, 0.45768477592962375], [0.8658437598357476, 0.7015059521248778, 0.8463315580765877, 0.22848165567358047, 0.7304560674084801], [0.9185266283960603, 0.2814161142143904, 0.6906535435911851, 0.40065741168985447, 0.2902266451589226], [0.9696234687543495, 0.3486332299682927, 0.10784968126068728, 0.3885892096203356, 0.44767829095998235], [0.7522108254992379, 0.9499142669470028, 0.8170569837942127, 0.930941311255106, 0.4750601299749311]]))), axis=1)+np.log(abs(7.178302162884231))-1.481547524796619, axis=1)

np.mean(10*(10*(array_x-7.797647086675497))/np.sqrt(abs(6.227347962595974))/np.round(1.1116349890457093)+array_x, axis=1)

np.mean(9.978219079884287*5.740708943522653-1.137743538607568*-(10*(array_x)), axis=1)

np.mean(abs(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x*5.663842906092071-4.5376763252825425)), axis=1)

np.mean(np.square(np.square(array_x+2.307224976741437+6.535919764252954)), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.square(array_x+9.00982812870295+6.024849454254983)), axis=1))

np.square(np.sqrt(abs(5.680939797007778-np.mean(10*(np.sqrt(abs(array_x))), axis=1)))+7.685943137635381)

np.round(10*(10*(np.log(abs(np.mean(np.log(abs(4.999301894652172+np.square(array_x))), axis=1))))))

np.mean(np.square(np.log(abs(np.cos(2*np.pi*np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))-array_x)+np.square(7.3753606199451935))))), axis=1)

np.mean(np.square(np.square(6.071093727983693)-array_x*3.507398721054763-np.square(array_x)/8.113868184600724), axis=1)

np.log(abs(np.sin(2*np.pi*np.round(np.sin(2*np.pi*1/(6.474750195873593)))+np.cos(2*np.pi*np.sqrt(abs(np.sin(2*np.pi*8.365938452895945)+np.sum(array_x*6.718597479457638, axis=1)))/6.272145910540692))))

np.mean(10*(9.881133659128412)+np.round(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.739866558764824, 0.3840980551084494, 0.5095620286443752, 0.888033136532694, 0.6497908567084307], [0.5355501320419, 0.07122246114189124, 0.17601521177791102, 0.20099158381562332, 0.6231482924522946], [0.10811275170455004, 0.02899486998162648, 0.3603505576518653, 0.7188591785088077, 0.6932493543020594], [0.7926703519503892, 0.6962482562693095, 0.6132855786014619, 0.4861620967576452, 0.20849832048797057], [0.568548055856618, 0.6366247482083411, 0.12374332841471625, 0.5651473386031348, 0.0977487131935516]])))))-np.square(7.626104647134769-(np.dot(array_x, np.array([[0.5470768023064918, 0.15891889101864154, 0.11901354720833301, 0.11310005601383666, 0.9110253128309069], [0.5980873174524421, 0.250158722202139, 0.07144887799209143, 0.5361814185121025, 0.1448029708768086], [0.7784029344045007, 0.4961096566018073, 0.7264488848573398, 0.39572659882014205, 0.7023227446203606], [0.6846143563430122, 0.5614162080757035, 0.8457396341581295, 0.5824735736023768, 0.5781104107798223], [0.3079831073423336, 0.9315062661175667, 0.5171395715537852, 0.3917146575735714, 0.5416422915626176]])))), axis=1)

np.exp(np.sum(np.sqrt(abs(5.599674207082117))+array_x, axis=1)/np.sqrt(abs(2.077382628235468)))

np.round(1.519980280399158+np.sum(array_x*6.740850658716796, axis=1))

np.square(4.5562458424534515)-np.sum(array_x, axis=1)/np.sqrt(abs(np.sin(2*np.pi*np.sum(array_x-np.cos(2*np.pi*2.157490540167285), axis=1))))+np.sin(2*np.pi*np.square(8.058590604310641)-np.sum(array_x, axis=1)/np.sqrt(abs(np.sin(2*np.pi*np.sum(array_x-np.cos(2*np.pi*8.610025064491758), axis=1)))))

