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np.sum(np.cos(2*np.pi*np.cumsum(np.square((np.dot(array_x, np.array([[0.3678845680533317, 0.31459739085535554, 0.448616506216976, 0.12825441886015643, 0.17253252118319218, 0.095394844280279, 0.8312875551112568, 0.5064171350320166, 0.2722731546567976, 0.40522081882539085], [0.07912621497975647, 0.8159167164003357, 0.713128614329728, 0.14219805142050945, 0.628858541633094, 0.07652908051091345, 0.43179274613376095, 0.9224163029553317, 0.2446987679408229, 0.20382198612378966], [0.11218744394761715, 0.6053461864395651, 0.2126179078923477, 0.3551647325392929, 0.33929309019508413, 0.12014041412940724, 0.696126828155504, 0.4908663915586642, 0.9476315863262769, 0.5717682213238506], [0.06009459849594323, 0.7390913231395787, 0.7034811462203299, 0.22375090126917052, 0.1507247222450051, 0.11940734201010106, 0.7798411624944118, 0.5495838688541879, 0.17384240281317398, 0.36342853134116715], [0.3008927387143512, 0.3386397655103133, 0.8318567751359618, 0.42142691390848586, 0.10572336346040379, 0.8895297549770752, 0.8402260849391978, 0.7809075561609544, 0.749331269129813, 0.1408632171499593], [0.39859131829198224, 0.5680897494862576, 0.685550122911492, 0.3429472097020978, 0.9916123997813326, 0.8152649262789825, 0.07764718274185889, 0.6171999952531312, 0.926448046668382, 0.7123576227802249], [0.8304212164429828, 0.9121125031310182, 0.6610876601164019, 0.09104301588061547, 0.7664518069406713, 0.48327785279178814, 0.42279290314903406, 0.957982874294677, 0.6761754645952632, 0.03932517075648878], [0.9212992219403052, 0.7050056517307014, 0.5740761450024264, 0.13490402822477077, 0.537297540253319, 0.07376812685495926, 0.7568874109156553, 0.285505280611866, 0.8345978045718807, 0.6670019060243282], [0.9923044103198437, 0.4786161263816827, 0.893278486666348, 0.06065426989680445, 0.8341232377871937, 0.588832001075934, 0.6366256147039896, 0.11929361433513097, 0.6774309239603358, 0.06083847352368199], [0.1767672731720279, 0.761437554254726, 0.6662646398741496, 0.5995560222556549, 0.6312668087051948, 0.8039399443143138, 0.9739625737675093, 0.9093323779143424, 0.13956245232316877, 0.9198752346563744]]))))+np.square(5.394391452872927), axis=1)), axis=1) |
np.mean(10*(6.410694456122923-(np.dot(array_x, np.array([[0.15968563804518487, 0.9405168718396787, 0.13653891632084947, 0.9249456913750117, 0.5102198399709956, 0.5076254217476737, 0.9251885519605756, 0.010431084175211436, 0.3987958328637392, 0.11978323036133876], [0.7798815506992927, 0.19054034676303766, 0.3947856659307215, 0.03398797022428146, 0.36716311748487074, 0.1789606068430567, 0.9741555897554817, 0.33775842753884, 0.044658173408198554, 0.1531351143266635], [0.014122447021401974, 0.5763588680463777, 0.43955910688300737, 0.43694383354517774, 0.21371754265223097, 0.3943398228131185, 0.5460053744951981, 0.20476637831582867, 0.373238391521062, 0.42509567387295877], [0.5154707999412776, 0.4357851310969074, 0.5657400276107483, 0.877260791967611, 0.7742262903966253, 0.5438221620035598, 0.9216209501789879, 0.21655864964413785, 0.9800380954568317, 0.5319607956626152], [0.15998176713608314, 0.5622266183963512, 0.9754285093504879, 0.6467577297077927, 0.6081061641720025, 0.02664729832161339, 0.46664267078406685, 0.5123234875972326, 0.09576036342263994, 0.09683171499669951], [0.051096126173030054, 0.751657656161905, 0.6810544961815496, 0.4133898456869044, 0.48394122664346095, 0.4752772405805191, 0.9012928023363479, 0.1930561952908958, 0.3252001122293079, 0.8768862372525309], [0.474889768440813, 0.734307492084221, 0.6734280399448072, 0.004667942666168989, 0.863723983474189, 0.6572928740182911, 0.016059993281454243, 0.14175277682198795, 0.6778476765112789, 0.510140893113997], [0.2671915260810449, 0.7621090212910562, 0.7754195693664259, 0.42302411877263746, 0.7693670992855812, 0.0188251059586938, 0.8154579991257973, 0.44936788128582816, 0.042328887901000534, 0.3254578802441648], [0.65458255093765, 0.9829864018484431, 0.9347309892415679, 0.8128104279472791, 0.1299538755278825, 0.9228646900013461, 0.9256937957582204, 0.1911373267826013, 0.5565311419064751, 0.04744682827939484], [0.10739229138069095, 0.39153299956026744, 0.22629914858077893, 0.2769823644063659, 0.4767819816313895, 0.012652087163485537, 0.7768661441299294, 0.5711672291938031, 0.1226532865453619, 0.7728976761203018]])))/2.755400200348104), axis=1) |
np.square(np.square(6.871470358588372)-np.sqrt(abs(np.amax(np.exp(np.square(array_x)), axis=1)))) |
np.mean(np.log(abs(np.square(np.cos(2*np.pi*np.cos(2*np.pi*3.1610221927798934)))))+np.square(np.square(8.837001168469925-array_x)), axis=1) |
np.mean(np.square(1.2119471356398255)+np.exp(np.sin(2*np.pi*3.257341245181408)+np.exp(array_x)), axis=1) |
np.square(10*(np.log(abs(np.cos(2*np.pi*1.784560956590093)))+np.square(np.mean((np.dot(array_x, np.array([[0.4582575241134974, 0.7756659698005774, 0.7617184459976859, 0.7195393361414031, 0.8835386513996714, 0.5163213170787905, 0.8611280226561147, 0.9173142985423726, 0.5098251021081532, 0.5875557928427785], [0.294446353871069, 0.5000265574997753, 0.0316510816826816, 0.8659517515794445, 0.9066907157994363, 0.9835613999556156, 0.8757429379387741, 0.959737620450908, 0.10673174786867357, 0.36396266351595363], [0.22446341267163405, 0.5436663719959086, 0.9076510060171366, 0.780524106403488, 0.5431869667697085, 0.13239293851334633, 0.05197921563588259, 0.793313221910668, 0.524499477963962, 0.14722390087124737], [0.6746066974395354, 0.4584301484129627, 0.6732848732941875, 0.7609256216807218, 0.1325848296068287, 0.5110321696362197, 0.9645716402035166, 0.09118635638696204, 0.7061254275813744, 0.8941872676398952], [0.7240930567723555, 0.7385037683510413, 0.44477378865641815, 0.4986136829100747, 0.7995528573064806, 0.5472657450268416, 0.2180019216099469, 0.05841334723537328, 0.5164949745206325, 0.5418445386366584], [0.18349733641239396, 0.9072892136194778, 0.027202202413575072, 0.5976699219120808, 0.855241449704283, 0.8502919378301227, 0.3288161508454699, 0.07942255799162612, 0.672736863911376, 0.07674755557851176], [0.14524863524732368, 0.8093930507683691, 0.34837525982295436, 0.6433079022754669, 0.822852788891256, 0.17077771166565348, 0.1746738427748853, 0.799552391403839, 0.4497717877729419, 0.6410613486504178], [0.5078595200564981, 0.17000668944908204, 0.7147279857831446, 0.4181958477503287, 0.3559774604599243, 0.6213382265420118, 0.756575049826895, 0.6064098955839344, 0.5454271015742951, 0.6382493529759353], [0.4232654900700592, 0.07968946438513036, 0.9205697459442933, 0.04579650162696047, 0.6233855753509968, 0.729178209499035, 0.026441343244377857, 0.6127009095978454, 0.39723845401861135, 0.6283125531868189], [0.18170805230759957, 0.8878405180771503, 0.7695829456511535, 0.18028391064753357, 0.6403610833784192, 0.33578609849354146, 0.9202033744955344, 0.05096540142447803, 0.4716174820841791, 0.9637568972143359]])))*1.0166755894577668, axis=1)))) |
np.amax(np.log(abs(9.547916786253978))+np.cos(2*np.pi*5.542787710669852+array_x), axis=1)+10*(np.sin(2*np.pi*np.amax(np.log(abs(2.2900215714242695))+np.cos(2*np.pi*7.45146849904854+array_x), axis=1))) |
np.mean(np.square(1.8754613661679667*array_x-1.2885758132359535*6.394699615852167+(np.dot(array_x, np.array([[0.9767421920933698, 0.3320763061847146, 0.8918239245050943, 0.38115930536032694, 0.6923685664540347, 0.31923067504927116, 0.8378517743374385, 0.04346461645302424, 0.07384395768984497, 0.20640554842068082], [0.10684278075525833, 0.3569793502469599, 0.8142441908477155, 0.5086790733659368, 0.47134190527111264, 0.19534886591953726, 0.14015556598402878, 0.28910172993102534, 0.8538985229763497, 0.11657347073289781], [0.335020784559674, 0.6852133866487172, 0.5608148407982148, 0.6725066489422654, 0.19545283296507376, 0.64140563064089, 0.34313609456752314, 0.8732387664776021, 0.4180859773626152, 0.6386809547248494], [0.008745891678278217, 0.783474651933009, 0.9092318714607305, 0.17443803885852704, 0.6109447319310293, 0.6201193828827177, 0.5177684661469024, 0.7514889438470913, 0.028605026138666667, 0.7498520896283226], [0.47154352114152764, 0.2736629537709492, 0.11581589347198162, 0.3291345767695566, 0.6777643235323876, 0.0021621256714218218, 0.26105945449445855, 0.6183109974229238, 0.5722920816413352, 0.9263626045238816], [0.7158881177708225, 0.4977236257817137, 0.2755636788132966, 0.9428872707118083, 0.08887964848682306, 0.9666143682451296, 0.2458623382363586, 0.49450905220953734, 0.4564248310821266, 0.08180003950157255], [0.7976649733548603, 0.2163027497999963, 0.11356089501135558, 0.43653191886762754, 0.7174618628736331, 0.9324529704285642, 0.5531601566634129, 0.8373629026495556, 0.19874956529640841, 0.38495270204624565], [0.5688840137386383, 0.2988369410728371, 0.5562803626757226, 0.040362165828981555, 0.749784136137485, 0.74377730017114, 0.9070762896782306, 0.8185455771865836, 0.03649551909568882, 0.21551180393484104], [0.28781806688697886, 0.6488585141637977, 0.7350478791095112, 0.48260119070647045, 0.9945304254241628, 0.987433928945454, 0.7473787232055135, 0.0234616637878311, 0.8377520675138401, 0.4216330720914109], [0.538762557596497, 0.6059507706642071, 0.3917982970433599, 0.8674509105409063, 0.8536264389461212, 0.6157903571693923, 0.13125219636395413, 0.7397327198941617, 0.8093792503424692, 0.304552883567897]])))+1.5625045582617592/np.cos(2*np.pi*np.sin(2*np.pi*6.672123036806989))), axis=1) |
np.round(10*(np.sum(7.514827026182496+array_x/7.281723100817486+array_x, axis=1)*np.square(np.sqrt(abs(2.797433648213704+np.mean(array_x, axis=1)))/2.291224929445409)))+np.sin(2*np.pi*np.round(10*(np.sum(5.951089228030108+array_x/8.424290617205028+array_x, axis=1)*np.square(np.sqrt(abs(4.14269186076095+np.mean(array_x, axis=1)))/6.2589216063646)))) |
np.prod(4.899773855047941-array_x, axis=1)-4.715770994742134 |
np.sum(array_x-array_x, axis=1)+np.sum(np.sin(2*np.pi*np.cos(2*np.pi*9.102719948052714)+(np.dot(array_x, np.array([[0.31857392312029154, 0.6317813133461497, 0.18429770547118818, 0.19910306648022502, 0.22363831281053148, 0.9248733733927811, 0.9494025776433974, 0.34294865346699566, 0.3612373696305968, 0.11015662245117042], [0.203211782608678, 0.5281445290507456, 0.04525269008303634, 0.02141523531448497, 0.8025961776142703, 0.6585853564809216, 0.09899894843525336, 0.06034539975944264, 0.7139508430600358, 0.7264312932149046], [0.6872127166617576, 0.3329588560289767, 0.7866025823655521, 0.5113756106642007, 0.9256336872342901, 0.05440099976716428, 0.40908492120820594, 0.7155175660225516, 0.7989328510509838, 0.14296284719347196], [0.2877984641483725, 0.35474131312059365, 0.6041012709834296, 0.1401288444783767, 0.48940275595223737, 0.07996294703736351, 0.5206881333360885, 0.778205909019363, 0.9360049617962702, 0.907508629008857], [0.09256424364398752, 0.048095190435934576, 0.5777274963840819, 0.14898766669749008, 0.6227679619603268, 0.9352101263002478, 0.345427716840196, 0.36336160782984894, 0.9456075293778186, 0.4954776736683024], [0.5104304335688229, 0.8706078340073828, 0.5427292747927617, 0.08258055579980106, 0.512952116564672, 0.2490630017013662, 0.9948415374580409, 0.3526558136966116, 0.9603928522041951, 0.1796818207678963], [0.6100756704716612, 0.14894302662803693, 0.24524805617956058, 0.3589165813249946, 0.29961638465473917, 0.978572826660965, 0.21533154445517688, 0.09075434250755654, 0.5939330983709332, 0.9594472505262869], [0.6380628110400792, 0.36014839375225205, 0.9942008683075938, 0.2610216730821967, 0.32180117239631834, 0.49380827978895847, 0.651334938867386, 0.18288235643442297, 0.29912469458022906, 0.7003333883675081], [0.9964370938739013, 0.6334417635382196, 0.4434098019094529, 0.21689022470790087, 0.8884351947669676, 0.15414386696653803, 0.6978183748444969, 0.5290066927166319, 0.7569357021241385, 0.8224859163133542], [0.02755381682200908, 0.0033112451558333644, 0.46120086209805056, 0.3144671295063386, 0.3528321896858967, 0.07232999837809018, 0.7539512352816174, 0.7205858061871887, 0.6368533130572905, 0.18761253203501593]])))-3.6826454099040213), axis=1) |
