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np.mean(6.075041924451809+np.exp(7.61403223581703-array_x), axis=1) |
np.square(5.215341588124016)+np.mean(10*(array_x)-4.976333604130268, axis=1) |
np.mean(8.18816239033642*array_x-3.655240065717609, axis=1) |
np.sum(4.225945288428815+array_x*10*(1/(np.sqrt(abs(5.944912992394381)))), axis=1) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*(np.dot(array_x, np.array([[0.3826903600700229, 0.6618237395321434, 0.5165986003485726, 0.16675474856832884, 0.70987001041401], [0.8883946997271486, 0.33766638550950956, 0.9965487966866502, 0.4908303165558806, 0.38970253370879415], [0.11667310062287228, 0.9274000291588704, 0.4693123784533255, 0.4317155546020669, 0.8955611173249316], [0.7726783814346657, 0.8899412705857322, 0.740458321435181, 0.25419896563228295, 0.9261653587068505], [0.6504852293660274, 0.4670006479394686, 0.6695716174078437, 0.7674005307326319, 0.8443862121384372]])))-np.sqrt(abs(array_x))+5.490555368370395, axis=1) |
np.sin(2*np.pi*np.sum(3.1338936384419744+array_x, axis=1))*2.0311150306208203+np.sin(2*np.pi*np.sin(2*np.pi*np.sum(2.417177393125868+array_x, axis=1))*9.654428736326157) |
np.square(np.mean(-(array_x), axis=1)+7.908363827354949)+1.3440950466026078 |
np.mean(5.734326522690749-10*(6.777256576130115*array_x)-np.square(4.755038677230701), axis=1) |
np.sum(5.511454933543327+np.sin(2*np.pi*array_x), axis=1)+np.sin(2*np.pi*np.sum(9.783301431978423+np.sin(2*np.pi*array_x), axis=1)) |
np.mean(np.log(abs(9.881277367794118))-np.exp((np.dot(array_x, np.array([[0.88062883340673, 0.9771115467968586, 0.928290259756157, 0.9647952848686885, 0.5089796796329716], [0.8739412788969221, 0.20372053776865873, 0.3292726519804682, 0.4070549423036537, 0.1237331197705559], [0.9986014236436771, 0.2960028883077376, 0.6831901154114073, 0.8476375383786, 0.39286180069495025], [0.7902839910076218, 0.40756572023473314, 0.013347850107980896, 0.961290057691656, 0.06105581724939624], [0.38099784660177394, 0.007700864659662021, 0.8185402168046552, 0.6879282430919532, 0.2911319047001877]]))))-1.357011447130731-array_x-array_x, axis=1) |
np.mean(np.square(2.7315433834205716)/-(np.exp(8.549957880341466/2.337618348309369-array_x))/9.855239344431114*array_x-5.801758577454433, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(9.062671677817828)/-(np.exp(8.915114444012719/3.7524576237428784-array_x))/4.087409286444835*array_x-5.2142679296994485, axis=1))) |
np.mean(np.sqrt(abs(10*(np.sin(2*np.pi*array_x)/np.sin(2*np.pi*9.576532318285942)+7.269315022255865-(np.array(range(1, array_x.shape[1]+1)))-4.278408890293275*-(8.932781657194123)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(10*(np.sin(2*np.pi*array_x)/np.sin(2*np.pi*8.938660679117909)+7.16604670785768-(np.array(range(1, array_x.shape[1]+1)))-1.2512945085234342*-(3.777627474014505)))), axis=1))) |
np.mean(np.exp(3.6418081692182187-array_x+7.195027269744698)+array_x*8.390311198052501, axis=1) |
np.mean(np.round(2.9953133357772943+np.log(abs(np.cumsum(array_x, axis=1)+np.exp(1.7435668548124719)))*10*(abs(np.square(np.square(5.480751790361016))))), axis=1) |
np.mean(5.32783011024094+9.932262628303617*(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(2.9660807984761006/(np.array(range(1, array_x.shape[1]+1)))-8.5963274481072+np.log(abs(np.sin(2*np.pi*6.551491979262268))))-3.101848994634509*array_x*np.square(5.992552076627231)-2.3389374683270905, axis=1) |
np.mean(np.cos(2*np.pi*array_x)+6.093317936855418-np.square(1/(np.cos(2*np.pi*7.176576067992373))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*array_x)+5.970066078212274-np.square(1/(np.cos(2*np.pi*6.709377022656938))), axis=1))) |