np.mean(np.log(abs(np.sqrt(abs(4.238580587807633))+array_x))*10*(2.167317609573459+array_x+array_x), axis=1)

np.mean(np.square(9.279031964721524-np.exp(5.608925653941279*array_x*np.sqrt(abs(array_x)))), axis=1)+np.sin(2*np.pi*np.mean(np.square(8.200265983498292-np.exp(4.530789054717885*array_x*np.sqrt(abs(array_x)))), axis=1))

np.exp(np.cos(2*np.pi*1/(np.square(1.4645530049101874))))+np.sum((np.dot(array_x, np.array([[0.5616247030681413, 0.474954644495594, 0.5961940429332528, 0.23214111172609309, 0.5213835409110541], [0.3500375382514712, 0.10891793499403313, 0.7914985973135433, 0.3607954228610627, 0.2789112105308523], [0.0005696174167706403, 0.038004487981462276, 0.25184447127852916, 0.3295302892589168, 0.6991276180280982], [0.36845606233299866, 0.796524887914078, 0.5871662145403442, 0.35056705308389646, 0.15739744875697415], [0.0013694093405861585, 0.3831142751656631, 0.4067203069274563, 0.363891243584077, 0.8024040447137513]]))), axis=1)

np.mean(np.round(4.186006481215478)-np.square(np.exp(array_x)*4.7022532379761826)+np.log(abs(2.3119800652612374)), axis=1)

np.mean(3.768705109683697/np.cos(2*np.pi*np.sqrt(abs(array_x))-9.061037590153816), axis=1)+np.sin(2*np.pi*np.mean(8.481592739433502/np.cos(2*np.pi*np.sqrt(abs(array_x))-2.5920412397366626), axis=1))

np.mean(array_x-6.421439267529622*np.cos(2*np.pi*9.325049476041922+array_x/7.150599028810033), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x-6.999312880326956*np.cos(2*np.pi*3.5038603596086952+array_x/9.249246669825496), axis=1)))

np.amax(np.square(np.square(8.669737852732162)-np.sin(2*np.pi*np.sin(2*np.pi*array_x)))-np.square(4.320270150270215), axis=1)

np.mean(5.85420510410307*np.square(array_x)-5.365984004858715, axis=1)+10*(np.sin(2*np.pi*np.mean(1.8484287566794873*np.square(array_x)-8.469733564282862, axis=1)))

7.597731325067704+np.mean(9.285438816609853*(np.dot(array_x, np.array([[0.7907331077229514, 0.3978794562599788, 0.7177293639412117, 0.292174220375387, 0.6577465844009773], [0.5175209486562324, 0.662227407428941, 0.41383578997582815, 0.15416755408644356, 0.4210430439060491], [0.47740911847182876, 0.6778617515894295, 0.2845136955685824, 0.6456507764029659, 0.23383749483744698], [0.7649086776791715, 0.7221115994544708, 0.11774830724293639, 0.592654903863343, 0.0880500269296034], [0.9691243185508291, 0.3392396282439475, 0.020261149647881282, 0.5036150487320212, 0.12186266862307837]])))*array_x, axis=1)

np.mean(-(5.70218156029605)-array_x*9.310327925077466*7.308950208488158, axis=1)+np.sin(2*np.pi*np.mean(-(6.620464108100945)-array_x*2.2268821468093565*2.4635545312964737, axis=1))

np.mean(np.exp(2.7384097259858375+(np.array(range(1, array_x.shape[1]+1)))*9.357531223916919/(np.array(range(1, array_x.shape[1]+1)))*array_x)-(np.array(range(1, array_x.shape[1]+1)))*array_x+6.9802844257038394-np.exp(9.650416007673028)-4.0537220892893355-(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.mean(np.sqrt(abs(5.937123040391118))/np.cos(2*np.pi*2.1508367347029065+array_x)+np.round(9.192332895268022)+(np.dot(array_x, np.array([[0.543202726225152, 0.5938182545143235, 0.5472262325117234, 0.0025362271463302832, 0.7707167719534188], [0.9003024475226163, 0.25277886428534946, 0.1332162517160571, 0.7684658162893137, 0.7300326974115938], [0.7930487456647983, 0.9189439789872554, 0.04424079455943797, 0.5370332293031123, 0.7890738693061556], [0.5012340253503839, 0.5521422895385487, 0.566623123184881, 0.010389553165235488, 0.9042885346449084], [0.21078767617572924, 0.025458097763114584, 0.6334389308178681, 0.5601739936405676, 0.127759270929393]])))+7.09171414424271+(np.array(range(1, array_x.shape[1]+1)))-np.exp(1.015528347404898), axis=1)

