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BasStein

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250000-randomfunctions-10d

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np.mean(np.cos(2*np.pi*3.171534891328044)-3.7892861967555955/3.455557175383193-np.sqrt(abs(array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*6.039113904428469)-2.468744142664618/5.670257194750149-np.sqrt(abs(array_x)), axis=1)))

np.mean(5.175795091251042-(np.dot(array_x, np.array([[0.906555499221179, 0.7740473326986388, 0.3331451520286419, 0.08110138998799676, 0.40724117141380733, 0.23223414217094274, 0.13248763475798297, 0.05342718178682526, 0.7255943642105788, 0.011427458625031028], [0.7705807485027762, 0.14694664540037505, 0.07952208258675575, 0.08960303423860538, 0.6720478073539145, 0.24536720985284477, 0.42053946668009845, 0.5573687913239169, 0.8605511738287938, 0.7270442627113283], [0.27032790523871464, 0.1314827992911276, 0.05537432042119794, 0.3015986344809425, 0.26211814923967824, 0.45614056680047965, 0.6832813355476804, 0.6956254456388572, 0.28351884658216664, 0.3799269559001205], [0.18115096173690304, 0.7885455123065187, 0.0568480764332403, 0.6969972417249873, 0.7786953959411034, 0.7774075618487531, 0.25942256434535493, 0.37381313793256143, 0.587599635196389, 0.272821902424467], [0.3708527992178887, 0.19705428018563964, 0.4598558837560074, 0.044612301254114084, 0.799795884570618, 0.07695644698663273, 0.518835148831526, 0.3068100995451961, 0.5775429488313755, 0.9594333408334251], [0.6455702444560039, 0.03536243575549092, 0.43040243950806123, 0.5100168523182502, 0.536177494703452, 0.6813925106038379, 0.2775960977317661, 0.1288605654663202, 0.39267567654709434, 0.9564057227959488], [0.18713089175084474, 0.903983954928237, 0.5438059500773263, 0.4569114216457658, 0.8820414102298896, 0.45860396176858587, 0.7241676366115433, 0.399025321703102, 0.9040443929009577, 0.6900250201912274], [0.6996220542505167, 0.3277204015571189, 0.7567786427368892, 0.6360610554471413, 0.24002027337970955, 0.16053882248525642, 0.7963914745173317, 0.9591666030352225, 0.45813882726004285, 0.5909841653236849], [0.8577226441935546, 0.45722345335385706, 0.9518744768327362, 0.5757511620448724, 0.820767120701315, 0.9088437184127384, 0.8155238187685688, 0.15941446344895593, 0.6288984390617004, 0.3984342586196771], [0.0627129520233457, 0.42403225188984195, 0.2586840668894077, 0.8490383084285108, 0.03330462654669619, 0.9589827218634736, 0.3553688484719296, 0.3567068904025429, 0.01632850268370789, 0.18523232523618394]])))*(np.array(range(1, array_x.shape[1]+1)))*array_x*6.599705374528786/np.sqrt(abs(1.4482816441558217)), axis=1)

np.mean(10*(np.square(4.492066485544211)+(np.dot(array_x, np.array([[0.17465838539500578, 0.32798800090807967, 0.6803486660150015, 0.06320761833863064, 0.607249374011541, 0.4776465028764161, 0.2839999767621011, 0.238413280924058, 0.5145127432987567, 0.36792758053704133], [0.45651989126265535, 0.3374773817642399, 0.9704936935959776, 0.13343943174560402, 0.0968039531783742, 0.34339172879091606, 0.5910269008704913, 0.6591764718500283, 0.39725674716804205, 0.9992779939221711], [0.3518929961930426, 0.7214066679599525, 0.6375826945307929, 0.8130538632474607, 0.97622566345382, 0.8897936564455402, 0.7645619743577086, 0.6982484778182906, 0.3354981696758996, 0.14768557820670736], [0.06263600305980976, 0.24190170420148482, 0.4322814811812986, 0.5219962736299825, 0.7730835540548716, 0.958740923056593, 0.11732048038481102, 0.10700414019379156, 0.5896947230135507, 0.745398073947293], [0.8481503803469849, 0.9358320802167885, 0.983426242260642, 0.3998016922245259, 0.3803351835275731, 0.14780867669727238, 0.6849344386835594, 0.6567619584408371, 0.8620625958512073, 0.09725799478764063], [0.4977769078253418, 0.5810819296720631, 0.2415570400399184, 0.16902540612916128, 0.8595808364196215, 0.058534922235558784, 0.4706209039180729, 0.11583400130088528, 0.45705876133136736, 0.9799623263423093], [0.4237063534554728, 0.8571249175045673, 0.11731556418319389, 0.27125207676186414, 0.4037927406673345, 0.39981214000933074, 0.6713834786701531, 0.34471812737550767, 0.7137668684100164, 0.6391868992253925], [0.3991611452547731, 0.43176012765431926, 0.6145276998103207, 0.07004219014464463, 0.8224067383556903, 0.6534211611136369, 0.7263424644178352, 0.5369230010823904, 0.11047711099174473, 0.4050356132969499], [0.40537358284855607, 0.3210429900432169, 0.02995032490474936, 0.7372542425964773, 0.1097844580625007, 0.6063081330450851, 0.7032174964672158, 0.6347863229336947, 0.959142251977975, 0.10329815508513862], [0.8671671591051991, 0.029190234848913255, 0.534916854927084, 0.4042436179392588, 0.5241838603937582, 0.3650998770600098, 0.19056691494006806, 0.01912289744868978, 0.5181498137911743, 0.8427768626848423]])))+5.240333458715373/9.833971182508643-array_x/np.cos(2*np.pi*array_x*8.18543096042766)/3.2368394256731485-array_x), axis=1)

np.mean(4.17866394832904-abs(array_x+6.180699743994513), axis=1)+10*(np.sin(2*np.pi*np.mean(3.090711261136323-abs(array_x+8.460815713691197), axis=1)))

np.mean(np.round(np.square(7.272369281331698-np.square(array_x-7.61086248995374/6.987398308887333)*4.568271937982389-array_x/np.sqrt(abs(8.734927098198915-array_x)))), axis=1)