np.mean(np.square(-(np.exp(5.196205356883972)+3.833236562810367-array_x-array_x)), axis=1) |
np.mean(np.square(np.square(np.square(array_x)))+np.cumsum(8.656919118705538*array_x+4.554522894851017, axis=1), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.square(np.square(array_x)))+np.cumsum(8.241226875444477*array_x+9.025737749761712, axis=1), axis=1))) |
np.mean(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)/8.920207018791555+(np.array(range(1, array_x.shape[1]+1)))*array_x-1.6135278956334544, axis=1) |
np.mean(array_x+7.29194918293728+np.exp(10*(np.sqrt(abs(2.6560981461522086))))+np.exp(8.262765685862405-(np.dot(array_x, np.array([[0.4302019330695315, 0.39606442728918556, 0.17353123491794697, 0.9942380924917138, 0.38720991485430833, 0.11049513517235221, 0.5593763643443829, 0.5542300236268186, 0.3021939956570333, 0.9245897531968533], [0.8908490023644966, 0.4986990398892768, 0.7663619496360292, 0.19445812963331566, 0.4233204349374554, 0.6199697726639584, 0.6921516843082443, 0.5034997451507854, 0.11405438792369982, 0.0011290312482625797], [0.25583320902574236, 0.8180615878125681, 0.12227543841556743, 0.6493602038607414, 0.7794082661880464, 0.2722311780156077, 0.7927744823591091, 0.9422323768466199, 0.3647695682394556, 0.43596887335357115], [0.34993807380485, 0.8334012813452123, 0.7649254479432744, 0.14545950837606214, 0.17669488995535276, 0.4743313327472308, 0.3341915885881833, 0.35480383554967454, 0.1975516606178488, 0.44596564053585797], [0.8240403852021162, 0.46943040412760784, 0.3397193593040464, 0.04651060532446705, 0.23860991034252654, 0.738229230706905, 0.06462095226949727, 0.3817414183827951, 0.8443450410794672, 0.5998463496407545], [0.2715641728976699, 0.34826689192128346, 0.9045846988132951, 0.43180193288850865, 0.809411431702865, 0.3975524662324993, 0.8091297741677407, 0.03060423865224271, 0.8112144470630027, 0.4467903718276901], [0.539925055028783, 0.26342033182877744, 0.40449636697893054, 0.12760225159291727, 0.7610731882986382, 0.3157658324096412, 0.7715854478995252, 0.5074169038915273, 0.34782388626302363, 0.4017876224410629], [0.7247919485352857, 0.43767342450505087, 0.49673750651258886, 0.3359065705104777, 0.5630994867789567, 0.39015022793699716, 0.62837202639595, 0.16247081936818852, 0.5056793335587688, 0.5537327241266127], [0.983449224702851, 0.8396704933522418, 0.5930544780438307, 0.00834748600924251, 0.49242235820642555, 0.002078458001753658, 0.4217336635091804, 0.32463603536075325, 0.06660412498147616, 0.5587606415979973], [0.7284169650404736, 0.05807466574979325, 0.0023347999239019535, 0.8610835907787933, 0.5217560193158147, 0.7496991978831025, 0.4181072422088583, 0.3064640007325504, 0.932988228823432, 0.055109598931138204]])))*5.437123595135112-2.176609327682308), axis=1) |
np.mean(abs(np.sin(2*np.pi*7.477131654285499+(np.array(range(1, array_x.shape[1]+1)))*array_x*2.61590987100453*9.4887568878026))+7.210628185582399-(np.dot(array_x, np.array([[0.8449955612673106, 0.6869036139719906, 0.9275355301893593, 0.8308921472207973, 0.7984965096883522, 0.39979732148173974, 0.688878662379897, 0.35528725052690413, 0.6513079358153248, 0.2812778511456493], [0.7256370704427134, 0.7458651660680073, 0.33424853359069995, 0.12685706066417368, 0.686173544669358, 0.10673197427621306, 0.33033679836905006, 0.14993722127331421, 0.31098097388432433, 0.7558347336742043], [0.7046809612637631, 0.16189102254313947, 0.0509067642058052, 0.8775800347975383, 0.16222880513030258, 0.8281196332532882, 0.15778630474638033, 0.954738079289219, 0.288665707280644, 0.4589122601679234], [0.05657013548150236, 0.06608574568015846, 0.45582222991691324, 0.5859573383077182, 0.8800660518095421, 0.7989040238618845, 0.9156754426348435, 0.11169487276311096, 0.38624591237116546, 0.1700698282650972], [0.6670193806792458, 0.2952364361572287, 0.3761544896632344, 0.5283117625056689, 0.9209896683027853, 0.09732917688398479, 0.32130110389630717, 0.9143724203607948, 0.5011445209675656, 0.39961167554063726], [0.2291112615123022, 0.5257182474864663, 0.09026246481346045, 0.24077878015503418, 0.7400039904529093, 0.4754480748083867, 0.4295430510413145, 0.8811158645783095, 0.8298521139570375, 0.5099849517144457], [0.6418920580921215, 0.6008191548508935, 0.9459432512361314, 0.46699285320705064, 0.8405251576097019, 0.19300963012922057, 0.41054755427921963, 0.8711195765467734, 0.4100791842575846, 0.9634448516777685], [0.18953715401437987, 0.11393545505677538, 0.5396560107178587, 0.4878430712254107, 0.8948806206777326, 0.24606404401130066, 0.5481112066634668, 0.6189563777965051, 0.6247421041406352, 0.7416313156053304], [0.4135211175675674, 0.5301348031993857, 0.627169797404463, 0.7285736111174863, 0.7703534183792718, 0.01427172174035607, 0.8945963343227649, 0.5053548538350884, 0.11700694548749024, 0.12556716517435385], [0.923719903344026, 0.059645786247852106, 0.379157928925952, 0.3885215565236112, 0.46167877640719557, 0.9396104103631081, 0.8002176738070021, 0.7837195338494409, 0.7619810257701491, 0.5749740229693264]])))+(np.array(range(1, array_x.shape[1]+1)))*array_x-np.square((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.sum(8.861134556852349*abs(np.cos(2*np.pi*array_x*array_x-3.5292978956987318+2.416410791586643))+5.339130866989271+array_x-1.6491354381817769, axis=1) |
np.sum(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.24113566074304338, 0.1355625394462736, 0.32331859314031774, 0.6923458876048361, 0.9165355606069614, 0.7156456193658062, 0.09145901145678903, 0.7508267549653437, 0.09938706480134696, 0.7538623373463742], [0.49538817339265295, 0.4812180443152789, 0.9863053974832203, 0.8328980810177617, 0.8516261149978529, 0.38515191205115484, 0.14450371827392627, 0.4620481935401224, 0.21707486826495825, 0.8050373251570926], [0.11633733342437413, 0.6330669766227572, 0.34465539408317014, 0.39736890485232323, 0.7806700583423749, 0.8747190708922911, 0.5825598377736281, 0.5991824293181762, 0.4456257509629319, 0.3911970939000079], [0.6789306244667285, 0.20786511835906074, 0.08198326379520426, 0.6363614725347497, 0.6192841260190777, 0.705786306022402, 0.824081758404728, 0.6429337429780553, 0.04863455158818275, 0.2914420026875908], [0.1920573974317803, 0.1918138249789294, 0.5445382834568626, 0.6149144163863791, 0.8210867242297609, 0.11808806318517118, 0.5296135727806449, 0.1931470330329157, 0.3873640251602666, 0.7203832747719845], [0.503438199277171, 0.9997706035565241, 0.8172477704023269, 0.27932056405629757, 0.5858595618771708, 0.5507616845801773, 0.8819604804570281, 0.25828448129521664, 0.05159712790237747, 0.8895959351285643], [0.40334150009521863, 0.8862449368167126, 0.30621889398115687, 0.5626887829176009, 0.4624824389421853, 0.5319987033552616, 0.813085018781041, 0.3587792089751689, 0.24343088880549002, 0.13883989141099673], [0.3376440996948009, 0.6528559515209884, 0.606582223946294, 0.5187446138144438, 0.7172166431938212, 0.3651791167045978, 0.4745357066618946, 0.026824298074966557, 0.2969810609839558, 0.10828439842295601], [0.811803896922521, 0.9589677840528531, 0.5983110788243547, 0.5250561014839463, 0.8171213552263593, 0.8594931619674104, 0.3938726864979133, 0.34414023005237215, 0.801380683639469, 0.05282424425903132], [0.6461242653105798, 0.6063368093830935, 0.024857201361516812, 0.9777954569559613, 0.8847393449231014, 0.4590554576163368, 0.05750992454580095, 0.78541933169527, 0.5044661691754393, 0.3950182064046821]])))-3.4278859096725607)*6.875100162460747+1.2431084517017714, axis=1)+9.769933999458644*np.square(np.mean((np.dot(array_x, np.array([[0.006288769303044894, 0.5283499657941441, 0.19197394365459508, 0.5810629093416585, 0.4477564141915815, 0.8962979438524956, 0.14815040713706373, 0.2762425092494246, 0.6819978103563565, 0.8411115481380846], [0.5726845229193117, 0.29958610905270555, 0.042495336742700784, 0.7245282012281951, 0.5895489571920243, 0.7757495509820734, 0.09475779589171385, 0.634058941274606, 0.1286140862454599, 0.39246306311349366], [0.9704521836891806, 0.4909477925806045, 0.5738154990257207, 0.44254757407026757, 0.01919955128226114, 0.7924459307973397, 0.742263886028586, 0.9401553095276444, 0.09973690848642802, 0.246548150283875], [0.6049625550452523, 0.19619325692454248, 0.527573612531034, 0.006346029901710737, 0.7193554310342779, 0.516602344958436, 0.21109610454554217, 0.24294101901225873, 0.497052006757832, 0.40548702222330757], [0.800864190725587, 0.15035702929616634, 0.022923576287338543, 0.5853781866513237, 0.5380897516098133, 0.3510581850359832, 0.5955573607403065, 0.08287306978643105, 0.06840617923778014, 0.7067159113289393], [0.5929422373675917, 0.11633317297948043, 0.9123409657716166, 0.0219174246677859, 0.8993861937066018, 0.7327958974926256, 0.406076075182079, 0.32121603189895975, 0.46651685360557227, 0.898463557470282], [0.39252177254279974, 0.3331527026947769, 0.5575649046962834, 0.9417346621358005, 0.533083807158332, 0.608768369702641, 0.3874514428700744, 0.22384747424269846, 0.3217643632772025, 0.4845540940339522], [0.45793531565841206, 0.46503894878064767, 0.23350987332244322, 0.18985416281832468, 0.1961022295074104, 0.4232808424392631, 0.0018661529389518705, 0.8020943590317258, 0.04633104098140839, 0.13297554455123173], [0.4126525287129592, 0.2481854674449565, 0.7381893116632282, 0.22719927282848007, 0.5402297765290223, 0.9380815957556566, 0.8013056280436117, 0.25612419320375646, 0.726361321158905, 0.09439143593080446], [0.2727373559144579, 0.619098597334449, 0.43945990030813675, 0.0935618768308526, 0.4782355596233496, 0.29891145803747077, 0.36944436647243795, 0.3732578567698399, 0.24612211006822138, 0.5666279251592178]])))*3.627767970600087, axis=1)) |
np.mean(9.690736835960072-array_x+array_x+6.324646741809732-array_x-4.291366420136659*array_x/2.281274862294331+8.99840503724605, axis=1)+10*(np.sin(2*np.pi*np.mean(8.88544956911236-array_x+array_x+2.4756668579923162-array_x-6.491130221081343*array_x/9.92770139930616+9.714355171289265, axis=1))) |
np.mean(9.088230095772003-3.1612214686697797*array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(9.498439586937412-7.772410668525302*array_x, axis=1))) |
np.mean(1.755021127153105+array_x*abs(array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(3.6819645294885333+array_x*abs(array_x), axis=1))) |
np.mean(5.181706190381495+np.sin(2*np.pi*array_x)/4.8250246533071905-np.sin(2*np.pi*3.260608542926314)+7.651685590569531, axis=1)+10*(np.sin(2*np.pi*np.mean(5.903348319821999+np.sin(2*np.pi*array_x)/1.1930232127887326-np.sin(2*np.pi*8.38820565029242)+1.5267919468409823, axis=1))) |