np.mean(7.1497300516980955+4.484030841317288*np.exp(1.7303235332809113-array_x)/3.382865525496002, axis=1)+10*(np.sin(2*np.pi*np.mean(5.198191400675875+5.474610595477865*np.exp(2.045735829580856-array_x)/8.63789689489521, axis=1)))

4.11025313912479-np.sin(2*np.pi*np.sum(array_x, axis=1))+np.sin(2*np.pi*3.9914485076451784-np.sin(2*np.pi*np.sum(array_x, axis=1)))

np.mean(abs(2.274053344085569)+(np.dot(array_x, np.array([[0.49006596729291363, 0.374577296000298, 0.46082953739926036, 0.1415038494017561, 0.27098446255382946], [0.1851013063790684, 0.880906155390773, 0.6899731640641824, 0.1333347578550118, 0.4304095136360707], [0.3256426178891674, 0.01494289387648684, 0.7394275136686482, 0.4402098161457113, 0.36165973410413865], [0.18215110167675275, 0.4882892666788057, 0.2716390141047752, 0.5077763447855582, 0.2503933499023442], [0.29264526451191597, 0.42516303846131565, 0.6109114614023318, 0.842562922040296, 0.27865464446321675]])))*10*(2.881980234785499), axis=1)

np.square(abs(5.4350389937095445)-np.round(np.cos(2*np.pi*np.sum(8.358783689932238-(np.dot(array_x, np.array([[0.16852335984611877, 0.34796124669001516, 0.10586803445028325, 0.7211829774945829, 0.5811331376284521], [0.04176896754559534, 0.9538298022152253, 0.7717621691239763, 0.9743897847722247, 0.6427701974472181], [0.9421137207142817, 0.8031539174397093, 0.3086644144542384, 0.7541478947377915, 0.12160651693961144], [0.5063493563373049, 0.9087721168803684, 0.5246203828941719, 0.9467119294012922, 0.3817710225786487], [0.5241535754092777, 0.5694954646106728, 0.9604976835456291, 0.7076815130051147, 0.594815122793816]])))*7.052393755504595, axis=1))))

np.mean(-(abs(6.252117121376122))*np.round((np.array(range(1, array_x.shape[1]+1)))*array_x*6.634397368955525-(np.array(range(1, array_x.shape[1]+1)))*array_x)-np.square(1.2490011734287827), axis=1)

np.mean(1.274484111853887-array_x*np.exp(2.25432172271422)+np.sin(2*np.pi*6.828747812157619), axis=1)

np.mean(np.round(5.565525631217233/np.log(abs(array_x))-np.cos(2*np.pi*np.sqrt(abs(2.769885457638739)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(5.34984074847396/np.log(abs(array_x))-np.cos(2*np.pi*np.sqrt(abs(9.95462482012343)))), axis=1)))

np.mean(np.square(np.exp(np.exp(np.cos(2*np.pi*1.024308631997667+array_x*9.468395046325712*1/(3.7475489328582023+array_x)+array_x)))), axis=1)

np.mean(1/(np.cos(2*np.pi*5.90365553457846+np.cumsum((np.dot(array_x, np.array([[0.6575585991315914, 0.020573595812578005, 0.7642609923485958, 0.16434037729669282, 0.25081768942294347], [0.7145072062436597, 0.6076560043099889, 0.2543906140777221, 0.8437143035743963, 0.05655129177371743], [0.20772386404171006, 0.17912088433166673, 0.04344724109150988, 0.5986304097586412, 0.9765698614378274], [0.892388733952331, 0.6610698450457575, 0.2413687344562624, 0.7572326511723687, 0.9272977663115081], [0.6735764246396558, 0.7553831933656093, 0.7928222631843066, 0.6302082071481697, 0.4721698082623794]])))-7.7745535071101735+7.034588704405181, axis=1))), axis=1)