np.mean(np.exp(array_x)/-(np.sin(2*np.pi*7.720359225176488))+np.sqrt(abs(1.9044767723689389))-np.square(array_x)-np.square((np.dot(array_x, np.array([[0.7748654492251879, 0.030288410155906775, 0.40694640027158935, 0.0445096271486205, 0.24783864699076974, 0.19288070275794833, 0.21518257924215722, 0.3391184140345286, 0.2774180418359983, 0.9622799113275983], [0.3524071200093821, 0.8941725152929719, 0.1810416715209251, 0.7637468671882143, 0.061345536024642766, 0.4627612294693413, 0.005510440235525937, 0.810290763069436, 0.9504860360377839, 0.035107370827145656], [0.9338463614733145, 0.773853891491353, 0.35886157879279534, 0.9088765512228052, 0.29625727125090096, 0.409295312234762, 0.09671126219202164, 0.656938961900144, 0.02960082874227188, 0.48490527519903726], [0.6831918502525338, 0.8212376009491109, 0.14994138962166215, 0.7540903662487174, 0.7190770235550296, 0.5595705341197842, 0.584644597787864, 0.09127072085677812, 0.6004710432403803, 0.3815222070708323], [0.867580851341647, 0.3130988817359902, 0.5765950773635247, 0.4265713997011944, 0.8762619382503131, 0.253916666989189, 0.07880236030946042, 0.7486557209831396, 0.41289661690832147, 0.5890983591262915], [0.026389331713351116, 0.952109791397402, 0.8776183773849765, 0.6238679005772775, 0.017029842112618043, 0.3779764739828766, 0.9787965377421489, 0.6813303587402346, 0.16098786131341913, 0.4605967486545006], [0.966737743843486, 0.5347081744165934, 0.024614528029824556, 0.6197720842759777, 0.2712165309311598, 0.09884595187690437, 0.402908444731893, 0.5585230974322448, 0.017040718047802716, 0.5590468086359475], [0.5112031468396374, 0.7939083830987601, 0.42354981681539716, 0.5534504296698862, 0.7420093040571263, 0.8971458717819455, 0.3800345760236875, 0.6016334957831465, 0.6946109034163818, 0.043270676442196176], [0.8007605084861348, 0.37743018521539384, 0.6702389414436098, 0.47166406620494905, 0.7023391426917073, 0.7243309792357789, 0.19157101348646732, 0.6621742342864505, 0.8513234378820872, 0.23556720050497482], [0.7581834698938779, 0.6338251131552236, 0.9551260344725638, 0.2602465772917608, 0.15427777765800355, 0.3121210152816999, 0.2579587781979187, 0.7568327604735129, 0.7331093038467983, 0.6696849008553338]])))+5.725269569715896/4.034698177716854), axis=1)

np.cos(2*np.pi*np.square(1.4520763842529962))+np.prod(array_x*4.798424740365348, axis=1)

np.sum(np.square(8.769554763651062)*3.3525584695543165+(np.dot(array_x, np.array([[0.129156396826558, 0.07319729852497836, 0.15411716937731978, 0.24499020990495046, 0.5929463194364691, 0.6124252490967343, 0.23260187745708427, 0.4892254993109375, 0.48206185276773483, 0.45311076628522495], [0.8035452949126581, 0.47169306987563187, 0.758365495956166, 0.16648113180479807, 0.531967344073168, 0.8241047760631955, 0.17771658391625766, 0.46492216869766234, 0.6090793160357167, 0.18477457483728732], [0.7567656059114031, 0.07899624779631742, 0.9464155089077187, 0.46822149421813575, 0.3361230863654765, 0.11680700716264036, 0.4325966430678647, 0.6598040293907718, 0.09448727914238408, 0.3062690951677486], [0.8360175827727111, 0.6461810255983272, 0.2063730118576239, 0.16090979579308917, 0.9044850262008999, 0.28702022134098437, 0.1795001688986303, 0.9999640185426024, 0.3091918277779059, 0.12474018780936369], [0.4617576297529471, 0.4343451101984821, 0.926802475830331, 0.13420494747630474, 0.09113640612930685, 0.0929786709066801, 0.9042874658222364, 0.4493691779610771, 0.15060652815522568, 0.5938300983792906], [0.5376233556508321, 0.19840674774889933, 0.603038139426371, 0.3402312343169456, 0.23837076773086996, 0.5104006108990281, 0.2916492615163876, 0.22213992176121322, 0.8797224082996742, 0.1557625765734173], [0.2805899772848325, 0.3861383798498257, 0.27363831030002284, 0.2107106425777696, 0.771146097054588, 0.3290161587235815, 0.11251295769933845, 0.5408951624566958, 0.1254884972364103, 0.3575196074409279], [0.8818267622722838, 0.3691611951062197, 0.63250897428825, 0.5146255360086313, 0.1377248457779756, 0.6490695977908352, 0.8560349969338062, 0.09321247404534405, 0.7536800891297916, 0.6902112815189781], [0.8666605749971459, 0.10409193213346701, 0.3056953793966788, 0.6942914872263741, 0.022017978886650247, 0.7340756777304887, 0.857978579001121, 0.4533040523767575, 0.4343299034266521, 0.278468962069366], [0.005051870005087333, 0.18630351666635603, 0.9798641184223712, 0.8672860771213566, 0.2665046387489214, 0.9637488571210464, 0.36771261595328586, 0.2975853747511801, 0.15280763544988518, 0.901611069510674]])))-4.015922523468184, axis=1)

np.round(np.mean(abs(8.232813567160084)*abs(array_x-np.exp(array_x)-np.square(array_x)), axis=1))

np.mean(10*(5.908871385593642*array_x-9.744497181016996), axis=1)

np.exp(1.3062378903455198)*np.sum(2.3669606702780404-array_x, axis=1)+10*(np.sin(2*np.pi*np.exp(8.166763898701808)*np.sum(4.812430703979211-array_x, axis=1)))

np.sum(8.989894269911105+array_x*7.12388170631988+np.sin(2*np.pi*7.179563794176874), axis=1)

np.mean(2.8687730736768464-(np.array(range(1, array_x.shape[1]+1)))*array_x-9.859655032573318*7.383675623330619-(np.array(range(1, array_x.shape[1]+1)))*array_x*4.797993939020377, axis=1)

-(np.square(np.sum(np.cos(2*np.pi*array_x-3.1855631717056934)+np.square((np.dot(array_x, np.array([[0.8068743135696138, 0.2220930223168458, 0.5727805812768629, 0.32404271547176233, 0.6589164331843178, 0.5226096468822, 0.6254888050590407, 0.6176700243373133, 0.7364358207481377, 0.7988822867835662], [0.061877768458453164, 0.9483768496816916, 0.4064449124008854, 0.1700970121859906, 0.9219801187179109, 0.7839914153748908, 0.420353809437209, 0.5999145144686872, 0.226197261451772, 0.4131303277190288], [0.4009399651127772, 0.754040439417072, 0.4317229954617603, 0.7548712885119567, 0.7900794282391057, 0.385619861610651, 0.44006333135797315, 0.5207159938186551, 0.003510696044525141, 0.19505500018472055], [0.8471371472082502, 0.76094030253111, 0.07715158047521509, 0.6348976650794832, 0.020735353158175984, 0.5342036691279171, 0.16148162818349876, 0.8534523470310466, 0.6148152219392081, 0.06398226218648662], [0.6412538606417006, 0.8921982110925353, 0.7337064022325901, 0.11520230767278927, 0.6650932640952021, 0.6107026804586011, 0.6313518640485368, 0.7951621306965382, 0.304419139269518, 0.35546852734595125], [0.2018161374633567, 0.4571760830790651, 0.4098433616918413, 0.0841018432522711, 0.2426065143827414, 0.11992758304959594, 0.5545083973033134, 0.18726652626029783, 0.32560219913276156, 0.817053335823799], [0.5718492202749439, 0.5195832923153049, 0.9443627442890116, 0.43735139078251306, 0.8836410845930377, 0.5778540051170096, 0.40533548163208066, 0.3460720743301008, 0.9033779832128846, 0.565245357783107], [0.2517135644049552, 0.5194688609363887, 0.24977089352473847, 0.24066889673321867, 0.12914135191368392, 0.6893092537821236, 0.7810436077246344, 0.3236119729431979, 0.4092966507907204, 0.7930680917284937], [0.01630319946630754, 0.33283095496479487, 0.30759438411770046, 0.5434516951075146, 0.8948692396828983, 0.04051673614469664, 0.8427713553455266, 0.5571278058016171, 0.5876669265986314, 0.9569656367103522], [0.1282831064381995, 0.2766435649769482, 0.2300518096949531, 0.468839957890701, 0.5798298600121987, 0.7278629745496702, 0.05303920645675886, 0.4261294201663096, 0.9242743346141207, 0.46259277420988687]])))), axis=1)+np.round(np.sin(2*np.pi*9.778682062763133))))

