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np.mean(10*(7.43355231834533+4.03644772139648+array_x-9.211528224398773-np.square(3.404871521843357)), axis=1) |
np.mean(5.012438347356219*(np.array(range(1, array_x.shape[1]+1)))*array_x+2.3746351866056026+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.mean(np.square(6.95647788238163-array_x)+np.square(4.080835340153763), axis=1) |
np.mean(np.cumsum(np.square(abs(np.square(7.1575668469390745-3.024168282985536*array_x-(np.dot(array_x, np.array([[0.13676177642289777, 0.7911053165012084, 0.9206546078783757, 0.8134433637954921, 0.8068202505857146], [0.5911919477586329, 0.5731803875987078, 0.5467976659219346, 0.14270633186208292, 0.13154976351520853], [0.37824822457224894, 0.10466792951544401, 0.08312859448192655, 0.05885545991489394, 0.3909403376875893], [0.7557507005046312, 0.7263487291802057, 0.2827477698634564, 0.062248617377688165, 0.7578531689366423], [0.10388891133436373, 0.31996290490446655, 0.4036414945618193, 0.4797675104795218, 0.04247062602629659]])))*9.04536168483466))), axis=1), axis=1) |
np.mean(np.exp(np.sqrt(abs(np.cumsum((np.array(range(1, array_x.shape[1]+1)))-8.229024950557665-(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)))), axis=1) |
np.mean(3.1029666698910896+np.exp(array_x)*4.157668581643909-10*(np.round(8.650203729099726)), axis=1)+10*(np.sin(2*np.pi*np.mean(3.5363166398966177+np.exp(array_x)*9.575182921895529-10*(np.round(5.862742806707341)), axis=1))) |
np.mean(np.square(np.cos(2*np.pi*np.sqrt(abs((np.dot(array_x, np.array([[0.3385476190109251, 0.5968531788334215, 0.7964932854546478, 0.5155705151769499, 0.08859249015766624], [0.6594845896621657, 0.11991482979334422, 0.3973674118575691, 0.899784872773116, 0.1934218061040578], [0.533417783374563, 0.29782038206237116, 0.14215185441039535, 0.6952569679265412, 0.9132237919513678], [0.27811537961301513, 0.060316536988244795, 0.9672042973689092, 0.6715796346631984, 0.22149023496163978], [0.20207982697653415, 0.4759593766876168, 0.4799075330630709, 0.3935762939464813, 0.28440396835327975]])))/8.35817559720195)))+2.8107829292429636-array_x), axis=1) |
np.sum(6.318382250657587+10*((np.array(range(1, array_x.shape[1]+1))))-array_x-7.17155324407606*np.sqrt(abs(array_x))-9.133071332759853, axis=1)+10*(np.sin(2*np.pi*np.sum(6.237133007891494+10*((np.array(range(1, array_x.shape[1]+1))))-array_x-4.051446727193349*np.sqrt(abs(array_x))-9.387171698319632, axis=1))) |
np.mean(np.square(2.8738017603848904*(np.dot(array_x, np.array([[0.631911157102174, 0.009823639460113553, 0.9090950170159836, 0.6033869188060575, 0.25861526829327564], [0.0782287397142164, 0.22444552395139827, 0.6287924989143013, 0.6691687718951812, 0.7103570420501926], [0.8463797577103302, 0.5354747593969658, 0.24972678230599477, 0.9703674873738966, 0.11327181145653098], [0.2937587273526767, 0.7134730229601053, 0.6056355084364154, 0.5438646503683903, 0.11447021004365043], [0.979377186768316, 0.14783994946669343, 0.3302447150059903, 0.381685586496169, 0.23460726848068425]]))))+np.cos(2*np.pi*3.5295063682777466)/np.sqrt(abs(6.499653374591388))/np.exp(np.square(np.sin(2*np.pi*array_x))), axis=1) |
np.mean(-(abs(2.439141763657219-9.360073271286044*np.round(array_x))), axis=1) |
np.sum(np.sin(2*np.pi*array_x+np.square(9.370265961023007)), axis=1)+10*(np.sin(2*np.pi*np.sum(np.sin(2*np.pi*array_x+np.square(9.045955010641993)), axis=1))) |
8.331054813774042/9.699452377050768+np.mean(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(np.square((np.dot(array_x, np.array([[0.9949443166805552, 0.3935361428228268, 0.9604900440160665, 0.5122852715247146, 0.8339603748182994], [0.8707031660728891, 0.18785496887407738, 0.5833938509770069, 0.8850212187843436, 0.7362882114684058], [0.7470049686559627, 0.49364169566849336, 0.40721053765386594, 0.08248711835905664, 0.8777783018146827], [0.2763245318513594, 0.5998558098993759, 0.3551288942420495, 0.8446111237887833, 0.6923563652760912], [0.6968599821090967, 0.7420909717669084, 0.6164621222098018, 