np.mean(8.917238942906042+np.exp((np.dot(array_x, np.array([[0.1546511041355937, 0.005055338753112482, 0.3021890505640964, 0.02571875654634259, 0.3684856407637135, 0.8724950394481048, 0.7702376604218396, 0.9608042146719395, 0.38396026096984226, 0.6622781169468569], [0.9596473919806088, 0.7005347545802124, 0.5365125122688117, 0.65234992167448, 0.0025601960831923565, 0.15955874746347676, 0.24074065285880353, 0.2361179620644589, 0.7948179599771827, 0.46582753608658756], [0.4954847562543351, 0.9891398284545245, 0.523350748035573, 0.9905089206859601, 0.17975978618364308, 0.005654410878977001, 0.913775559668553, 0.7163106829218892, 0.587329220289496, 0.6328795426674754], [0.565998652928023, 0.47406564831913567, 0.46439237659730637, 0.7622218498712183, 0.28982590860129964, 0.8541522419517585, 0.791309532807943, 0.19225862712197395, 0.6207104877647731, 0.23094722230761966], [0.8619151011881743, 0.6753566799929253, 0.8378575480478575, 0.2438446231312208, 0.029390593916396535, 0.30263080491489425, 0.9004899803252735, 0.0008634495416862675, 0.7135096611635224, 0.5829348693467945], [0.6301309392067295, 0.7182484293473382, 0.7121055791839156, 0.709215865169986, 0.7936761954570376, 0.5190865091064578, 0.2702925555703727, 0.39838289159928264, 0.5469551405639547, 0.629814435009979], [0.26010102098932086, 0.8424347954920659, 0.3764405581447414, 0.5984882473758021, 0.6681536349098441, 0.1912210827481573, 0.5191960281915607, 0.753037432111351, 0.5256936533852135, 0.9034322432598386], [0.6860579886462145, 0.9355077306322318, 0.5980476230896337, 0.4959072081449756, 0.08611361145528384, 0.5658137177802148, 0.1118177641575776, 0.6091441810621268, 0.8613210957656142, 0.13778287364916586], [0.786647380992426, 0.7362838455141357, 0.2053797338805372, 0.43586649834968916, 0.3312943450488084, 0.5636064927686885, 0.5842934444689722, 0.1907530583210405, 0.5662879984792052, 0.9976319913999914], [0.6217826626914057, 0.8565129189026013, 0.8216102477112452, 0.17409543014876583, 0.7474750772902973, 0.3753167966216572, 0.06772551782318192, 0.6706375607461484, 0.583074247185256, 0.782617737511045]])))*2.9144903162602)+9.858588184430339+(np.array(range(1, array_x.shape[1]+1)))-(np.dot(array_x, np.array([[0.0023513286821592594, 0.8222273864055625, 0.8476695469098787, 0.10945325274992768, 0.9776347189142075, 0.7189232106228122, 0.2312770355042929, 0.7862584142794379, 0.5688593868997304, 0.44643272665873024], [0.14045063358404053, 0.12707815266171296, 0.06379922944552119, 0.09306569730938008, 0.8546345060905773, 0.5080664879424086, 0.5057083462219588, 0.3369654034935644, 0.5589070152342537, 0.05465560291876648], [0.347934015595341, 0.3070254415491568, 0.12518975042853153, 0.2863254254584324, 0.9198141155661136, 0.036758760889930575, 0.3698993311141592, 0.491185799430805, 0.048355447697333465, 0.6562299238817748], [0.7921658696336294, 0.14600854053211976, 0.3305119237876195, 0.3499866703933926, 0.2650090222758169, 0.6386170144983425, 0.5207166460914607, 0.7571092059786022, 0.5250155133800665, 0.04660762617299463], [0.4355863983977629, 0.9377300891576532, 0.15609791656269267, 0.12187942057709544, 0.3416028689770314, 0.9928202518568155, 0.8107615764019114, 0.5951569909893825, 0.8073124378096858, 0.5412481142055914], [0.5678234707728421, 0.41708313219742665, 0.4320728319638535, 0.13695224976793652, 0.9286529686170379, 0.503716904850164, 0.5453939500590699, 0.4926127222758808, 0.5492015399477405, 0.5783531942637379], [0.1977680376612615, 0.7797063106039576, 0.7588302236568675, 0.9255023404418083, 0.8230319585317681, 0.18525369987274476, 0.3162356232917245, 0.9753094757489434, 0.35736531672799476, 0.7449656073908034], [0.43436431010388665, 0.013973051997281938, 0.8270724118164227, 0.26220427327532536, 0.681549368045555, 0.27680089240126293, 0.22630379516877486, 0.6526843656858805, 0.5234164059052259, 0.2253147182980333], [0.918892789453167, 0.6810470667043885, 0.08481878194657466, 0.6663245154575039, 0.9941001395196927, 0.3512777073387614, 0.43418726152407583, 0.9077558790003688, 0.6111778397504095, 0.8597856778294524], [0.8146214422811485, 0.9245250023830663, 0.709212222213939, 0.9533457608647855, 0.6006962224950044, 0.7931752491471454, 0.8620201572295441, 0.2506495784565548, 0.7608381718458588, 0.8976812219803283]])))-np.sin(2*np.pi*array_x)*3.907975661577199, axis=1) |
np.mean(9.474966852990345*array_x-array_x+abs(10*(np.sin(2*np.pi*7.4803702674997075-array_x)+2.6309197989137263)+9.745537470440848), axis=1) |
np.mean(10*(6.7560764547698895-array_x*4.4843142546138335-array_x), axis=1) |
np.mean(np.square(np.square(np.cos(2*np.pi*np.cumsum(np.cos(2*np.pi*array_x), axis=1))+(np.array(range(1, array_x.shape[1]+1)))-1.1675898030189003+np.sin(2*np.pi*array_x)/np.cos(2*np.pi*6.90972608590867))), axis=1) |
np.mean(np.round(np.sqrt(abs(array_x))*6.740914665682677*8.303719627977328)+8.861074497330792-array_x*np.sqrt(abs(1.0358447114973812+array_x))*6.376684545306247, axis=1) |
np.mean(np.cos(2*np.pi*np.square(array_x-np.sqrt(abs(6.358236688350282))))*7.413766775066127, axis=1) |
np.square(np.log(abs(np.square(np.sum(array_x-1.0901489935348958+5.953421973169614, axis=1)))))+10*(np.sin(2*np.pi*np.square(np.log(abs(np.square(np.sum(array_x-5.3099827678549385+8.526395532729758, axis=1))))))) |
np.mean(1.7704649603245617*1.8083471500881583-np.square(array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(1.2460310242680044*1.2034101582739685-np.square(array_x), axis=1))) |
np.mean(8.624922980955223/np.cos(2*np.pi*np.square((np.dot(array_x, np.array([[0.14401036492065122, 0.7947028375956994, 0.7306221498469317, 0.3794551334190266, 0.8064149400174934, 0.6089684672845999, 0.23148911881183432, 0.6331961260092831, 0.25541759847138357, 0.5538061889392718], [0.5486546327476888, 0.16230723395532576, 0.2345052982311039, 0.664147871130174, 0.11523510012460625, 0.10761162169149419, 0.8459358892281225, 0.6058964973364129, 0.3374843092649351, 0.1834170667154016], [0.04395185097650933, 0.28399612542966546, 0.5735394323586698, 0.5912549180730341, 0.658500094538066, 0.322032825879105, 0.8198877740536658, 0.10985569752987723, 0.6935607971694977, 0.30831710217341035], [0.7344844012988171, 0.4024715991541734, 0.8018201599115636, 0.9944744371955907, 0.17795870972824923, 0.3426690018518863, 0.01668777663141008, 0.3596108511320858, 0.18069694647021206, 0.4951493224236283], [0.3307820721848421, 0.30775061505952805, 0.18687205208569935, 0.8466012604277792, 0.530694207459433, 0.6156265762911715, 0.2897068385792734, 0.2596694734442828, 0.3746728240705376, 0.8940046927498357], [0.4041737704614884, 0.10540235369630002, 0.34566435111825566, 0.48263471059163565, 0.5412320016791223, 0.06321199853917525, 0.9551852125550956, 0.1652504012911966, 0.101487472807957, 0.3717056064411999], [0.27219659078857017, 0.7212233457430405, 0.5463800910491424, 0.6062924378104407, 0.44726040282390056, 0.48876244822726433, 0.10300046613320779, 0.15337278041667324, 0.30940433851444604, 0.8711331249335443], [0.29965691764026614, 0.3536088982573018, 0.41204286984346816, 0.789055336267053, 0.8365846240274384, 0.37801129307869397, 0.3922057574231963, 0.8512188447563145, 0.16791536758476633, 0.6768243091667184], [0.4252078058450993, 0.9054245523214924, 0.702990240020388, 0.1649119012384782, 0.030113942008094363, 0.36991466044710053, 0.18775391438327704, 0.19142549808270537, 0.9662082158256556, 0.19098460098104753], [0.18737955537576978, 0.9289385690192612, 0.4089092863226602, 0.9909343286023794, 0.5677833620287948, 0.12815785669103708, 0.29072124614612826, 0.8244564594042655, 0.5406380362422568, 0.5370366853824303]])))))*np.cos(2*np.pi*np.square(np.cos(2*np.pi*9.42891734557819)))+abs(array_x)+array_x+(np.array(range(1, array_x.shape[1]+1)))*9.985183460809695, axis=1) |
np.sum(4.388787583083296+np.square(abs(7.753919149182432*array_x)), axis=1) |
np.mean(np.cos(2*np.pi*5.132832630413135)-np.square(10*(np.cos(2*np.pi*3.3356404447345076+array_x)))+(np.array(range(1, array_x.shape[1]+1)))*8.059438978986222, axis=1) |
np.mean(np.log(abs(np.sqrt(abs(np.exp(9.809518349580586+array_x+np.log(abs(8.455967007350836))))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(np.sqrt(abs(np.exp(2.2458157960166147+array_x+np.log(abs(9.647277953914438))))))), axis=1))) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x*4.9260196844150235-6.054967631355121*6.120971598375197-np.cos(2*np.pi*np.square(abs((np.dot(array_x, np.array([[0.3490072391060085, 0.32643113173844795, 0.9584531190462097, 0.3648591490604389, 0.8051236779496125, 0.8959022769855249, 0.25068449106635304, 0.8612219407026186, 0.6403602687232631, 0.8858574011265864], [0.10588466494208748, 0.6298290928647742, 0.5611081924784339, 0.9023440733176933, 0.7096999864571986, 0.8890822696432101, 0.9970240320899886, 0.08497234055357927, 0.756174157381042, 0.04537001504654048], [0.4197592707702489, 0.9750093507506802, 0.504592157050199, 0.7702812175414783, 0.8608030639695475, 0.8966119910168013, 0.45841721003678093, 0.6066506883230653, 0.9712824036143154, 0.7196520077907446], [0.26476484973536485, 0.7170079309101227, 0.6362987374965557, 0.4582712687618634, 0.3472551686047781, 0.8749330929466572, 0.6589738445367816, 0.5774338352687655, 0.4014014704582971, 0.5482402121513092], [0.9142356678063046, 0.10931619942816229, 0.09486788280662017, 0.39969835125183384, 0.8759503338530257, 0.24641104390675295, 0.15080997407018748, 0.9856793806452977, 0.681882763940897, 0.751679044099879], [0.4219050669546016, 0.1114289778800367, 0.593547806626887, 0.255432195805933, 0.8716755605779275, 0.630018846007601, 0.23201596209844866, 0.3608754875357998, 0.3694337525356467, 0.6450437342057915], [0.7388502392620632, 0.04927744502866116, 0.9649305406288419, 0.34746113285648483, 0.6451964507175006, 0.5936638011535179, 0.49565115718941366, 0.12615527587406072, 0.3233303765615211, 0.9110556486490542], [0.5729620521828079, 0.1337984102489238, 0.9681800132743842, 0.8061854347381758, 0.23421010737692294, 0.94080430001619, 0.8626067614796542, 0.3744732681343661, 0.11056392127282666, 0.49995894752600456], [0.30298707788023793, 0.9379025918510514, 0.686062912773299, 0.027680824639586876, 0.9152638878491416, 0.11712340530911736, 0.4745142187788238, 0.4518182172047198, 0.29643304966222395, 0.570255431072486], [0.4077637511787615, 0.7562442536259749, 0.7935830153540431, 0.13298409867411665, 0.09775206745237175, 0.001801694514614538, 0.9034164331108123, 0.44584339778139725, 0.900494281790446, 0.7928411667431589]])))-(np.array(range(1, array_x.shape[1]+1)))*array_x+1.8334974996713247))), axis=1) |
np.mean(np.sqrt(abs(np.log(abs(np.log(abs(4.577517337469372))/np.cos(2*np.pi*3.172841766022991)-array_x))))+np.square(np.exp(7.373632817199301)+array_x*7.377738701525792)-10*(3.538052946833343), axis=1) |
