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np.log(abs(7.279161714393687))+np.amax(np.cumsum(np.square(np.square(np.exp(np.round(np.square(array_x))-4.843394438701392))), axis=1), axis=1)-np.prod(5.938136264412377-array_x, axis=1) |
4.953427690633173-np.square(np.amax((np.array(range(1, array_x.shape[1]+1)))+8.32366013244105*array_x, axis=1))+10*(np.sin(2*np.pi*5.898588681317748-np.square(np.amax((np.array(range(1, array_x.shape[1]+1)))+5.7472848123411335*array_x, axis=1)))) |
np.mean(10*(np.square(array_x+6.715283062368282-8.322499407769099)-8.790647765880374-array_x-array_x-2.7022454266991156-(np.array(range(1, array_x.shape[1]+1)))), axis=1) |
np.mean(np.exp(np.sqrt(abs(9.413168592606015+abs(3.8190287172982798+10*(array_x))))), axis=1) |
np.mean(np.square(np.square(np.square(np.exp(np.sqrt(abs(abs(1.0675321440571115-array_x)/1.0853937615909386+array_x/7.32981329638123)))))), axis=1) |
np.mean(abs(10*(8.187476587610833-np.sqrt(abs(1.4624256017192332))+(np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1) |
np.mean(np.log(abs(6.033655948005727+np.square(array_x)))+4.529139982449237*np.cos(2*np.pi*array_x), axis=1) |
5.8030346429270905-np.sum(-((np.dot(array_x, np.array([[0.10426082457382779, 0.11484858136859932, 0.6637530414076664, 0.8136338293666119, 0.7925205553312642, 0.4056464103527724, 0.7941686432642524, 0.17132615337935964, 0.8456697891014945, 0.5053305812566203], [0.7566441488741068, 0.8185913773674012, 0.04159303120329094, 0.7179718266514656, 0.06087183746611724, 0.9030163984412464, 0.7156220387127435, 0.5309003279986149, 0.8328791977199647, 0.6094807875810443], [0.8595456339218347, 0.15427020032817584, 0.4732840465748458, 0.3664908038747362, 0.9432267038727966, 0.48190420438303594, 0.2624578683595946, 0.5649496012822443, 0.5143328003294846, 0.653012882017557], [0.9502713161809653, 0.4750185461771631, 0.8444049877066456, 0.21175644971466534, 0.27693029269434666, 0.6954450675767513, 0.8348166241646412, 0.2188879505470649, 0.4306277930570537, 0.9851405130396251], [0.3298000376503898, 0.7255276689547083, 0.43709243228342887, 0.24139618844170807, 0.7539973790062009, 0.8772495857299397, 0.6171090784134234, 0.7934474703880875, 0.2265197556447398, 0.6621290532514471], [0.4744168464009991, 0.8246727381375103, 0.9892519220069729, 0.05041000173059862, 0.22339678323518797, 0.5908429589151638, 0.8308580291586465, 0.13994926609272174, 0.5347178702008395, 0.5107633066217144], [0.019448026073642932, 0.4710179581963866, 0.8505230317102046, 0.7275152449751702, 0.9384763908964107, 0.8962927101160226, 0.8001101965825989, 0.37039525422908337, 0.9623135499338703, 0.4673981719299185], [0.07960132776549922, 0.8766097899386234, 0.5682036859728429, 0.23933213161541544, 0.818784727065968, 0.8121173133152167, 0.25432620295872566, 0.9459799214701203, 0.862564377884555, 0.5044660390213138], [0.3636356968178781, 0.65913296768156, 0.29669480743567345, 0.4821629005158984, 0.14987551054560166, 0.1721753357151099, 0.9446080324758449, 0.5889729121763414, 0.05419451215252691, 0.006113093030312489], [0.6549216768386705, 0.24754754409186552, 0.5157708456304371, 0.5391595106070706, 0.7023628155612291, 0.4849704762573023, 0.7456125154246827, 0.3490191401264948, 0.7434046653106806, 0.27828144927746123]])))), axis=1) |
np.sum(np.log(abs(np.cos(2*np.pi*np.cos(2*np.pi*np.square(np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x)))+1/(8.002535523716187)))), axis=1) |
np.round(np.mean(np.cumsum(np.square(np.square(np.log(abs(np.square(4.931881922576194)))*np.sin(2*np.pi*9.20247119922375+np.square(array_x)))), axis=1), axis=1)) |
np.sum(10*(np.sqrt(abs(np.log(abs(4.615976750897877))))-array_x), axis=1)+np.round(7.793422601623913) |
np.mean(np.round(3.367278204424239-8.4291660790782*array_x*np.sqrt(abs(8.295510629240493))), axis=1) |
np.mean(6.083219398112664-np.round(np.sqrt(abs(7.765301264388357)))-np.square(5.614788282998998+array_x), axis=1) |
3.500178262236887-np.square(np.sum(3.6796474642104675+array_x, axis=1)) |
np.mean(np.sqrt(abs(array_x))+np.square(np.square(np.square(6.737931706382546)))+np.square(np.sin(2*np.pi*np.sqrt(abs(9.650872663184442)))+array_x*3.926924501371461), axis=1) |
9.216197226212362-np.sum(10*(np.square(array_x*np.sin(2*np.pi*6.06136246922045)*9.812630517476236+(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.mean(1/(np.log(abs(array_x)))+np.square(-(4.8908800422806245)), axis=1) |
np.square(np.sum(-(abs(array_x)), axis=1)+3.3016494034155968) |
np.sum(8.728273389477144-(np.dot(array_x, np.array([[0.9180139206085892, 0.6093886075668892, 0.7727935121281647, 0.7893858173313341, 0.2913660714951587, 0.6383095728411055, 0.6309366163561757, 0.5050727734565833, 0.17026591915429667, 0.5070074267828224], [0.6093257462521644, 0.9293576496335234, 0.5552398681181724, 0.786672092844584, 0.7546063370237036, 0.3614412487828086, 0.04140083689592289, 0.007215888644637181, 0.8450361747105256, 0.8496849462412617], [0.4440464218872884, 0.29981896382691164, 0.7218552687473788, 0.29148324518142943, 0.59372491989536, 0.3787895152352807, 0.6413974334927398, 0.5517622330574304, 0.10192367868659591, 0.6687074844326356], [0.12317904018753245, 0.34408092718579897, 0.39441953212838676, 0.8581100442287167, 0.1667458540345319, 0.7925380069244581, 0.5417408605622682, 0.08075030092001334, 0.33369431050471454, 0.22045644875478443], [0.4404940316170076, 0.028991299299468754, 0.43303084544222126, 0.16769694717725092, 0.18764325863762532, 0.5947527715728071, 0.7547377684389168, 0.2766706867598727, 0.015447295887406343, 0.17251092589448735], [0.40539381273976893, 0.2531446624198719, 0.6678626086257133, 0.011956582544409788, 0.23883661011835178, 0.25573396204008514, 0.3934958187900365, 0.5850164602789732, 0.611110618764675, 0.20931537177002935], [0.8470279177596386, 0.9424918550729474, 0.4659187630694004, 0.9950286063515583, 0.6444072954345292, 0.9938010736281598, 0.47259422303932597, 0.6331558149820735, 0.07188053345065204, 0.155574470448279], [0.2257661456256721, 0.698946969618782, 0.7565567289529653, 0.8419084743288315, 0.02287878611998384, 0.31168871738673076, 0.15288815888887675, 0.20151011248938067, 0.9659097932126924, 0.70971197966621], [0.004381318199072948, 0.5737264673668528, 0.0665061079635747, 0.9875109475674045, 0.31010611114054254, 0.7614832966662719, 0.5554463421240172, 0.26576546496971465, 0.984824431424065, 0.08690064002147813], [0.6207655410619645, 0.813505805178222, 0.7812566795352979, 0.4898569816162145, 0.758977061829859, 0.46413112202977724, 0.176072503432545, 0.8389772596992603, 0.7065393027288295, 0.7296033339748955]])))-np.sqrt(abs(np.sqrt(abs(5.9948599204377695))))+(np.dot(array_x, np.array([[0.6984480098392426, 0.008488949521611389, 0.0961756137389167, 0.8157138939798108, 0.48154825972481485, 0.8178855995827191, 0.5193150114881986, 0.9937515282627668, 0.2314929832436624, 0.9951559068843232], [0.15258438332120172, 0.9871329645042252, 0.02772154466339427, 0.6404231931201498, 0.8122715572433946, 0.08606702814514011, 0.5125542561803181, 0.638320857124299, 0.3662046851075813, 0.37814407404353534], [0.43485080348471294, 0.27446773121839885, 0.9788558021651336, 0.2832370679081445, 0.6005857640378784, 0.6184612237446416, 0.2571578277437452, 0.5036300528783711, 0.44902832377535196, 0.006424871524058129], [0.9322646638509551, 0.13847045197809027, 0.20024746189708043, 0.8799423306525465, 0.08512179619436067, 0.4358017043573561, 0.5530079422711771, 0.8027408519065959, 0.3170286906225782, 0.12859093630591256], [0.7526093242353, 0.4026579636444878, 0.3781102291715408, 0.8363460861208021, 0.7845225212043364, 0.7587940652437148, 0.5309569694429646, 0.6066925489466624, 0.9796848673561493, 0.5803298873418471], [0.46736705171232706, 0.25877634590880294, 0.4829784343711935, 0.37382829163413256, 0.28387764080672206, 0.43403552010411584, 0.8945594293283184, 0.6995582174006324, 0.7295434395441389, 0.4087264452808017], [0.8981109946085679, 0.7407446704626659, 0.8968915909346096, 0.9906196685654085, 0.3549803731075799, 0.6378904897185897, 0.5275181380011051, 0.18828516878952573, 0.2530231437111994, 0.40620441105970406], [0.7071967533420636, 0.1157156692729936, 0.4569794222987186, 0.49799548369190083, 0.012879686636394827, 0.24376176288144158, 0.38747862293054847, 0.16737751935719603, 0.9529316601360377, 0.7977528240928209], [0.6673956034215609, 0.696692244728324, 0.07831342349863757, 0.7184474466136113, 0.6554321389838396, 0.6672459892759474, 0.05420208534888393, 0.6959184919862316, 0.8262904623112576, 0.716392215905058], [0.257727715418441, 0.6877319063810929, 0.3047336517774689, 0.812265778968029, 0.9932913059515448, 0.2583441406859698, 0.03071692419382588, 0.7384592956409407, 0.8164229998054224, 0.46971659699861834]])))+3.0479794594714935, axis=1) |