np.mean(np.square((np.dot(array_x, np.array([[0.5950522370013422, 0.2869565359610352, 0.16567215697940418, 0.33347154522930544, 0.08120473999647482], [0.7148548947688431, 0.9148975433660045, 0.9360714939191059, 0.8110273562533793, 0.1459093893084773], [0.341408157349703, 0.016871131766955316, 0.9770413580000057, 0.9580973499888913, 0.25890493839799134], [0.41731014844713643, 0.40973154055063754, 0.7605144551056071, 0.41587646047877047, 0.42312204237013307], [0.08615687834690855, 0.38374945469439126, 0.18681203148305237, 0.09657394364219418, 0.24603407968863744]])))-6.241751435992152+abs(1.4343087607166907)), axis=1) |
np.mean(np.sin(2*np.pi*4.247575230684656)*np.cos(2*np.pi*np.sqrt(abs(5.458118340015341+(np.array(range(1, array_x.shape[1]+1)))*array_x)))+np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x+9.991354130003858), axis=1) |
np.mean(4.815035298561385*np.cos(2*np.pi*np.square((np.dot(array_x, np.array([[0.4797384781676459, 0.24330020304917932, 0.624825159876809, 0.8808733385106241, 0.6845091920265122], [0.23576929919626866, 0.2528662624499971, 0.5365487913691491, 0.6285382511879473, 0.16131844960084485], [0.7311611978027015, 0.3879209092276451, 0.2972756041747796, 0.7291338503605614, 0.18120212352642273], [0.18910110907497513, 0.7753328298487512, 0.8610794528235423, 0.20241218742303213, 0.49032012170648964], [0.01474670797887312, 0.5065799573532601, 0.46706489670321427, 0.4954778833100454, 0.5350653411433681]])))))-3.1539124212388163+(np.dot(array_x, np.array([[0.6640406876172534, 0.8163823099692087, 0.5337663756197317, 0.36529376545674774, 0.7572746663599282], [0.4083515042893072, 0.20251310615112195, 0.8041121566744533, 0.42040694156975256, 0.1102383081094015], [0.08967254291019189, 0.545412646932041, 0.682482519697741, 0.17517167018025515, 0.2104502881008048], [0.006828927661224449, 0.9156968306261606, 0.6759223981670874, 0.04770571935308476, 0.14853603633624202], [0.909085139826214, 0.9268082331860265, 0.8977110255739226, 0.5023897114384072, 0.7531201742209903]])))-4.52093671355624, axis=1) |
np.mean(10*(np.sqrt(abs(np.cos(2*np.pi*np.square(10*(array_x+4.8119495978425055)+4.017537145647308+array_x))))-np.exp(np.round(1/(4.300760503753061*-(4.956402732086277*array_x))))), axis=1) |
np.mean(np.round(abs(1.9061897959601064))*np.exp(7.923469900585103+array_x/np.round(5.745007411298944)), axis=1) |
np.prod(array_x*4.344900145754051, axis=1)-np.exp(10*(1.6211121821065069))*5.475002134482034 |
np.mean(np.cos(2*np.pi*np.sin(2*np.pi*7.126138221879205))-np.square(array_x+6.875822807114403), axis=1) |
np.mean(10*(array_x), axis=1)+np.log(abs(8.260052638731961)) |
np.mean(np.sin(2*np.pi*array_x/3.3867411141266364)*np.sin(2*np.pi*np.sqrt(abs(2.7039144293186492)))-10*(2.228189701360911+np.round(np.square(array_x*4.514122554839868)))*8.807643386344346*1.0864089870426956+(np.array(range(1, array_x.shape[1]+1))), axis=1)+np.sin(2*np.pi*np.mean(np.sin(2*np.pi*array_x/9.825993360123844)*np.sin(2*np.pi*np.sqrt(abs(3.29407099397318)))-10*(8.212161639144963+np.round(np.square(array_x*3.8576049358049174)))*7.7685274845369925*8.866969054327242+(np.array(range(1, array_x.shape[1]+1))), axis=1)) |
np.mean(10*(array_x-4.03501337392473/abs(6.223602057670395))-4.960561894467762/-(np.round(8.335769377697476)/array_x), axis=1) |
9.8508654910561-np.sum(np.cos(2*np.pi*10*(np.exp(np.sin(2*np.pi*array_x))))+1.7683069258566824, axis=1) |
-(np.prod(7.07164675126098+array_x+5.503972565957007, axis=1)) |
np.mean(np.square(-((np.array(range(1, array_x.shape[1]+1)))*array_x)-np.sin(2*np.pi*1.2534337437993361)/9.283451658775284-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(np.exp(10*(array_x))*2.9632494239594807, axis=1) |