np.mean(4.158931650954559+array_x*4.478755151011102+array_x+10*(array_x), axis=1)

np.mean(np.sqrt(abs(1.7264221571850542-1/(np.cos(2*np.pi*array_x)*np.exp(6.408634220312717))))/np.sqrt(abs(np.cos(2*np.pi*1.25608985790167)-array_x)), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(2.845827668921101-1/(np.cos(2*np.pi*array_x)*np.exp(6.0402034480192786))))/np.sqrt(abs(np.cos(2*np.pi*4.893086793441691)-array_x)), axis=1))

np.sum(array_x+2.1757616193302507, axis=1)*2.984031056873294

np.sum(np.square(9.498322623972912+abs(array_x))+3.54643227749399, axis=1)

np.mean(np.sqrt(abs(np.sin(2*np.pi*np.cumsum(np.sqrt(abs(array_x))*3.2052892824634287, axis=1)-3.2791008647743)))-np.cos(2*np.pi*array_x)*8.067867455696678-np.cos(2*np.pi*6.692459213037771), axis=1)

1.2563551098415777+np.square(np.square(9.892023885154584))*np.sum(array_x, axis=1)

np.mean(1/(np.cos(2*np.pi*10*(6.037014115837308-(np.dot(array_x, np.array([[0.011787739180724288, 0.9962678716348444, 0.4881966580554369, 0.37202476397191997, 0.19617209456397833, 0.8071922450885463, 0.705752719530148, 0.0015561979296321304, 0.7712266730402363, 0.11148274515070988], [0.9486326801840331, 0.3327360839555057, 0.4511027753265111, 0.004980905673753422, 0.8243926884444718, 0.3081682542302372, 0.556446876169447, 0.9266007970596744, 0.1562223832155636, 0.8673296129309216], [0.5002389308425548, 0.9244561740566206, 0.8223550501864259, 0.44298003065908886, 0.08871177071474401, 0.03019745488202563, 0.8742308098340758, 0.47428491745836876, 0.6636949079277515, 0.8816484142088524], [0.3046589914846971, 0.8957630183913475, 0.02753243942768835, 0.27992567873413254, 0.8117663950996238, 0.8532203183236466, 0.9448353953575863, 0.30301127246390347, 0.7802355856161365, 0.985130302648835], [0.5270060151117548, 0.2525962896380215, 0.038962315397181646, 0.7596530627838091, 0.13884538443846695, 0.3869426064309628, 0.30927788165325387, 0.41685067295365186, 0.905998994932563, 0.42594776122186195], [0.9127443126236229, 0.8054008597007135, 0.2883783320685043, 0.31808901905133224, 0.3698442246014255, 0.605647191620102, 0.6740625171413107, 0.9722298357062036, 0.9265160634970615, 0.6118717214256995], [0.04623877949911093, 0.5453076203927011, 0.403640221272776, 0.5447787254319155, 0.040176373259916964, 0.9957622074271772, 0.056958370419600834, 0.2726799343332931, 0.07212402893799752, 0.5476111060042707], [0.3518182965541563, 0.3752998428527162, 0.7891675208579388, 0.9040201155693187, 0.16569698311586423, 0.278976940059957, 0.4727286201965848, 0.11288167014198824, 0.38653808205160267, 0.30208804561488445], [0.9219763370334326, 0.19853412043549779, 0.7212113980733806, 0.2158932087534554, 0.6256583787620588, 0.5819907281404807, 0.4587242927095082, 0.8169073021819279, 0.22271719437819004, 0.47728022402490655], [0.9824176014231915, 0.183261789288234, 0.5454324548403714, 0.2931057833084715, 0.12380605950108436, 0.8515929808925421, 0.5776788415716316, 0.5788292635102961, 0.5453347372646765, 0.18446810682017512]]))))+np.sqrt(abs(1.9165787893835424))-(np.dot(array_x, np.array([[0.568633165586175, 0.09138901443541514, 0.6826418704197971, 0.05145716805952549, 0.1891511169716894, 0.29421779709416485, 0.27893121765987905, 0.8121022889603224, 0.28775064460174904, 0.07395790212188069], [0.06798269358489328, 0.1494135193849384, 0.3941919004624236, 0.19681423373603557, 0.23502235948749772, 0.1839756554953268, 0.29873539721452047, 0.504858728623104, 0.9713470716842646, 0.7919904418762828], [0.17252070346015436, 0.8028506904108322, 0.7100930298974862, 0.9057984098984114, 0.4347067335244227, 0.8670986825639548, 0.6980902525277356, 0.27309257611294324, 0.38977614848977926, 0.8384785896604684], [0.8077122798086224, 0.2455059727184039, 0.8362738906161136, 0.9457570719536152, 0.8777694586274829, 0.3812880277129903, 0.5411617955795399, 0.8459606423014996, 0.7164004275445698, 0.7091049401281583], [0.28525146110424493, 0.6108018899406755, 0.054461115748946964, 0.2971473995618096, 0.20278886619064784, 0.3870145742968444, 0.7382295862478772, 0.26054112777298355, 0.17891509131097527, 0.8420356753224081], [0.9150979639464029, 0.44621659632947264, 0.24449777185584276, 0.2523659018024208, 0.4161798877403149, 0.8024429375694436, 0.5614816808370013, 0.5773359468594568, 0.9781573156377867, 0.07767519686195612], [0.4750133933108883, 0.47940229511558097, 0.553973975768774, 0.16794023736286134, 0.1689221483990183, 0.4154485609508135, 0.6062172562914766, 0.3028025250340949, 0.497519204116432, 0.9867572743319002], [0.03043101738982701, 0.4658672858255014, 0.47390469734258545, 0.6918063887205457, 0.26640011528741636, 0.7999340674693095, 0.40099342002648575, 0.09516239261882609, 0.6300604990619585, 0.026393144399509683], [0.7168021936284159, 0.012140297760557917, 0.5966702023597915, 0.8708919794868603, 0.6984084272619435, 0.624343754739019, 0.4317911252873432, 0.33619551690675753, 0.3702955209441793, 0.3750264199817449], [0.5298836294296025, 0.1433300810670205, 0.8818819351339124, 0.6258755443056147, 0.37443622782909747, 0.9822121641145255, 0.8725275366252805, 0.6068161895353213, 0.3717488184535388, 0.36909385599884226]]))))), axis=1)

np.mean(np.square(10*(array_x)-4.574875779678284/8.627757624241799)+abs(np.cos(2*np.pi*4.500223386892673)+3.969157613194363*(np.array(range(1, array_x.shape[1]+1)))), axis=1)

np.mean(4.928938352432059+5.471185275413662/(np.array(range(1, array_x.shape[1]+1)))*array_x/7.928232646930112-(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)*np.exp(np.sin(2*np.pi*np.square(5.397698671466905)))

np.mean(np.cos(2*np.pi*7.213810218931249)+np.cos(2*np.pi*array_x)*9.533592973806538, axis=1)