np.log(abs(np.prod(np.cos(2*np.pi*np.cos(2*np.pi*array_x-array_x)+6.676887692483624), axis=1))) |
np.mean(array_x/5.267381422737378*9.237875070695042-array_x-9.546684404309302/5.101564083229966/np.log(abs(array_x-1.116246126218012))/5.35139628299404, axis=1) |
np.sum(array_x-8.994352211944157+np.sqrt(abs(6.517766419175847))-10*(array_x*array_x*3.797031521899162), axis=1)*8.293916303756554 |
np.mean(4.353923616590237+10*(np.sin(2*np.pi*10*(np.cos(2*np.pi*array_x)))), axis=1) |
np.sum(np.sin(2*np.pi*9.059816385425203*array_x+np.sin(2*np.pi*7.204024856919717)), axis=1) |
1/(np.sin(2*np.pi*np.sqrt(abs(np.sum(array_x/7.944092821395074, axis=1)-1.8439051821816057))))+np.sin(2*np.pi*1/(np.sin(2*np.pi*np.sqrt(abs(np.sum(array_x/3.5967378629360685, axis=1)-3.8026016535659033))))) |
np.mean(np.exp(array_x*9.74047030260759)+np.square(array_x*array_x)+np.cos(2*np.pi*array_x)-np.square(3.4995964802332113), axis=1)+np.sin(2*np.pi*np.mean(np.exp(array_x*2.142685527316865)+np.square(array_x*array_x)+np.cos(2*np.pi*array_x)-np.square(5.576202250011924), axis=1)) |
10*(np.sqrt(abs(np.sqrt(abs(np.cos(2*np.pi*np.mean(array_x*array_x, axis=1)+10*(np.cos(2*np.pi*-(abs(8.026363861565347))))))))))+np.sin(2*np.pi*10*(np.sqrt(abs(np.sqrt(abs(np.cos(2*np.pi*np.mean(array_x*array_x, axis=1)+10*(np.cos(2*np.pi*-(abs(1.9214791060289893))))))))))) |
np.mean((np.dot(array_x, np.array([[0.8654047179739734, 0.8946948295750371, 0.9277559509344632, 0.5770269893674268, 0.18393374115780226, 0.6205061619247874, 0.05790489874197913, 0.19799407891545773, 0.5109818299476634, 0.34339026031143216], [0.6328909681830265, 0.7691673922319632, 0.20011669258361198, 0.5427761082783189, 0.8552642469020161, 0.7968293338350996, 0.5975627112654426, 0.27288085260342654, 0.9843632122255115, 0.9782332884704137], [0.6092230016585098, 0.14875031708787334, 0.8231041607606694, 0.1831094090014389, 0.45617289673937456, 0.7572159918735302, 0.5529969062060208, 0.9107810986638593, 0.49557004881124145, 0.7596939862821803], [0.04021881859950216, 0.342248028531403, 0.030173151466998616, 0.7622585207347781, 0.4695978668857569, 0.5331806669392853, 0.6222240752970442, 0.19683075470950762, 0.8954878419065538, 0.7040094665429237], [0.26358192297164096, 0.15820679177408126, 0.5244030919205982, 0.7352888072114964, 0.761041949412872, 0.9653712928637602, 0.5705467475236201, 0.5132834212330215, 0.48052436905882634, 0.4810875359120057], [0.9500274882395203, 0.4593183641214672, 0.3526307004346576, 0.3246810011450607, 0.9360405810804542, 0.2768800900542614, 0.6912226811936998, 0.8204789505908101, 0.39809433160601104, 0.9640970156597597], [0.4406046693742771, 0.759624097504608, 0.5410338555187885, 0.36310889612581776, 0.3112937602788789, 0.6826939237076941, 0.5711164364588635, 0.48192586330943366, 0.012917431962559234, 0.20668186545304879], [0.31591799658604236, 0.6422205389495651, 0.9919620785810489, 0.7347527315969171, 0.5243055625891136, 0.6075709642816441, 0.05539973177530866, 0.39294281376060225, 0.948072802019248, 0.1483506018348142], [0.7677115010428643, 0.9496376002585097, 0.7768828718067328, 0.7137920842162719, 0.35074447073281767, 0.5046545031456029, 0.34815327112905226, 0.37575614493703047, 0.9102158398976998, 0.06850652236578125], [0.8017579862403543, 0.9278148872990991, 0.5370691080586378, 0.4172032421536457, 0.04781083198542524, 0.3738035332492917, 0.3615987885569144, 0.46135920783767015, 0.14660121002885118, 0.7781214806807866]])))+5.3844970657587945-np.exp((np.dot(array_x, np.array([[0.2457098949462233, 0.2592072481722776, 0.5923057214711037, 0.8975972612808053, 0.1701268355050478, 0.9825324902097543, 0.7846748765894984, 0.02832993202390721, 0.12213619140994902, 0.36553638106791764], [0.6674485107236747, 0.37065347952911165, 0.9353801500101494, 0.0004977688143050774, 0.6791659807541782, 0.7709337144843558, 0.5264056826524215, 0.40102393991478436, 0.45374089388750094, 0.6306390423726297], [0.3575422513798626, 0.053828126782361374, 0.18895224962101143, 0.762987466317554, 0.9835433174443504, 0.48057086319717657, 0.7001310413284497, 0.8754076662091598, 0.46880675066568134, 0.49934036254497705], [0.5073655540933372, 0.2114374704131322, 0.16313000008810874, 0.08384571953087849, 0.6235546900038109, 0.4515078090744812, 0.03386989462135492, 0.348217823022841, 0.687384162895615, 0.0879356999946651], [0.02291560515052382, 0.06867780838395343, 0.4182737483188833, 0.23738414094227256, 0.6718254162080918, 0.9071787058883842, 0.15260909598674977, 0.37631131000024176, 0.89914288697721, 0.5880308642435395], [0.4272610995159828, 0.16617105851992797, 0.053085034555448773, 0.6585520145677463, 0.777561373880784, 0.587893106185701, 0.0722767971115249, 0.0744514611753393, 0.8551754469268434, 0.8237796046213479], [0.26729275762555926, 0.29052064845668046, 0.525345825374662, 0.9708983108150027, 0.9286730383100266, 0.48159149415610936, 0.21804560679838303, 0.14554057824081446, 0.7545816640342641, 0.09615791351405245], [0.7662953562380678, 0.9972714752610456, 0.5940634330133165, 0.2418000256966979, 0.12192152412184798, 0.7205563766650627, 0.4897716619927941, 0.9699947445405346, 0.3847822080160421, 0.32104487241296753], [0.03853892751771382, 0.574271635782181, 0.5579528628483316, 0.3335402735514911, 0.1895490145666564, 0.47934000277647226, 0.6439594007416152, 0.1704498185589487, 0.6932418087737233, 0.6331335653596831], [0.6874969341174167, 0.3870597322148378, 0.9372907291440075, 0.5862793380078146, 0.49500343070092745, 0.767010042890221, 0.21856870167097708, 0.9299523489721444, 0.21214508068644522, 0.2651004254905842]])))+4.000520281940104+3.582830163846138*np.sqrt(abs((np.dot(array_x, np.array([[0.42421632727355474, 0.14924409591052512, 0.6390614438802493, 0.08867835306009031, 0.4984408268182008, 0.13939165772321316, 0.3390143453353237, 0.7282736451777326, 0.01841886648166602, 0.854448355595811], [0.8195031852525141, 0.6556746014411475, 0.3942158266247646, 0.5628277976907511, 0.6029529183426678, 0.20213390411608456, 0.10409729914483146, 0.3849414389224005, 0.003133760314160794, 0.19751364046459274], [0.25973174725405324, 0.4018271464769716, 0.6497244584252321, 0.10215291617312117, 0.9696720453170519, 0.05059333066580196, 0.6883622746525926, 0.7340991368970059, 0.5008732284418285, 0.30282516060755504], [0.0477503413397703, 0.31747935852620923, 0.9578746127759371, 0.06123640938902819, 0.8388594137558193, 0.31453989784594616, 0.5442728431911379, 0.263484354346065, 0.7682562636440831, 0.13470017180264604], [0.9818759223425518, 0.7836343313699343, 0.32699536841856147, 0.25906681924123887, 0.8368796809541612, 0.5169277402017128, 0.6434117513672127, 0.8430296623405744, 0.5745943979193137, 0.4692057749105376], [0.689030754552407, 0.34617788833242535, 0.01142486395394815, 0.9031832402508283, 0.2615991736223455, 0.7665631698182148, 0.28771854591512425, 0.47510461702464646, 0.43528514729427403, 0.6401288900837381], [0.6415216160991433, 0.11964482964306744, 0.9758637882370869, 0.5981382324684906, 0.9783839515431056, 0.2343447761499201, 0.9201932389013547, 0.11557576437361616, 0.31925110875447604, 0.9144094849308614], [0.02840662275458572, 0.9604116700205001, 0.17813381160293362, 0.2574353315057232, 0.5311504453227639, 0.9654304005458929, 0.6828348198188063, 0.6010193618316907, 0.22770503569074396, 0.4816899482849899], [0.8835207215091652, 0.6138419747747899, 0.3682495786508192, 0.3922374847317541, 0.971398329128071, 0.05732774997579326, 0.9859097054432951, 0.08597617165189486, 0.40668081663600164, 0.5125896884014985], [0.09697852699261744, 0.536865672486617, 0.42365785799020184, 0.21989178491096628, 0.862239480420484, 0.908060290573414, 0.43311475496125584, 0.9651347770343388, 0.0011944040272424683, 0.5028089425260426]])))))), axis=1) |