np.amax(4.811191823645583+(np.dot(array_x, np.array([[0.7232520193440632, 0.8481272710228998, 0.8062765801241809, 0.013568480015742734, 0.8897199799679567], [0.6423689256503584, 0.7992767561865608, 0.20561556198849662, 0.7535352594638269, 0.9252999480826759], [0.30783654299340124, 0.8665451920277923, 0.3267152180650137, 0.9453125032036643, 0.9704887419533272], [0.5818833684829616, 0.6384672379322474, 0.2553982756755727, 0.5773185599527629, 0.3975904514177555], [0.22469088186487385, 0.3866718568587061, 0.23658280214850524, 0.8756793415079788, 0.3348896810312174]]))), axis=1)*np.exp(6.8686232153690145)-10*(4.041292477610219+array_x[:,0]/4.101418812984756) |
np.mean(1.7159064056750242/np.sin(2*np.pi*4.6868395025780245/(np.array(range(1, array_x.shape[1]+1)))*array_x-5.616721476932568-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(7.9017299601402105*array_x-9.080229913831426, axis=1) |
np.mean(np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))+1/(1.7810193470554523)/np.cos(2*np.pi*1.5174740840651728))+10*(np.square(8.488215910508725-np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x*(np.array(range(1, array_x.shape[1]+1)))*array_x)*2.844810823289512-(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.mean(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x*6.342640256707527-3.1080970820069282), axis=1) |
np.round(np.mean(4.0054915333637595+10*(np.sqrt(abs(array_x))), axis=1)) |
np.mean(np.square(array_x*7.142021547702784+1.102198378619708)+4.14038576156337*6.9761037881775225-array_x*6.288520999191465, axis=1)+np.sin(2*np.pi*np.mean(np.square(array_x*7.351140782971737+4.954143691196302)+2.956461791778594*6.341178513210636-array_x*6.826926222665169, axis=1)) |
np.mean(np.sin(2*np.pi*2.0162160681937165)+9.698971214589024*array_x*9.509938878222318-2.463348586176677, axis=1) |
abs(np.sum(np.log(abs(np.cos(2*np.pi*6.144115592753929-(np.array(range(1, array_x.shape[1]+1)))+np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x)))))-np.sqrt(abs(6.363377532233799)), axis=1)) |
np.sqrt(abs(10*(np.exp(np.mean(array_x/9.195526824286132*np.square(5.409256667027386), axis=1))-6.796249364656563))) |
np.mean(10*(np.round(1.724794971313493-array_x)-1/(7.346802953684355)-array_x-np.log(abs(4.095035842348306/(np.array(range(1, array_x.shape[1]+1)))/5.594396539300154+4.201834108948638+5.074561011315305))), axis=1)+np.sin(2*np.pi*np.mean(10*(np.round(8.864464886271438-array_x)-1/(4.663648522809314)-array_x-np.log(abs(3.995044166302551/(np.array(range(1, array_x.shape[1]+1)))/4.543462694692029+4.235072234777213+5.707332830094081))), axis=1)) |
np.mean(np.cos(2*np.pi*np.cos(2*np.pi*np.sqrt(abs(9.930355626809856))))+np.square(8.594545690476505-array_x), axis=1) |
np.amax((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)-array_x[:,0]-3.3585112477862866+8.092259208900076-7.3216967018180785-8.668634528205924 |
np.mean(np.square(4.333489861902494+array_x)*9.808350934416787+np.log(abs(array_x+2.5316665262167763*np.exp(array_x/3.7564831745250147))), axis=1)+np.sin(2*np.pi*np.mean(np.square(1.7055824010210952+array_x)*2.0052270310429297+np.log(abs(array_x+5.243988794153488*np.exp(array_x/7.055039526842939))), axis=1)) |
np.mean(np.square(np.sin(2*np.pi*3.3616681009292977))-array_x*2.472819238334232*8.418260193668456*(np.dot(array_x, np.array([[0.3264558574759412, 0.2549570953373411, 0.8086186945116343, 0.9610552879581923, 0.1542419592210551], [0.5416418213790828, 0.9579853536892753, 0.8043159314481926, 0.6355148262128776, 0.6545969798010368], [0.8520268775497412, 0.2082572874738955, 0.010555740634170419, 0.6925671875390184, 0.4173992877430124], [0.004523212995977355, 0.4485735960670907, 0.7493826590499356, 0.8525239545565158, 0.18288377568689085], [0.2299819883455002, 0.2846147431123529, 0.14327786620068916, 0.6077588476553589, 0.29027331929463385]])))+np.sin(2*np.pi*np.log(abs(4.34175305570758))), axis=1) |