np.sum(np.square((np.dot(array_x, np.array([[0.9309108559518301, 0.8747025932519616, 0.47706130400794866, 0.5443856744744053, 0.8524692491673806, 0.41318112910489757, 0.9777416700486419, 0.26510480465052544, 0.53825432187485, 0.3652864377538646], [0.6349763376721956, 0.7373244228487597, 0.5063263849089248, 0.49246161436592184, 0.4373058816445713, 0.4590272464306919, 0.003420571050488097, 0.9480140892733866, 0.4136811445774915, 0.5284600712227983], [0.9978489240397623, 0.36917217224825827, 0.7197001992531185, 0.6832849522313962, 0.3118935573888838, 0.08553176249403327, 0.7075844799929862, 0.948533613961846, 0.9295172330861826, 0.8508923553851845], [0.725365263074581, 0.8798293304245353, 0.21819171857833952, 0.6179017729503978, 0.7156044133666661, 0.5583511272230637, 0.8477426857230841, 0.5492839591842202, 0.04096622663157856, 0.32274317380548], [0.9778280117119754, 0.48625974409717454, 0.9837365893767457, 0.154816087215204, 0.041691272163626114, 0.4740079976692495, 0.8683818647687915, 0.07207288044580407, 0.7599644456376965, 0.8648750416668789], [0.6845102453668229, 0.009242661074602254, 0.8728499108551252, 0.8278665894172871, 0.8366345838777786, 0.36755535451153387, 0.417478092694305, 0.5142249386745349, 0.8855867609378245, 0.07040365693976558], [0.45745691731500937, 0.15730647488556715, 0.23818715640667198, 0.4427211450033881, 0.3657076745410833, 0.766250340789445, 0.7924306357278335, 0.9526902991185803, 0.9303335588352946, 0.8493593808293515], [0.8593512820153641, 0.7847263380935015, 0.6077556653054406, 0.6672190396945039, 0.5263315690303798, 0.9234134073759482, 0.5241400980657538, 0.1986066239449421, 0.6057150960474509, 0.5996489610983251], [0.6327415878413682, 0.5967835690793865, 0.1275301469059562, 0.07893204030925494, 0.05923987533428432, 0.7162714393500862, 0.1989137628822535, 0.5386894035849933, 0.7986335319432221, 0.7312138973587599], [0.6811909020993786, 0.6883093223486804, 0.43098563195660555, 0.09777755003753308, 0.4500978302317794, 0.09067426925584599, 0.43769299764590686, 0.6876722876594656, 0.32153979892676654, 0.10349521459674249]])))), axis=1)-np.mean(array_x, axis=1)/np.mean(4.870277396245385-(np.dot(array_x, np.array([[0.38708084128898834, 0.8707535304059539, 0.18630163710930547, 0.9019257683490077, 0.7260200909747873, 0.6819363072817434, 0.0302436670749483, 0.7154091833242768, 0.7885755233961759, 0.19010572587842378], [0.8005604209578586, 0.045436598785287385, 0.03850335472132527, 0.7598736050727521, 0.4791538907477717, 0.6960059069939407, 0.29099822101894535, 0.9898940553119255, 0.7300579700492559, 0.08788335318269824], [0.7403094771360557, 0.010075944450823937, 0.9122399062723741, 0.38021699281273214, 0.9172405115609112, 0.3026487984070687, 0.23323259416309983, 0.9731947193053374, 0.9836506593691289, 0.7280399420874918], [0.4161827287994989, 0.3674406655700606, 0.015773285325837882, 0.8691540797667842, 0.5614022775492173, 0.07631167576521114, 0.3107375032945411, 0.47063647060655933, 0.7241431246122009, 0.2799033671651008], [0.2692825265719829, 0.4738489950702819, 0.8081951504470836, 0.3791752887934694, 0.1744736058097549, 0.2729395564369903, 0.12184409764294901, 0.9048503465226526, 0.9262714034867149, 0.2617305795024607], [0.09490322755497616, 0.25921362267759807, 0.6720254107520417, 0.04506262080376122, 0.7633993184475412, 0.7273667008841578, 0.130978771649777, 0.5363967620451074, 0.6905431382260051, 0.8515016996477814], [0.265024564810432, 0.2949701963795175, 0.7768945765236758, 0.6741327203781234, 0.06441801951133441, 0.8349706898288292, 0.6494902680326257, 0.5505225986352033, 0.2658983760811344, 0.8815713459054427], [0.5479519205090261, 0.7407438418154116, 0.8394306001625893, 0.9788477712701331, 0.15117624510545102, 0.1489166173260582, 0.20087912842042466, 0.4009348594044736, 0.916713802206217, 0.34626891342693467], [0.2978785575010553, 0.26758131520822326, 0.8365371850173592, 0.9239888029806063, 0.707356967790881, 0.3234899243514089, 0.8737397348938032, 0.5971892810640674, 0.013862919058476808, 0.15967764697770503], [0.7121925894685358, 0.42674919890543583, 0.03303087692156392, 0.10891344922162083, 0.7479983829521215, 0.17859966461416243, 0.8822633502170356, 0.4212168442150007, 0.5487854534621965, 0.8707595209006324]]))), axis=1)-5.394816679710307+np.sin(2*np.pi*np.sum(np.square((np.dot(array_x, np.array([[0.36946651390822416, 0.6711567703257462, 0.19896861778937758, 0.8436076972358354, 0.6673041957285456, 0.8354271466802597, 0.19857475854463236, 0.3720107565930081, 0.34046697755494715, 0.7461285306055877], [0.005002944720660207, 0.1483013151207785, 0.30895037868143715, 0.7530215648267308, 0.07679679023894703, 0.4183294649815301, 0.026364404488934068, 0.27896700102934435, 0.7661625795845015, 0.8562937375100049], [0.32141733606099765, 0.33715093126451534, 0.15691641460180783, 0.6830205461435913, 0.15732357395481977, 0.18382150399458597, 0.5763837423783587, 0.43258660407608485, 0.3248185415198974, 0.40018252816513133], [0.06413602956856856, 0.12740169772887033, 0.7765644029882248, 0.4190512703895697, 0.3544648791800665, 0.9016867079214576, 0.05409916405838666, 0.9845411858073954, 0.36893636524539053, 0.297124534774595], [0.6723157996173974, 0.0727640665878414, 0.22833276457770946, 0.24378032078872047, 0.3981154752483489, 0.3754494364861056, 0.3705452763595286, 0.2096450021814037, 0.17321172322261125, 0.22131879704725488], [0.5601926987294693, 0.40263633462754544, 0.17697828325683962, 0.06596562074352674, 0.04273379987711967, 0.47726349486468467, 0.772457723503793, 0.8049873534285693, 0.18116680835867272, 0.5737634262430119], [0.5135060780140982, 0.8720484164666832, 0.21585804260394192, 0.7657796718162835, 0.5870402887704519, 0.5894046811701444, 0.41596056779423796, 0.09719524164985305, 0.21328979628344213, 0.47911223337152553], [0.6443974136491044, 0.008932964470296123, 0.4696060749606181, 0.13100434110323844, 0.12987830934189504, 0.3370444496424917, 0.6808393910473076, 0.18390626967135937, 0.5744312387288184, 0.96758769943795], [0.11162872004215496, 0.15713592754108485, 0.6753314954081862, 0.5548009304551078, 0.8873920736675525, 0.8243583797506363, 0.478476683248962, 0.3878952323072541, 0.21860720601263828, 0.3915966308603577], [0.23177890574718085, 0.7937127156100502, 0.5135092651221499, 0.4901348069920286, 0.23585905949744035, 0.20656373365056657, 0.45933147931675844, 0.7727879902330443, 0.8386135802484679, 0.8071104464789969]])))), axis=1)-np.mean(array_x, axis=1)/np.mean(9.773332363099192-(np.dot(array_x, np.array([[0.451056527914775, 0.11967667918599967, 0.021604875337595697, 0.5206294072926003, 0.15860289809584205, 0.6941153317054741, 0.8373345342918861, 0.30262068102967166, 0.8270794473406856, 0.8634492242428152], [0.8025478965501374, 0.7743893939783099, 0.3521139666859461, 0.8220670229413587, 0.14781101228864457, 0.4373751080256627, 0.3270718524585434, 0.31432253069233285, 0.6705829466743061, 0.0663494782117986], [0.8064993539072326, 0.758869967692507, 0.8817439979725364, 0.29004398096685446, 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0.5422610386290014, 0.556904027100425, 0.4413868904416062, 0.17444775829000558, 0.20201773157302094, 0.012584663599454426, 0.7031106022422231], [0.2103626998758168, 0.33535184880824453, 0.5227161494984435, 0.6929297420253707, 0.9432948328919257, 0.9683342987332895, 0.15178922172363696, 0.36354650796242793, 0.004882984560984682, 0.657346620221937], [0.5050194833350135, 0.3629301633554364, 0.17196471487982146, 0.32161555050124, 0.27762519848164013, 0.615148700572628, 0.6513041079748629, 0.10120134318973428, 0.6062178160812023, 0.02443853556237019]]))), axis=1)-1.322679246109994) |
np.sum(np.exp(7.519149080662286)+array_x, axis=1)+2.987144039256181+np.sin(2*np.pi*np.sum(np.exp(3.508053804280209)+array_x, axis=1)+6.002661469469085) |
np.mean(np.sqrt(abs(4.942684424471993-array_x/2.651010133772482*3.2820708942497077-array_x))/np.cos(2*np.pi*array_x+7.3225894405663725-np.sqrt(abs(7.378705945009205))-8.137054058717322), axis=1) |
np.mean(1/(2.2095487059959344)-np.square(5.690311666516174+array_x)*1.8657914154469881+(np.array(range(1, array_x.shape[1]+1))), axis=1)+10*(np.sin(2*np.pi*np.mean(1/(1.6681748375767729)-np.square(9.174302551328381+array_x)*8.736866165295423+(np.array(range(1, array_x.shape[1]+1))), axis=1))) |
np.mean(np.round(np.exp(4.8797414018976495*array_x))+array_x-6.112690819113509/3.6066351659496694, axis=1) |
np.mean(np.square(5.852058561307306-np.square(np.cos(2*np.pi*array_x))), axis=1)+np.sin(2*np.pi*np.mean(np.square(8.079766373928397-np.square(np.cos(2*np.pi*array_x))), axis=1)) |
np.prod(3.5909625840740063+(np.dot(array_x, np.array([[0.17800384362314914, 0.7918952464419324, 0.5339848453336742, 0.6081810500235872, 0.013462259122340448, 0.462699473867221, 0.7715264685680057, 0.08070760968525048, 0.1676507088994197, 0.1768444848764853], [0.3078526294579649, 0.25020783297223337, 0.5448869327245406, 0.4143813778461639, 0.22753832773818872, 0.24394178469989425, 0.09527979265097297, 0.7273698762627231, 0.6711148891131891, 0.46644715138575077], [0.16639905940699884, 0.5382326032449085, 0.18878333625556787, 0.006362863312639577, 0.5376370850966236, 0.2858189997488739, 0.08209297596406107, 0.35581304326484287, 0.75276569635794, 0.5630763221054227], [0.3734354848939183, 0.10438186714081255, 0.43719083097434164, 0.5007793958559165, 0.33212985135556283, 0.6053442915895011, 0.4951885476067417, 0.37827628967770355, 0.013143218182439687, 0.1979056467030822], [0.9271314129672991, 0.02068137872510023, 0.8144580116991492, 0.18699270429112236, 0.7706945979186157, 0.39343628310161793, 0.7979323669279995, 0.7503143391892677, 0.6174317951894024, 0.241077578780598], [0.9754606578001359, 0.08138203184085868, 0.3433133457110198, 0.7425297149686259, 0.5010948644287462, 0.042441845924796606, 0.6806873258295253, 0.40371398782381696, 0.49120769799358566, 0.6742254088030315], [0.07175077851371547, 0.9566650826450158, 0.3529959716584885, 0.17541390589632155, 0.9571169084394944, 0.015264531561315686, 0.6534111410912554, 0.5089865900071862, 0.8640049241545557, 0.11519104586140283], [0.5649855125272386, 0.24722000335155858, 0.045256574606029276, 0.664076537183172, 0.4449658609577327, 0.9178631048177126, 0.6391491257903312, 0.5578455879378278, 0.9488293636234766, 0.5886908559069607], [0.6285377948627324, 0.9569524224221654, 0.09826918780646232, 0.32245963599771554, 0.4504071990078855, 0.5545646628191551, 0.4935421213101506, 0.6372328447373208, 0.6309612360715898, 0.27510468278725375], [0.3354444655915809, 0.05180644922829736, 0.6363911270706316, 0.15197870252715429, 0.9481986728150765, 0.606622217444673, 0.9389559350358925, 0.37182625849591056, 0.7861789583409396, 0.3020937920917316]])))/8.048169628576987-array_x+2.182548072799807*3.981983772843525/5.5019095292483255-9.639388735011995, axis=1) |
np.mean(np.round(np.square(4.146840874088578)*array_x*array_x-8.686550982931914), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(np.square(1.7698441494241277)*array_x*array_x-5.593469240662916), axis=1))) |
np.mean(np.square(3.264928722856598+(np.dot(array_x, np.array([[0.927954775229624, 0.9029369851284533, 0.42761682978555027, 0.5108056976079839, 0.5832001247153724, 0.9523303071278175, 0.2807886990777726, 0.7953453173992925, 0.9751400025284155, 0.46315728362223363], [0.7124592745207211, 0.10283157971677626, 0.38714922703851173, 0.448450151263333, 0.36039683423920355, 0.17294903279974527, 0.06502653492485067, 0.4146979935962528, 0.5538204043513066, 0.9428238355688667], [0.0785561216995947, 0.4503980195277517, 0.10048979962255367, 0.21781206863092828, 0.2480722855745644, 0.7591923409066286, 0.5442463259025984, 0.7507041191050535, 0.23970918193059865, 0.051353459022319914], [0.28893851395700165, 0.8065461148372808, 0.05652281864517483, 0.5795372369734049, 0.15759090678465315, 0.8938875618844997, 0.06860982738563637, 0.4560122506420813, 0.9451890983047913, 0.17636708225519704], [0.08282849066212083, 0.6087385507387364, 0.8326048129785752, 0.889804366367337, 0.3932411128701673, 0.10517106087413486, 0.5808006379360812, 0.8703442310147966, 0.8232351116309399, 0.5006682362066417], [0.12546544295116924, 0.8091247085414337, 0.10238018672952998, 0.07635199752846689, 0.8377610057959771, 0.5461459114518106, 0.9817780712908619, 0.9202281086744, 0.17234721007142395, 0.5143001466858739], [0.13693765795905877, 0.6461314173073437, 0.16219859575540452, 0.8854834346698766, 0.5950459516142619, 0.44007562202413375, 0.577146306131825, 0.2302888490684475, 0.9575174435977284, 0.3832488750495464], [0.2402284168134975, 0.6775581866072331, 0.021228482797886805, 0.8315329367323104, 0.6398629158897015, 0.34294884200392395, 0.5332148320817262, 0.046434328318763485, 0.03150869662356026, 0.8111508486975442], [0.8633190099883765, 0.7688202230837842, 0.18106647537734488, 0.952226742924272, 0.03813790681703111, 0.2944849192974379, 0.5921189516671097, 0.2545285327320089, 0.7738369098201687, 0.28728153821688374], [0.7162255837864235, 0.17188791744372034, 0.478861027826876, 0.034927549054460605, 0.05378415392068914, 0.582496479349252, 0.4809554314629746, 0.9278925309845025, 0.8572799062961275, 0.522696759463937]]))))+8.557653385187216*10*(array_x)+2.6653431511639765+5.196366852554322, axis=1) |