np.mean(1/(np.square(np.log(abs(array_x))/10*(6.454033305133371)+1.9838576711623448*(np.array(range(1, array_x.shape[1]+1)))))-1.7832169417947301, axis=1) |
np.mean(8.02050272529507-np.exp((np.dot(array_x, np.array([[0.7834680998407356, 0.8209174890937614, 0.8216928539599173, 0.03785645686357897, 0.9065480507717051, 0.4379084862358932, 0.09923904250128701, 0.6053124659774574, 0.8707108365222543, 0.12795991851581423], [0.9987396965117478, 0.1494525461378876, 0.6470383531978745, 0.15942993596141353, 0.14545275383514245, 0.006301182619198764, 0.11887577297886309, 0.6822993990888284, 0.7154417036591721, 0.7797841925988811], [0.6369343372832812, 0.1116196745267849, 0.3639307245776542, 0.14114911314103817, 0.9527181686059248, 0.2044941701466617, 0.04807781162935254, 0.9712020305540479, 0.4653570398720078, 0.6805932015916974], [0.9744431862919886, 0.6757987490791114, 0.10457922175510292, 0.1964766586089396, 0.2231966456636406, 0.32461739910616216, 0.8217806339714243, 0.6511131260833015, 0.04402328558151025, 0.9734811737517971], [0.8822457580017077, 0.5756592500032942, 0.45247037119679834, 0.2031550939636696, 0.9695941971356917, 0.5191744872609416, 0.4491757565384278, 0.07664269670982893, 0.8638857190813538, 0.04847900642527714], [0.9153375152369688, 0.7307929608970516, 0.1509973433823767, 0.9794558155102394, 0.45195531662276023, 0.22535866104205649, 0.4504788889287983, 0.8061601149602761, 0.22060514477760051, 0.7291194152366292], [0.46873054830360095, 0.5466392784530235, 0.8982817105369593, 0.5773183407355783, 0.6699912893737193, 0.11132339744351816, 0.2836307992059489, 0.3091231912465754, 0.2727827803105627, 0.42790131274998666], [0.8717251845900742, 0.9641602931574687, 0.1526108936877264, 0.2676763517645727, 0.9710941520228447, 0.6749447229269014, 0.6792219525688281, 0.7154448857162727, 0.699376125093637, 0.5999183036863563], [0.9640230951249361, 0.4184299244382865, 0.3398643077211674, 0.6311551749915605, 0.09489900491120995, 0.8144706471550001, 0.9154987045418779, 0.07701694244365931, 0.6592509174386789, 0.14574004985375433], [0.40438860689884193, 0.769351772986131, 0.4470787957991237, 0.32865180250868997, 0.7128231989155088, 0.2360641323527085, 0.48744781465088005, 0.11222300140321295, 0.7863002914752247, 0.5684167173059111]]))))-np.sqrt(abs((np.dot(array_x, np.array([[0.3090717006221402, 0.21609828249277074, 0.6818869102540154, 0.3396042802745366, 0.9537379756927837, 0.7510425270978798, 0.4787654513467504, 0.29669375997221614, 0.5116377412240413, 0.9457584455172726], [0.4670478452296295, 0.22360648832228502, 0.26543589203382834, 0.8721450135925871, 0.150980585028208, 0.48156127798266046, 0.6705396886943529, 0.3784648789025915, 0.5058220584277122, 0.8061887874708407], [0.5574350366099062, 0.7765705784798019, 0.7938477060780591, 0.011426965293654723, 0.28487445351797447, 0.6950131955072641, 0.16959143094145568, 0.2844316342486374, 0.025693828125124774, 0.6007714390909873], [0.4293307792196407, 0.11037925550355177, 0.18737226208142432, 0.11613676222837488, 0.19681099282516168, 0.8028986547353445, 0.9643800733761119, 0.7203109612579224, 0.782245285526469, 0.8722955257644207], [0.5442884464568574, 0.9390068366731441, 0.9484949750256296, 0.10582762818144009, 0.4024829080457323, 0.10391546779236216, 0.9705921823075379, 0.7610664238668541, 0.6496434465462985, 0.4246701431694012], [0.8149045000129164, 0.4103467741529987, 0.5983585054330987, 0.05429574215823485, 0.3497910094475266, 0.9035185264616263, 0.5205457819292961, 0.12220429921116305, 0.3552476581582754, 0.058278218926960945], [0.004335260324119017, 0.2534250147549372, 0.43420360699118954, 0.36399299599905466, 0.7483927169010265, 0.4599362060709292, 0.11356213753194522, 0.6810927219607528, 0.22154349892535918, 0.0518466080458031], [0.19834770235245003, 0.1758033281991136, 0.3641035085459091, 0.13038303903027815, 0.13627143365002836, 0.8839395681928908, 0.02371547006499386, 0.11834718671174804, 0.24818121445066588, 0.4783699706982242], [0.6061906066754525, 0.1754431523558032, 0.22698638572819296, 0.5873243980774691, 0.9781625958895266, 0.03260413337927759, 0.4978122352357125, 0.5379919258441206, 0.2072534979637778, 0.1911908322099818], [0.5063490930115725, 0.25754537700015256, 0.3194021196030905, 0.7856813630041384, 0.9774950756848343, 0.47622650537276834, 0.5856209339977481, 0.30683757050405647, 0.6813390108258426, 0.7530412847647376]]))))), axis=1) |
np.mean(1.051023989369779-array_x*2.484022032569931, axis=1)+10*(np.sin(2*np.pi*np.mean(6.1263962345969185-array_x*2.453101309819863, axis=1))) |
np.mean(-(np.square(5.436992879389331)*array_x+1.8351518458068559-np.exp(5.237462068027533)-array_x), axis=1)+np.sin(2*np.pi*np.mean(-(np.square(4.842433423766671)*array_x+9.083088393149792-np.exp(9.153066534705248)-array_x), axis=1)) |
10*(np.mean(2.0497365811212735-np.round(10*(10*(array_x)))*np.cos(2*np.pi*9.722359449365037+(np.array(range(1, array_x.shape[1]+1)))-6.350109590830993), axis=1)) |
np.mean(np.round(3.3571477321838135)/np.cos(2*np.pi*10*(9.566618636971278))+(np.dot(array_x, np.array([[0.6724417301553743, 0.5870139316163889, 0.515732444378858, 0.5396729039839676, 0.07343312347771791, 0.3726452872304382, 0.3880679481335616, 0.8551073691014307, 0.41899795937760353, 0.9705882922501726], [0.548865335160926, 0.8408387321787367, 0.4338916119109181, 0.10100070445160969, 0.22300236356853975, 0.6817196340568644, 0.12226232588369657, 0.8524889488179123, 0.5779414009991793, 0.06335539262036038], [0.09552974292633487, 0.8166595144800068, 0.4580105122215763, 0.38757371593667544, 0.5637612380543585, 0.3350368704042289, 0.4901424167463756, 0.46473963903982496, 0.7873215450430133, 0.4394962664496248], [0.8354865724187417, 0.40605663981452544, 0.8501183476784688, 0.7719942163982252, 0.7006877528682238, 0.04767754065535501, 0.848678769713339, 0.6077777410575684, 0.19630583491133946, 0.6765899196673592], [0.2804027365511551, 0.3664825796376967, 0.5109546071928127, 0.7512282034442024, 0.7651563708692921, 0.2666355035259318, 0.004769395691507139, 0.7395096164361024, 0.594639484960395, 0.27087408440336613], [0.09146741951497794, 0.8252695856353806, 0.023811111187715506, 0.49465963196598073, 0.2097058871563222, 0.2471976627462773, 0.8146523100582668, 0.6140920908973388, 0.3266982174751427, 0.15802225279549764], [0.37387616219160213, 0.8738065620569433, 0.8455160632787978, 0.02815839781993812, 0.9164113821140291, 0.08685322257864325, 0.0780076917695357, 0.9014917872672511, 0.22771408545786886, 0.751710939271811], [0.9614364131979302, 0.9635320890944227, 0.5914006416296553, 0.1198895080040917, 0.7134635527709591, 0.9726540200614816, 0.5648839366080799, 0.6193717923907447, 0.4136355660560348, 0.9550706734944095], [0.27893218104471795, 0.056349027467376356, 0.6654452395886653, 0.21100211280815007, 0.16376791653735767, 0.6222871037080028, 0.7149447349095356, 0.11157207350739806, 0.012812056310386621, 0.2529194214663736], [0.31939401930959366, 0.10859062381694407, 0.15115629418974508, 0.6408102057397014, 0.5488528169634189, 0.9384012782559157, 0.944545072338009, 0.8627904253145932, 0.3844804660685034, 0.5241973436911015]])))*5.782982599807714, axis=1) |
np.round(np.mean(array_x*array_x-7.992163594503233+10*(10*(array_x))-array_x-4.850817125268173*(np.array(range(1, array_x.shape[1]+1))), axis=1)) |
np.round(np.mean(6.780005337076828+1/(1.018845173207466-array_x), axis=1)) |