np.mean(np.log(abs(1.5338859283533837))-np.round(array_x)+2.069709219651131, axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(5.54606067509117))-np.round(array_x)+4.147839373259398, axis=1)))

np.mean(5.778811653579995*np.cos(2*np.pi*np.sin(2*np.pi*array_x)), axis=1)+np.sin(2*np.pi*np.mean(3.4176875635630193*np.cos(2*np.pi*np.sin(2*np.pi*array_x)), axis=1))

np.amax(np.square(np.cos(2*np.pi*9.965298282898372))-1/(2.9705697218566636)+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.sum(np.cumsum(array_x, axis=1), axis=1)+np.log(abs(np.square(3.7204319584124455)))-np.sqrt(abs(np.mean(np.square(4.629163074342188*array_x), axis=1)))

np.mean(np.exp(3.989815928401601*2.367245904393959*array_x-np.sqrt(abs(np.round(2.8123028572775146)-(np.array(range(1, array_x.shape[1]+1)))))), axis=1)

np.mean(10*(4.186625066770651+3.5154822082164574+(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.sqrt(abs(np.exp(array_x)))/2.034002770763631+5.989918240321291+np.sqrt(abs(9.649763398169805))/2.620389101221983-array_x*np.square(8.94626501716355), axis=1)

np.mean(np.round(np.square(3.2282946768406413)-np.sqrt(abs(2.55785856248639-array_x))+3.121835561629332-array_x)-np.square(6.282396540153856/np.cos(2*np.pi*np.sin(2*np.pi*array_x)))/6.433901459145758*8.63060384751569, axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(np.square(8.074818639065015)-np.sqrt(abs(1.9877568521526183-array_x))+1.9105545033468474-array_x)-np.square(4.5098581959152115/np.cos(2*np.pi*np.sin(2*np.pi*array_x)))/9.126968879411939*2.019025062486772, axis=1)))

np.mean(10*(8.691295800079399-abs((np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1)

np.mean(array_x*5.692803429990118*abs(np.square((np.dot(array_x, np.array([[0.5130835411483123, 0.35610824679544173, 0.8192761480586795, 0.5117867028343176, 0.18866314395275452, 0.03170573378325958, 0.15299395117797632, 0.7297262823878592, 0.9859156852183389, 0.7243225364665207], [0.6841021501106309, 0.19555604923928516, 0.2906343584162858, 0.04351123243060284, 0.527927310000479, 0.28758326905780807, 0.5996938506466981, 0.50919791573742, 0.4255484643781251, 0.2174665553006464], [0.5320575593269047, 0.7537536008049095, 0.7294159692741273, 0.8655801151399123, 0.089474077142642, 0.37897500061134126, 0.8699662409948912, 0.4688279643914395, 0.08977781090074044, 0.30765677574331884], [0.3083187452384053, 0.40855794358288455, 0.6405301409259936, 0.12009341164721021, 0.5178135062077008, 0.10459447630592178, 0.18804752775005862, 0.35520133606688686, 0.556937411397443, 0.5032873231325624], [0.46005705779281547, 0.42921006404829465, 0.3429879951939834, 0.06076141937467339, 0.5979030234769301, 0.4993711533041363, 0.4872064766017197, 0.7283603005606499, 0.6537942618709064, 0.5240724016899062], [0.011945511323690483, 0.9528565031342866, 0.6082797063174833, 0.04347041132411278, 0.182396423779828, 0.4401544623991175, 0.8296597578605286, 0.08484605862306205, 0.42902571851748006, 0.3141481360735223], [0.6502810628691295, 0.8847027487140622, 0.5665619362273915, 0.6571680963957189, 0.4516303247823664, 0.925820723655512, 0.6273683753983099, 0.02570499502918644, 0.1658365584224275, 0.825737492657843], [0.08942186535113517, 0.7494879859476326, 0.08001295148634802, 0.5740082667343935, 0.16602427506326878, 0.996998355333622, 0.7746991381443226, 0.6043709998690417, 0.7981742348915024, 0.2605004197619075], [0.8040340516219602, 0.13179986733350824, 0.7751778080471918, 0.8309471673105038, 0.21872577690129968, 0.972935722207534, 0.15347596030372468, 0.7013361282347909, 0.7052753953314725, 0.9854532208947087], [0.13763634096581767, 0.672617922150033, 0.3461449394182462, 0.2532462573545379, 0.934873349302298, 0.018069167763773653, 0.8935049166584016, 0.34474149011344035, 0.5080158305161082, 0.4097474574670842]]))))-7.606051204168961)-3.6326962402258722+np.square((np.dot(array_x, np.array([[0.8233153417405125, 0.5152597105596881, 0.1035826907922206, 0.07531562914285916, 0.7973368227252503, 0.3420494102664723, 0.3921444905565925, 0.9892897696213884, 0.17775698697773978, 0.07483134447375117], [0.4010302394042764, 0.8586255241253675, 0.10182590055588414, 0.6028967875805774, 0.09795489899061749, 0.5049771624536397, 0.17471973265295726, 0.08653568416195623, 0.45378341831030133, 0.5423073881051861], [0.8228607468192273, 0.5763836679929532, 0.5863469352951418, 0.3177784414244954, 0.29746806189944, 0.2988271973979286, 0.0471555567076114, 0.28301202842448436, 0.7920115414358521, 0.7931631959912447], [0.7443527879805913, 0.6423085708990665, 0.8781550438250374, 0.865458682521074, 0.3823268523318032, 0.6652689077560975, 0.8853358991984259, 0.7398494147252377, 0.9251644534553729, 0.9773148910927173], [0.5078561055899011, 0.5965621399932542, 0.4579807949632896, 0.43550308690860695, 0.08982933507658808, 0.3673389667512993, 0.44733962840321384, 0.5965606514334484, 0.12621411930923276, 0.9813679799900986], [0.36155846245942125, 0.25545586983706603, 0.4098265350163608, 0.47970085016434216, 0.3429066262451358, 0.7690167948310811, 0.95319423682041, 0.2229124384178438, 0.045385616333122436, 0.5660045497172818], [0.6397677150763456, 0.8557450705316619, 0.9046240049929487, 0.01116648125450459, 0.17982460680896328, 0.3261642529201383, 0.3114543624670819, 0.4931837730226314, 0.14000808111874752, 0.8468781718995003], [0.5207779961540844, 0.9819423368641778, 0.6222657670208015, 0.40766915330506837, 0.33151075830279, 0.8654075064344626, 0.8781888274439438, 0.24355324134660772, 0.49035212633632075, 0.4320947829145333], [0.8660156276063637, 0.2282449821772553, 0.15096500769655274, 0.4491658675579091, 0.45312579816863285, 0.2976512526787829, 0.8118456635058693, 0.8205450582909171, 0.6930845818944277, 0.9527203152310205], [0.09013374421313691, 0.9452312013042192, 0.15289587876104194, 0.7239385462081479, 0.4453441866305, 0.5257300838549014, 0.38154501241633487, 0.3576993773832543, 0.6022106921127843, 0.5965188147686975]]))))-(np.dot(array_x, np.array([[0.7137767239352668, 0.7166259932637926, 0.029733336524887077, 0.5418518574910878, 0.6658172668067286, 0.7791708261392903, 0.8886723073235371, 0.32600582786672816, 0.3938052973722296, 0.09885289164458744], [0.44454794142717124, 0.9158217231681511, 0.29522782814371384, 0.2380629100310241, 0.16514506064924384, 0.5805767910694241, 0.5234164668673017, 0.4468762258804676, 0.2674736162527378, 0.9774277811793922], [0.16086401275177697, 0.2769773591831626, 0.31403168117385905, 0.418807830732858, 0.1802631542433889, 0.15367242076321674, 0.24228508787444836, 0.5246357745620236, 0.7646531495028456, 0.2776226851143422], [0.7884323054347522, 0.06722202661071242, 0.23422713180349863, 0.4219332424105604, 0.3435251842825333, 0.27706990944461374, 0.37122866517977393, 0.825103915085288, 0.18388638112275046, 0.43769315549554977], [0.6461723844146787, 0.41399453522376795, 0.903575287414244, 0.09957892885632702, 0.4100121246718047, 0.9554397798773574, 0.46600111335754646, 0.834370093901097, 0.06844718086676915, 0.7323455072848898], [0.27829350551372756, 0.07991619329357902, 0.6322372603860336, 0.8047176039622268, 0.13145937212966863, 0.06599488604289017, 0.9297590825913412, 0.40166244848538923, 0.20943529169672315, 0.9623305679713339], [0.9364653265056838, 0.2707102618925915, 0.1915828444991805, 0.12555313885528963, 0.409120819321405, 0.7734856548513942, 0.3471733556762394, 0.24905087153598626, 0.7808895591062971, 0.3822891040369688], [0.06272855302724623, 0.44385272179809276, 0.8601325286547671, 0.9880349199660033, 0.5775492403417305, 0.39623131040003057, 0.5606745046961693, 0.3833689199321234, 0.2876394235097576, 0.7353145134445792], [0.20885333211718027, 0.11589756778927152, 0.8211846181564642, 0.05342826087514274, 0.08563109320586482, 0.4369386182725997, 0.9998014490331347, 0.10703949905961074, 0.37945056672188304, 0.0988906031271668], [0.44118413296869496, 0.341347486376956, 0.6282104751259658, 0.9560388003441809, 0.19718933588230236, 0.6797848912533593, 0.9486190933145859, 0.7983165440558451, 0.8655415751306923, 0.4200287067335188]]))), axis=1)