np.mean(np.sqrt(abs(8.545954260966532-array_x))*np.square(9.860436218657004), axis=1)

np.mean(-(8.075885701737628)+1/(np.cos(2*np.pi*np.cos(2*np.pi*np.square(array_x+8.117212685346006)))), axis=1)

np.mean(abs(10*(10*(5.154349853029576+(np.array(range(1, array_x.shape[1]+1)))*array_x*8.762833280863635)*np.log(abs(2.5599322941026053-(np.array(range(1, array_x.shape[1]+1)))*array_x))+np.exp(4.6499035994414175))), axis=1)

np.mean(np.square((np.dot(array_x, np.array([[0.9167546436237031, 0.7122454429399688, 0.18910954878355668, 0.2676793527657123, 0.30978290462320734], [0.44089483336211943, 0.1860410324814542, 0.17544510543550695, 0.07011595751764288, 0.1993259903496305], [0.37431617412984675, 0.7422591873242415, 0.6240828593485872, 0.4035354422566553, 0.7111983691433996], [0.22582434753815128, 0.3389777030433415, 0.5480218102192392, 0.4926883406577557, 0.7830713541884422], [0.22786824256627813, 0.8642977072836249, 0.8157216180642227, 0.40000959772124967, 0.8738403065925879]])))*4.541660544672023+8.552648758399304)+np.sin(2*np.pi*(np.dot(array_x, np.array([[0.635515246193691, 0.5505212837523169, 0.587098188346141, 0.5144872984531137, 0.647451289442173], [0.4347154811612902, 0.4536817229053053, 0.22806944940484986, 0.51196016879985, 0.303903019842924], [0.35636132647821006, 0.10384842420917295, 0.5734500848586189, 0.9790812928656492, 0.2701225969201576], [0.8858286031353617, 0.02006494798908731, 0.4350402314360613, 0.8608886347822639, 0.3021346698878361], [0.16934767381933202, 0.6588502940947246, 0.9496467372856535, 0.6283034530399805, 0.09030753613737741]])))+5.900760005415247+(np.dot(array_x, np.array([[0.7490908725278697, 0.005935057952418665, 0.5418630366692918, 0.849123691470037, 0.4297568402076717], [0.21960902136977245, 0.8769430510166122, 0.1546180822412757, 0.398086576939911, 0.46050555798133286], [0.0314678304902285, 0.5250974071230563, 0.6030924115473005, 0.41571808231981444, 0.03510509869754974], [0.11030550219123036, 0.39227100860703, 0.4240113420840387, 0.9133955226352884, 0.3724801214317499], [0.7244615154964198, 0.61536444070697, 0.038461668023950346, 0.46014857153105004, 0.6725455997253357]])))), axis=1)

np.mean(np.cumsum(np.square(2.724177340365275+(np.array(range(1, array_x.shape[1]+1)))+array_x*1.3510111750750338), axis=1)+1/(3.2852590157963006), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(np.square(3.5061087227066263+(np.array(range(1, array_x.shape[1]+1)))+array_x*7.0323178862134), axis=1)+1/(6.530158010009471), axis=1))

np.mean(np.exp(6.269967729914366*np.square(np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x))+(np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))*array_x*1.0499971153807044), axis=1)

np.mean(np.sqrt(abs(-(6.995024885558742*np.exp((np.array(range(1, array_x.shape[1]+1))))-2.107752056653839)))-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x*7.449246093207782+(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)

np.mean(np.square(array_x-np.square(1.5189977130412835)+np.square(9.17275566501972)), axis=1)