np.mean(1/(np.cos(2*np.pi*np.sin(2*np.pi*4.392677527483611*(np.array(range(1, array_x.shape[1]+1)))*array_x*(np.array(range(1, array_x.shape[1]+1)))*array_x-3.2099354240041342))), axis=1) |
np.mean(abs(np.exp(3.234023737898147-np.square(array_x))+8.937546261758126+array_x/2.515062286148402), axis=1) |
np.mean(np.square(np.square(np.round(np.square(np.sqrt(abs(array_x))+6.9585705265477715+array_x/10*(8.806205818613082))))), axis=1) |
np.sum(np.sqrt(abs(array_x+1.8220319823192064))+9.949721532969406-np.cumsum(5.872681937112424+5.135275176238656*-(array_x), axis=1)-np.cos(2*np.pi*array_x+8.922932529744632), axis=1) |
1.6680225661484407-np.sum(np.sqrt(abs(3.101913389611053))*array_x, axis=1)*5.429820662982543 |
np.mean(7.722009946517475*np.cos(2*np.pi*6.345311581724315*array_x*6.537347198848015), axis=1)+np.sin(2*np.pi*np.mean(2.409518997579248*np.cos(2*np.pi*9.693975270707552*array_x*7.640161532166427), axis=1)) |
np.mean(3.198749391577391/np.cos(2*np.pi*np.square(6.6028776533999265+abs(3.580295277021617)-array_x)), axis=1) |
np.mean(10*(8.5102335342633-np.cumsum(array_x, axis=1)-np.square(6.563362392933573)), axis=1) |
np.mean(np.square(10*(1.6261506215975328)+array_x-np.sqrt(abs(2.8846327470482245))), axis=1) |
np.prod(1.872172142742338-array_x*3.2613385080515527, axis=1) |
np.mean(np.sin(2*np.pi*np.cos(2*np.pi*3.684789355200336-np.square(array_x))-array_x*np.sin(2*np.pi*1.4511944632557983+array_x)/np.exp(6.937502104033621)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*np.cos(2*np.pi*4.060228186169843-np.square(array_x))-array_x*np.sin(2*np.pi*3.8697289681721063+array_x)/np.exp(3.207099874889045)), axis=1))) |
np.mean(8.134217535277786+array_x-6.256357307273102-array_x+np.sin(2*np.pi*array_x+(np.array(range(1, array_x.shape[1]+1)))), axis=1)+5.3220116273302525+10*(np.sin(2*np.pi*np.mean(2.132010019049664+array_x-3.8419265478207647-array_x+np.sin(2*np.pi*array_x+(np.array(range(1, array_x.shape[1]+1)))), axis=1)+3.0493447002697898)) |
np.mean(np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x)+1.5536088631554161*3.3976392183396156+(np.array(range(1, array_x.shape[1]+1)))*array_x*3.97373193653464, axis=1) |
np.mean(6.55291781623759+np.exp((np.dot(array_x, np.array([[0.5482315762636677, 0.5960304646358408, 0.8509225552718481, 0.7055103177705843, 0.46249249334561715, 0.5083954036066087, 0.5945929257590786, 0.5861213125360777, 0.86333643455486, 0.7861068703919291], [0.5588526662141944, 0.26188808631021143, 0.7770811521618585, 0.8999788063048579, 0.4928632526585395, 0.8192087532152187, 0.5157351155748446, 0.1747450143027064, 0.34264093135368034, 0.9978505275084883], [0.3362690139457998, 0.7224516264143211, 0.8039058589296667, 0.6334636254090028, 0.018051925374752198, 0.5187247491675823, 0.748554250803355, 0.7183708994714567, 0.5852017368808423, 0.23202846980832748], [0.0525155172417221, 0.13839173139181926, 0.13973209915239237, 0.5552511064957071, 0.034901228837666554, 0.12531969286454792, 0.30275511024747326, 0.12515885616114997, 0.365393548752016, 0.6048042935565768], [0.5117163402063977, 0.7811820030129231, 0.9695247262773328, 0.7550534406590774, 0.6042266881892724, 0.20628941730251604, 0.31150296215093176, 0.0028071339395217, 0.07388287466779908, 0.5150533953721792], [0.13486229078324719, 0.6647549028720495, 0.7631768639647104, 0.5280315512086539, 0.24685622904914073, 0.26933039680971904, 0.6811745004995764, 0.9650229964824119, 0.4664497223983146, 0.29822409053352217], [0.5596316957828983, 0.7334113954086487, 0.6792867696017999, 0.27010350966942087, 0.16272665235636508, 0.3690500979099105, 0.1824926160481044, 0.20737133368694716, 0.22191012275598143, 0.5262820099399671], [0.7862661718843613, 0.5081739433664492, 0.8269119068149506, 0.9697136100738657, 0.6048835844430204, 0.5670963301035288, 0.7879718711426305, 0.6144312520460933, 0.3583653201850727, 0.5594353685635023], [0.363329608348795, 0.874617304444664, 0.7523112990264281, 0.692777489096487, 0.7518375071311986, 0.9574810099857716, 0.4550368143753809, 0.3051098539417292, 0.05795710366071427, 0.7705311708424775], [0.7041745435317569, 0.9360128639603859, 0.09997513219732668, 0.6312512641147214, 0.18869944831383878, 0.5636002619410414, 0.49774081771253975, 0.04589381264352144, 0.6223933951437819, 0.21358639000672075]])))), axis=1) |
np.mean(np.square(np.log(abs(9.337452866700032)))+np.square((np.array(range(1, array_x.shape[1]+1)))*array_x-1.5227448561452792/4.841228224074629-(np.array(range(1, array_x.shape[1]+1)))*array_x/6.523481300661187-8.007610472200778-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(np.square(np.round((np.dot(array_x, np.array([[0.723939688033082, 0.17667364315726453, 0.6919121096475218, 0.5960597506642424, 0.5578922387037171, 0.16657890228240935, 0.9896003203862916, 0.009632831294368871, 0.09076808705369377, 0.18935856313152977], [0.3524534024626843, 0.4295945604258906, 0.9537896559279583, 0.19800754164990497, 0.08680670521890677, 0.8309838537676215, 0.737929449998923, 0.9652891654667916, 0.8030676845945334, 0.11967108010325744], [0.276843288632019, 0.28149800733701424, 0.3329398623476445, 0.7092921058585596, 0.1553967421705006, 0.3699683929851866, 0.8916011163526252, 0.33606379461253, 0.39382254914661385, 0.4769395750578814], [0.9435248177622558, 0.35597245188872295, 0.2779697573520943, 0.7554884713397135, 0.008136438886275021, 0.5134280849883249, 0.09800483728809428, 0.6918384080218467, 0.8750860434001704, 0.3302520683399913], [0.9567382147298953, 0.5588695539017066, 0.2946891894454279, 0.6320914873898029, 0.5707417191985003, 0.9360996829846523, 0.9520134938354592, 0.832195413930566, 0.15544400531205949, 0.9716327963048796], [0.9603289804297432, 0.7218433666983031, 0.6288144104694217, 0.9725684993760718, 0.7534483879130416, 0.882995933473007, 0.13797923507214316, 0.02290162486470726, 0.1408997614187003, 0.13587373795237767], [0.27786236891744576, 0.546206442464253, 0.6827277842776637, 0.717061811630468, 0.9404066344854419, 0.7713689247950892, 0.0071616229772141216, 0.9081852898613951, 0.6985007488843183, 0.6908297867251434], [0.32293086643978086, 0.5789249087095902, 0.6289770672384695, 0.5556597175615897, 0.3604392265902252, 0.537982658065304, 0.1942035583723637, 0.6717686553884215, 0.5369006355267827, 0.49471493187973803], [0.9645318251712949, 0.951249061787267, 0.7635511556500493, 0.33877807091363765, 0.7098482084848913, 0.48845620563553616, 0.037346095801284895, 0.8076933472184668, 0.8214251777856241, 0.15195672953684625], [0.18788719019868694, 0.5062743159534363, 0.12141277025538388, 0.15243959506083438, 0.9736709078473436, 0.5312857747861994, 0.08782387612702247, 0.0260226759122546, 0.5328938911591377, 0.7038880673353866]])))-9.051282047805126)/np.round(8.585237438929639))*10*(9.369480459147672)+array_x/np.round(9.0645984213163)*3.5297390885300217, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.round((np.dot(array_x, np.array([[0.023624316184151617, 0.2811097888133559, 0.30879189038463617, 0.00924394395860384, 0.7644347096657009, 0.8347666139833197, 0.01462642898032096, 0.46011920862827016, 0.8811320473175506, 0.6987675255772451], [0.20081832100977837, 0.28816827804500256, 0.6555750471418095, 0.6653727916154861, 0.9813505998257179, 0.931654867466758, 0.5494096108772125, 0.09735794570489442, 0.7414364745996735, 0.9811699054850647], [0.5644839363361563, 0.8669030445925873, 0.2646870857137996, 0.425711439326677, 0.19009474719271846, 0.504815628217953, 0.45281191726340186, 0.274482313849191, 0.024672716509996695, 0.9961516588291968], [0.23275408113139584, 0.8520106520472839, 0.6063273263484019, 0.03246876344060412, 0.9187771486105334, 0.5992942253969428, 0.26794663655847684, 0.5950290912849251, 0.10018234396057901, 0.3593199594680452], [0.3793953187638639, 0.777913491618275, 0.1663849404625274, 0.07322458170127732, 0.042543758770266504, 0.6464845306474766, 0.18427759992834092, 0.37940378556046206, 0.5503874648695674, 0.7297291097896651], [0.08894897944704228, 0.10211649774642317, 0.41996624983259545, 0.5876153043454353, 0.09625501604163511, 0.6168019054623582, 0.9489162702614148, 0.23333792373618611, 0.22826504346045684, 0.7466767566952883], [0.7720216515443619, 0.7601766709115206, 0.34470549597759037, 0.15044798155142036, 0.29338482407109456, 0.5399340990496907, 0.09880721435296969, 0.6945599786905785, 0.3402122095908835, 0.3922266367159488], [0.9748518488689769, 0.46532052777718946, 0.15807162465332192, 0.25076445265536684, 0.24207081104266892, 0.25492834519244656, 0.11398032040758066, 0.589942495323773, 0.15069811493133334, 0.854739243005283], [0.47108320116469626, 0.633454589606096, 0.5807851276316013, 0.5620911144315653, 0.49021018012475226, 0.19333204625172673, 0.05692899983121391, 0.8163879999056705, 0.40266948110225353, 0.4214702525896702], [0.12748579038254382, 0.7877431601941883, 0.14298777870407198, 0.11831410936742137, 0.6115901608413795, 0.4202471320043434, 0.7492353531543803, 0.5097072078755559, 0.06040236018004319, 0.058775904351537456]])))-5.123303337885013)/np.round(5.285872857846552))*10*(9.813476091662654)+array_x/np.round(2.028512497519457)*2.035408568490361, axis=1))) |
np.mean(5.22027571447052*array_x+array_x+6.362704222867389*array_x*1.4848887327820335+10*(5.406184090647153), axis=1)+10*(np.sin(2*np.pi*np.mean(2.2670152263877905*array_x+array_x+1.6019641115049792*array_x*9.360782184133184+10*(2.1732265678170823), axis=1))) |
np.mean(10*(2.821049401010743)*np.square(9.487968295115705+7.737876845779598*array_x)-9.548233149906629*(np.dot(array_x, np.array([[0.4660266465096309, 0.8393812073289757, 0.3333519821111619, 0.12981558831150797, 0.9918374413448418, 0.6494273820631163, 0.03738390911278866, 0.8560819665259999, 0.4649497854754777, 0.5713879095786847], [0.31805511886735716, 0.28150754101108155, 0.4012137063098542, 0.8101788762870149, 0.24644590025455282, 0.7461948404296893, 0.2486382827706538, 0.4621390955307709, 0.6843815636450647, 0.6999749929985513], [0.8479657798047143, 0.5375675997107398, 0.4969660914969545, 0.5948999798467097, 0.6764025630653767, 0.6630084106770519, 0.7260708867399691, 0.8480897669343489, 0.30138190759601735, 0.14383606628549872], [0.5392490678734414, 0.48959101114501624, 0.42579831787754374, 0.9561737589309515, 0.4944086047645597, 0.4132563826426907, 0.19878952500900937, 0.2211998636128869, 0.677532809221302, 0.6602245618312017], [0.3221284228373025, 0.021962431653009262, 0.05191671066675407, 0.8940702954073657, 0.900721279507114, 0.753939427836161, 0.753464843076155, 0.2736874936689043, 0.43898573224690773, 0.7355372755223814], [0.7626999183871735, 0.009467395723795291, 0.6436553218729906, 0.936442398710413, 0.8291931475376586, 0.6320836256191639, 0.8356196622695625, 0.33162566755760425, 0.47278470484116375, 0.8138113362721053], [0.6643066567902046, 0.26115626014737736, 0.30498438599609834, 0.3876033773877712, 0.07301594700701541, 0.07047575675601747, 0.9831544453877185, 0.559598109708155, 0.09866984758921049, 0.5354383943355292], [0.23911470817485214, 0.22812403094942058, 0.5844572817817799, 0.718021301308856, 0.38298452308493014, 0.38384183407022154, 0.8596536643234999, 0.9650517579251614, 0.9808960601950556, 0.8230352020223253], [0.1341485929243985, 0.5432466248071827, 0.31628783172672237, 0.274230846357745, 0.557705647191057, 0.5221278174853453, 0.38730307894521177, 0.8675171817826199, 0.8227553071057766, 0.5380099509490749], [0.48962836457986725, 0.3196630560530084, 0.9224936499950873, 0.911265598315045, 0.3847346313601979, 0.06661634708413455, 0.7812999750470525, 0.7969798683110786, 0.09951466762531636, 0.1221670236675052]])))-4.4828895259519514, axis=1) |