0.8931167142716999, 0.37851652778558065]]))))-(np.dot(array_x, np.array([[0.6212093059470644, 0.6642335063139962, 0.595137467231789, 0.7261098330939025, 0.7598382992123913], [0.22988181632110782, 0.5597353925104536, 0.9758925174389954, 0.8139439905218422, 0.149700396915303], [0.03517583179617245, 0.5535738848276092, 0.018280923779674385, 0.02987350483526896, 0.6012850266553117], [0.6181911010343859, 0.35609943266275856, 0.5381524356005615, 0.828811973923338, 0.3539064010650156], [0.7407778456033197, 0.4082377914282671, 0.45067039059752834, 0.6127013966671725, 0.14799230820986042]])))-6.478056453504664+np.cos(2*np.pi*np.sqrt(abs(np.round(1.8686220142887908)-(np.dot(array_x, np.array([[0.4644952056628928, 0.9645149699800193, 0.5798150452722658, 0.7806043610752755, 0.8980136909833001], [0.4328666782199603, 0.7924589317945611, 0.3692626381496289, 0.7311117830383519, 0.32810674153652675], [0.33061667338756184, 0.6039013213912325, 0.2795228568606595, 0.6709459497438469, 0.9970063965246604], [0.8198121700856035, 0.7293409227891772, 0.16943711676037176, 0.7299651805743461, 0.327238376774297], [0.0029624461605943786, 0.745892328118826, 0.7479749272831834, 0.5487540077642867, 0.1670855812676526]])))+5.088684833283207))), axis=1) |
np.mean(10*(abs(np.square(6.104597145017518))-np.square(array_x))*5.67091206694255-np.sin(2*np.pi*array_x-3.840062330241725), axis=1)+np.sin(2*np.pi*np.mean(10*(abs(np.square(6.641503789914852))-np.square(array_x))*9.426068901031709-np.sin(2*np.pi*array_x-6.559021582415688), axis=1)) |
np.mean(8.717379339476413*9.034945576046171*(np.array(range(1, array_x.shape[1]+1)))*(np.array(range(1, array_x.shape[1]+1)))*array_x-4.769691430623388, axis=1) |
np.mean(abs(np.square(np.square(10*(4.606049334874835+(np.dot(array_x, np.array([[0.6220826579612017, 0.9491943546597481, 0.7955176792141383, 0.11821492888251539, 0.8125098563419623], [0.05371053909074064, 0.06158344374558844, 0.5887250139443402, 0.11046873322452466, 0.8392213191373936], [0.460105975382179, 0.6130878615063148, 0.3865343626209361, 0.9329525398420004, 0.5559578958994689], [0.01207518802727181, 0.021367833784186385, 0.6260200415524012, 0.5378247208687025, 0.2883353407443804], [0.6190267461383155, 0.13455769670992446, 0.23037686036203409, 0.7447531938114231, 0.8757972771382984]]))))+7.837942931235542)-(np.dot(array_x, np.array([[0.20507156756038203, 0.6734653310457449, 0.297391645514994, 0.31415760711946283, 0.5044941251837541], [0.17708331015282985, 0.693771894206541, 0.439394272914824, 0.2825428124754955, 0.22375005629379097], [0.024051092151124664, 0.8674463704694028, 0.6781451877067234, 0.3320998094672003, 0.4197900276432005], [0.6896992455710247, 0.7279336489660603, 0.6703617080358025, 0.45325793234777034, 0.84407437928291], [0.9600000196572585, 0.33466921393324434, 0.8176696615693528, 0.6321004523190851, 0.43600038140337805]])))+(np.dot(array_x, np.array([[0.9396055084003726, 0.6387730623971991, 0.08833723722489151, 0.7371026415745662, 0.5141081688640022], [0.7000731581148809, 0.008237245005537797, 0.33413496296234424, 0.4084801677584997, 0.2837483697169879], [0.7175537163322577, 0.8775375943491125, 0.3749230978671627, 0.08660913445644736, 0.7800729872510627], [0.32115580260559873, 0.7239966052534202, 0.267619096201028, 0.668008274697514, 0.336501244306903], [0.8164957607466726, 0.9233839290605232, 0.8391615832171763, 0.9148105572027567, 0.3598388130010247]])))-(np.dot(array_x, np.array([[0.09798107158546387, 0.9219782356501114, 0.9076365275928934, 0.5475073711914289, 0.4352004467624163], [0.6664570774035988, 0.077114649244855, 0.3146949399832657, 0.6906597059150238, 0.8853122350122794], [0.7636785855222612, 0.06908785349379931, 0.05650395564882171, 0.47913366519034795, 0.49264169852820483], [0.4725176829365395, 0.5394152277005918, 0.19928724886940075, 0.7152088718257393, 0.33176872203202157], [0.21309545119895434, 0.109800617196121, 0.1557089895826127, 0.04460279981006021, 0.6293749823309496]]))))), axis=1) |