np.mean(10*(np.sin(2*np.pi*abs(array_x)-1.470696926454382+10*(array_x))-np.cos(2*np.pi*np.exp(np.exp(np.cos(2*np.pi*8.527888312685974))))), axis=1) |
np.mean(np.log(abs(np.cos(2*np.pi*array_x)))+array_x+8.101509335523435, axis=1) |
np.exp(np.mean(7.432652346516297+array_x, axis=1)+5.079669172362847)+np.sin(2*np.pi*np.exp(np.mean(3.4526053789758073+array_x, axis=1)+3.3047415502507937)) |
7.0683812663782275-np.amax((np.dot(array_x, np.array([[0.7284584625298017, 0.061996428007497384, 0.7473878942359087, 0.07351965306424757, 0.8870968053866992], [0.5335747495338454, 0.9160442045329139, 0.7044909520653581, 0.17382209891656752, 0.51923090174918], [0.15868335788610244, 0.295363478792969, 0.8646365072455268, 0.7449549373010446, 0.38021062884217427], [0.33646620146761763, 0.22767393644955514, 0.5496999826608683, 0.19441805512810062, 0.9325390024639364], [0.3211055179495905, 0.11121380927188151, 0.9169550126874785, 0.21211992549102587, 0.08121345842051175]]))), axis=1)*2.129329746624129/5.349836911852775/np.mean(np.log(abs(4.792869424301411-(np.array(range(1, array_x.shape[1]+1)))-array_x)), axis=1)-np.square(4.840508174747307-abs(array_x[:,0])) |
np.mean(10*((np.array(range(1, array_x.shape[1]+1)))*array_x)-np.square(3.57565160870657), axis=1) |
np.cos(2*np.pi*6.397761547394472*np.prod(array_x, axis=1)/10*(4.888027746420503))/7.745451687424495+10*(np.sin(2*np.pi*np.cos(2*np.pi*5.00326463082217*np.prod(array_x, axis=1)/10*(3.477923031157723))/2.3523787045096247)) |
np.round(np.mean(np.exp(np.exp(1.9684842507304934))+np.exp(abs(array_x)+9.049039025209176), axis=1)) |
np.mean(8.445975619837196-abs(array_x*array_x*6.967966762880962)+np.sin(2*np.pi*array_x), axis=1) |
8.015596620435552*np.mean(array_x, axis=1)+3.4541000893437683 |
np.mean(np.sin(2*np.pi*2.252731765323513+array_x-np.sqrt(abs(9.085402129202967))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*2.0194370785078215+array_x-np.sqrt(abs(7.625920518131631))), axis=1))) |
np.exp(abs(1.3684881335087953*np.sum(array_x, axis=1)))-2.462916320967532 |
np.mean(10*(np.sqrt(abs((np.dot(array_x, np.array([[0.9064766733668392, 0.8046676399826361, 0.8018042857601577, 0.8578594799570161, 0.4538761504763148], [0.7053073482871783, 0.14442049499045517, 0.811059523252072, 0.6189037618349894, 0.16004644578420413], [0.931381150299713, 0.944221872192477, 0.9773706167669912, 0.36505731602425984, 0.25813379953245763], [0.9685680933546696, 0.5645784103807286, 0.8369256159145959, 0.8675608240864373, 0.28671020182314544], [0.8057020696025682, 0.012379460832415035, 0.1739578693927144, 0.6174607096586181, 0.5845044199421864]])))*1.7008111644081696))-7.822549395239398+array_x*array_x)+np.cos(2*np.pi*3.816481895063884), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.sqrt(abs((np.dot(array_x, np.array([[0.16167238493185476, 0.3333184967107611, 0.14711633063167728, 0.9609240345266605, 0.7246500417054812], [0.49045273992376104, 0.05334880961080524, 0.47723031655375325, 0.5823036989198211, 0.822355530359272], [0.9610089110095522, 0.37022018563080983, 0.04528841283146756, 0.20111245064378935, 0.24377845182777513], [0.040925237829265715, 0.2076612324261633, 0.5976714165017376, 0.9532235747592173, 0.7563820728588561], [0.7932554758945595, 0.03487878621362561, 0.28665889755417795, 0.7560834605132001, 0.9647938226875636]])))*1.8066491948086392))-3.8861303800035483+array_x*array_x)+np.cos(2*np.pi*4.770923072017904), axis=1))) |