np.mean(7.936974823684233*array_x-(np.dot(array_x, np.array([[0.6705506488680774, 0.08137101041541872, 0.8675973732528465, 0.2583030136404838, 0.6104524470262634, 0.3731651004825185, 0.22192072805643348, 0.8393434653662224, 0.09998571400006928, 0.9290543339571226], [0.7667846802145671, 0.06528612924604338, 0.8907821725824779, 0.6294218154347493, 0.6743796715544333, 0.3067825675715484, 0.7904635621763803, 0.39737640833852084, 0.09237259826091337, 0.36203875766070903], [0.2602815267905103, 0.168665624202345, 0.6396585579696555, 0.33093069155640864, 0.3115667904135997, 0.5297003983543763, 0.9597150190619884, 0.29435389244365806, 0.6107114443484821, 0.2194492719262352], [0.5262578572534632, 0.7513507381268298, 0.042832526988234254, 0.7426925955815717, 0.6417408951330605, 0.8361965575892608, 0.9394181175751078, 0.10525335209865683, 0.7215689303606915, 0.8419098183576702], [0.1420493321222278, 0.3909936926591253, 0.7775150361083896, 0.5301596197957049, 0.9879955640338708, 0.1579577330215789, 0.8728233095720384, 0.9016570231921827, 0.1988194966138641, 0.00535587891254008], [0.718207526164327, 0.2542743757201601, 0.4382041330520491, 0.8413835890580461, 0.670325139230796, 0.792953481564326, 0.9794363245633415, 0.9589833266687997, 0.7424204311166683, 0.9999544780934178], [0.5248018417796623, 0.0525696110960967, 0.6661169571585586, 0.29489559764893847, 0.5723150222467489, 0.9556227538144055, 0.4333091911057556, 0.4365235036211771, 0.35035377679737645, 0.35484640969392156], [0.03774756874105334, 0.40799056187495286, 0.8451798244788048, 0.02524541882242093, 0.6736857509741883, 0.5895703232973393, 0.23717881026389853, 0.6518744632121709, 0.4062696275470242, 0.08719413070508886], [0.5786693748774079, 0.34602484337664396, 0.0509492691819603, 0.5522743163225818, 0.7493529489314776, 0.5582426018560548, 0.09804082266005198, 0.18705796533809305, 0.9829952450265471, 0.709938116605026], [0.5864254613429288, 0.45412120742938034, 0.1242040623522761, 0.026487423666793686, 0.5447859265291313, 0.41164301583386687, 0.601808282263009, 0.8261988772867274, 0.772316152577946, 0.009051538928495528]])))*3.9806578969496216-np.sin(2*np.pi*np.sin(2*np.pi*np.exp(4.105083937790461))), axis=1) |
np.exp(np.cos(2*np.pi*6.893194028796125*5.817835721998985+np.mean(array_x, axis=1)))+10*(np.sin(2*np.pi*np.exp(np.cos(2*np.pi*2.9760257631928915*8.391049638297098+np.mean(array_x, axis=1))))) |
np.mean(np.square(np.square(1.9946286738669108)+np.exp(array_x)-9.815127717977347), axis=1) |
np.prod(np.sqrt(abs(9.989120390816588))*np.cos(2*np.pi*array_x+3.0909291571750144-np.sqrt(abs(array_x))), axis=1)+10*(np.sin(2*np.pi*np.prod(np.sqrt(abs(1.6884660025741876))*np.cos(2*np.pi*array_x+9.28543786890531-np.sqrt(abs(array_x))), axis=1))) |
np.exp(np.sum(array_x, axis=1))+1.0333965597974912 |
np.round(np.mean(-(array_x+7.270552591451424*7.6966807021558585)-np.exp(8.791061250364127)*4.978120870138094*array_x-6.161716036529401, axis=1)) |
np.mean(abs(7.92771236805967/np.cos(2*np.pi*(np.dot(array_x, np.array([[0.2579693258327078, 0.7513178927179152, 0.5626511316084399, 0.6506344277677583, 0.8612545280662018, 0.5680846158854097, 0.47279928974477914, 0.21531543805025732, 0.5033244647561658, 0.936147476307489], [0.8975301866083798, 0.5854668660924562, 0.5324812988450002, 0.18905921453711838, 0.6624266600049877, 0.9937154059013912, 0.04209082785972995, 0.6035606445377221, 0.41946080005260966, 0.04643765636994568], [0.718583828505469, 0.7216430734978293, 0.819385488386943, 0.5806983283055059, 0.552958275524723, 0.3495534128312303, 0.47033185111061504, 0.36102689003113486, 0.1999351220816632, 0.6862174317925931], [0.28278523678596856, 0.3357313235646704, 0.3964314810725542, 0.5453487842688112, 0.8481605404970101, 0.6412328463126586, 0.15316098641761955, 0.5659407420008049, 0.6803034452474107, 0.22869320869772236], [0.8442603363503459, 0.021818997434955967, 0.34796809852794675, 0.5649749589076595, 0.006760664460577659, 0.5763340423418526, 0.9515502801086431, 0.719281726803649, 0.0045502460739261785, 0.28349720610401075], [0.019467578819446496, 0.5101162636097738, 0.15432424638148423, 0.9419133782185818, 0.7842197535742881, 0.36223711263710934, 0.2791190742597275, 0.38027959942232403, 0.4823838334489985, 0.8880158017487535], [0.8344371999329733, 0.23903162922486731, 0.8790793290795796, 0.8958773186207005, 0.07762723877682554, 0.3287936969766201, 0.6045442042384648, 0.8554261160669995, 0.2476397953676892, 0.18170254132426833], [0.8357337522903643, 0.9367335826856327, 0.4676618658442864, 0.01930137953619504, 0.696134421499513, 0.4826658433608716, 0.18028093086145658, 0.6099846120354304, 0.4974468519822336, 0.9687972077293027], [0.47663261114678, 0.211687412276362, 0.8305883136422562, 0.8113853971677569, 0.6645971870706602, 0.09349371648469251, 0.8307450224962551, 0.12306502437124811, 0.9867606372820109, 0.9193566317371326], [0.887759537222792, 0.1124776967138944, 0.8676629430281068, 0.8125062755498185, 0.9355002168404779, 0.5633826008206586, 0.366327360888692, 0.7533685220012013, 0.6446402999107235, 0.919682851329086]])))))-np.square(np.cos(2*np.pi*np.square((np.dot(array_x, np.array([[0.9006939102670336, 0.12426529798435892, 0.9922467271264832, 0.9893378810916243, 0.02135268006897595, 0.4968098362213542, 0.9000872525887056, 0.4426835815468366, 0.938133783589064, 0.095778824185071], [0.1990170206419416, 0.5709628592366122, 0.23542965205596267, 0.4728282164276435, 0.46070301321272444, 0.9984092933592476, 0.8250461686822141, 0.528670298506089, 0.24293769502318907, 0.00038202806564147096], [0.5368371409073307, 0.9273471543977035, 0.13678987719837632, 0.629775532523964, 0.9659655644266728, 0.5262355149485567, 0.5351646788491936, 0.298397435576215, 0.06497908781140838, 0.017258097536719252], [0.12220263002605147, 0.1387032621266886, 0.4805245157410749, 0.15491893458896, 0.25932310702053385, 0.7855540876328608, 0.26484491285914014, 0.7717364485915122, 0.04802726128909085, 0.9274148037020294], [0.22964881366326206, 0.5299346220415808, 0.5795872543262384, 0.6283734835196281, 0.07895988626517203, 0.9595022759528071, 0.3474106583194405, 0.45764242650590936, 0.2388047384194958, 0.11501960080225182], [0.2435269994263093, 0.745848185570449, 0.6432027224860842, 0.059988868544974316, 0.10506724391478495, 0.36773350239554714, 0.9635144049147358, 0.2376665121275855, 0.867804184345804, 0.058724873452793314], [0.15987900370491914, 0.3443504853389192, 0.7585745909055849, 0.14705438905991575, 0.4364731053820491, 0.3144250560698699, 0.0245663800862419, 0.8330453930874354, 0.15392754891193527, 0.1608735698534678], [0.7730548965251945, 0.5854089726591003, 0.4357391282974161, 0.24076945690832185, 0.625999811946954, 0.2992932745024479, 0.2339542623059343, 0.9466414304750558, 0.4752684972569271, 0.09781171030064228], [0.8831149120883092, 0.6426472241222763, 0.8928500323478161, 0.08506646070704105, 0.8146499517821438, 0.5964361591486905, 0.44061744497920896, 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0.7364704488273068, 0.288150263984723, 0.8103206703626658, 0.012277366949852153, 0.09407928340228522, 0.4298151498320226, 0.24566795455575485, 0.703208883475681, 0.5378853890927988], [0.5308198063642245, 0.9257598934793043, 0.6954194498224242, 0.8905994354144777, 0.2694959691110643, 0.7278291968432038, 0.5306293723338938, 0.2661598197922439, 0.14113597804296907, 0.541565025361892], [0.16868042484784962, 0.9469401751549197, 0.9246316442996445, 0.07448445700738848, 0.45612918984905226, 0.09498164619186078, 0.5024643912177775, 0.06018681917896895, 0.7430207548232967, 0.8805509724044155], [0.9769359805414989, 0.7970147805743458, 0.974984681551187, 0.9087437552790897, 0.02326733018868543, 0.15650531002621804, 0.5873119075232629, 0.40596623093602613, 0.9700348064066227, 0.8146111109758161], [0.5270523921225481, 0.5269941387480743, 0.9630929708246823, 0.20390162589621996, 0.11224490271534793, 0.5261869401197563, 0.14925375651036177, 0.8900772759166423, 0.14444702774783724, 0.3655136118448219], [0.4381685664091721, 0.00885683456368569, 0.07668972193226231, 0.8517019462549918, 0.3448560337866713, 0.021315119979556374, 0.41660324624380474, 0.4692886151453659, 0.2983702318235735, 0.6779187702478068], [0.5206195203049848, 0.41394263451818747, 0.2842147841275965, 0.24989140721897973, 0.37871830361122505, 0.9054624197033246, 0.17408523940212317, 0.06662512485277428, 0.28780909153246514, 0.0218509000799344]])))), axis=1))), axis=1) |