np.mean(8.143279554881678+array_x-(np.dot(array_x, np.array([[0.2282449821772553, 0.15096500769655274, 0.4491658675579091, 0.45312579816863285, 0.2976512526787829], [0.8118456635058693, 0.8205450582909171, 0.6930845818944277, 0.9527203152310205, 0.09013374421313691], [0.9452312013042192, 0.15289587876104194, 0.7239385462081479, 0.4453441866305, 0.5257300838549014], [0.38154501241633487, 0.3576993773832543, 0.6022106921127843, 0.5965188147686975, 0.7137767239352668], [0.7166259932637926, 0.029733336524887077, 0.5418518574910878, 0.6658172668067286, 0.7791708261392903]])))*7.040318544810323*5.344525021493889-(np.array(range(1, array_x.shape[1]+1)))-3.2388782732059584+5.6594850435208635, axis=1)

np.mean(np.square(array_x*(np.array(range(1, array_x.shape[1]+1)))+1/(8.570613908854984))-4.810135281978985+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(array_x*(np.array(range(1, array_x.shape[1]+1)))+1/(6.128143551549535))-6.12639805784087+array_x, axis=1)))

np.mean(np.sin(2*np.pi*np.log(abs(np.square(2.686950764016884))))+np.sin(2*np.pi*array_x)*9.01329778344284, axis=1)

np.round(np.sum(np.sqrt(abs(6.680102512204544-np.cumsum(array_x, axis=1)))*np.square(2.1200652591004205), axis=1))

np.round(np.log(abs(7.5476582398265))-np.mean(3.388511596861472-np.square(array_x-7.59729566021633), axis=1))

np.mean(np.square(np.square(9.23019954647118)+np.round(array_x+np.sqrt(abs(1.4547156011586742)))*array_x-4.898279726192138/4.7013750417802775), axis=1)

np.mean(np.square(3.7297240780752636/np.cos(2*np.pi*np.log(abs(2.9772135033781213)))-array_x), axis=1)

np.sum(array_x, axis=1)+array_x[:,0]-array_x[:,0]-8.607092801840384+np.sum(8.41261836808959+7.364909587954819*array_x, axis=1)

np.sum(abs(2.706946965385572)/7.470317643047536-np.sin(2*np.pi*8.846883151362846+array_x+9.007892508492782*array_x/9.226587172045484), axis=1)

np.mean(np.exp(array_x/9.208002109513238+np.sqrt(abs(5.216981120848476)))*1.817779542603665*array_x+np.cos(2*np.pi*10*(9.418978647409082)), axis=1)

np.mean(-(np.square(np.log(abs(array_x*array_x-3.1651847029614784))))-array_x*(np.array(range(1, array_x.shape[1]+1)))*9.630186589077647, axis=1)

np.mean(-(np.round(np.round(np.exp(2.619089454537506)*np.sqrt(abs(array_x)))+4.6876130497292685)), axis=1)

np.mean(10*(abs(array_x/5.317678930490065)+10*(np.sin(2*np.pi*array_x))-6.034407569733098), axis=1)

1.5913926541943306-7.430091959655805*np.sin(2*np.pi*np.mean((np.dot(array_x, np.array([[0.3390067176179068, 0.9725986588404333, 0.13527586722630713, 0.485313933970845, 0.316077675848472], [0.13656701419543782, 0.405780669083432, 0.6422149295815317, 0.4603681468849996, 0.30466093131982586], [0.887065325617688, 0.3091426920055751, 0.018336638311006292, 0.5614300662641278, 0.0902630741469308], [0.8189853468909697, 0.4518825040948017, 0.07509307125812859, 0.47390805086746557, 0.43299239122709565], [0.4203772573557988, 0.666593224515368, 0.9051940719262022, 0.6782073523751007, 0.22679029150007346]]))), axis=1))+10*(np.prod(array_x, axis=1)/1.1821038761300868)+np.square(1.9806822440917942)+np.sin(2*np.pi*3.8146025646610444-8.534600773979506*np.sin(2*np.pi*np.mean((np.dot(array_x, np.array([[0.17085082433859322, 0.20029072126091707, 0.5147137666026848, 0.420834505983414, 0.14178650232235768], [0.4650935336968116, 0.0028268445745950332, 0.623881354513013, 0.4444372050085683, 0.9850653695121748], [0.16011534498338476, 0.4411880646937315, 0.018639006163226335, 0.17142201871372464, 0.9660446764358439], [0.9829738420066471, 0.016555442103573292, 0.8079123253089756, 0.4346527211757877, 0.8047834024298998], [0.48032056184058014, 0.9289536804940578, 0.16210294938885583, 0.24491893433209255, 0.2761321069812691]]))), axis=1))+10*(np.prod(array_x, axis=1)/9.536707084757928)+np.square(5.334980095247139))