np.mean(np.square(np.square(array_x)-2.513628086657282-2.8603884981836174-array_x+array_x)+np.cos(2*np.pi*7.714889433965277+np.sin(2*np.pi*array_x)), axis=1) |
np.log(abs(np.square(np.sum(1/(np.square(5.955522532773108-(np.array(range(1, array_x.shape[1]+1)))*array_x)+(np.array(range(1, array_x.shape[1]+1)))*array_x-5.306077130074538-8.877882176505619*(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)))) |
np.sum(array_x-8.500725158147173-7.109520338840002*array_x, axis=1) |
np.mean(np.square(np.round(1.7447968450943845+array_x+2.0373416517858245)), axis=1) |
np.mean(np.cos(2*np.pi*3.8853396207892428)+np.cos(2*np.pi*np.square(8.384587117686962)+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*5.641295392336438)+np.cos(2*np.pi*np.square(8.033938510406198)+array_x), axis=1))) |
np.mean(np.sin(2*np.pi*np.square(6.7975456226837)-(np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x*9.8512261959412)*np.round((np.array(range(1, array_x.shape[1]+1)))*array_x+1.5247306092986208)-8.156778911298845, axis=1) |
np.mean(np.exp(4.772951448593931)/9.308584631967873-10*((np.dot(array_x, np.array([[0.44944981888265034, 0.9943981672879274, 0.6794783809581638, 0.29279566947678737, 0.05246994877131661, 0.16549491225688817, 0.9691117083800691, 0.683896511652948, 0.8138840807250178, 0.7405146187008759], [0.4084431386187103, 0.5556357048449109, 0.24668254966545466, 0.772470172053477, 0.110516031534447, 0.05668819307759809, 0.17282158410328663, 0.9275862109012835, 0.8874807631351606, 0.919498347667743], [0.46080046001798347, 0.2661314307551238, 0.03797721048801239, 0.1912385833885516, 0.42797593600228523, 0.9172929085577273, 0.28193522815818495, 0.5718558761561917, 0.9290144012376652, 0.9596106498886972], [0.6615638940138514, 0.6304581731414968, 0.4660057434157354, 0.7908730749777327, 0.9855543499006079, 0.9962206484308237, 0.342850864631598, 0.09386425014363198, 0.4923814747472909, 0.2515169985983938], [0.12807659687557793, 0.11679319259972953, 0.799644746224759, 0.7647743211621929, 0.6760482402294365, 0.888285242887119, 0.7259474223785559, 0.9181909302490658, 0.6975002718920373, 0.2658808643535462], [0.030775352562644964, 0.025028478269740084, 0.2554668047148787, 0.8454553162801908, 0.3965058627749444, 0.3614090906323677, 0.11314434031283693, 0.7334130944717089, 0.7678493864286475, 0.619067018257545], [0.0029480428151745786, 0.9811524730885057, 0.08557828295036896, 0.7666971454250877, 0.7227258892532337, 0.3441873733559295, 0.7092983255504499, 0.04282007655002318, 0.564048213755146, 0.7473469337048223], [0.9586184447101929, 0.4810322318250939, 0.2860771713658138, 0.5697278249280615, 0.6178532328105237, 0.5356940314730718, 0.659581164485134, 0.8643630624819135, 0.905298973082577, 0.2726850456622878], [0.09368602172976737, 0.9263814909139179, 0.8611996492919007, 0.8323498317865542, 0.06964169052087466, 0.7501858885252576, 0.8163128053118436, 0.3800443145102872, 0.8448018181700034, 0.6227262889356727], [0.3259065221169334, 0.34109138528641636, 0.3696076627760726, 0.7597099085953678, 0.3663488740341345, 0.6810729443350726, 0.6028156175477866, 0.047828184367033844, 0.7996697310199646, 0.7635521447502847]])))), axis=1) |
np.mean(5.731432073809984/np.cos(2*np.pi*(np.dot(array_x, np.array([[0.4528412879131942, 0.5407933756883033, 0.7859317167798835, 0.3589526234614415, 0.334428132787687, 0.32625923342368424, 0.12488787210650265, 0.8022323281916821, 0.3636466161163885, 0.06603315274312482], [0.9745554738868929, 0.48287763903946546, 0.895157233135831, 0.011217033146071365, 0.3733931972533192, 0.131389499021475, 0.1478493632654473, 0.03917780605210841, 0.5753880300511025, 0.10552539103721659], [0.8167759701457228, 0.7614712550108239, 0.504780707713666, 0.7294267007118796, 0.3145314948266822, 0.03131456691001977, 0.3855880482236935, 0.8425953545411258, 0.7198499707016732, 0.19710931984063573], [0.8827324622236921, 0.31179026159600076, 0.5831952023192348, 0.9839185975930205, 0.3377172559117765, 0.9180505611376357, 0.715832402002616, 0.3089769609005222, 0.7519370633775082, 0.178978530354735], [0.11736687050503747, 0.14028197130786102, 0.9213477845646094, 0.38358908572457984, 0.06606256851566428, 0.4383487903044002, 0.4913817641083197, 0.40168132986233296, 0.48825602238322674, 0.44702920747610964], [0.822007200366933, 0.8489553000473545, 0.1837541625847331, 0.9034769141568932, 0.7541494436738638, 0.9682401275894107, 0.42650887074731436, 0.19280768721001007, 0.8764173486939301, 0.7228354497465623], [0.5626388364193716, 0.1409094479263433, 0.3247495136912074, 0.3342812539533302, 0.9773184166573059, 0.5330112760786591, 0.04205478705665722, 0.4085984398700149, 0.12953930835666483, 0.6342405488461523], [0.12104034129434971, 0.861070955053441, 0.8596697409182362, 0.7786251866840326, 0.7585634366022275, 0.8407881191198411, 0.1942116978688564, 0.2560318653022816, 0.9021254681191444, 0.6778884462212199], [0.053117296312107376, 0.9404472914365949, 0.5588856992437293, 0.9348247390079863, 0.87221529275326, 0.39398074436740416, 0.6873291898474494, 0.8335688317841853, 0.40946926775569326, 0.21782653030089605], [0.2780610918912072, 0.34096768316479464, 0.21306090666224042, 0.23799848759641473, 0.28987590817622255, 0.4526610209682047, 0.42783531421986076, 0.0683224505228015, 0.8670395477807261, 0.5493078974899387]])))), axis=1) |
np.mean(np.exp(abs(5.544283851199025-8.515432280784962*array_x+np.log(abs(1.6083205579841973))/np.log(abs(3.7812300665580683+array_x)))), axis=1) |
np.mean(9.997644501396222+10*((np.dot(array_x, np.array([[0.06717853362312887, 0.9250269463849199, 0.028489531710937044, 0.9300090087181726, 0.22876355793702408, 0.7447133648241305, 0.24710076380906243, 0.6126188023149388, 0.09543698849191884, 0.5631328584924762], [0.22772589686659095, 0.5994626710861418, 0.10104358293096971, 0.2806839409218491, 0.4770046311242365, 0.9377970642220251, 0.2017675243956344, 0.14724651888265883, 0.006256228125306684, 0.30175924087072215], [0.10850763820006382, 0.42016479680375196, 0.6082375271493952, 0.6756440766267772, 0.9656201360222278, 0.8347631915441925, 0.7571215772312372, 0.5836446480209158, 0.9582850050332363, 0.32345622917661565], [0.11107160784917847, 0.8237238268287435, 0.23262693094803888, 0.14427216786543784, 0.9078534817142572, 0.9819639005351972, 0.6354415527246103, 0.5136950402313862, 0.19682381606883137, 0.1308593840834933], [0.5427346231862951, 0.7644235457245955, 0.4977134104293419, 0.5022115728497425, 0.7560898743636902, 0.9976104749240812, 0.18622147525724186, 0.12575845757510218, 0.43245244779097225, 0.7299562753279677], [0.513599014017753, 0.7090602654447613, 0.5161129334999779, 0.6067336544403479, 0.6760865847591536, 0.3113226879527886, 0.9424705185658571, 0.2507951925793015, 0.3138149005153007, 0.3791532853931505], [0.8299496064388558, 0.6476057469169323, 0.7318795170684761, 0.5564487213310708, 0.7842273436309928, 0.8553738578912232, 0.5440048652273207, 0.16674760220088125, 0.14007892260863286, 0.2877222380261676], [0.8523163697165301, 0.9499604691982546, 0.7485127707744719, 0.7621089098320514, 0.32872883037437495, 0.544628441295667, 0.5623746994622402, 0.8648348663165185, 0.17690747951595243, 0.9309565082668595], [0.03229475807200011, 0.6538454034594063, 0.9347149536020667, 0.6638501842724511, 0.07927854373539178, 0.5288561858787793, 0.5948846398485151, 0.09247127185139936, 0.42489534537853235, 0.9170616684012871], [0.630933852426539, 0.9122105823091375, 0.4239970743778704, 0.8863019986053867, 0.3261733151517179, 0.08752031008844707, 0.29696386457438895, 0.4030569850677823, 0.013583914688860776, 0.0003443341136378786]])))*array_x), axis=1) |
1/(np.sum(np.log(abs(np.log(abs(np.sqrt(abs(np.sqrt(abs(5.394125145372905))+array_x/8.523383483845183))-np.round(9.330172799378484*array_x))))), axis=1)) |
np.mean(8.852628043296544+np.square(2.3611392795123507)*(np.array(range(1, array_x.shape[1]+1)))+7.6717358714405215*1.7582280442755138*np.sqrt(abs(array_x)), axis=1) |
np.mean(array_x+6.597554399846682*9.292972781600328*np.exp((np.dot(array_x, np.array([[0.04549840967900498, 0.23072484054373632, 0.9911462747450746, 0.6129747833292624, 0.7838285551357543, 0.3967885044021747, 0.19593042959863594, 0.5737774159598031, 0.047590646490323296, 0.8503683279728244], [0.9644472604667742, 0.856324184978486, 0.15614153548794985, 0.24882263292041118, 0.4765776743923352, 0.13442052402561377, 0.15639713010625478, 0.8155256243285628, 0.27281774545705983, 0.3368090825811987], [0.9253740601050758, 0.7874660870895713, 0.2537500602882248, 0.023084508730149, 0.6560260171355474, 0.8624137111284309, 0.13822447808973648, 0.4995986666721379, 0.459154828333723, 0.5065547823926564], [0.12212399356925119, 0.35613692743743264, 0.08763578253387083, 0.31937631671594047, 0.814332235411614, 0.27826054480714135, 0.3649918484722505, 0.676514449812532, 0.6654033186231916, 0.3438047367937197], [0.046234255847533934, 0.3774048197975256, 0.7670688397917955, 0.1453916305184817, 0.4178434253996848, 0.4071110901771141, 0.768657305041894, 0.6615006413601113, 0.5976215365777189, 0.41411257488656894], [0.4966719647587797, 0.09837106113804583, 0.3833897339505785, 0.8226532590317508, 0.7546745591666761, 0.06537511891755965, 0.8576597851303747, 0.0728025543969284, 0.2817162421871784, 0.8239161349740323], [0.9362548145727473, 0.35671509434674675, 0.2827125485401203, 0.22821896462952762, 0.1407432653051025, 0.45342126133065375, 0.09198587935589397, 0.21372902035331143, 0.19841582124028456, 0.8464978491368998], [0.5705336987337772, 0.5260529997797362, 0.14806658542094175, 0.49398485958021, 0.44796751290291015, 0.2688561659726155, 0.8015047096718173, 0.12743431254142523, 0.4601508195071693, 0.8256202122766216], [0.1786726391211091, 0.849242332378038, 0.48936640452390745, 0.666412880729861, 0.23536227297901258, 0.016845296557695844, 0.36844763869737485, 0.08467537058697183, 0.07345813480551755, 0.04334158746008843], [0.6317876641373095, 0.14486585550934017, 0.10880561307198244, 0.10420103472785314, 0.0033827631746199405, 0.7588291483414584, 0.8201955253120439, 0.7221671387819311, 0.21541955994698048, 0.012465931499593741]])))), axis=1) |
np.mean(np.exp(3.2888996129945283)*array_x*array_x+array_x+2.7796055103710184, axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(5.866852498785933)*array_x*array_x+array_x+1.8461002697613735, axis=1))) |
6.630554754580856-np.exp(np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x+np.sqrt(abs(5.812400534074264))+np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)) |
np.square(np.cos(2*np.pi*array_x[:,0])+np.sin(2*np.pi*2.6670424703884947)*1/(9.39281458951288)-np.cos(2*np.pi*np.prod(array_x, axis=1))) |