np.exp(np.amax(array_x/3.3648911209182684+array_x+7.861453136761754+np.round(7.756065864438625), axis=1))+10*(np.sin(2*np.pi*np.exp(np.amax(array_x/4.523163034017596+array_x+9.589455518067457+np.round(6.157297832783329), axis=1)))) |
np.mean((np.dot(array_x, np.array([[0.9179053037679038, 0.8718752734690717, 0.12817705531748103, 0.277833478836748, 0.4767157390882899], [0.03751395755352227, 0.3504941652621777, 0.6244416380275217, 0.4555531476261774, 0.3482691954113265], [0.7821481019254101, 0.7705367595573365, 0.2771356236021779, 0.029917541860597807, 0.0017243561335372748], [0.7362350649926526, 0.6232072521927636, 0.2981544573134063, 0.001153208795239724, 0.6825632767037707], [0.8151469956360785, 0.7777909989574036, 0.32520443541103805, 0.566738806516925, 0.8116129026989187]])))*8.155598976518595+8.40751330205046/abs((np.dot(array_x, np.array([[0.40320931885417055, 0.038790380611068986, 0.9007246140287878, 0.04329676763821766, 0.44526195863687146], [0.431500914097642, 0.2890095129689936, 0.7607307744040444, 0.14730958685254292, 0.06522938874433704], [0.7220873038515808, 0.14137695658029503, 0.852418708702652, 0.12380466698173997, 0.06629811738411506], [0.7004295968831753, 0.460257273672104, 0.26293716885065954, 0.8484012728104667, 0.06451991853547745], [0.9219754311889145, 0.17115781888104709, 0.4791206156290455, 0.5251372391259044, 0.18339435301977636]])))+4.9655429520926795+4.162469097373858), axis=1) |
np.mean(np.exp(np.sqrt(abs((np.dot(array_x, np.array([[0.7576953794869344, 0.6215088042054355, 0.9689255711914652, 0.8597832518551987, 0.1869224034163165], [0.8252008905396966, 0.5769228973354752, 0.19584515403937186, 0.0323673557100862, 0.942079718871661], [0.3762705855964481, 0.8506002471871797, 0.9791499886972667, 0.32841187966560814, 0.10756450767027925], [0.01842735708964316, 0.027384415775783988, 0.9544218984161112, 0.9643294232998272, 0.5257242260535687], [0.20058303725639637, 0.9960784970881713, 0.3825032448843694, 0.047969715522274914, 0.9222186650692699]])))*np.log(abs(np.square(np.exp(array_x+5.575713582691338))))))), axis=1) |
np.sum((np.dot(array_x, np.array([[0.1861864941522131, 0.046324196837429055, 0.8127841930554064, 0.5318326649638794, 0.6822719833189921], [0.6235961855133575, 0.16479000944752276, 0.9834617056187913, 0.0630415149801613, 0.9825498461664647], [0.4202114211821607, 0.8438797309693403, 0.20986413007825622, 0.6403969865248309, 0.5627396515006134], [0.08011916335970659, 0.6499665535200427, 0.9207103599667099, 0.47261952151761855, 0.06655716036182102], [0.9125082371429712, 0.329596789979265, 0.9056652802214693, 0.6794678402780356, 0.32159455380644963]])))-2.3307159402640636*5.894927122639102+(np.dot(array_x, np.array([[0.6808774344235112, 0.24780522536115634, 0.0006296806988718151, 0.3659974793277033, 0.7705497772507915], [0.7933866761647371, 0.46037401212009565, 0.4774910837185442, 0.808744986184209, 0.8949076578221498], [0.16028171460803087, 0.8580752750080525, 0.7025500420292181, 0.2491521167120878, 0.3317952935261266], [0.554221147011205, 0.5531633749187388, 0.3788037810003325, 0.7352672418294908, 0.27980920234474727], [0.478004039009152, 0.23324331711539215, 0.735348264737928, 0.8822434124640888, 0.461314695736482]]))), axis=1)-np.cos(2*np.pi*np.sin(2*np.pi*7.834915455950605)) |
np.mean(10*(1.652508713997333-array_x), axis=1) |
-(np.sum(np.square(3.4707132678750856*np.sqrt(abs(2.311499582700304-9.084759817475412*array_x))), axis=1)) |
np.mean(2.4086413014120907*array_x*3.897220826443513-3.5296866770415516-6.893852529780374+6.5627290504472855-6.767573866377-array_x*array_x, axis=1) |