np.mean(np.round(np.round(1.9733419151376022-7.950392236458903*array_x*-(8.845818304661211))), axis=1)

np.mean(abs(np.square(9.631972476546633+np.exp(array_x)-7.504917841745293/7.095896835672281)), axis=1)

np.mean(np.exp(np.cos(2*np.pi*7.952825445603051)-np.round(array_x)*5.598349412002589+2.792903085473327), axis=1)

np.mean(np.square(9.869655143708496*array_x+array_x+7.46449655930424+3.353005902400324+np.log(abs(6.3288316429266125))/4.946142993395345+10*(np.round(7.883922279592278))*np.cumsum(array_x, axis=1)), axis=1)

np.square(np.prod(np.exp((np.dot(array_x, np.array([[0.060935855889475365, 0.5975222953575832, 0.397235991772808, 0.19844068798590297, 0.4422533983832482], [0.4302496058291916, 0.5640408916955618, 0.22463566981456384, 0.7079458178798078, 0.8413948262138282], [0.6976592485634826, 0.8569814376332836, 0.49886235965570624, 0.0029230711208823035, 0.5928149302012805], [0.9864728315096124, 0.3197653168554492, 0.9959126271198526, 0.1581883216955594, 0.05121155600635574], [0.3321827053336671, 0.37556337255868844, 0.4185546875476992, 0.5342669742698084, 0.3619356408707656]]))))/np.exp(7.784578858842168)+np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*(np.dot(array_x, np.array([[0.8300790536689325, 0.248380967441159, 0.5913023502789062, 0.6666301253680327, 0.3733881125065869], [0.20615068864944364, 0.800320442513193, 0.35316359612482484, 0.2578998640346616, 0.7623046978361647], [0.4286172029136036, 0.7674095387095426, 0.7394054664027682, 0.017605794965370736, 0.7266613069177461], [0.20650200291468046, 0.6486273518081079, 0.31979362192995664, 0.528330161592105, 0.5455522694434692], [0.8437577387187739, 0.43962983320235294, 0.009449542139249312, 0.7423363747467127, 0.5880459106406416]])))-7.971558455664818)), axis=1))

np.mean(10*(np.cos(2*np.pi*2.646401220334169)-np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x))), axis=1)

np.mean(abs(array_x+9.180594362385257)*array_x-np.log(abs((np.dot(array_x, np.array([[0.948949497410991, 0.09228665191194008, 0.5205621889326273, 0.06642158888346661, 0.6979010067235951], [0.6654196573879165, 0.01702092948564038, 0.8370391916225702, 0.199349959511539, 0.05801671980922818], [0.23977314887374612, 0.83003958023771, 0.311190829824354, 0.910502606828073, 0.34155396416058925], [0.6127511482274156, 0.7354055471560539, 0.9707694871918185, 0.836790598462941, 0.8366326648973212], [0.18978861119161017, 0.07185144416099676, 0.41392748437481597, 0.5197775630195007, 0.30097034980584403]])))+8.522550982322777/7.187910449133544*(np.array(range(1, array_x.shape[1]+1))))), axis=1)