np.mean(4.212766100997034-array_x*8.009929681413444-np.cumsum(4.061648784925787*array_x, axis=1)-array_x*7.753256625758894, axis=1) |
np.sin(2*np.pi*np.sum(1/(5.391538589017015/array_x/5.518340360453714-7.3448227878544765)-(np.array(range(1, array_x.shape[1]+1)))+1.874207236723493-array_x, axis=1))+np.sin(2*np.pi*np.sin(2*np.pi*np.sum(1/(1.8544063062414988/array_x/6.386449179568848-1.5038077982850657)-(np.array(range(1, array_x.shape[1]+1)))+6.4361038665172305-array_x, axis=1))) |
np.mean(np.cos(2*np.pi*6.014134556625855*abs(np.square(1.078488481115993)*array_x+7.8069664053209)-(np.dot(array_x, np.array([[0.4849651320538674, 0.18610931788554463, 0.5986379866539083, 0.7284991972404886, 0.6376249926684414], [0.321506706085259, 0.9324546387155597, 0.6830184350952198, 0.65590582502334, 0.3182455839936794], [0.06887006628656234, 0.061871585016422426, 0.9871411648102877, 0.9418945852908424, 0.3306208692037955], [0.38371347943185796, 0.9236383668436481, 0.33220773991834807, 0.5309319414846777, 0.2845381513543437], [0.3806852321311901, 0.5666221544328275, 0.03604235843440373, 0.16731991317483752, 0.9118308766848017]]))))-np.square(array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*4.1686161686185*abs(np.square(1.2759278009817865)*array_x+3.392642183918717)-(np.dot(array_x, np.array([[0.07971902726782887, 0.6541589536874706, 0.2886185099565215, 0.10702400966231063, 0.19678875071721136], [0.34487183699364177, 0.8884839662465809, 0.4725265158596156, 0.26362825386405875, 0.7661088865374005], [0.45306257299241637, 0.7545210458764388, 0.06820160164367728, 0.6309375993749833, 0.4019245160361212], [0.6462230635155304, 0.5144161933936037, 0.5911712393862666, 0.7482169252161004, 0.6382978796651161], [0.6274342754176666, 0.14220166961448466, 0.9603134956306812, 0.5121338582449592, 0.3046174000462887]]))))-np.square(array_x), axis=1))) |
np.mean(np.round(np.square(np.square(2.924577383510824-array_x))), axis=1)/4.019321387028161 |
np.mean(4.534815687568232-np.sqrt(abs(np.sin(2*np.pi*array_x+3.1643826633173604)))/np.sqrt(abs(9.010050359784852*array_x-6.039184517777017-2.2905325056366284)), axis=1)+10*(np.sin(2*np.pi*np.mean(8.19050339755531-np.sqrt(abs(np.sin(2*np.pi*array_x+7.354537247003263)))/np.sqrt(abs(8.474692632743167*array_x-4.346678559395017-3.1024258126930504)), axis=1))) |
-(np.prod(8.248393090195009-array_x+7.315797393783116, axis=1)) |
np.mean(8.086436071932663/np.log(abs(2.943951027657838/(np.array(range(1, array_x.shape[1]+1)))*array_x))+np.cos(2*np.pi*3.2989139601450166-(np.array(range(1, array_x.shape[1]+1)))*array_x*(np.dot(array_x, np.array([[0.7662410750008924, 0.7826132508450401, 0.6460963214102662, 0.2291121736852023, 0.5316314403926196], [0.08988096514206212, 0.8015118679716247, 0.9628760592522929, 0.10468590247994403, 0.7195655587413498], [0.08742169521156318, 0.8202742274823048, 0.8953467601630272, 0.013492107463467407, 0.44352902926052895], [0.13545349208594926, 0.8006563405204333, 0.7073741358261336, 0.8740318080226069, 0.8123768314964201], [0.5517999720794987, 0.09985683718451965, 0.8294449740491451, 0.48577078388334227, 0.4649946793119868]])))), axis=1) |
4.368811214042227-np.round(np.sum(np.sin(2*np.pi*np.sin(2*np.pi*10*((np.array(range(1, array_x.shape[1]+1)))*array_x-1/((np.array(range(1, array_x.shape[1]+1))))))), axis=1)) |
np.mean(np.square(array_x+1.1312606758368382/7.020701895653669/(np.array(range(1, array_x.shape[1]+1)))+6.225103577879861)-3.666340043085505, axis=1) |