np.mean(10*(10*(np.log(abs((np.array(range(1, array_x.shape[1]+1)))*array_x-8.038956590946452*3.553648607850225))+np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x/9.899364129627362)))*4.284730622039624), axis=1)

np.sum(8.58001076127951-6.4859354310461015*array_x*array_x, axis=1)

np.sum(array_x*5.976705096533142/8.405386015255395-5.963190679158666+array_x, axis=1)+10*(np.sin(2*np.pi*np.sum(array_x*3.7171481874375947/8.637054125818459-3.411655458680884+array_x, axis=1)))

np.sqrt(abs(np.sum(np.exp(5.445406961556522-np.square(np.exp(np.sqrt(abs(array_x))))), axis=1)))

abs(np.mean(1/(array_x+9.153241478422634)+np.log(abs(1.8670471453523736))-np.square(np.exp(np.square(array_x))*1.9550551124241888), axis=1))+10*(np.sin(2*np.pi*abs(np.mean(1/(array_x+1.1995580274940019)+np.log(abs(1.2839736702489986))-np.square(np.exp(np.square(array_x))*6.176341835974036), axis=1))))

np.mean(array_x+4.6548872321218004-np.sin(2*np.pi*7.91450972771714), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+4.049540955750494-np.sin(2*np.pi*4.174359718298964), axis=1)))

np.mean(np.round(6.615019733695302)+(np.array(range(1, array_x.shape[1]+1)))*array_x*4.35210744723023, axis=1)

np.mean(8.88983461115217*2.715870851639645+np.square(7.6039255337358105-(np.array(range(1, array_x.shape[1]+1)))*array_x)-5.48924058109653, axis=1)

np.mean(np.exp(5.854162750533218*array_x-1.6079472002273714+(np.dot(array_x, np.array([[0.09565288721333931, 0.9267084386020217, 0.9993610708310922, 0.46685725965057046, 0.8298837802664197, 0.19393458805627184, 0.4783673529685162, 0.1114090018091487, 0.6991622756042054, 0.38293158096762026], [0.14621316655834748, 0.22192197777129674, 0.13120094527502746, 0.35128401638753626, 0.8434933874117387, 0.4642157730054711, 0.6691162963022589, 0.955593883525169, 0.6963138328642843, 0.242824279191461], [0.08114309532907815, 0.8223897126838138, 0.2792469702010507, 0.6270547424473533, 0.9176788128848525, 0.5263063942479221, 0.5153784371217757, 0.38275700392334255, 0.48837658940570383, 0.6769905272580778], [0.6198744286436156, 0.8692984097153905, 0.9896987024059339, 0.4192549285267321, 0.9800936668690882, 0.6647115782403775, 0.7408070932661079, 0.39449869858886444, 0.7121603428785216, 0.8320903266908347], [0.6012747630110841, 0.118812814736583, 0.5842281225377521, 0.22723105476687555, 0.4169860702806959, 0.4489808711232681, 0.7048374107674906, 0.4204665193167326, 0.5039311634662187, 0.11699168505407775], [0.6820721840619755, 0.06233553969352157, 0.31261931039457425, 0.6650779818751699, 0.301245481423972, 0.007690493012202149, 0.44336417361362856, 0.6497701290924287, 0.8467329178711905, 0.8895003851505621], [0.24522965128691043, 0.9542856965164147, 0.372151915061658, 0.0441106339124715, 0.309095818663229, 0.17505369848718266, 0.2714445752776833, 0.5548197686323529, 0.5955250034890454, 0.15207825875547942], [0.3932451781304509, 0.6207792045402649, 0.5240851147854245, 0.557882562469137, 0.914578652294737, 0.7167030908984271, 0.30163854120612, 0.48122767329670324, 0.1009409932318871, 0.29790084223358904], [0.9378953172825425, 0.6934750091358407, 0.9913760899808953, 0.41759552523727406, 0.5259174541486545, 0.9642729361919938, 0.6214227866123074, 0.45823327076125175, 0.730690455162145, 0.6248936960228196], [0.9694682265466342, 0.9554493538925882, 0.7969919336952096, 0.2538350550515731, 0.5926995210705815, 0.9384922209843597, 0.0016084273062878518, 0.10571433272738817, 0.46805235650375965, 0.646426300809957]])))+3.0294389505656176), axis=1)

np.sum(8.917868503579449*-(2.6394049607375605*array_x+4.15488610791968)-4.13452026894937, axis=1)

np.mean(np.square(array_x-9.428700503250962-np.cumsum(array_x, axis=1)+(np.array(range(1, array_x.shape[1]+1)))), axis=1)+np.sin(2*np.pi*np.mean(np.square(array_x-7.0384216790782-np.cumsum(array_x, axis=1)+(np.array(range(1, array_x.shape[1]+1)))), axis=1))

np.round(7.0379166913627085)/np.cos(2*np.pi*np.cos(2*np.pi*np.sin(2*np.pi*np.prod(array_x, axis=1))))+np.sin(2*np.pi*np.round(5.714826390912901)/np.cos(2*np.pi*np.cos(2*np.pi*np.sin(2*np.pi*np.prod(array_x, axis=1)))))