np.mean(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.4735651670679437, 0.15897313586912298, 0.6801444295125338, 0.9084953054010161, 0.506619002515107], [0.8644156274000412, 0.6271985676462636, 0.44814708604507814, 0.4950212951597903, 0.7286393702984619], [0.03795874147142919, 0.33593419376233913, 0.6750358001173914, 0.6889423265362856, 0.2187736994599807], [0.19705846671470473, 0.8278174352092793, 0.5947309267652653, 0.4011114836440798, 0.11105088054416068], [0.48832132495458436, 0.7279892946213913, 0.30220770445882095, 0.8444833205750595, 0.1569070206155624]]))))/np.square(2.195926268043418)/np.log(abs(5.859125925989356))-np.cos(2*np.pi*array_x)+(np.array(range(1, array_x.shape[1]+1))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.16056167355079554, 0.0739896165628291, 0.8158378494625413, 0.28395669103173604, 0.7248680613719009], [0.47222198729146836, 0.7619928894419027, 0.5346100958659902, 0.06431028154254015, 0.3886307350410346], [0.4356235048279402, 0.29706392897345113, 0.5141551342620867, 0.11573718704342018, 0.324054490645426], [0.9341433484411608, 0.9926445234360649, 0.4632955744277778, 0.4254415222879748, 0.47130355116661293], [0.5105051573467421, 0.4502619146116187, 0.7154122464650401, 0.7165059904223021, 0.12375517380660683]]))))/np.square(2.176165029352605)/np.log(abs(1.1669843611054496))-np.cos(2*np.pi*array_x)+(np.array(range(1, array_x.shape[1]+1))), axis=1)))

np.mean(np.exp(1.4558225049959783)-array_x*8.987017007597391-3.6335719288978274*4.488850431476784, axis=1)

abs(np.square(np.sum(array_x, axis=1)))/2.035755720736043+4.161596436026697-array_x[:,0]-3.8345639530871223

np.mean(9.28364398782136+array_x-np.sqrt(abs(np.exp(array_x+7.678152089520239))), axis=1)

1/(4.320484499314259)+np.sum(array_x, axis=1)*np.sum(array_x-6.130921971856489, axis=1)+2.364097406242179

np.mean(np.square(2.4994260921183256)+np.square(array_x-(np.dot(array_x, np.array([[0.36316637695406717, 0.046335734161900066, 0.7934817996962461, 0.37943237446636, 0.4667075623110791], [0.14765509902693275, 0.6056422301645394, 0.24038283135099692, 0.8344622763711468, 0.9732217641606761], [0.22470638539666188, 0.5603707911560452, 0.4137172666129685, 0.8433491973256517, 0.007274749684474946], [0.4315933477804015, 0.6847638071385054, 0.9008306879789147, 0.7994014754561537, 0.41904073190639957], [0.7439972237190722, 0.22033540875697544, 0.6060116145191081, 0.642210323263415, 0.652759294010021]]))))+7.080020013067435-array_x*4.740981203687822-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(4.716932323733381)+np.square(array_x-(np.dot(array_x, np.array([[0.14623322175405584, 0.213989112784999, 0.31703180108683027, 0.5814455743370941, 0.5088235858526817], [0.13086447730876294, 0.5349092739717808, 0.6976579916843173, 0.18642869329270895, 0.5640817019025087], [0.5202446051691122, 0.6597832077725381, 0.7068172290818197, 0.44255482959210246, 0.595509864907663], [0.0726775604524037, 0.25857724189937015, 0.42533738129148135, 0.859940013177941, 0.9403766619126044], [0.9508109879384778, 0.4303082390304863, 0.5476094647607245, 0.44193610755562995, 0.05832786032036941]]))))+6.475887856134737-array_x*3.548132671769811-array_x, axis=1)))

np.mean(10*(np.square(array_x)/2.910770970765567-6.9013273504646975)*2.1398320404033027*3.9774653456754767, axis=1)+np.sin(2*np.pi*np.mean(10*(np.square(array_x)/6.087995534226223-4.133428901261497)*7.8912322793278555*5.390763050087883, axis=1))

np.mean(array_x+1.7865288884437558+np.sin(2*np.pi*array_x-5.333268603758815+5.322406808154255)*np.sin(2*np.pi*np.log(abs(10*(np.sqrt(abs(2.424309269822181)))))), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+2.6224329297552176+np.sin(2*np.pi*array_x-6.788488945417481+6.530389897227736)*np.sin(2*np.pi*np.log(abs(10*(np.sqrt(abs(9.51919335439431)))))), axis=1)))

np.mean(np.square(np.round(np.sqrt(abs(4.398007802342186/(np.array(range(1, array_x.shape[1]+1)))))-4.438630462437143+array_x-np.sin(2*np.pi*6.389182999105624+np.sqrt(abs(array_x))))), axis=1)