abs(np.square(np.sum(np.exp(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.33109917532737476, 0.7894327911000686, 0.17507437632738487, 0.35614808627580685, 0.30729132503656087, 0.6051347405061543, 0.5197278669874622, 0.08519798615579732, 0.5472332632344397, 0.381214217864175], [0.6577908452422742, 0.6729095144377293, 0.2352284302305121, 0.014450680704091612, 0.3270300982623283, 0.1783452672013215, 0.3326114792048728, 0.8543889346983055, 0.9607746753225275, 0.8349070641019196], [0.8743531031016776, 0.7378077137307774, 0.5205790667709643, 0.5633490658988684, 0.29701328029536866, 0.696366746341382, 0.4988701632238204, 0.2481258556570789, 0.09370673020462328, 0.753206485666467], [0.37923638713674257, 0.41724441602121154, 0.36481824336357715, 0.8726385217823692, 0.2955464094907374, 0.757973882578258, 0.9641860665437006, 0.02838137170830002, 0.09240540677459408, 0.1063373079272647], [0.7046072142203565, 0.9675843286286839, 0.5771297995513284, 0.40919857554419725, 0.3250870201086057, 0.2760544850804414, 0.9343320343733619, 0.005395982060468096, 0.8745021330539497, 0.9702253236541467], [0.4416623779686083, 0.8287777084931292, 0.29937719762853865, 0.8682485522589027, 0.6096301114897231, 0.2036294367961654, 0.6057499860208962, 0.6694922607945053, 0.38619123136122535, 0.23630150361721836], [0.7492936957382911, 0.4232302924605449, 0.27209166147136865, 0.031060756873934392, 0.851017201180861, 0.7476898404322757, 0.018485339851041394, 0.9549877145231518, 0.3063932668904643, 0.41711114194016485], [0.5816423696827642, 0.6520015401051034, 0.948579158225411, 0.47036349673605204, 0.7799028144623715, 0.7082886045238659, 0.022776271571273266, 0.0662744122390091, 0.7431290825906086, 0.37716313823554837], [0.6971166609266428, 0.050334873593033014, 0.9465162446390734, 0.01880691901671483, 0.046515528336209755, 0.2681926233025119, 0.045101202424177145, 0.19249813731280163, 0.444514407266961, 0.8335940006524252], [0.5203167771641597, 0.8105653168812358, 0.49020673389054104, 0.5027822582305416, 0.5250869769736596, 0.406467065578498, 0.9648000875107313, 0.8834163582380375, 0.7544801257048254, 0.4797135323089968]]))))), axis=1)-2.9882416402211875)) |
10*(np.mean(6.574951543634079*8.015363045467149-9.448247834113056*array_x*6.472246789109634, axis=1)) |
10*(np.sin(2*np.pi*np.sum(6.5226580330406*np.sqrt(abs(2.9060042532116466+array_x+8.876308601685027)), axis=1))) |
np.mean(1.8820114852842704+array_x*-(np.square(6.1236088220790705))*9.570552271495673, axis=1) |
np.mean(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)-7.765220229930335, axis=1) |
np.mean(np.exp(5.156482471825487)*abs((np.array(range(1, array_x.shape[1]+1))))-abs(array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(4.619761422869297)*abs((np.array(range(1, array_x.shape[1]+1))))-abs(array_x), axis=1))) |
np.mean(np.square(array_x)*5.789142594121544*np.sqrt(abs(3.25766364498702-array_x+4.800052262753263))-8.269474263309474+(np.dot(array_x, np.array([[0.6056389536569123, 0.690453141362662, 0.006907372854415117, 0.30375702696206885, 0.9116628919512373, 0.4993964598401818, 0.7051300178238542, 0.23147810473829378, 0.25268473818720627, 0.1837817036459033], [0.2572175641893151, 0.8230204708219918, 0.8899570701961076, 0.5675249532497926, 0.41540780658249354, 0.7101440871909437, 0.7959816611987726, 0.2328271564006532, 0.9657429994814337, 0.5678250577477689], [0.5574271917852087, 0.875054711378177, 0.09332216441500396, 0.7169981514133983, 0.11757514594040597, 0.8130171895812512, 0.01621170330943289, 0.5212410770379471, 0.3038607057379362, 0.17434633751103645], [0.6363513791162708, 0.4629789407420358, 0.7933470311712423, 0.09586186447683198, 0.9709391769184192, 0.9048990461776202, 0.9706009978195455, 0.5374504008173718, 0.05251016369253225, 0.2512352225182347], [0.4226196268439215, 0.7517233486009391, 0.2056003331578533, 0.7457296365439355, 0.7416770034270579, 0.629235307386113, 0.1269734489388784, 0.4888716217378849, 0.6462974646117486, 0.11772337551832024], [0.05091954283888567, 0.8904460494283035, 0.009480407734171137, 0.46458613755834566, 0.9516982690292949, 0.7138503035295346, 0.26550502998527903, 0.6826601466034082, 0.12493442406747357, 0.971656038963975], [0.5387013570770892, 0.33862920759556236, 0.08923900643201732, 0.40644374517980364, 0.08026438856396212, 0.3354519825070812, 0.21324738265445753, 0.660507029132662, 0.3209318409552492, 0.5015120697387524], [0.9884030721655408, 0.5979407610434667, 0.4640742936200192, 0.8763005115492345, 0.8808822259855579, 0.6816642225086731, 0.9792862904419665, 0.4673303031168162, 0.8482528665784125, 0.5224481727776543], [0.9549790003280316, 0.4753597063367271, 0.13805883049255574, 0.5953646858714386, 0.5297237060419477, 0.4545266502885207, 0.41230239919182576, 0.18896071768996048, 0.6322819943591439, 0.17340687011739764], [0.2737676748395591, 0.20066148641239068, 0.44766590299758857, 0.7231195095986845, 0.2866975778497003, 0.14652710438913874, 0.5229566805094897, 0.01568067320338984, 0.2677959406316849, 0.601561262387627]])))-8.336237084554243, axis=1) |
np.mean(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x-10*(7.22243752472079)*(np.array(range(1, array_x.shape[1]+1)))*array_x-np.sin(2*np.pi*6.02532538982604)+5.091688745073039+(np.array(range(1, array_x.shape[1]+1)))*array_x/np.cos(2*np.pi*6.155096685616287)*4.127249117561055)), axis=1) |
10*(7.8724346275171015-np.round(np.prod(array_x*np.round(3.0240728759448), axis=1)))+np.sin(2*np.pi*10*(8.577802444159467-np.round(np.prod(array_x*np.round(4.879171072745851), axis=1)))) |
np.mean(np.exp(np.sqrt(abs(np.round(4.33940755584109-array_x/np.log(abs(array_x))/2.520562395474742)))), axis=1) |
np.mean(np.sin(2*np.pi*8.550433662934235)+abs(array_x-8.688513767464269+2.6903240118167293), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*6.753373989946782)+abs(array_x-8.309762049220991+5.548057459939551), axis=1))) |
np.sum(np.log(abs(1.4816409280587417+6.997899818751888*(np.dot(array_x, np.array([[0.2885309856463828, 0.7782526919840907, 0.8521732235780334, 0.10846141252630637, 0.6573115241386748, 0.19857675012388087, 0.6531240674387727, 0.1096653993942498, 0.06864304190398018, 0.40379616431313037], [0.4772300903461162, 0.5740421047759363, 0.24611551629834416, 0.9125794032391901, 0.36226436358814995, 0.7858592267203183, 0.7618903293871883, 0.5992811293101017, 0.6522975929839971, 0.4822005087991348], [0.7795792913317103, 0.8968518109094343, 0.2864042231662197, 0.13400952143765266, 0.18076053046902252, 0.8196558216783857, 0.9792158752790688, 0.15492879195852272, 0.6565868620126795, 0.4054359778557146], [0.8041052383378671, 0.09509704621860116, 0.5521614701046598, 0.3023684065061174, 0.4677733953474399, 0.7982673098877907, 0.9545498040564252, 0.11505863318195264, 0.2892789206013773, 0.669682868628998], [0.5607256811684764, 0.7842819217961726, 0.8775558981766897, 0.5052237123696109, 0.07025470107159071, 0.9294227419213248, 0.012701453825092024, 0.669269846487519, 0.5681371312904485, 0.4272174544302453], [0.1553947687976931, 0.5636673616080825, 0.6233945694877187, 0.08295776740243976, 0.06563853098983607, 0.6403016254519476, 0.5502913942685469, 0.971508257327539, 0.16834152605817765, 0.7818199135850471], [0.3322123298058429, 0.2358501913192833, 0.08938225821909473, 0.9246932590552012, 0.28568394996282276, 0.20072321755614553, 0.4047809826513489, 0.13094132359078559, 0.6565866049540036, 0.40673838297882126], [0.3282964065020012, 0.38381895183489556, 0.8538253472720411, 0.9378796262327846, 0.35300489111858135, 0.18383560848517932, 0.7978776413655506, 0.23797999747440401, 0.38298163306135136, 0.07119781746473852], [0.8743389390412837, 0.5983744066636097, 0.5271999201653531, 0.7648359430313014, 0.19344213332161342, 0.6568556880882572, 0.45319251876781286, 0.673638380874509, 0.23615753992313993, 0.26126931097967465], [0.285811279666877, 0.7396401917207971, 0.12459546727188231, 0.679524483379547, 0.8066171494798255, 0.30341717702226767, 0.14158564314473077, 0.5112947811425765, 0.21813647268803005, 0.2857898781206031]]))))), axis=1)-6.455896124896892*np.log(abs(3.4047446028543593)) |
np.mean(6.443575473546611*10*(array_x)-10*(6.360555548490143), axis=1) |
np.round(7.632640763828378/np.mean(np.log(abs(array_x))/3.4779069680185666, axis=1)-abs(2.9328440038990897)) |