np.mean(9.069887410831196*10*(array_x+array_x)+np.exp(8.782267238270915), axis=1) |
np.prod(4.018675337572621-array_x*1.8378076154189653*9.108664190704229, axis=1) |
np.sum(np.log(abs(array_x/(np.array(range(1, array_x.shape[1]+1)))-4.423333779275177/8.152410153533442-5.970535721352743)), axis=1)+10*(np.sin(2*np.pi*np.sum(np.log(abs(array_x/(np.array(range(1, array_x.shape[1]+1)))-5.070673887693857/6.704595028006471-2.4576608345533066)), axis=1))) |
np.mean(np.exp(2.562767777263739-10*(1.6658306678514376/np.square(3.0471241341142186)*np.cos(2*np.pi*array_x))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(1.7949297921070362-10*(5.39861191224265/np.square(1.129492030624902)*np.cos(2*np.pi*array_x))), axis=1))) |
np.mean(np.sqrt(abs(array_x))+np.square(7.548400959609147)+array_x+np.sqrt(abs(array_x))*abs(np.square(8.635269692375724)), axis=1) |
10*(np.sum(1/(2.709703455086988+array_x)/-(3.2901804620034047)+array_x, axis=1)) |
np.mean(np.sqrt(abs(7.3246228421086315*array_x-abs(6.708585795341601-array_x*np.exp(8.2265419855984)))), axis=1) |
np.mean(abs((np.array(range(1, array_x.shape[1]+1)))/np.sin(2*np.pi*9.984728349767574)+2.6941564850216304/np.cos(2*np.pi*np.exp((np.dot(array_x, np.array([[0.40621449674745036, 0.10758322256394748, 0.5935867098730435, 0.22515126715607958, 0.8156400148401275], [0.7474322659988707, 0.7436714825608944, 0.751051613536733, 0.49223253169809855, 0.1549248494686749], [0.6523051431008711, 0.6539656803677873, 0.9814006417174009, 0.6969934659635919, 0.2999339259862823], [0.8708702393978627, 0.30727258363230603, 0.6990746490421227, 0.3198268177939967, 0.03230890346079729], [0.9878189152752741, 0.8686092299057946, 0.39460626650458264, 0.4020771982282948, 0.311673355926227]])))))), axis=1) |
np.mean(np.square(np.round(-(6.531352815911234+(np.dot(array_x, np.array([[0.3789986070273772, 0.08441769754748218, 0.8964644183672457, 0.354055723229846, 0.8447162353515284], [0.3513686625393566, 0.31964584127053786, 0.1587771551327456, 0.7718486100218915, 0.05704470743156154], [0.7116025890734521, 0.7235823166602576, 0.7305864243665559, 0.46468745987162163, 0.7813090704977627], [0.28778261226801405, 0.9213799817049069, 0.9165586465467381, 0.3284924468036682, 0.6954069401513122], [0.6794570646755909, 0.030154795983493132, 0.5635825495931698, 0.6594107310889733, 0.06359999767352353]])))/5.7828086657526105+(np.dot(array_x, np.array([[0.3472914752947831, 0.7157416074401202, 0.30704952719808376, 0.03646139254430636, 0.16735956846853173], [0.952081156683314, 0.16787262733478336, 0.20194117650532573, 0.7327621355826539, 0.9181072314380178], [0.010523535700159625, 0.25618970796584684, 0.6732877158377514, 0.819567336809186, 0.4037450135927959], [0.35015366624724886, 0.48467829223741576, 0.45363712803005996, 0.4509502710880693, 0.588832029344162], [0.09470171328539467, 0.26009229399022427, 0.92916233453497, 0.6005368183357245, 0.7808487521100042]])))-4.924116680142845)*(np.dot(array_x, np.array([[0.9122740414587216, 0.3249127557568794, 0.22449843719225893, 0.7671541288295801, 0.7764721116976059], [0.8964348663045807, 0.37618499298578156, 0.21281034792814957, 0.2626566698922266, 0.4882119185646935], [0.6934676560004783, 0.48274978337978114, 0.32538897265373357, 0.7086368525198097, 0.35434277713885753], [0.42272178011900485, 0.632524969654192, 0.9759907405606562, 0.5052886443015475, 0.41096847441224194], [0.4918296810475933, 0.37230092765420264, 0.3696756282060766, 0.17232624688928888, 0.5090655496821131]])))+8.524743718751136))*np.square(9.203414700591049), axis=1) |