np.square(np.sum(array_x*9.616128803530737/5.736813538109261+(np.dot(array_x, np.array([[0.003190606661562345, 0.6794682724770941, 0.0767343239489453, 0.6430960656757682, 0.6960803828110224], [0.4733783687523736, 0.477214650028858, 0.46442668778373897, 0.03516872958673212, 0.6707913145636861], [0.06136904682116906, 0.5087598881808089, 0.4338987268366937, 0.22068837355060955, 0.7135297110675388], [0.5935788094648855, 0.1276651856388823, 0.10559394962975899, 0.856484195013706, 0.942475411154056], [0.664288308916714, 0.21536309092037775, 0.3578182875439424, 0.7776524564087717, 0.5449905660388435]])))-(np.dot(array_x, np.array([[0.33801402922115587, 0.17964224822953268, 0.7522807167901456, 0.47506756347749146, 0.8942197398953801], [0.3552138731884793, 0.42004205999492494, 0.6315972065775679, 0.6484029700400674, 0.8171782746281633], [0.9432597830784175, 0.2672590113073414, 0.10701045257783137, 0.8367513216626035, 0.08999722640005159], [0.09439474671041181, 0.45211120606394695, 0.40459818856032936, 0.6541666988493275, 0.5573594668536347], [0.4546265436328174, 0.18613671911786933, 0.6758663406713645, 0.47755906919233415, 0.24523943851718122]]))), axis=1)+np.round(np.square(7.010780719744194))) |
np.mean(np.cumsum(3.6114524830099963-np.exp(array_x+array_x+1.048330602938484), axis=1), axis=1) |
np.prod(np.cos(2*np.pi*3.22395572146358)*np.cumsum(array_x-3.952636657089659, axis=1), axis=1) |
np.mean(np.square(3.1371507093364617*np.sqrt(abs(array_x)))-3.410222893090947, axis=1) |
np.mean(10*(3.9054031383127916-(np.array(range(1, array_x.shape[1]+1)))*array_x*8.909267511931942)+np.square(abs(np.square(10*(5.4365428705356535)))), axis=1) |
10*(abs(np.mean(4.777446372230815+10*(5.66454326329779+array_x)-np.cos(2*np.pi*array_x), axis=1))) |
np.mean(10*(4.251884596177129*np.sin(2*np.pi*1.38164193168394*array_x-8.235516073986233)-7.827572110188536), axis=1) |
np.mean(np.cos(2*np.pi*2.378034593725415)-(np.dot(array_x, np.array([[0.3070309719354849, 0.9349277983437907, 0.4450204542582167, 0.12746079473532945, 0.8922402834309741], [0.07765251335405354, 0.6566139418768032, 0.4212364284533475, 0.18950190958278956, 0.0867297558242357], [0.14826581205546574, 0.5825450646671909, 0.5019170509833873, 0.448117039596612, 0.11114042438618643], [0.5380965305201207, 0.031924075025770327, 0.5267482070934518, 0.06653316061186498, 0.0495449640800536], [0.8607338907896755, 0.9145440561352927, 0.21909978227643956, 0.8869986622486757, 0.5539280382287121]])))*7.231170050169439, axis=1) |
10*(6.7221265748660635-np.sum(np.sqrt(abs(2.1581788499293806-array_x)), axis=1)) |
-(np.square(np.sum(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.5874115079124433, 0.7100203800180054, 0.39009624959999656, 0.5472675160840788, 0.21476297120222332], [0.6475209024608315, 0.757596965404011, 0.438727949010853, 0.2281048092743877, 0.16310400154175264], [0.7509392245802534, 0.6890639315005128, 0.34340135388504267, 0.6165716608533478, 0.6823527928860759], [0.265751871110244, 0.900785328816501, 0.7762465103774615, 0.6269660455641977, 0.12374244642556542], [0.4324064122315241, 0.8335039440150704, 0.1992336859742304, 0.8602910108091687, 0.08722599402206865]])))/5.902782100105081-6.581139906243179)*np.sin(2*np.pi*5.191865575237741/6.981080057200421+array_x), axis=1))) |
np.prod(1.832279816778259+(np.array(range(1, array_x.shape[1]+1)))*array_x/(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)+5.410448053573074 |
np.log(abs(np.square(np.sin(2*np.pi*np.sin(2*np.pi*np.sum(np.square(5.319879718419796-array_x), axis=1)))))) |
np.sum(np.square(np.sin(2*np.pi*np.sin(2*np.pi*10*(9.457272060627057)-(np.dot(array_x, np.array([[0.3531834910973084, 0.24810836021016414, 0.013541141668590329, 0.4580214168162381, 0.7148139382966142], [0.043412359318030225, 0.1215498585171092, 0.9745869680020774, 0.38127712271575376, 0.9724331889186527], [0.6267413784323631, 0.8988274860642731, 0.43104131228484155, 0.3627977637150095, 0.6514747114239207], [0.7882216948554304, 0.009143710074430156, 0.6005598614686999, 0.2687644464614146, 0.6901652257285674], [0.8159523254768072, 0.011207313063852142, 0.971059654519276, 0.33028454855981515, 0.37189092562454884]])))+array_x))), axis=1)+np.sin(2*np.pi*np.sum(np.square(np.sin(2*np.pi*np.sin(2*np.pi*10*(1.9907285112272506)-(np.dot(array_x, np.array([[0.8867149673265763, 0.05464889316421451, 0.7345773344028913, 0.3197523226062402, 0.9102109389174499], [0.46715839110488855, 0.9835938625901065, 0.7362609747517174, 0.02836988915392402, 0.19589407097686395], [0.7407377212484505, 0.4364560591725599, 0.3886772650914473, 0.7591310333510627, 0.9245830924668031], [0.8716114877718875, 0.7337679615840447, 0.6216938505690033, 0.6129645537653592, 0.04874892632233663], [0.5152622185030113, 0.10909898197418766, 0.8556856513939324, 0.028713521324072455, 0.46763046662878405]])))+array_x))), axis=1)) |