np.mean(1.6147531667325747-np.exp(8.451238150025421+array_x), axis=1)

np.mean(np.square(4.364365750333839/7.202283664514586+4.668530087131769*array_x), axis=1)

np.mean(np.square(4.272678543835286/8.36714106671988-array_x+8.822854103241859-8.946997028746914*(np.array(range(1, array_x.shape[1]+1)))/8.819893751474645), axis=1)

np.mean(10*(4.036779639767248-array_x), axis=1)+np.sin(2*np.pi*np.mean(10*(4.591924523405943-array_x), axis=1))

np.mean(np.square(6.10310874155697-array_x)+np.exp(np.round(np.sqrt(abs(4.730272663575995))))-np.cumsum(4.830299570839277+array_x, axis=1), axis=1)+np.sin(2*np.pi*np.mean(np.square(1.8475846544214254-array_x)+np.exp(np.round(np.sqrt(abs(1.176942838540008))))-np.cumsum(7.029442644493184+array_x, axis=1), axis=1))

np.mean(np.square(np.square(8.684029681651946/np.exp(array_x)-np.square(2.0996642566110286))), axis=1)

np.mean(np.exp(np.square(np.round(np.sin(2*np.pi*array_x)+(np.dot(array_x, np.array([[0.6881650277568054, 0.7608481810352581, 0.4354447098672497, 0.40685124961474894, 0.4170595760368133], [0.6121067105097884, 0.1104098270244478, 0.2068684282686274, 0.1604478565830254, 0.20825606792892015], [0.9330551914304391, 0.05619217633080642, 0.9201694505635042, 0.9481380494861665, 0.6531176439878345], [0.9866464092463006, 0.6716734529046843, 0.2693729290244615, 0.18192445637549726, 0.8962818123809436], [0.36679287890589485, 0.7280642717705504, 0.25549263806967315, 0.605823273994619, 0.1486691778320156]])))+1.1618277451047505-6.808867870075484+3.931049083433139))), axis=1)+np.sin(2*np.pi*np.mean(np.exp(np.square(np.round(np.sin(2*np.pi*array_x)+(np.dot(array_x, np.array([[0.4245669585866304, 0.9952307891119746, 0.968127366853894, 0.27631922488202865, 0.19128973529106252], [0.7151433274200323, 0.4613821079878776, 0.1507906166856332, 0.40619238842025807, 0.02647705834457037], [0.1508756762102813, 0.780606517261451, 0.4948293085404145, 0.7803020967661392, 0.07725569030348922], [0.007287515258405541, 0.764002135334996, 0.42336010432286353, 0.9697040890999405, 0.33053308482302435], [0.3207820821386621, 0.8089825815706927, 0.1882072127115142, 0.5885933625777596, 0.2789289658330575]])))+9.47598754874932-8.39566631887516+5.278615349487839))), axis=1))

np.square(np.sum(np.log(abs(np.cos(2*np.pi*np.cos(2*np.pi*array_x/3.5377262028785923)*9.468493384576222+array_x/-(9.086696237047493)-abs(8.19596581526242)))), axis=1))

np.mean(10*(np.exp(np.sqrt(abs(np.cumsum(array_x, axis=1)))+4.137328440064916-array_x)), axis=1)

np.mean(np.exp(np.cumsum(array_x, axis=1))+5.9399204314035146-abs((np.dot(array_x, np.array([[0.9362876993441422, 0.3507085695985961, 0.24129960629790248, 0.5021746496111648, 0.13236803454672608], [0.3362441646022958, 0.38087541650662726, 0.41512969751550455, 0.2015322181881425, 0.9880191736049786], [0.325368727291487, 0.7771962460781929, 0.5160557620328253, 0.005700720191840425, 0.3159664492566485], [0.3719058415233424, 0.5835724775560858, 0.7430282542832622, 0.7193771586443424, 0.8531050413405283], [0.7598575014064004, 0.5272649827837599, 0.9912349907575442, 0.8032798940602958, 0.3321386992552742]])))), axis=1)