np.mean(np.round(array_x/8.054728689633691-array_x+array_x)+1.918886327206864+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(array_x/3.3519754400991206-array_x+array_x)+6.484353302277093+array_x, axis=1))) |
np.cos(2*np.pi*np.square(10*(np.sum(array_x-4.694869052468406, axis=1))))+10*(5.619868276985825)+np.sin(2*np.pi*np.cos(2*np.pi*np.square(10*(np.sum(array_x-5.330065229303591, axis=1))))+10*(8.659123406168526)) |
np.sqrt(abs(np.round(4.315007649751019)))*abs(np.square(np.sum(np.square(array_x*array_x), axis=1))+np.sqrt(abs(3.4796940269008525))) |
np.mean(np.cumsum(10*(np.square(8.942424473519633-array_x)), axis=1), axis=1) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x*2.2835922999567906-3.5575979074747166+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.mean(np.square(np.exp(np.sqrt(abs(array_x-np.cos(2*np.pi*abs(array_x))+np.sin(2*np.pi*2.5089289954308023))))), axis=1) |
np.round(np.mean(np.round(np.square(array_x-np.exp(1.1388803029412866)+array_x-5.3648591475714325)), axis=1)) |
np.mean(np.round(np.square(abs(9.408282744341552*array_x+(np.array(range(1, array_x.shape[1]+1)))+np.log(abs(8.154514894350378))/4.849576938306665))), axis=1)+np.sin(2*np.pi*np.mean(np.round(np.square(abs(9.4157135396421*array_x+(np.array(range(1, array_x.shape[1]+1)))+np.log(abs(6.335336989458692))/6.057481239363014))), axis=1)) |
np.mean(np.square(np.square((np.dot(array_x, np.array([[0.38150077495862156, 0.5249415021453951, 0.41098617604724685, 0.37295667834557655, 0.35653479686418477], [0.7141120130805922, 0.7045953290262085, 0.11073507380069492, 0.799141292212441, 0.20804450492810844], [0.40296282651518767, 0.3525808703402957, 0.9187824585180592, 0.675182092056839, 0.4815898632233482], [0.7921304767615653, 0.2699736522380306, 0.720291421985405, 0.8756142172425226, 0.9574712475423616], [0.14719242600367466, 0.47008288440594315, 0.3829307045711109, 0.25914681297345277, 0.5225117783052461]]))))*1.7607849401935542)+3.6998978338085555, axis=1) |
np.mean(np.exp(5.1193247017653265+array_x-np.square(np.sin(2*np.pi*5.704944287149791))), axis=1) |
np.round(np.sum(1/(9.670254495956039)/np.exp(array_x-9.743328659558077), axis=1)) |
np.log(abs(np.mean(1.5356663890933735-np.sin(2*np.pi*array_x)-np.cumsum(array_x, axis=1), axis=1)*np.sqrt(abs(np.log(abs(3.37159870468087)))))) |
np.mean(array_x/4.292196692807286*2.2060659742797664-array_x-np.square(np.square(10*(3.7393448502133975)/4.127281108466775+array_x)), axis=1) |
np.mean(np.exp(9.241795270842912+array_x)/8.230275128743848+array_x, axis=1) |
np.mean(np.round(1/(np.log(abs(array_x)))-np.cos(2*np.pi*1/(array_x+5.809630524861963)))-np.square(2.488303096363832), axis=1) |
np.exp(np.square(np.prod(abs(array_x)+array_x/np.sqrt(abs(4.588733996617556)), axis=1))) |
np.mean(np.square(np.square(1.719263375594109)+array_x*5.070187515090709)-10*(np.sin(2*np.pi*4.27028411372048)-array_x)-np.sqrt(abs(np.sqrt(abs(4.716351468454329)))), axis=1) |
np.mean(1/(abs(2.544188215550803*np.cos(2*np.pi*array_x)-8.000381746780686+array_x))-np.log(abs(array_x+array_x-5.800391038349048))/np.cos(2*np.pi*6.58045208503517)+10*(array_x), axis=1) |
np.mean(np.square(np.exp(8.990476644727746-array_x)+4.780370383745826), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.exp(7.160533737928853-array_x)+2.4382856059018256), axis=1))) |