-(np.cos(2*np.pi*6.767814401528992))-np.sum(array_x*5.687309912255877+9.777077902150008, axis=1) |
-(np.sum(np.sin(2*np.pi*array_x)*array_x*9.202461345208725, axis=1))-np.cos(2*np.pi*np.sum(array_x*10*(9.73241593781346)*-(9.392260971295437), axis=1))+10*(np.sin(2*np.pi*-(np.sum(np.sin(2*np.pi*array_x)*array_x*9.073372122607829, axis=1))-np.cos(2*np.pi*np.sum(array_x*10*(9.17696398134669)*-(8.433314724138086), axis=1)))) |
np.mean(np.sin(2*np.pi*array_x)-1.821418173444/3.4777349005651117+2.582225433923635-np.sqrt(abs(np.sin(2*np.pi*3.6275104135972835+array_x*2.0121622635229235/4.20983364382545))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*array_x)-9.566540343712587/5.629866177246336+8.08963141022895-np.sqrt(abs(np.sin(2*np.pi*1.9334094778386408+array_x*1.7561142730918626/4.2044581506840615))), axis=1))) |
np.mean(10*(5.56107243837981*np.cos(2*np.pi*array_x-7.44411469361457)-1.365690758015524), axis=1) |
np.mean(7.647030972582305-array_x-10*(4.249142812645244)/np.cos(2*np.pi*5.711642138883377+(np.array(range(1, array_x.shape[1]+1)))-array_x), axis=1)+np.sin(2*np.pi*np.mean(7.333324077635154-array_x-10*(7.470162563613375)/np.cos(2*np.pi*3.1407358389727777+(np.array(range(1, array_x.shape[1]+1)))-array_x), axis=1)) |
np.mean(np.exp(9.905101656736967-array_x)/np.exp((np.dot(array_x, np.array([[0.7806610261090319, 0.5421421083017176, 0.9468166816596025, 0.996527818591122, 0.9673739446295526], [0.8412710317336936, 0.6346487234842305, 0.2349872338734209, 0.19724321420040103, 0.8406520588296001], [0.315439859451246, 0.6971742139281207, 0.05697662358204614, 0.9228225640871619, 0.9457320209056499], [0.836986264800156, 0.5690400079377887, 0.693558680305352, 0.5025066667945358, 0.6604775966710669], [0.4121949550536641, 0.9580700146952769, 0.3339849918814344, 0.43128761310773966, 0.5920291806293881]]))))-np.square(4.741749267698647)*array_x+4.618463724598454, axis=1) |
np.mean(3.415118086348894+np.square(3.4348378938316504)+(np.array(range(1, array_x.shape[1]+1)))*array_x-4.096562013006972+(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.mean(np.exp(abs(4.5332157790158085))*np.square((np.dot(array_x, np.array([[0.49406248950016807, 0.377873037191904, 0.2645306410319165, 0.4662829095407258, 0.723915342687703], [0.9758572455914822, 0.543978811790596, 0.3971641269824123, 0.6935414185428221, 0.17931965760427715], [0.3816124549069321, 0.7071022361817771, 0.8274153851909737, 0.8210714958030191, 0.6861603190499375], [0.7621883626387866, 0.3374898819536911, 0.5820620866407117, 0.05141193480020223, 0.8576034944636728], [0.5980653291860244, 0.3462456649947213, 0.20108144082174695, 0.9665065523010514, 0.48860243068819054]])))*array_x-6.955791055574003-array_x), axis=1) |
np.mean(np.cos(2*np.pi*np.square(4.7615625583060375)+array_x)/9.497365256883949-np.square(10*(array_x))-2.8544417361110743, axis=1) |
np.mean(10*(3.078016091996382-np.sqrt(abs(np.exp(1.4901246800917924+np.log(abs(array_x)))))), axis=1)-np.log(abs(np.log(abs(8.932608613158884)))) |
np.mean(np.square(6.110270314207809)+array_x-np.exp(np.sqrt(abs(10*((np.dot(array_x, np.array([[0.33362440579843566, 0.41458489718413905, 0.42369724331156444, 0.5441297664318566, 0.4331516904385061], [0.10048390257326123, 0.8463338501566382, 0.6383260788333901, 0.9448749025875466, 0.8450752417361018], [0.16774610793229117, 0.10394536287837886, 0.6111412761164681, 0.04959872267131071, 0.9094227298039593], [0.6500191213096654, 0.9060877521079904, 0.19571446132295955, 0.3969524004558669, 0.07856895952997822], [0.5582697875852969, 0.0320025155630419, 0.6025540343212045, 0.09696107357209982, 0.6345184369841513]])))))))+np.cos(2*np.pi*3.071517249996294), axis=1) |