np.mean(np.square(np.exp((np.dot(array_x, np.array([[0.02317320176180593, 0.21030491681457897, 0.9633757031020774, 0.39476911236797885, 0.09437180241890986, 0.3938984889042566, 0.1268704337861286, 0.8625573128346384, 0.8823526524900677, 0.9039359246271013], [0.022008983127335235, 0.8327701410088579, 0.38236518499746064, 0.17653681723033865, 0.2341912416805373, 0.4678452952294777, 0.29165749601271984, 0.8679839311905121, 0.12377312206174285, 0.5741660811217354], [0.3224816963428869, 0.11749151035548644, 0.22178846805910002, 0.6164455739907636, 0.7997439027996363, 0.607339802490966, 0.5765994191400103, 0.4551674636933777, 0.48652074746346907, 0.3124174220395428], [0.5493521340321222, 0.647659815875767, 0.5028257952778433, 0.14834229049553604, 0.791624780499774, 0.8236966012179241, 0.8390152201352484, 0.27703163407434084, 0.771209630137672, 0.7055446212974092], [0.34309842451535577, 0.8173474595781165, 0.6113227496307069, 0.648452025220548, 0.3440342480016456, 0.23168940556788187, 0.8710849245788563, 0.20110921295907702, 0.5173592850519398, 0.019873612422435905], [0.27710320969149826, 0.011751072256620532, 0.05476788787947895, 0.3444479882720154, 0.5510458861070637, 0.7969464745109307, 0.8038772604385184, 0.059406769354876654, 0.9714108185497649, 0.7559158116518137], [0.9866372980029573, 0.08203584143911768, 0.08739852643726942, 0.5034115740070785, 0.40051367418516803, 0.9629487563065362, 0.3033262562340151, 0.35342538717327865, 0.01524278181917782, 0.913452358981965], [0.7420825398325048, 0.24232793814013898, 0.46281637731698766, 0.12613633809279112, 0.14221981775752457, 0.6856798623063335, 0.42223815627251804, 0.589039321819921, 0.014868630482833156, 0.45409676634617113], [0.24357742694029494, 0.5923278392182371, 0.2539318655380681, 0.057786719377569296, 0.23960792438434964, 0.017526774345228757, 0.18708238989857406, 0.7137117417872949, 0.8557866187056903, 0.7905569719569067], [0.20782528124629263, 0.6522151935921096, 0.12367784183491348, 0.17254085481815584, 0.6661984338764809, 0.44414808165982034, 0.45992698606369986, 0.33070609637440385, 0.34602697222821244, 0.4476576575549074]]))))*3.840563065623442), axis=1)

np.mean(np.exp(6.404788405826996-array_x)/np.sin(2*np.pi*9.096224530134226)-array_x, axis=1)

np.mean(np.sin(2*np.pi*array_x)*8.550451487196852+6.0485066600151836, axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*array_x)*5.3315827025408575+8.247130833049884, axis=1)))

np.mean(np.log(abs(np.sqrt(abs(7.679599465022906+10*(array_x)-2.759860892494413)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(np.sqrt(abs(8.927225911573949+10*(array_x)-8.038137441410996)))), axis=1)))

np.log(abs(-(np.sum(np.exp(10*(np.exp(array_x)+1.1899710876039995)), axis=1))))

np.mean(np.sin(2*np.pi*10*(1/(abs(np.square(9.971372690385106))))-np.sin(2*np.pi*array_x*9.552215313601044+array_x+1.0000149065821016/3.7778596178422315-np.sin(2*np.pi*8.752435674980246))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*10*(1/(abs(np.square(9.663580493358724))))-np.sin(2*np.pi*array_x*3.203527842110963+array_x+9.013984593586038/3.2160966516236846-np.sin(2*np.pi*6.205459312624329))), axis=1)))

np.sqrt(abs(-(1/(np.mean(1/(np.sin(2*np.pi*array_x-5.5388344092132025)), axis=1)))))+np.sin(2*np.pi*np.sqrt(abs(-(1/(np.mean(1/(np.sin(2*np.pi*array_x-2.5182078278329847)), axis=1))))))

np.mean(6.085571238655993+(np.dot(array_x, np.array([[0.7371429294304833, 0.8306163051048975, 0.09427664722462592, 0.5735718521766399, 0.17803851628082934, 0.36907607139234877, 0.6681234681719559, 0.03193396525092651, 0.00034494409119278924, 0.9610306195449035], [0.4461638120702819, 0.05025165019558764, 0.05286206845658925, 0.9523573281560087, 0.9732723491403733, 0.3459325568366546, 0.18097697245903388, 0.4318017806695401, 0.03107298400036773, 0.5642000477910235], [0.2755011148783081, 0.47405186137359634, 0.27704767009577924, 0.4120716613969053, 0.7620788356628977, 0.38154900124473423, 0.1857389311914598, 0.9115229081923157, 0.7046490277637554, 0.10801941797961279], [0.2771905722040069, 0.48266330671916047, 0.6054701993744307, 0.36220722346537004, 0.8480587444756204, 0.369766943343109, 0.5814493508859025, 0.408685801848377, 0.939702781595396, 0.19620920814345288], [0.7021080797615981, 0.8032161081157346, 0.7332353007258607, 0.1193876803371624, 0.07912352932090005, 0.8983693920360152, 0.566230176608897, 0.7388112623960783, 0.8849856857055078, 0.2469563296763071], [0.4772891283342151, 0.9476076000242375, 0.40865169876626684, 0.48042607180467145, 0.457103737478865, 0.5484731784567192, 0.848029828879747, 0.40635429698667813, 0.3540848686025261, 0.6269749371023279], [0.10721828713176296, 0.9491928046061672, 0.18833115832182656, 0.3626632115338394, 0.7437798222039946, 0.5818100862177267, 0.2148940261142469, 0.6913200454902086, 0.8717691902167513, 0.09817161244802086], [0.5067603405114971, 0.2834399907116979, 0.17222971089633388, 0.5713108160447287, 0.9817326420206818, 0.9306144873029419, 0.9199034373955609, 0.8983176321760842, 0.809845474409947, 0.7203453655943632], [0.8825863650572607, 0.034074659457490775, 0.1691951580021167, 0.8329532152214628, 0.16007933998531831, 0.6017148659837018, 0.17178426639889932, 0.5415448925125017, 0.9400948457253399, 0.6100370060065725], [0.9763467215203185, 0.4122973889854572, 0.4303035849722616, 0.490599659215061, 0.49625862904426676, 0.1364103581252345, 0.4961307038114533, 0.8066930872748147, 0.06130301018818973, 0.3412092773356308]])))+(np.dot(array_x, np.array([[0.09417109748195984, 0.014549254777506793, 0.8449866953069078, 0.3437999049724715, 0.3027066962966335, 0.2079837617619762, 0.29878148911337676, 0.9813194044883762, 0.7440070884861493, 0.49617590916237453], [0.2104697919601638, 0.018593476826753608, 0.9549924934872229, 0.7086102477739146, 0.11836618193706272, 0.18068449283184762, 0.2164009971976214, 0.20515427129332375, 0.521520776894945, 0.3999960270579336], [0.8298308080565587, 0.9531291761977764, 0.5384552469858013, 0.13948214715433405, 0.3030499487144256, 0.5889476326873601, 0.272405403555824, 0.8024044537170854, 0.28334938673428567, 0.5708730368363736], [0.299882259866037, 0.06647325831539441, 0.247765660415737, 0.9590628153803494, 0.8786919570653057, 0.3336565756824902, 0.7405485593265658, 0.8329962918667964, 0.9008772264265148, 0.449234861190445], [0.5354934017990888, 0.35032187004124715, 0.19592247666871365, 0.4030012761778444, 0.5663963747271852, 0.5981682594694906, 0.9165076148000941, 0.6478296578022114, 0.35141237006253034, 0.6654520758928043], [0.4114554907243875, 0.14895170636719324, 0.7751322125937081, 0.15884564920619348, 0.7939069979649267, 0.8575317032612841, 0.7382472821216968, 0.6236007391349674, 0.057004222465352306, 0.9334408811291056], [0.07114559983470636, 0.7944443021470371, 0.13830086837648237, 0.0327711192679887, 0.9300944640601766, 0.18606330129669368, 0.5372782132789548, 0.11347559873391877, 0.6362588584578407, 0.8683034826818372], [0.22803037474604848, 0.29984150781931984, 0.6216569637228124, 0.6104522900929213, 0.8067392873041721, 0.8257410890505928, 0.010417986719260752, 0.8289982395850193, 0.6148144945761811, 0.2971609661977501], [0.006422504307683763, 0.45962761913658245, 0.58639059726825, 0.37273835034905356, 0.7458901750949104, 0.07721715038615085, 0.16736652831595944, 0.8666996763960444, 0.921687786475068, 0.45001718416863234], [0.5566649461004366, 0.6892769287527833, 0.4143987522907301, 0.7799810342964971, 0.525234527373945, 0.8442459825265904, 0.6732405619468824, 0.7412039761971244, 0.05882409254440879, 0.753023027881549]])))*4.401371006752675*7.96417085610562, axis=1)