np.sum(np.sqrt(abs(8.442652010084618*(np.dot(array_x, np.array([[0.7560182725599128, 0.8682919771208439, 0.8524969488713721, 0.46952156947244483, 0.9657963033739754, 0.5223825186975708, 0.6335713182413338, 0.8491203143106241, 0.530016097087599, 0.27819736417469265], [0.8964759235248482, 0.9511483154050181, 0.33506620026745115, 0.6107388992972967, 0.7618834439314385, 0.919204028581634, 0.34256332236219555, 0.07464368224695295, 0.028360268637396935, 0.08897819683085595], [0.24758300428137714, 0.2385279859521815, 0.8597239184926507, 0.49672495997885624, 0.9118289477831844, 0.454000639475047, 0.515807958596153, 0.4077429905062532, 0.26959231367147474, 0.01191692948604084], [0.016118341425209293, 0.6657737216138696, 0.5473334887412077, 0.9105919485106275, 0.7806503810370615, 0.5094645608577163, 0.5195299633351548, 0.6328762438099669, 0.6987918812805539, 0.0026218495587774227], [0.5163675804747563, 0.7489586983772046, 0.7992026803960134, 0.6751064931492085, 0.15315555515936974, 0.031629011393680906, 0.4343206056997283, 0.9600297158950831, 0.6235550876444962, 0.10252349648911174], [0.8752804443191226, 0.3654773956275199, 0.77496739640376, 0.25046804166667236, 0.1495206195409564, 0.0681958261497232, 0.7516910676343932, 0.3292533091015323, 0.7724398037518827, 0.5852160031010423], [0.5014713985114612, 0.7559166064442174, 0.9789823676767803, 0.850354492012814, 0.13606883383895907, 0.7708711043878821, 0.9131669368143595, 0.6770026993041978, 0.8001925503541996, 0.7526904304007435], [0.3617221502181508, 0.27707657440181943, 0.8458660787543962, 0.42572293349269974, 0.5094881557596549, 0.9758665701536519, 0.836611520985157, 0.3433598302867308, 0.8980366661151251, 0.9472711844183016], [0.8957617583618827, 0.03862052871495836, 0.9538402567245511, 0.6815839580052294, 0.10662939054555587, 0.7161602238068858, 0.8960277088101954, 0.767341664951718, 0.9001321115514513, 0.3277538402731385], [0.43385273192189555, 0.17701374261044345, 0.4047513200001477, 0.7699638810483814, 0.7405890504444407, 0.7957863404740745, 0.3078816410127828, 0.23188077091271353, 0.5972726074658468, 0.023446879819305932]])))))-5.682940977263703+np.exp((np.dot(array_x, np.array([[0.7124843786703885, 0.8197433271122933, 0.7783254921387835, 0.43000982496689333, 0.11398821876632148, 0.9433923463250349, 0.2464926035870193, 0.5847803976249779, 0.6975042533255941, 0.42179914096920246], [0.3774191599864254, 0.3543242846493365, 0.1773058065792803, 0.3498351722091486, 0.33187456519982494, 0.07511673399796859, 0.8090631261065969, 0.3832747830016312, 0.3246045141384539, 0.07498689094578037], [0.36717045248948266, 0.09927834119976187, 0.8252843778032692, 0.5602933760641389, 0.4450720646823232, 0.21137570145103024, 0.9015415018460067, 0.1843913672513965, 0.5589575620794904, 0.15259383302295404], [0.925204907991614, 0.7961128579064545, 0.615189431781593, 0.42281720953792357, 0.41641600068204965, 0.6710946311018151, 0.8822566429278031, 0.10951477348157501, 0.47710046235034187, 0.9289938168384353], [0.5438803304631182, 0.707822535507148, 0.4342029647333613, 0.8284811646994815, 0.20504530336949855, 0.4640811262315293, 0.9337622734656926, 0.8005916459719511, 0.818344726360793, 0.4652135106963735], [0.8833883441388056, 0.09830393321352338, 0.9538992690182353, 0.42982391656955277, 0.07582200426105623, 0.8410125077765869, 0.5055582880627003, 0.20470452244716952, 0.1474509154244802, 0.21797122160044602], [0.09137314724367418, 0.22794554532532818, 0.8592533400266028, 0.3104735862547783, 0.989208182550708, 0.7345444205193963, 0.8900406679455284, 0.561619373928642, 0.9486033651921406, 0.6077975160886364], [0.2516002140515907, 0.7087077129802806, 0.8231784893170065, 0.34990146578859327, 0.6110362016822833, 0.34649634007086405, 0.7258600014771327, 0.2182585164698414, 0.5631380689829112, 0.2818430730509055], [0.4278585859299615, 0.5088161721140505, 0.7430518214925185, 0.23943136711784363, 0.14921769410897212, 0.5027969512387316, 0.21457566573611242, 0.9678388856900839, 0.368484001820892, 0.6238838059947981], [0.45752339314855195, 0.10770693400037701, 0.914220077392251, 0.28322078465956557, 0.3775047122343026, 0.9556643324448714, 0.9183394999522716, 0.11106509426857247, 0.7125537607380882, 0.7240910995449832]]))))*2.489704014063139, axis=1) |
np.round(4.1164582997708-np.square(5.659186401661145+abs(8.379408735085198-np.sum(array_x, axis=1)))) |
np.mean(np.square(np.sin(2*np.pi*array_x+7.5781685575145765)-3.609326306483838), axis=1) |
np.mean(np.cumsum(np.round(6.247556516927183*array_x+7.687381728144058), axis=1)/2.9432171287229663, axis=1) |
np.mean(6.505671238740965+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(5.244885198796827+array_x, axis=1))) |
np.mean(6.01437636061632/4.82122911172779-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(7.108870812566314/1.8607396721288352-array_x, axis=1))) |
np.mean(6.128766620373317+(np.array(range(1, array_x.shape[1]+1)))*array_x-5.768733739285764*(np.array(range(1, array_x.shape[1]+1)))*array_x-np.sqrt(abs(np.sqrt(abs(4.915713610035645)))), axis=1) |
np.mean(3.5242359951461184-array_x-np.cos(2*np.pi*abs(7.465103818468106)), axis=1)+10*(np.sin(2*np.pi*np.mean(2.847918880685052-array_x-np.cos(2*np.pi*abs(2.7184889350481556)), axis=1))) |
np.exp(2.8605200799860464)*np.square(1.957338398836268+np.amax(array_x*9.875645029070519, axis=1)) |
1/(np.mean(np.sqrt(abs(array_x+3.4594638130307254))-2.1148409991543846, axis=1)) |
np.mean(6.32518197882179+np.square(6.068813086781883)*array_x, axis=1) |
np.amax(np.square(6.0797720161982465+array_x), axis=1)+np.sum(9.29064801840081-array_x+np.log(abs(2.1427236564708814)), axis=1) |
np.cos(2*np.pi*np.sqrt(abs(abs(2.4456074978878055)+np.sum(array_x/2.668690654709973*4.835376068279502, axis=1))))+10*(np.sin(2*np.pi*np.cos(2*np.pi*np.sqrt(abs(abs(7.944818276489232)+np.sum(array_x/3.421527869914304*1.7687994681092611, axis=1)))))) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x-9.143790952116994+1/(np.sqrt(abs(7.116154253146752))), axis=1) |
np.sum(np.square(-(6.814510980187699)-array_x/np.sqrt(abs(3.5130111536709463)))-np.sin(2*np.pi*9.853036662539084), axis=1) |
np.sum(np.sqrt(abs(array_x+7.212200922352237+2.1475552594513605*-(np.sqrt(abs(4.3250234173107485)))))+np.square(np.square(4.187291952538119-abs(array_x))), axis=1) |
np.mean(abs(4.712620424157924-array_x*3.7529304733888207), axis=1)+10*(np.sin(2*np.pi*np.mean(abs(7.043040773868385-array_x*1.0030429132770735), axis=1))) |
10*(np.mean(6.342551903232638*np.round(np.square(4.654583421171672+array_x)), axis=1))+np.sin(2*np.pi*10*(np.mean(7.183794979950207*np.round(np.square(9.671815737612134+array_x)), axis=1))) |
np.square(8.314430739565688)*10*(array_x[:,0]+np.mean(array_x, axis=1))-1/(np.square(np.log(abs(8.030052708210611)))) |
np.mean(np.log(abs(np.square(array_x/9.373514359715987+np.square(3.495049308646971/np.cos(2*np.pi*array_x)+6.432060308255756)))), axis=1) |
np.mean(10*(np.sin(2*np.pi*1.127221200283226)*abs(6.749128313913253)/(np.array(range(1, array_x.shape[1]+1))))/np.square(1.2987136523083262+10*(array_x))*6.223645824737874, axis=1) |
np.mean(np.square(np.log(abs(np.sqrt(abs(-(9.09272906506915)))))*np.exp(array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.log(abs(np.sqrt(abs(-(3.5144336629583286)))))*np.exp(array_x)), axis=1))) |
np.sum(np.exp(array_x+1.964220568750997)-4.584527749453352, axis=1) |
np.mean(np.sqrt(abs(10*(np.sqrt(abs(np.exp(array_x)/np.sqrt(abs(5.364608838505991))))-10*(1.3432225003609615)*array_x*6.419481909401849))), axis=1) |
np.mean(np.log(abs(array_x+8.25617792194957+array_x/1.7900921240881824))-np.square(array_x*1.2936992333995736-3.275287308394959-3.3791815780028216), axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(array_x+6.890666044350603+array_x/7.112275962436154))-np.square(array_x*7.58188039156057-1.4387999959438327-8.628380942182073), axis=1))) |
np.mean(np.square(np.square(np.square(3.5277417024271465)+np.square(1.5184899131051002*array_x))), axis=1) |
9.776020735325144-np.sum((np.array(range(1, array_x.shape[1]+1)))*array_x+3.1597920411977873, axis=1)+4.161648888424894 |
np.sqrt(abs(np.mean(5.403007989346403-3.12903270350354*(np.array(range(1, array_x.shape[1]+1)))/np.sin(2*np.pi*np.cos(2*np.pi*np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x))-np.square(4.755399491092672)), axis=1))) |
np.mean(np.square(10*(2.5859361033090034)*np.cumsum(np.round(array_x), axis=1)+3.586206857484084*np.round(array_x)-1/(6.364194396516984)), axis=1) |