np.mean(np.square(np.cumsum(np.sqrt(abs(array_x)), axis=1)-np.square(4.298607394214519)), axis=1) |
np.round(np.mean(8.196079250562988*array_x+np.log(abs(6.748774865802053))*np.exp(array_x), axis=1)) |
np.mean(4.189768163742119*3.7021411081861744*array_x-4.423253543593489+array_x-8.51230777101065, axis=1) |
np.mean(np.log(abs(2.5185644719402696+array_x-np.log(abs(3.078520018735519))))--(2.4064763887359675+array_x-7.305245769847985*array_x-array_x)-3.2900154041949587-(np.array(range(1, array_x.shape[1]+1))), axis=1)+np.sin(2*np.pi*np.mean(np.log(abs(8.79706335699267+array_x-np.log(abs(9.8986608197911))))--(6.865935697072907+array_x-3.4475221851707425*array_x-array_x)-9.146581244125633-(np.array(range(1, array_x.shape[1]+1))), axis=1)) |
np.mean(np.cumsum(5.126137846434465*array_x, axis=1), axis=1)+8.388100508080514 |
np.mean(7.3661345512605205*-(np.sin(2*np.pi*2.7938309467208158+array_x))*np.square(8.577399658572467), axis=1)+np.sin(2*np.pi*np.mean(7.837972130960425*-(np.sin(2*np.pi*3.115371907504594+array_x))*np.square(8.72182808223179), axis=1)) |
np.prod(1.912368305184661-1.7031081940348556*array_x-8.329208265292669, axis=1) |
np.mean(abs(10*(np.sin(2*np.pi*np.square(np.log(abs(4.381054049570659)))))*10*(1/(1.0655835535670857-8.867518791467734*array_x))), axis=1)+np.sin(2*np.pi*np.mean(abs(10*(np.sin(2*np.pi*np.square(np.log(abs(1.6648640597662454)))))*10*(1/(7.716221250557533-5.681547189638243*array_x))), axis=1)) |
np.exp(4.294711705635095+np.sqrt(abs(np.sum(array_x*2.45010220598893, axis=1)))) |
np.mean(7.075932660648361*(np.array(range(1, array_x.shape[1]+1)))*array_x-7.597783146012788-(np.array(range(1, array_x.shape[1]+1)))/np.log(abs(np.square(1.8724484234429692)+(np.dot(array_x, np.array([[0.06555733744984138, 0.6560085416237864, 0.6162960962686519, 0.09955446071512242, 0.2052268595913903], [0.29208341735199017, 0.8964261668507922, 0.4344128572678553, 0.10077421296417177, 0.41450538395334857], [0.3115171037809039, 0.20922024528173444, 0.9260960923445524, 0.657471115698606, 0.46191019351294826], [0.9897986392076766, 0.02482712788797692, 0.5572796446625192, 0.16417398013926277, 0.522276669456124], [0.28403000405400736, 0.5786536203188725, 0.9106543646069528, 0.9510705853721093, 0.6196213579208276]])))-7.208885264412385+(np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1) |
np.round(np.exp(abs(np.sum(5.004969482588348-array_x, axis=1)+np.sqrt(abs(3.1555814999394434)))/5.709126303575484)) |
np.mean(np.exp(10*(np.sin(2*np.pi*np.square(abs(np.sin(2*np.pi*3.0797275599791645)-np.round(10*(3.0758424472340624*array_x))))))), axis=1) |
np.mean(np.cos(2*np.pi*array_x)*4.083536895963231-np.square(array_x/9.955217766930925), axis=1) |
np.mean(np.exp(abs(4.5616603513936305/3.718626542938884+array_x-7.367862432529908)+6.9489748337731845-array_x), axis=1) |
np.mean(10*(np.cumsum(array_x, axis=1)*np.sin(2*np.pi*7.18734392141916))+np.sqrt(abs(2.1893437967297213)), axis=1) |
np.mean(np.square(6.996833910030762*-(np.cos(2*np.pi*array_x)))-1.2135149967993653, axis=1) |
np.square(np.prod(4.665076634541884+array_x+array_x, axis=1))+np.sin(2*np.pi*np.square(np.prod(1.4412074367211416+array_x+array_x, axis=1))) |
np.mean(np.sqrt(abs(np.square(np.square(np.square(array_x))-4.5372468630893685)/np.sin(2*np.pi*np.sin(2*np.pi*array_x-5.3231188752855925)-8.849902938771052))), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(np.square(np.square(np.square(array_x))-9.591466449041237)/np.sin(2*np.pi*np.sin(2*np.pi*array_x-9.64759060413047)-8.694683161667012))), axis=1)) |
np.prod(6.273425363411192-np.sin(2*np.pi*5.575857170236476+array_x), axis=1) |
np.mean(8.89160495379952-(np.array(range(1, array_x.shape[1]+1)))*array_x/9.997219664401404+10*(5.558582178104663)*(np.array(range(1, array_x.shape[1]+1)))*array_x+5.2021698049138205+(np.array(range(1, array_x.shape[1]+1)))*array_x+2.9476374864685106, axis=1) |
np.exp(array_x[:,0])-np.log(abs(np.sqrt(abs(9.09913786203698))))+abs(10*(np.square(np.sum(array_x, axis=1)))) |