np.mean(np.log(abs(3.955723550464599))+array_x/(np.array(range(1, array_x.shape[1]+1)))-10*(array_x), axis=1)+np.sin(2*np.pi*np.mean(np.log(abs(6.06839452928097))+array_x/(np.array(range(1, array_x.shape[1]+1)))-10*(array_x), axis=1))

np.round(np.sqrt(abs(np.sum(np.square(np.square(np.exp(np.exp(np.square(np.cos(2*np.pi*7.608788915659586))))+(np.array(range(1, array_x.shape[1]+1)))*array_x)/1.2278997036263655), axis=1))))

np.mean(np.square(np.square(np.exp(np.sqrt(abs(4.806959356865639))-array_x))-2.7666380855625174), axis=1)

np.mean(3.1220536435832225+np.log(abs(np.sqrt(abs(7.1322194517378925/np.cos(2*np.pi*array_x)*4.14576443854324)))), axis=1)

np.mean(1/(np.sin(2*np.pi*6.586364840089608/5.412708980021528+array_x+9.33588928465536*array_x)), axis=1)

np.mean(np.cumsum(array_x*3.0397397146394844, axis=1)-np.square(8.408797796965569), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(array_x*6.0839476426954615, axis=1)-np.square(1.6439378631654826), axis=1))

np.sum(np.square((np.array(range(1, array_x.shape[1]+1))))/6.91262434681797+(np.array(range(1, array_x.shape[1]+1)))/np.log(abs(array_x)), axis=1)

np.mean(10*(10*(5.753090044159928))/np.sin(2*np.pi*9.283373617145664-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)

np.mean(np.square(np.square(7.336064606331081+array_x*3.3941857984950303)*np.square(array_x-3.426682647183403+array_x)), axis=1)

np.round(np.mean(10*(np.round(8.69481876396712-np.square(array_x)/np.exp(array_x)+np.cos(2*np.pi*np.square(array_x*5.625459846517301)-2.0469996523316154))), axis=1))

np.mean(np.exp(np.square(3.7307215392732176)*array_x+np.sqrt(abs(5.096451844831753))), axis=1)

np.mean(np.sqrt(abs(array_x/np.log(abs(array_x))*6.513548295673774-1.1159333241447955)), axis=1)

np.mean(np.square(3.600253835942835-9.156911224542316*-((np.dot(array_x, np.array([[0.8759935980743886, 0.4410794023682435, 0.5323709528458608, 0.2723347866640553, 0.1829244565875643, 0.6457912652894194, 0.531708709580533, 0.8266144755327182, 0.6804343787558941, 0.17235453529118938], [0.282700920691099, 0.8288809011924607, 0.08889045584940825, 0.6454797768002745, 0.32538072214531333, 0.5927561513255296, 0.6876045596113562, 0.9706414742625145, 0.7504293099089354, 0.5225972689795142], [0.8996170548417898, 0.3089785040763666, 0.0435023301961589, 0.43617951668257793, 0.45564264609289906, 0.8575323450405424, 0.2808665305661088, 0.8494305113763515, 0.4502879757876779, 0.8823357267360088], [0.0944165109812094, 0.29684877244283936, 0.508784639855427, 0.45917013051654576, 0.7344696431821384, 0.10309772779840454, 0.9071338879765294, 0.853480231203158, 0.025788517773085218, 0.033791733737220664], [0.9645835480475298, 0.3983511802871601, 0.44911484549614966, 0.1245507008814658, 0.1084387234033033, 0.809907288483579, 0.13363062760361466, 0.9484464911815629, 0.18572818050704032, 0.007999752778197089], [0.3170893978512057, 0.4566764584795271, 0.49718205528057324, 0.32652415414386005, 0.9902110159196237, 0.3468981801508446, 0.03165278126978688, 0.013296294844655887, 0.8147048902993764, 0.5216267395722158], [0.9946686378304264, 0.43737650937498795, 0.14229218161153256, 0.5916026433196274, 0.41862062526105626, 0.20742512528552282, 0.6223457192644302, 0.6357926659806066, 0.3443704701021971, 0.11103474722397011], [0.9264377656558045, 0.5954295854394267, 0.16969662741175617, 0.2976886003116743, 0.9637190503182146, 0.5645873859114761, 0.5651278369697563, 0.5124642603440656, 0.8344599697737352, 0.9708386061717563], [0.6642351435807533, 0.7901304955554757, 0.9077466802245336, 0.30458019769133615, 0.7712628094832484, 0.7290143213157628, 0.1707360805675845, 0.9773671636894503, 0.06289450728494839, 0.5794191812599843], [0.5562635695151498, 0.5358548306372976, 0.4172518994474842, 0.9108500143866981, 0.6157395080192116, 0.05452866738930651, 0.9923877699840614, 0.4753986290719633, 0.07714139330797609, 0.8401756698004745]]))))), axis=1)

np.mean(8.466081731516674+array_x+6.14673769447765-array_x/np.sqrt(abs(6.410200013168679)), axis=1)+10*(np.sin(2*np.pi*np.mean(3.2319724162417773+array_x+8.548424605855459-array_x/np.sqrt(abs(7.232769801817313)), axis=1)))