np.mean(np.exp(array_x)*np.square(np.exp(4.004719803051222))*-(np.sqrt(abs(1.2050447981061987)))/np.exp(8.415365628484096*array_x), axis=1) |
6.105751906565172-np.sum(np.sqrt(abs(array_x)), axis=1)-np.sum(array_x, axis=1) |
np.sum((np.dot(array_x, np.array([[0.17264090799056808, 0.34263977522644906, 0.33895685621114513, 0.24345167985680471, 0.7528573788591889, 0.8823348549723868, 0.9355773981690985, 0.3764690594957919, 0.9431088459045868, 0.4875393978550454], [0.5355875768632762, 0.46163345263793965, 0.9796025534059405, 0.23375899506309317, 0.8346869818084596, 0.24279816430473233, 0.8017308179510952, 0.5450317379036227, 0.011327655906423817, 0.6511175109042824], [0.366032158331972, 0.24824559216796627, 0.38076862463851147, 0.774645478102393, 0.7731309596374437, 0.7020563892545523, 0.7843064384841194, 0.4238211748551297, 0.5369868594787679, 0.43168245529139004], [0.8335991284784815, 0.08601307996298613, 0.9430658436904432, 0.5385978309563312, 0.025092973481738445, 0.9525681759794208, 0.5864127201628798, 0.735826428483256, 0.8115703068530831, 0.7895741365570171], [0.7302064698802865, 0.06793032322248393, 0.5853052416666235, 0.2935110456297676, 0.3381297792814363, 0.04851977994494405, 0.9368472491933298, 0.8557446510738267, 0.18413888329239048, 0.5773168327974048], [0.12343627382976397, 0.3653623729297777, 0.7941380649928137, 0.838083311739545, 0.7302945494725603, 0.17099305194967818, 0.4516683127601877, 0.3317855228427755, 0.3611550928617522, 0.3289438572078094], [0.062106876664306476, 0.773498871927582, 0.007233796805190984, 0.7852374094290413, 0.9008783271448263, 0.5040795906833146, 0.7057890914979629, 0.22542103756569398, 0.8702425731769315, 0.8555690472650214], [0.8531797138335928, 0.1396247857130376, 0.1695605475518469, 0.46341725444061066, 0.2286188353231453, 0.6400507057256967, 0.38131090669929013, 0.4415488621245439, 0.4042307692265661, 0.04973208551444941], [0.6341931897099813, 0.8219660058100945, 0.8853275664218394, 0.16584044367365702, 0.05589344974085009, 0.7122128272062209, 0.8177480404362485, 0.6274305405568826, 0.945287105290942, 0.255685693527948], [0.5990410235858125, 0.4567226402128939, 0.9676914305786664, 0.7591768470115535, 0.07086344346073958, 0.9433986604535509, 0.024637830915138048, 0.31950255793947946, 0.4787356619431741, 0.18251523476913867]]))), axis=1)+6.661135646975745/np.exp(5.528794183380429)*-(np.square(2.474675743014865+array_x[:,0])) |
abs(10*(np.log(abs(np.sum(np.square((np.dot(array_x, np.array([[0.6253527138173595, 0.4840469184076265, 0.2547440669062324, 0.6924015592069235, 0.00020504969927304106, 0.9413857227785949, 0.8730394047330698, 0.3716111601567973, 0.8925952935757446, 0.3167262529907584], [0.8266938042875649, 0.4224254150435163, 0.41093755755294814, 0.7375073372573142, 0.3884363913705101, 0.6695763314390707, 0.6994948590604035, 0.5352038943203614, 0.9166207419939757, 0.30761075153355955], [0.039269564030669835, 0.8647329400228886, 0.5724045908794897, 0.9563918543147985, 0.888828649306328, 0.4547634080430024, 0.7297272154764464, 0.5909463052764636, 0.6586839121165065, 0.026771562923219472], [0.5380204429242001, 0.2298367118587794, 0.981256945058585, 0.5043334057772652, 0.3172295706393943, 0.9719603469288713, 0.1821847953224497, 0.007986522341469904, 0.07500958530051505, 0.5866486055165172], [0.8833518931704695, 0.6046323715048464, 0.23931633647896033, 0.2891277949294476, 0.8116453486673932, 0.3029359059833603, 0.6636627334714035, 0.04850580767936308, 0.8723480208454989, 0.5509168424876347], [0.40132612316765415, 0.018381983105312916, 0.5101864601266592, 0.850500703385747, 0.8673404132809003, 0.4848024821421133, 0.1938726562185148, 0.5467381419006033, 0.11295228043502659, 0.17164405588346365], [0.5702493943610156, 0.6091556284620149, 0.97401525778585, 0.19234000189260825, 0.6786396698765322, 0.3141545237927944, 0.42357769569062453, 0.8519334342593233, 0.840834469011886, 0.18055140660957814], [0.8241816845822107, 0.4765727288544358, 0.004470671954623029, 0.8845579413619213, 0.31340505608237623, 0.7628073019746671, 0.9493573525234615, 0.44220297691758537, 0.6139673723935396, 0.5751291579873412], [0.6235561049947591, 0.7865583392241401, 0.47616868155870695, 0.38116006533963764, 0.8020966576019011, 0.026648634043112907, 0.054310308063196144, 0.33190649118070215, 0.19368693269283066, 0.7248823410989675], [0.06536059034555108, 0.7938738304061496, 0.9402069366419151, 0.7872211967493115, 0.14904306718148286, 0.5309690568262528, 0.1888531976268356, 0.12405699012524463, 0.7414559555099823, 0.8933130444199281]])))-1.9524247978389697)+array_x+np.cos(2*np.pi*7.354681387509866), axis=1))))) |
np.mean(3.2260758989013123-(np.dot(array_x, np.array([[0.8348447298573312, 0.004191918565801589, 0.7480349982545398, 0.9670600303427749, 0.1776215885778204, 0.45976297153462675, 0.4713311266411704, 0.49790926262951507, 0.752271995813137, 0.36869500057471327], [0.9239113201958793, 0.6872441697440262, 0.09157837964603988, 0.09027425566887481, 0.09751615006668679, 0.12164325690945288, 0.7066877582691231, 0.2621447128233447, 0.5437549227214801, 0.8236861184086293], [0.31561673511995647, 0.16131978681026993, 0.9130003807678155, 0.866254136662217, 0.7516516173990776, 0.0256753071392688, 0.6644024819410859, 0.9231378131288992, 0.12110757209032208, 0.6897962271310678], [0.6200321595090302, 0.6616295411376252, 0.9986109171984977, 0.34686242800383227, 0.6764424202456168, 0.191263645797476, 0.06681728571098866, 0.5583494926487401, 0.2998013550382512, 0.1098696041231696], [0.6785107596400223, 0.05027306028017264, 0.7119605333397238, 0.8097029345548835, 0.5044178609898997, 0.1769377248065075, 0.658371820526868, 0.2180574788332339, 0.10138625471304508, 0.5860007902166452], [0.3717123787385087, 0.05526658079726188, 0.9014524032996396, 0.7182493118854847, 0.4036295020508902, 0.3015139497137941, 0.3733081432066997, 0.09496300881116271, 0.07808238416485369, 0.5421235774716813], [0.15686951717881226, 0.21633343255438742, 0.9231306538783352, 0.11522354912888155, 0.13719703874636657, 0.8192741419982138, 0.45105688960641177, 0.3277872863163286, 0.426248342263549, 0.5260009689111185], [0.827644974481436, 0.8656725757740549, 0.4222752133532168, 0.6561835570587362, 0.5445035200588558, 0.9952501432416506, 0.4566364174229798, 0.09299569971731991, 0.1401271521054145, 0.7850176224031055], [0.01166694904071286, 0.3975623825158646, 0.5294934656109832, 0.333469518361067, 0.7827260513889798, 0.4785465698807083, 0.8200713150228011, 0.7748843759499604, 0.7621312225478797, 0.9150234354347234], [0.6683277595488574, 0.062154276044292356, 0.26526938243036857, 0.2899266379769704, 0.6818314760646306, 0.5052644366434804, 0.159011531445038, 0.7363766112689428, 0.2487428590353712, 0.9681711662437941]])))-10*(array_x)+np.sqrt(abs(8.947697821871492)), axis=1) |
np.mean(np.sin(2*np.pi*array_x)-np.square(1.4467140544875965)*3.7709029047863645-np.square(array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*array_x)-np.square(9.858415702350879)*4.94323241369113-np.square(array_x), axis=1))) |
np.mean(np.exp(abs(np.round(np.round(array_x+6.189719960039199)))), axis=1) |
np.mean(-(np.square(6.1622660230443485)*array_x)-np.exp((np.array(range(1, array_x.shape[1]+1)))+2.7992885577638815)*3.487163894230438-3.9258846552665805, axis=1) |