np.mean(np.square(7.487713191831134/np.sin(2*np.pi*3.51749844438273/3.12263483555652-array_x))/np.square(1.6214955609320516), axis=1) |
np.mean(4.284770023358613-array_x*np.square(4.05742540165513)+10*(np.cos(2*np.pi*np.round(np.cos(2*np.pi*7.940045348471457*array_x)))), axis=1) |
np.mean(8.164333392130466+np.square(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)*10*(2.5916231452068974)+2.5399879782339254), axis=1) |
np.mean(6.98980568004976+array_x*3.4789437413803115+3.634153081619942*(np.array(range(1, array_x.shape[1]+1))), axis=1)+10*(np.sin(2*np.pi*np.mean(7.455093966040888+array_x*9.758312900423151+4.255450633988672*(np.array(range(1, array_x.shape[1]+1))), axis=1))) |
10*(np.sin(2*np.pi*-(np.log(abs(3.7353074598788742*np.sum(array_x, axis=1)+np.cos(2*np.pi*np.sin(2*np.pi*7.515514679055478)))))))-np.log(abs(8.52817374134355*np.sum(array_x, axis=1)+np.cos(2*np.pi*np.sin(2*np.pi*6.229364462643927)))) |
np.mean(np.cos(2*np.pi*np.cos(2*np.pi*6.528088421219661*(np.array(range(1, array_x.shape[1]+1))))+array_x)-5.332180632317947/np.cos(2*np.pi*(np.dot(array_x, np.array([[0.22924607767548677, 0.8522942490802021, 0.6038539174454548, 0.719975708500707, 0.08062148487282983], [0.9141218237923177, 0.0765135523074606, 0.42937088873029305, 0.5496510541500861, 0.19976644585946546], [0.03376181868142469, 0.4423826394769331, 0.08598344867750463, 0.21482161683888834, 0.3773231887347509], [0.5213201724845835, 0.19387744190036293, 0.3629842076309895, 0.062337037078839086, 0.7107533477249645], [0.8679034807297411, 0.21954718787642735, 0.4246476827322857, 0.9907816446545105, 0.11022144945466095]])))+6.083439670877208+(np.array(range(1, array_x.shape[1]+1)))*8.77135115200345), axis=1) |
np.mean(7.244827706801443-np.exp(9.97067004136793+(np.dot(array_x, np.array([[0.6186162244652903, 0.36090470034315436, 0.024733111110285644, 0.7372567099759201, 0.22645974294576043], [0.1332439995140875, 0.2131733512498739, 0.9917262792372741, 0.952743258544802, 0.31219886716953305], [0.5674950959049104, 0.6912644857223934, 0.0992127531107374, 0.6513524628920939, 0.07202218320147347], [0.4795731555401881, 0.9813546835426299, 0.9809999626837932, 0.9616626667222024, 0.2326794833749306], [0.7723438100792123, 0.14314898087366623, 0.6121443583813508, 0.15946172935686376, 0.8527314763017002]]))))*3.9797563460231267+array_x*8.168905395336218, axis=1) |
np.mean(np.sin(2*np.pi*2.5119023896987067+array_x)*9.986919480762003+np.log(abs(np.exp((np.array(range(1, array_x.shape[1]+1))))-1.0236329285896397)), axis=1) |
np.mean(10*((np.array(range(1, array_x.shape[1]+1)))*array_x+5.917435676527798/2.147307951218689*10*(3.200936798993003))-np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x-3.54543543837402-(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.mean(1.3555141647656264/np.cos(2*np.pi*5.151282588635277+array_x)*6.626813931395047-np.sqrt(abs(array_x)), axis=1) |
7.4601432156837-10*(np.sum(5.77702212794009/9.71125731677738+array_x, axis=1)) |
np.sum(np.cumsum(array_x, axis=1)+array_x, axis=1)-np.sqrt(abs(1.0761048709760366)) |
np.mean(10*(array_x)+array_x+7.780602030223854+8.510739753912004+array_x, axis=1) |
np.round(np.mean(4.728369830044017+np.square(array_x*np.square(5.3201495581432185)+9.1412872959677), axis=1)) |
np.mean(6.72885970987754-np.cumsum(np.square(array_x), axis=1)/np.log(abs(np.round(np.sqrt(abs(6.398255841987227))))), axis=1)+10*(np.sin(2*np.pi*np.mean(5.415222481376624-np.cumsum(np.square(array_x), axis=1)/np.log(abs(np.round(np.sqrt(abs(5.1910430344359675))))), axis=1))) |
np.cos(2*np.pi*abs(np.sum(10*(array_x)+2.931290997377557, axis=1)+7.873221805978039))+10*(np.sin(2*np.pi*np.cos(2*np.pi*abs(np.sum(10*(array_x)+2.310293078652448, axis=1)+2.3038097381139426)))) |
np.sum(array_x-array_x-np.cos(2*np.pi*np.sqrt(abs(array_x)))*7.909071212019634, axis=1) |
np.mean(np.round(array_x+5.86967365622161)+6.390972371863751/np.log(abs(array_x)), axis=1) |
np.cos(2*np.pi*np.log(abs(6.1051932845114925)))+np.mean(array_x, axis=1)*8.734904774142631 |