np.mean(5.85882873673239*abs(4.598283177752425-(np.dot(array_x, np.array([[0.3235145511695664, 0.7442361861253948, 0.39139722982657754, 0.07315162533824526, 0.7251783512576526, 0.16109485216010777, 0.5705452665106994, 0.9591294452281158, 0.08983118707756987, 0.6701048304079598], [0.15144274117165335, 0.19986514614889384, 0.8139349194281497, 0.2078499124333708, 0.9944518924862634, 0.9275116477592705, 0.11229198143133068, 0.6047189172801187, 0.23324693306597788, 0.3779236787736636], [0.1871963972368026, 0.18832193199640157, 0.2295533022590479, 0.05631704283464389, 0.6512474464444042, 0.683488446295752, 0.47024768896469094, 0.2189466821672419, 0.5908790807260471, 0.6986074214545651], [0.653552239028016, 0.6961760883087962, 0.08460071463307184, 0.009022919505256577, 0.3087895741895357, 0.2614817204659923, 0.13450055233549818, 0.5650161882478493, 0.3633224796340876, 0.15833045541856472], [0.049621600107005115, 0.81797737389063, 0.528734931114744, 0.7046441872239161, 0.7980435618948956, 0.9068128263030665, 0.1339400487100293, 0.46278271119838854, 0.18310179652132785, 0.31592163316620225], [0.8815387966709813, 0.8237151954501559, 0.4524958237924456, 0.7885530738733718, 0.1016545430719964, 0.9945965935555336, 0.8419190358024295, 0.5269637826217503, 0.3403519179959136, 0.38816191796935007], [0.3415165889529215, 0.3355571278822803, 0.2072965282124386, 0.047460333946031885, 0.08177045854347886, 0.32218890388984023, 0.8379269491239554, 0.056105147996656, 0.5680954923823708, 0.5169913685860822], [0.3410963256841839, 0.8182981573005574, 0.8830107099938957, 0.1942593123194628, 0.3390008089945761, 0.3121920693958592, 0.4945457896488804, 0.5329591193412077, 0.6953670868744267, 0.6455429061041367], [0.9039471896854925, 0.4547061983180257, 0.03297461246860112, 0.17327633172335033, 0.5646625855016113, 0.23318258900999167, 0.22612838394372614, 0.24644999968508197, 0.3539851213917897, 0.4706344641710902], [0.8261447695048786, 0.07082677782827917, 0.8977506337633201, 0.6790686895080214, 0.9118245017259308, 0.645360672679038, 0.04895379449796533, 0.08835001339612902, 0.4758974234907547, 0.5380209857540198]])))), axis=1) |
np.mean(np.square(10*(np.square(np.cos(2*np.pi*array_x))*array_x+2.2740930056146547-array_x+np.square(np.square(3.0244637502166176)))), axis=1)+np.sin(2*np.pi*np.mean(np.square(10*(np.square(np.cos(2*np.pi*array_x))*array_x+1.4478334447401324-array_x+np.square(np.square(2.405753425868083)))), axis=1)) |
np.square(3.7610512675744623)-np.square(6.541849471836602*np.mean((np.dot(array_x, np.array([[0.4424289912465664, 0.1509100007421036, 0.9451945677211077, 0.29606468006209397, 0.40968034447606483, 0.7604444868553503, 0.8927289040125597, 0.8434071894747737, 0.050635805847088156, 0.011100302247847527], [0.41504807240549835, 0.5999611569308151, 0.514754113719552, 0.17950004036647005, 0.007291395495891084, 0.10446321286844984, 0.4403102150494965, 0.7636949313973307, 0.04449681247867732, 0.18864390322512836], [0.676037686325765, 0.8547814427587036, 0.830349639259925, 0.030752839020511735, 0.8275156467837572, 0.6358375580904471, 0.12139134231580107, 0.544717833852577, 0.21042700280576343, 0.9640244278598142], [0.1486192193423499, 0.6103540889046954, 0.18384727375747423, 0.1430647571748267, 0.1323793485336452, 0.04685585362973954, 0.386584154041856, 0.6424804795670164, 0.8638007142591853, 0.5205361823649317], [0.24973181268994804, 0.4613452679821106, 0.7318679449604187, 0.46814842708622484, 0.7027931809220792, 0.40946499969653516, 0.12461825899158008, 0.5792721441632194, 0.5362329739389057, 0.14735649464719347], [0.46435459379461663, 0.4987498967128672, 0.827969214774871, 0.5295124657277388, 0.04591484153365788, 0.17585986339557602, 0.9994076018814669, 0.6320996395217484, 0.3060966049306233, 0.46989897418104554], [0.7751686446842705, 0.10312150379542473, 0.6497850915741649, 0.90480517983556, 0.7787992597609402, 0.6927835158139318, 0.9967521021887372, 0.3306159730887903, 0.6772109534877223, 0.5406837964152169], [0.05328243892783646, 0.54482941863927, 0.2652270083723487, 0.48129591982883946, 0.3780237220220467, 0.01688688006527428, 0.22035371854175534, 0.42520636592428296, 0.9039034878531086, 0.39723910744856594], [0.4666788791498645, 0.7442191039515627, 0.18669214029515013, 0.631376390252458, 0.32073639357196526, 0.43022025265130315, 0.04213658608063109, 0.3544861048982608, 0.05614237778855813, 0.8877785277084809], [0.989591532790956, 0.5869290446505582, 0.20110094215639074, 0.5599577351430322, 0.930024857949026, 0.6860358808967236, 0.08880856525678016, 0.20005470127195435, 0.8397844337357365, 0.012283693722736744]])))-4.192194779370225, axis=1)) |
10*(np.cos(2*np.pi*np.amax(array_x, axis=1))+1/(3.2820421960366266))-2.465866469134105 |
np.mean(np.cumsum(8.893783599414121+array_x-array_x*4.295218207880834, axis=1), axis=1) |
np.mean(np.square(6.9798072602060985)*abs(array_x-6.372093156087804/9.511513073742641-np.square(array_x)/np.square(3.769106348601534)), axis=1) |
np.mean(np.exp((np.dot(array_x, np.array([[0.0851073842800063, 0.022347632587613964, 0.2171701950900521, 0.9888628484625742, 0.13822644172247045, 0.2953495611282345, 0.27622790936187136, 0.5417610229519321, 0.6321874511852251, 0.24662136421323233], [0.45418379175549917, 0.8014608636857473, 0.2882026718498222, 0.12738987829820114, 0.41164696749246166, 0.6229918852468674, 0.17294397067235645, 0.4704625913398589, 0.31641852986190977, 0.015000350180244815], [0.17630717813575947, 0.8713528256573844, 0.4752669833086737, 0.16072760710904088, 0.9148808986292777, 0.32259642879707406, 0.770619730938101, 0.05008475585826344, 0.6436545236542329, 0.2399190640442862], [0.35675161499166075, 0.20718924518530046, 0.3288872012720665, 0.09512378485088202, 0.3355150986642528, 0.02162770692608429, 0.22177075698854443, 0.3877448278185561, 0.45475399880083067, 0.1483575032142691], [0.25927445088937284, 0.4586540631893261, 0.7191093503911071, 0.22716220395299802, 0.34498897055623523, 0.6241369876837057, 0.7981543428408773, 0.24540698813006945, 0.8356400344184042, 0.20436897456910919], [0.12158320247874432, 0.007440187031130252, 0.9476495268514513, 0.17620053118927803, 0.5471694002845228, 0.038044810334539214, 0.5659134090519755, 0.9678370815904462, 0.6754998089443665, 0.4536344003103886], [0.6661895036426854, 0.9571646920301796, 0.35111033497987953, 0.505173345311768, 0.7658831248056991, 0.6109313571364527, 0.415984364299984, 0.2649797002791566, 0.9427895369674312, 0.1246062476291192], [0.2983460755232582, 0.6536634813529008, 0.8158312930025983, 0.8695535862733347, 0.9063520966048604, 0.36720306199632147, 0.2608902749457429, 0.28234569913487173, 0.5950691100892198, 0.800505203441326], [0.07618017398561483, 0.6139545695796053, 0.7276346338814716, 0.5703933734661328, 0.5752338338099492, 0.6245799401235835, 0.10532196629735846, 0.109462223122471, 0.7295724234393389, 0.8292415756216556], [0.7852226686306066, 0.2235578197845267, 0.7171856644492629, 0.7108674893836697, 0.8080476220258416, 0.7987289454420042, 0.38726688702692025, 0.7051000799239736, 0.48237972917526684, 0.09840952963211269]]))))/8.719923888458077*5.743266747176628-1.4427573866386867*10*((np.dot(array_x, np.array([[0.1306050171702371, 0.46609786041789625, 0.804552819967426, 0.14368153671356143, 0.5345602924333992, 0.34927399089855915, 0.3844828700206978, 0.7698530173756026, 0.24412431181760796, 0.5123992116770487], [0.49802141180918347, 0.18298121856471572, 0.6126525412001608, 0.27139123720138536, 0.7061647250471322, 0.7476895224386803, 0.037959080527601885, 0.5912399741204704, 0.8668716283953279, 0.10595176707278797], [0.8986666854992988, 0.8039113082830774, 0.15683867718924438, 0.0458804893165603, 0.9697590874560958, 0.026723565588378406, 0.7421609152284239, 0.5021782604073638, 0.4713975364758747, 0.4058489873686133], [0.7365118795381603, 0.36601074836888803, 0.22697208095245858, 0.8518564443400957, 0.06921408740154356, 0.06247534947224409, 0.1341283214634642, 0.20303219043148912, 0.29464487518469784, 0.12386394548219981], [0.8212063364535522, 0.2413013230591724, 0.9600396703610131, 0.7669835278098291, 0.08961814473098784, 0.5539812239701479, 0.3963862512109896, 0.8548934724257969, 0.8470505590049394, 0.9663474581276094], [0.06667913375124623, 0.934892388682379, 0.5525159003510353, 0.7980367742270763, 0.5068795065236412, 0.2393965411628608, 0.9032253118636867, 0.26935330447518135, 0.5630316374751663, 0.655205699324576], [0.9353564767466971, 0.49398481398787564, 0.5394795709550283, 0.09716422635073008, 0.21219889078936838, 0.4089486949816651, 0.14297763477704384, 0.6372413621333841, 0.8746918674912993, 0.8291570837678844], [0.3385002985920116, 0.8668478877696941, 0.4776528463983104, 0.03346303407327034, 0.3531919717611586, 0.9850087470211859, 0.8700436316902, 0.6792085826781902, 0.3343315164950057, 0.3609541324139459], [0.7822286534328311, 0.9999595533904155, 0.9849670125079829, 0.231440261838463, 0.8540246156570162, 0.9768724210175712, 0.9063895214916649, 0.2722301755641351, 0.2283225888812126, 0.5907898790580146], [0.3103227808899134, 0.14754305429121262, 0.30732429137419626, 0.46755055241169485, 0.5447653701607503, 0.6989447873032579, 0.8887543948122545, 0.774221854389762, 0.48276697612320607, 0.6577717480823444]]))))-(np.dot(array_x, np.array([[0.21465281266687086, 0.9447113627919833, 0.7463857615452117, 0.5692019845085239, 0.3016695348559786, 0.5075560159174108, 0.5287536504873848, 0.18790742685312278, 0.7392701395183254, 0.36432601321435854], [0.4310255273694189, 0.7070912549807191, 0.22926218823733435, 0.6682940893614508, 0.26836331673737446, 0.6837715925086261, 0.28180196278491676, 0.9430886456477449, 0.6835569811399915, 0.7270830214332452], [0.8500246005891401, 0.7583282157882588, 0.4184069721691065, 0.18423069108401813, 0.6080858874253556, 0.057879873071687404, 0.5657319439377264, 0.937964756130403, 0.6298078876077428, 0.2599890245746107], [0.07898484183788712, 0.24301989486545694, 0.6319859560731623, 0.8800156238997531, 0.5987824684179627, 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np.mean(5.127332968827295/np.log(abs(array_x))-6.065854614004037-np.sqrt(abs(10*(array_x)))+1/(5.590861178313439), axis=1) |
np.mean(6.743656193904368-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x/4.065263768138618-4.888821749385896), axis=1) |
np.mean(np.exp(4.693995157123144+array_x), axis=1) |