np.mean(10*((np.dot(array_x, np.array([[0.7665464744915765, 0.06810151314852098, 0.6663874710207603, 0.020474757724425197, 0.7996808309310902, 0.4653519394780956, 0.10757950646293268, 0.6327282928372064, 0.9695050860464618, 0.5179775428229473], [0.5446768737253301, 0.27350204080188767, 0.4065876033610425, 0.797221893742342, 0.5078976860508769, 0.3178386690964411, 0.514378438430494, 0.7223528910864201, 0.7890256620735969, 0.4726663090999106], [0.1579403871250321, 0.7784529784078311, 0.09977100010858331, 0.16116566842489288, 0.624002555968676, 0.233781320917968, 0.2822113737743446, 0.7207492070185323, 0.6703419879614303, 0.5071246514757739], [0.11789373553284987, 0.793471648477702, 0.8814125378599594, 0.3478245993809599, 0.7804237894072191, 0.5146634539147583, 0.5570231584967408, 0.5312528657238937, 0.060446034321717135, 0.5347768456902257], [0.11697568420273197, 0.23037381598527462, 0.8365175629882811, 0.5545665774107055, 0.35186811045884236, 0.2447022226436738, 0.7081337342849404, 0.013626787913744498, 0.7882556158196243, 0.04697036911057961], [0.6808580890585413, 0.3241010686121929, 0.669845013053528, 0.6071745268081914, 0.47934439445542343, 0.1159724160574721, 0.9960907992493783, 0.51584508610011, 0.017742713613414174, 0.15314904649692762], [0.10980574901233775, 0.4000888579862536, 0.6405150210044827, 0.8613693129391076, 0.9361135355642968, 0.9422124373855177, 0.67902234052082, 0.5831864167348678, 0.1254323992241858, 0.8883756151370527], [0.10861580798767623, 0.19035432337937574, 0.45446134147222583, 0.44011449269165537, 0.1555908673477837, 0.882075481874325, 0.11878554112882012, 0.4227945987642674, 0.746555782067105, 0.3250112224868107], [0.44083805116353825, 0.3891217846370093, 0.9770027870466667, 0.2844774012390836, 0.5870112260782467, 0.9608305935000275, 0.44739176682984605, 0.8580746581242923, 0.06271919341316556, 0.8812441529972306], [0.059414969209232615, 0.6157311917321867, 0.1340451011560051, 0.22998702484925682, 0.3825120017177496, 0.25493786234273064, 0.22114337062309375, 0.4678637692322788, 0.9369148222218573, 0.11125720672212491]]))))-2.555886165668409-np.log(abs(4.516188809505273))-abs((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(array_x+abs(6.284382166863633), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+abs(6.950672140820502), axis=1))) |
np.mean(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.4458348413802212, 0.8487725194744069, 0.4624260409283618, 0.46960736075651, 0.6862264198719114, 0.9347590099346889, 0.36822967546383334, 0.6000241446035436, 0.8942255121372538, 0.3346442052965689], [0.19197246914390997, 0.05867976103243122, 0.20322649417055294, 0.14794421894366283, 0.304623503971304, 0.12101328952767576, 0.6922110018310663, 0.8142471778220374, 0.2967720172259749, 0.27070395143357784], [0.6793318755879748, 0.39656213165840903, 0.7784927719733313, 0.9758445106032636, 0.10674613461085869, 0.19019258791316151, 0.15852112184324318, 0.9677727396117085, 0.8880954273141378, 0.22871231311080042], [0.5154416308584517, 0.14614352412114529, 0.03286007871283314, 0.034029842527405485, 0.19551284037189232, 0.036693523602260636, 0.5478169532799393, 0.7640137082095158, 0.5977460014715819, 0.3374656540823173], [0.05185852471428076, 0.6642065128090108, 0.8400313155720709, 0.5899062138149964, 0.11498443246406609, 0.2500648363974879, 0.058339105184824325, 0.7056907554030526, 0.30875216307590014, 0.962230782432486], [0.9972495582193072, 0.5515075437134751, 0.1115167252633823, 0.09252363684314802, 0.014991207031488818, 0.7355079896291745, 0.21032479999428966, 0.6911448236804654, 0.5257659223779549, 0.5399821388530894], [0.27205539896735964, 0.638481492588475, 0.3306965697241854, 0.7987357907176599, 0.10821962289505827, 0.4501339099909002, 0.9392593590011346, 0.5832946408228109, 0.5863690172357575, 0.9861923673369244], [0.5776212406815013, 0.8207593988140863, 0.9143414678573889, 0.8507671921719392, 0.8659821959654019, 0.21116420894271737, 0.5931892049404023, 0.19628308715476528, 0.4191685517680982, 0.24623596918348667], [0.20742880193781743, 0.4705549134884829, 0.8796647800840262, 0.567468233455322, 0.8875053480282311, 0.6367668877718027, 0.464411389579908, 0.46348163948151366, 0.4467724198966724, 0.9008749808757146], [0.5224381554304105, 0.06272027957509108, 0.9583018774388855, 0.4290334304992781, 0.6916149763123326, 0.7616518130716154, 0.29284576417307706, 0.02821646849128201, 0.8243573357708336, 0.2979623951807201]]))))+4.025352778114304-np.square((np.dot(array_x, np.array([[0.47942243730887113, 0.8208957531434276, 0.45897368165946983, 0.7480987941496643, 0.898021505411662, 0.15282966694737987, 0.9750320910530852, 0.28088501826198964, 0.1859556603695227, 0.22336218281862075], [0.49788961771412477, 0.5840095341218312, 0.7791498684318297, 0.16208982551341, 0.13315364516496397, 0.09338105917868689, 0.19732718073864053, 0.08824110499091087, 0.837559363330899, 0.8902788537123254], [0.5570951498378345, 0.4436482830738231, 0.11057858516482855, 0.6827006682323878, 0.379687663947453, 0.7991371225308986, 0.7238150832826532, 0.06050067000723436, 0.34402691787880435, 0.13339300857492276], [0.5400907631998467, 0.4678976739549975, 0.17092000043910338, 0.09362274407452031, 0.705925266819922, 0.6922035391543667, 0.3977204721521498, 0.27605389008483094, 0.2685979673485255, 0.21996813252485858], [0.1362787144402583, 0.8937763240023263, 0.4976764932738823, 0.765128421635483, 0.35978478368548583, 0.36464696443007527, 0.8095989803791209, 0.8103636064650466, 0.7250140972586774, 0.6527333325214827], [0.24943258882824992, 0.40212805167402244, 0.7856476035753964, 0.35509827365155, 0.5565481287982567, 0.8694208051265527, 0.5086567684800604, 0.48804900915011684, 0.8697444115822297, 0.9297470186792889], [0.951519874987024, 0.8117652053013853, 0.9275334538008678, 0.40124783215179227, 0.11520430792823655, 0.918758148739768, 0.4588579625515016, 0.5815418782386561, 0.846162418025328, 0.9639811353543764], [0.504848515329193, 0.4815045808105973, 0.225137768266508, 0.6885846848030275, 0.26890428967645286, 0.2709080859032982, 0.469614509814279, 0.5119995926366605, 0.7005150355530233, 0.4633493924976313], [0.7890908945802946, 0.5650925015877704, 0.00739668677949723, 0.9552187001094956, 0.3861006547443303, 0.6777978061906921, 0.42873172910750235, 0.07023410492738746, 0.731878456333781, 0.42055597883915496], [0.06076665679816795, 0.7749085081040699, 0.7254761680323477, 0.717917597772729, 0.042057551698011286, 0.12475661933431359, 0.2373716090590463, 0.7470621593845422, 0.17560131480587793, 0.26817756428565787]])))), axis=1) |
10*(np.sin(2*np.pi*np.square(10*(np.cos(2*np.pi*np.sqrt(abs(np.mean(array_x, axis=1)))+np.mean(np.cos(2*np.pi*array_x), axis=1)))))) |
np.mean((np.array(range(1, array_x.shape[1]+1)))+7.964291972099484*10*(array_x*9.855573801146935), axis=1) |
np.mean((np.dot(array_x, np.array([[0.2976208620962223, 0.9968108456698311, 0.5260013728701466, 0.14566445829952257, 0.40342050989591005, 0.7141247886268043, 0.10262984037590994, 0.042179404240879514, 0.8810397300699133, 0.05225518836334919], [0.06616013724389103, 0.4687084105573155, 0.761261282179816, 0.18896105366820015, 0.14304145831420367, 0.8020412420519786, 0.21052807179697663, 0.824489759028728, 0.8957999817474195, 0.0771637637417647], [0.4066187998868035, 0.7183986826370367, 0.6124682402793883, 0.8130857417032881, 0.05139713719538441, 0.0637630354438321, 0.9429245543905688, 0.16471852619818672, 0.43701531440509667, 0.5752311965970729], [0.11934668937346604, 0.9111134147360073, 0.10179806357299703, 0.517223267006924, 0.11026115284321447, 0.9720060184245729, 0.9952404438386535, 0.4891747606716088, 0.16879136525924976, 0.8931299855133292], [0.9186914419195951, 0.9170707888130266, 0.6641633558860338, 0.47849369352990134, 0.7574655247924322, 0.17796774072127663, 0.9948754901776283, 0.049672491888796966, 0.22766504106416807, 0.39749499852223324], [0.6303754863370189, 0.009217664892798738, 0.3599020501603518, 0.8952903028066819, 0.9846674375730233, 0.662013967860319, 0.4017706130973918, 0.8496741469848992, 0.08019099124609841, 0.6122546901760683], [0.4777005185660458, 0.7131440821422084, 0.5216735980184848, 0.7039215528556478, 0.6471230324203708, 0.4890572508461659, 0.8942720131433316, 0.06251441740288555, 0.5443236503833631, 0.03278002911884559], [0.6612387363295764, 0.3649292922097408, 0.7171842155943495, 0.4688789189876983, 0.1642352230366062, 0.6589662549620678, 0.44598732088419657, 0.3410327455175759, 0.04841225663977178, 0.1966870655694516], [0.9576369045216507, 0.4787329928426327, 0.41534498152112564, 0.9700304937239081, 0.8069442487157807, 0.20501109849974086, 0.7182911037760495, 0.39662547544589877, 0.5688334990858819, 0.23203260341299736], [0.2232701220834964, 0.8123022088733505, 0.6754855057000292, 0.6053204294333807, 0.9722637542323364, 0.8029805010869873, 0.2816716101726501, 0.614130082204299, 0.4367057925747665, 0.29311911112707434]])))*7.316284211414415+7.807831435325257+array_x-6.040227189643189/np.sqrt(abs(np.cos(2*np.pi*3.5670044574978554))), axis=1) |