np.mean(np.square(np.square(5.86543061150198)*(np.dot(array_x, np.array([[0.9096997509228166, 0.2521005290194408, 0.8366690933471952, 0.8802256758565397, 0.1777766046078617], [0.7358232329307415, 0.7663203726255773, 0.007651436464902628, 0.8647744413344565, 0.011799768793462784], [0.04833170434994849, 0.016494221993373692, 0.24796840022384758, 0.7481751428736237, 0.09830741362184259], [0.8864595176263588, 0.18179662786560558, 0.09359832726035955, 0.5451911663723114, 0.9834309139438343], [0.03925960031913445, 0.9102601780001449, 0.6523790256312388, 0.6095496552302907, 0.5763482859494238]])))-abs(np.sin(2*np.pi*7.379211933494503))+np.round(array_x+1.9132822286012883)), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.square(7.092603993168655)*(np.dot(array_x, np.array([[0.06839205370029577, 0.0829040238732226, 0.2313982255298136, 0.0532545302988785, 0.775088207279874], [0.5424622394080458, 0.6808817960680006, 0.7413319608061129, 0.5045098285741287, 0.9032439863739564], [0.6160002476130046, 0.8517674366765736, 0.4797932616840167, 0.5433787500012383, 0.9235737955668966], [0.850014440567374, 0.23891946567280742, 0.4328606098724008, 0.32399439376702366, 0.6481466065019948], [0.22939566908440545, 0.21117137411332343, 0.6282778551685666, 0.8769786021277053, 0.9822205043726928]])))-abs(np.sin(2*np.pi*3.2443478326583097))+np.round(array_x+6.612855507267263)), axis=1)) |
np.round(np.mean(-(np.exp(array_x*1.5058622097479772/np.log(abs(8.61504712505882))*3.536908864462673)), axis=1)) |
np.mean(array_x+7.503031004086752*np.exp(array_x)-np.log(abs(6.798462485111457))+9.090242577539877-np.sin(2*np.pi*(np.dot(array_x, np.array([[0.6242287359320027, 0.6800464101758766, 0.17672948808368216, 0.30775487358385, 0.8499095476874601], [0.6539828825684761, 0.4530855676410991, 0.7833080520980605, 0.4381856739651653, 0.16264057157227085], [0.5289048138205549, 0.9147230522879629, 0.3996122206090965, 0.3413019196511369, 0.5033170852241533], [0.9233724004456162, 0.021681839114378265, 0.20410974407210847, 0.3340429219732044, 0.5437084934375054], [0.6721526447391922, 0.7956452754427423, 0.9014601762100254, 0.7296433417312697, 0.7805534053384968]])))), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+3.814342206432076*np.exp(array_x)-np.log(abs(9.040837369954069))+1.1114884835060506-np.sin(2*np.pi*(np.dot(array_x, np.array([[0.2943760231296816, 0.13261556577041012, 0.6710220154550806, 0.5847370421323972, 0.5622404164235916], [0.8022484625646238, 0.4961129212083878, 0.3112415352313882, 0.19995706057085372, 0.26052905720667263], [0.5470073419111192, 0.824925611779189, 0.6161833185396537, 0.04651454038448255, 0.6098516923142678], [0.9676709795054876, 0.7509495620502595, 0.425698934081735, 0.08213674331824627, 0.4911585898515536], [0.6691172229715122, 0.9652912278972161, 0.5982358458407429, 0.5925410025165903, 0.7931182394802747]])))), axis=1))) |
np.mean(np.exp(4.7854010471213915-array_x*8.586125899332076+4.39166459249646)-3.8453308082643636-array_x-7.794436402572673, axis=1) |
np.prod(3.247360464683703-np.cos(2*np.pi*(np.dot(array_x, np.array([[0.6827650811047112, 0.9251646739331592, 0.84597789393052, 0.8188789126880887, 0.5235298646608622], [0.5311800290390776, 0.10012634862466874, 0.8880498019138919, 0.014161768593402124, 0.9423081172066288], [0.064912426052119, 0.5937332179722001, 0.3992501478773236, 0.22669455126942117, 0.4384994379168452], [0.8881683424298458, 0.7860555515565105, 0.396817088855145, 0.70002441186608, 0.3971232248493629], [0.921512810102442, 0.9562292519270249, 0.4825121838436234, 0.4601924569678514, 0.794945587733076]]))))/8.405798677673051, axis=1) |
np.exp(np.prod(np.cos(2*np.pi*np.sqrt(abs(2.132674710839523))/4.069718127742709+np.cos(2*np.pi*np.sin(2*np.pi*array_x))), axis=1))+10*(np.sin(2*np.pi*np.exp(np.prod(np.cos(2*np.pi*np.sqrt(abs(5.279092085513084))/2.565863276652172+np.cos(2*np.pi*np.sin(2*np.pi*array_x))), axis=1)))) |
-(2.058298310326974)*np.sum(abs(array_x)-3.054097525460893, axis=1) |