np.mean(np.exp((np.dot(array_x, np.array([[0.5565781809656648, 0.0246718751233852, 0.10548488194294836, 0.7689652184666437, 0.2961479614604947, 0.9838692272423684, 0.7949683188297193, 0.72044886077377, 0.3463680855563741, 0.44004152927413787], [0.6706705034952873, 0.02193754943235604, 0.12131283766625744, 0.88985761245123, 0.21629085010819826, 0.3558486499793435, 0.7553523366344526, 0.798110686061024, 0.7074966845672906, 0.0753742616286206], [0.31060315278407036, 0.06606118371618364, 0.11963017999755843, 0.18732837962658233, 0.07894907550431773, 0.014235580648134238, 0.8457978853089709, 0.05646462774698846, 0.5507200033792784, 0.1258613579377702], [0.8087296526212191, 0.9760396105109351, 0.9812341870500983, 0.694003468254279, 0.7396508316180253, 0.35531680277899014, 0.977223998861183, 0.06708681605377587, 0.9363540367749897, 0.9119238350345285], [0.678016468611062, 0.5240429475192554, 0.7008202540351492, 0.6044373701414494, 0.9228537025597241, 0.03198119065658911, 0.7548009114580684, 0.21211738675270042, 0.009681036096286011, 0.3810674414463462], [0.6833160696623033, 0.23423400813946216, 0.4403752609629915, 0.5755027565513362, 0.9080566457463454, 0.10582889502708903, 0.9957614507296659, 0.37071346506968217, 0.4004732387187342, 0.9084932703297961], [0.2581596274753015, 0.6547101024437882, 0.08877805777299408, 0.8477170573873279, 0.8037243706320758, 0.820977815013217, 0.5834686265274834, 0.27046853093521317, 0.23599198752215889, 0.025990981567922233], [0.5170362210848011, 0.8229324885608299, 0.28032708343799007, 0.4735655267384137, 0.6113963622222883, 0.7907856446698918, 0.13063345501107082, 0.6288577660781514, 0.5550811559710231, 0.7882616720527884], [0.954648368758954, 0.3133180118487482, 0.7859467498858128, 0.8369803135032582, 0.5693246992882314, 0.9596287805459832, 0.7707104916257286, 0.07854301583319101, 0.5329087950991791, 0.8117160700372129], [0.17360973322762163, 0.5612401757277343, 0.12954987661251527, 0.7862842889227657, 0.7248177155774223, 0.3666319905649057, 0.7193968663050551, 0.21591518183962677, 0.7736113822172933, 0.2813254119590439]])))*1.652959147368064)+np.square(3.4251129190505836)/np.square(10*(4.748879434743499+(np.dot(array_x, np.array([[0.35561897345632587, 0.5615059878233802, 0.9636624230941303, 0.43945943806302934, 0.03790657974874956, 0.2818594511682314, 0.20272175925150682, 0.38773857905377995, 0.7937573551485984, 0.40271081669392794], [0.647912330836518, 0.605033183427129, 0.871464412740851, 0.8799257078655532, 0.7559249209394027, 0.14968176689995127, 0.24915263710645041, 0.4331274309755633, 0.9809613956006555, 0.7556536931730381], [0.42540016168776273, 0.4571822366947561, 0.30119455111457305, 0.349455669081543, 0.594743096704443, 0.506424681708482, 0.28872523944995543, 0.4103967168285597, 0.7245584714575989, 0.41801970541676736], [0.9434732698893505, 0.43231101611687817, 0.9153579613137778, 0.3511541353139692, 0.734372893355487, 0.3612476699861463, 0.669576765083032, 0.31229024392145743, 0.24498533426466207, 0.2251704815169867], [0.5634039305711296, 0.47142611450148, 0.7493019601296231, 0.8766317305440505, 0.41005273646038054, 0.06423333023346656, 0.05131713098377111, 0.030360947149059236, 0.042234691211071373, 0.24110288318939221], [0.6929146747408981, 0.30565379230933665, 0.26638775819871563, 0.7158369283163891, 0.3253607343221574, 0.8929262944769594, 0.44979276459789985, 0.9426121718271698, 0.14281638760785453, 0.37939789250162403], [0.52783290038576, 0.17055634482313942, 0.41409568091151683, 0.8322270413034424, 0.4252960969003299, 0.1932430513636243, 0.7649560493594082, 0.3212711622490583, 0.7452375469382224, 0.046637275686728374], [0.415106947718508, 0.99253578804783, 0.4509278923157508, 0.45995359242110223, 0.6467834707090944, 0.4146983321102421, 0.36201774816913, 0.2861221053861772, 0.4336050368421819, 0.5596704235965214], [0.830087542016323, 0.3696524386761235, 0.26597068573537597, 0.08007883367388324, 0.2770608459746604, 0.02305310718498288, 0.8193427164608051, 0.05467073718034532, 0.5656657883717057, 0.918131212513334], [0.8349977942109684, 0.3472479909792632, 0.3340625984483885, 0.5923094686334566, 0.059397330387452874, 0.22369436438518941, 0.20240849384912463, 0.05105584466877322, 0.3122622521715628, 0.636989048734548]])))-np.square(np.exp(np.square(array_x))))), axis=1) |
np.mean(7.553940178569383+6.936304707032866*array_x/np.sin(2*np.pi*array_x+2.4469834967949526), axis=1) |
np.round(np.mean(10*(np.cos(2*np.pi*9.11604604014393)+np.round(np.sin(2*np.pi*array_x-7.216110479393347))+np.sqrt(abs(2.87709237872732))), axis=1)) |
np.mean(np.sin(2*np.pi*1.6340926134125686)-np.sqrt(abs(array_x+1.4204915535437226))*10*(3.4192076492930354+np.exp(array_x)), axis=1) |
np.mean(np.exp(np.sqrt(abs(array_x/np.exp(np.sqrt(abs(9.318422880864905)))*np.square(array_x)-5.691679010879463+array_x+2.6832595078359156*np.square((np.dot(array_x, np.array([[0.3801975094691371, 0.7517730785477549, 0.3233254583018087, 0.9233767946359777, 0.7335542278610758, 0.4044955146393393, 0.7074226335944747, 0.7190247624645716, 0.07247195604240597, 0.749461320501356], [0.9446888878361599, 0.1709560574291238, 0.5310684004386667, 0.4320828169267198, 0.37446353210037897, 0.985263760597284, 0.44905728131614775, 0.12929319826151742, 0.3415809747591928, 0.20806183423471336], [0.361470968336817, 0.29529353132883795, 0.18487390844097584, 0.24310317091715283, 0.3048259457995608, 0.9995289936053157, 0.8118542849191364, 0.8796631604780943, 0.9815566334369135, 0.2288118374385919], [0.786167828790589, 0.973720044369289, 0.5975451039754116, 0.40537767183804585, 0.046613674699470975, 0.574790157769202, 0.6173846809699235, 0.2915492472498975, 0.24542768686780203, 0.23080667483968742], [0.3835950381377585, 0.24802482279995808, 0.0351059149997236, 0.08756965485632584, 0.04019788997399343, 0.9793163419135749, 0.17652973982590892, 0.4934434297982473, 0.07593454132366673, 0.20380244531072933], [0.6814992806693507, 0.26683219549974013, 0.7207282778348457, 0.8437178712641096, 0.702162255016941, 0.07814342578783051, 0.2576990791774906, 0.9638060221094407, 0.04478847804918862, 0.20250702894172246], [0.13641135958530803, 0.3866093045451837, 0.4936965575401242, 0.3046233489127447, 0.212723552751666, 0.5450337599696659, 0.9611587545826717, 0.7773512857526473, 0.26405560094080904, 0.632856591766152], [0.4691808624715421, 0.4074094273613419, 0.3028705567685751, 0.7334472769418778, 0.6357727410646091, 0.8741721961783497, 0.3220367310494189, 0.9024331141390142, 0.6319136743774908, 0.4891381539944626], [0.40160491249066044, 0.5720905874319815, 0.3864334602616748, 0.35841319002836514, 0.6361017249596264, 0.013905715103922467, 0.3936199816532576, 0.4744397416655203, 0.180757789639024, 0.27843970934399487], [0.8924668636597176, 0.26728259503531715, 0.5827416013737517, 0.45202022521022855, 0.8379470858105462, 0.017070844321318446, 0.3827050042416247, 0.21984051894228196, 0.15899185219492162, 0.7629238293199265]]))))))), axis=1) |
np.amax(abs(6.456282195521045)*array_x+1.2977404737773846+array_x, axis=1)*np.exp(3.707696208837417) |
np.mean(np.square(np.sqrt(abs(np.cos(2*np.pi*4.745220454860895-np.sqrt(abs(array_x))*2.7700006687597996/np.sin(2*np.pi*4.740420668048254))))*np.square(4.219930904110242+7.63873877683136*array_x)+np.cos(2*np.pi*8.880281306931085)), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.sqrt(abs(np.cos(2*np.pi*3.1371426077493436-np.sqrt(abs(array_x))*5.141888159468816/np.sin(2*np.pi*7.049803902770391))))*np.square(3.940857864342169+2.6004140009869876*array_x)+np.cos(2*np.pi*2.490585147180494)), axis=1)) |
np.mean(9.860068838097128/np.sin(2*np.pi*1.6976410465360567+(np.array(range(1, array_x.shape[1]+1)))*array_x+np.round(4.993841435619409)-4.534573996510762), axis=1) |
np.mean(np.square(np.cumsum(2.6762149696761175*array_x, axis=1)-3.021579906291806/np.exp(2.30946972774209+(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.mean(np.sqrt(abs(np.square(np.round((np.dot(array_x, np.array([[0.5460446289544882, 0.2812810753605899, 0.3852356772361649, 0.46473069862896466, 0.9659509722697953, 0.03873946118959248, 0.8067698980267276, 0.5103033997641326, 0.7014111467527343, 0.2899018938531944], [0.6080861237464639, 0.2954585111903233, 0.7365841236868086, 0.08492927137735529, 0.7397512541792057, 0.28131715024395365, 0.9817055618264308, 0.5413029587462768, 0.034346181752412, 0.5838381192147648], [0.0019124992750692016, 0.5440174843372196, 0.1934240813767909, 0.8643548960076363, 0.10926794466819678, 0.5262493435882715, 0.9719053201464027, 0.2776871908567057, 0.0017295694580000687, 0.5250092555321595], [0.0703723139951915, 0.9448467475761418, 0.3445501470557757, 0.6335281340003464, 0.7971589690678428, 0.11927256226458438, 0.3850321152425784, 0.8957316578802529, 0.842943343377308, 0.42352438370524137], [0.8754815842812377, 0.08616855726036798, 0.3857607828085814, 0.7550629762993979, 0.24089264860070958, 0.7659547247483816, 0.007142207856118632, 0.507155591649736, 0.20501379821414178, 0.12371947685893403], [0.1187983107753735, 0.3883923744699338, 0.870579959806174, 0.5965023362571453, 0.16660420881216498, 0.9575808557369692, 0.8013400832518148, 0.5136050714319597, 0.26457357252821523, 0.8108948632832714], [0.5470546023441033, 0.6105851352926073, 0.7101896814992005, 0.9331925708645986, 0.65315234863819, 0.7832276656471285, 0.5336876767146185, 0.03721173876743822, 0.07636583448861844, 0.42518522047435314], [0.10319458016934424, 0.9221710086630451, 0.626633202352888, 0.8563641546930323, 0.3883665743836091, 0.13638310957520494, 0.8405205343438544, 0.5210899602592095, 0.7038788230126622, 0.024050293417195223], [0.9574178997241691, 0.7439463785448222, 0.22440454676584298, 0.050052126998270996, 0.1507116210748185, 0.32409154481608715, 0.9580006477925345, 0.34213913311883815, 0.1757145188127648, 0.7478613907755928], [0.714759578599591, 0.4013845243251899, 0.557680461247967, 0.5397324026324789, 0.2768233787495321, 0.5674023467574301, 0.09304974684068357, 0.7695505928072705, 0.6727392001157901, 0.81769981062627]]))))+8.820346480306656)/np.exp(2.3438623081520475-(np.dot(array_x, np.array([[0.12997617301521636, 0.16651256500435307, 0.9385758286307981, 0.5139962706143555, 0.23529305326211747, 0.30310487148235987, 0.3892436495776789, 0.43160948020634093, 0.6837961697353928, 0.6160786050642095], [0.007811367468004149, 0.11421408411116796, 0.6456073672106027, 0.1505339587330573, 0.8470488695531438, 0.6802542694665147, 0.6374674937177826, 0.4216465647504968, 0.3661834174802181, 0.962192871735615], [0.4901160200952791, 0.4214299084299742, 0.8423832110009393, 0.39006571034535076, 0.0805340085935623, 0.020847239367054127, 0.23131492384020114, 0.4071209435996246, 0.1610422292524818, 0.4491953911535075], [0.724054501854137, 0.1463064064373032, 0.6346878637070159, 0.10316929008308506, 0.45896780182779573, 0.37039294450233895, 0.9633798506617643, 0.636603523524034, 0.36620197281493727, 0.6932202047999707], [0.35648860973089636, 0.14817550648629263, 0.36155843997029335, 0.4018281819607119, 0.9462034913160722, 0.07018871237074165, 0.8011873077369726, 0.3790512184484254, 0.8921933744019002, 0.7688167055079655], [0.23743825966817833, 0.5238438898960673, 0.6739373124610599, 0.3402836249358697, 0.9542183103445379, 0.21001578088675044, 0.26956599987411, 0.20960548324027106, 0.33419545603103085, 0.8686236953526864], [0.7981920964049108, 0.7458495491493236, 0.19862677910407234, 0.5643645549494994, 0.9439368343562602, 0.2615096696880854, 0.2986958193542464, 0.3787878948132757, 0.80012870646988, 0.9408569688821883], [0.7770222070183892, 0.6928507393982986, 0.4404127910038077, 0.07976784867144804, 0.809854331558018, 0.10712034329644204, 0.1827920154218402, 0.9650896678891729, 0.08848764939291653, 0.3407938196416923], [0.7918798470891568, 0.45711631502505234, 0.3315724690841033, 0.18629864040215527, 0.7386279560349565, 0.3008818003861996, 0.9409501534571519, 0.32502222770327627, 0.5744562639727492, 0.47321026104349206], [0.7747079498012077, 0.26036736355075163, 0.7865677060534335, 0.1312921992318561, 0.7926289945791983, 0.2727528660351617, 0.08104584883998078, 0.5231344834308714, 0.9842347298600438, 0.23609428848221303]])))-2.5602870397800728))), axis=1) |
np.mean(np.sin(2*np.pi*1.3278587785484057/np.log(abs((np.dot(array_x, np.array([[0.81374967375432, 0.6024213369634007, 0.08103804851340035, 0.8821630113906977, 0.3135190243546121, 0.14597815887776955, 0.10029515681095813, 0.6800867115365931, 0.04297231357423836, 0.22034764530054518], [0.22843428917955344, 0.8795926146776333, 0.3454189107175626, 0.8625853073328001, 0.0860392361931176, 0.2554819197275503, 0.48751374342788545, 0.1943932691685254, 0.7115234557343556, 0.3880316122027433], [0.7398991449864876, 0.6124202259981629, 0.01226935011406749, 0.7134726764008551, 0.21424306967972795, 0.45575997754131004, 0.06356613347761308, 0.285852057122004, 0.8321909869570886, 0.36263428743428083], [0.45748728670962735, 0.24679998721287777, 0.9298245923402096, 0.735573761548797, 0.47953018024361616, 0.746696904678925, 0.300385144611535, 0.29445380569426594, 0.470323123361326, 0.24402265290938951], [0.06947783737048796, 0.13793288783969737, 0.8939139936480747, 0.6743901309970003, 0.4066999613573714, 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