np.mean(np.square(-(8.91051160369195*(np.dot(array_x, np.array([[0.3360262295523122, 0.7301609293485926, 0.7931769329693737, 0.9784695478144392, 0.724286267632532], [0.6854348600872442, 0.8597596105178591, 0.36056042352963746, 0.7148052247348867, 0.7078936090248379], [0.5485226128574995, 0.6330592126322382, 0.7279746439214464, 0.8830027579845129, 0.9379766431587129], [0.5389240658445101, 0.6795739132830383, 0.520285143635391, 0.6888824419934607, 0.3315660318589867], [0.26959508446557867, 0.34348858079273725, 0.2282788567481443, 0.3537549329259928, 0.32101477566195724]])))-10*((np.dot(array_x, np.array([[0.846637669744068, 0.6424614090622176, 0.05668837620176714, 0.6184609310613176, 0.8381485460615102], [0.02587029754739123, 0.47286038483057646, 0.4500614216217107, 0.9899482502697051, 0.8260871283506286], [0.28235385705249505, 0.023849163164403553, 0.35807832688743924, 0.06495188438137323, 0.5938905596741764], [0.8868310880069353, 0.23048779549776366, 0.16959203269504686, 0.3339552641462039, 0.05968974875347566], [0.7872024938572637, 0.9915763994678817, 0.5886139734858159, 0.060143726481862725, 0.6849936484685455]])))-2.300938691322205+(np.dot(array_x, np.array([[0.7745153336577915, 0.2625032513605211, 0.34771762568354525, 0.1338018898447303, 0.6741672792752457], [0.007133830113275508, 0.22241944925225443, 0.7483364743187231, 0.8220778293668148, 0.9961816441494004], [0.8604942264620739, 0.14286107491264977, 0.9207109961300746, 0.5584823638997515, 0.1675745130486056], [0.07513474326967384, 0.19763865514282408, 0.4370830558694204, 0.8599176614738218, 0.5188168303907927], [0.3063916382067007, 0.8153353874686557, 0.6721100144382164, 0.9945937679781456, 0.6577539406966006]]))))/8.84694246265289)), axis=1) |
np.square(np.square(np.mean(abs(array_x*5.885526457936695+np.log(abs(np.cos(2*np.pi*np.exp(array_x*1.424431028408566))))-7.33887739172399), axis=1)))+np.sin(2*np.pi*np.square(np.square(np.mean(abs(array_x*3.448530246483776+np.log(abs(np.cos(2*np.pi*np.exp(array_x*9.818035616952244))))-2.8786857119248856), axis=1)))) |
np.mean(np.square(np.exp(5.920376131485519))/(np.array(range(1, array_x.shape[1]+1)))*array_x/5.80159009545087+np.log(abs(9.901328598092523-(np.array(range(1, array_x.shape[1]+1)))*array_x-np.sqrt(abs(2.1951008394358045+(np.array(range(1, array_x.shape[1]+1)))*array_x)))), axis=1) |
np.mean(np.square(1.5817724577694943*(np.dot(array_x, np.array([[0.8598971174561104, 0.315832646093021, 0.1351854999568447, 0.04152388947662533, 0.9929698130884896], [0.8906759805347535, 0.47095599488823103, 0.7759207492670344, 0.22253079432151956, 0.80195150269788], [0.24583902206807295, 0.4874706295247787, 0.15754482650808055, 0.26304094617682217, 0.3883258818420675], [0.04870400560674559, 0.1604795283553767, 0.5429486058811411, 0.6384871279767751, 0.6888383886149423], [0.6647352998914862, 0.745276370879141, 0.14399966597661407, 0.44517949761999764, 0.5395412803962875]])))+np.square(np.log(abs(4.3475724068751935)))), axis=1) |
np.mean(np.sqrt(abs(np.exp(array_x-array_x)/5.259305024299177*np.square(np.exp(np.exp(np.sqrt(abs(3.1422112467823458))))/np.exp(array_x-2.862442247392522)))), axis=1) |
1/(np.prod(1/(np.square(-(array_x+8.696478026807425/4.422953331731861+np.round(np.sqrt(abs(np.log(abs(1.607799236989832)))))))), axis=1)) |
np.round(9.517592610942039)/np.log(abs(np.cos(2*np.pi*np.mean(np.sqrt(abs(np.sqrt(abs(6.503473822777678-array_x)))), axis=1))))+np.sin(2*np.pi*np.round(6.224149631639932)/np.log(abs(np.cos(2*np.pi*np.mean(np.sqrt(abs(np.sqrt(abs(1.6208974532013969-array_x)))), axis=1))))) |
np.prod(np.sqrt(abs(np.log(abs(np.sin(2*np.pi*np.square(2.9674275154133958)*np.cos(2*np.pi*array_x)))))), axis=1) |
9.02418316596594+np.mean(1.6543483841202222-10*(array_x-np.sqrt(abs(4.54573867980109))), axis=1) |
np.mean(np.round(np.exp(5.046893126231353-array_x)), axis=1)+np.sin(2*np.pi*np.mean(np.round(np.exp(2.2345823699763727-array_x)), axis=1)) |
np.mean(10*(1/(np.cos(2*np.pi*3.5795248411401053-np.exp(array_x)))), axis=1) |