np.mean(np.exp(4.002239424145777)-(np.dot(array_x, np.array([[0.6746871546639971, 0.2070150156814673, 0.7857111882337645, 0.8754694774459341, 0.2552172543207294, 0.11490137614844409, 0.6630273993788887, 0.7068296467095977, 0.048087658091848895, 0.38236070812537115], [0.5497643079118211, 0.8622202965227295, 0.3218157849447677, 0.6722314274529292, 0.5063903735785475, 0.6418744877715156, 0.044242723212528756, 0.33354038988616064, 0.3195285144794048, 0.017537681018440887], [0.467437832122792, 0.22107711021035947, 0.6048229253368534, 0.2067459580035419, 0.9307257842260349, 0.13351320219118878, 0.4832745598743535, 0.2039114579930006, 0.27275833267681693, 0.9452542435439798], [0.901647477071386, 0.16839920905048555, 0.4140286961641223, 0.322448331297744, 0.15492313417679493, 0.09070672591339057, 0.21969168243068127, 0.4968327833923467, 0.5754845368654256, 0.1959535554804861], [0.12249339834285733, 0.2292147918947831, 0.40272132618848, 0.4398489470807815, 0.4208465231929863, 0.21472643503242728, 0.5355905190932735, 0.31981352756525416, 0.07878052292818749, 0.6495015957820416], [0.5884525834596764, 0.848274897721781, 0.10038595388376259, 0.5576036836177644, 0.9817773905275801, 0.06110192314404417, 0.3859142164266912, 0.7403517086031112, 0.9137351464729093, 0.5672656389009345], [0.09690725391636656, 0.4245381044796469, 0.2081409883585763, 0.7320016707448643, 0.14000458529183268, 0.31684531327097165, 0.5924310334265238, 0.9805808098905043, 0.5394355542504058, 0.1895613982766977], [0.5619676900701387, 0.7871433168188037, 0.19980913683888712, 0.22285644345105626, 0.12484331291874595, 0.7953398122919947, 0.6085731244062011, 0.8606616242197698, 0.9269635140925381, 0.07970618540628782], [0.11109869943192352, 0.6481103575070291, 0.38368863301130895, 0.27084329218482306, 0.38936020641373603, 0.41862409096936715, 0.9041074772036489, 0.16780273055119044, 0.0010190015767813643, 0.432410382997267], [0.03181791756689489, 0.5736495339181942, 0.5915102333232032, 0.5432048159690888, 0.12341607301959101, 0.5867650113057745, 0.2007142048449817, 0.2204982541660827, 0.6889768336396768, 0.9640625072162002]])))/np.sin(2*np.pi*5.94010588752424*(np.array(range(1, array_x.shape[1]+1)))*array_x+2.9140821145948657/3.8620989146557045), axis=1)

np.mean(array_x-np.sqrt(abs(2.5334541499377554))-8.83101304223714-5.0951359145532065*np.exp(7.5227231974971795-array_x-np.sqrt(abs(6.318038141963205))), axis=1)

np.mean(np.cos(2*np.pi*3.911072589647454/np.cumsum(abs(np.round(1.9316360689681622/array_x+8.272833567734578)), axis=1)+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*7.971446716261158/np.cumsum(abs(np.round(3.7296403150811637/array_x+1.8029795425880843)), axis=1)+array_x), axis=1)))

np.mean(np.cumsum(np.square(9.28720640899031)+9.88874037589232*array_x-8.792557506518548, axis=1), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(np.square(5.872244134282613)+2.5517003859702436*array_x-5.186846604297971, axis=1), axis=1))

np.mean(10*(np.cumsum(np.sqrt(abs(np.round(array_x)-array_x-abs(7.559552724132466))), axis=1)-7.9350431447692245), axis=1)

np.sum(np.exp(np.cos(2*np.pi*7.5354201193761945))-array_x, axis=1)+np.sin(2*np.pi*np.sum(np.exp(np.cos(2*np.pi*6.8296345017158435))-array_x, axis=1))

np.mean(5.225896090534747-8.904655202114562*array_x-array_x, axis=1)+np.sin(2*np.pi*np.mean(3.9970286950711618-8.96133922959165*array_x-array_x, axis=1))

np.mean(abs(10*(np.sqrt(abs(np.round(np.square(array_x+np.sqrt(abs(np.cos(2*np.pi*5.190368093503703)))))-np.sqrt(abs(1.091909108737036)))))), axis=1)

np.round(10*(-(2.023285632846567))-10*(np.sum(np.log(abs(3.9528160659886415))+array_x*9.932462709217772+array_x, axis=1)))

np.mean(array_x-4.7975322441763755*array_x*4.7888007763853135+(np.array(range(1, array_x.shape[1]+1)))*7.249724109637262-9.701454806127398*array_x, axis=1)

np.round(np.mean(np.square(np.exp(2.9057013390939734)+array_x/7.519183754425794*8.118499181443498+array_x), axis=1))

np.round(-(10*(np.square(np.sum(np.square(np.exp(array_x))+np.square(-(9.637590304866068))-array_x-np.sin(2*np.pi*4.614380830854333), axis=1)))))

np.mean(np.sqrt(abs(np.exp((np.dot(array_x, np.array([[0.658257778552691, 0.037179033221872104, 0.3441864617773833, 0.2670150551080781, 0.8867779108934798, 0.14053079401965451, 0.7256981541643092, 0.8780966629529128, 0.2193481113874889, 0.15681730432672691], [0.5489331137054699, 0.3777093858726648, 0.5073043130075007, 0.8204785655095178, 0.7933013561187656, 0.6605332902288528, 0.13619176383854525, 0.03733314873252669, 0.3353387996448194, 0.09320917741515322], [0.41540729122798314, 0.2805894122224537, 0.7903347760189523, 0.8392205377607458, 0.9779552860493268, 0.9707012066286492, 0.02151159948373671, 0.410706701223604, 0.9984863255964821, 0.3524757253280294], [0.4630943680866544, 0.7748201433523242, 0.7370964373248163, 0.8336091720729656, 0.26517630650845503, 0.020583878972307845, 0.6260517069044674, 0.6904277723038275, 0.5375852924606106, 0.1389740310934241], [0.28051358610462396, 0.11255691605069851, 0.3464709273102704, 0.18569382854497507, 0.4909352583636891, 0.3553635057316876, 0.15739718747056786, 0.3576780191472966, 0.8827913015696883, 0.2623599837491878], [0.3940904069355595, 0.1728081319804281, 0.7966295479596555, 0.6211175141818187, 0.003918251823730068, 0.4488795078868707, 0.9769969229650122, 0.9181303101450793, 0.9584431796998604, 0.9289759577839172], [0.6279228281173356, 0.3281099811006488, 0.7808749309472962, 0.004237331772400266, 0.45571551829287205, 0.8928516493171758, 0.2696474951189869, 0.07570850054734168, 0.1036510172444326, 0.42505379211861605], [0.7626991692120406, 0.48757048407503745, 0.5006148150032423, 0.5626192907300324, 0.36707505744305413, 0.31860035744921533, 0.10819826218607953, 0.6741762926803891, 0.20233628633861045, 0.28878553434004517], [0.947157511158908, 0.5720843377756729, 0.8469102691551258, 0.447594651569703, 0.6326302538485707, 0.36998146505118823, 0.8110372427207408, 0.03438121627872481, 0.7854338974879524, 0.8371857521570609], [0.8233794355276168, 0.828933344888204, 0.9143082541924665, 0.5234944958611777, 0.6708633913656941, 0.337107090682243, 0.4977538698168875, 0.32313858606298873, 0.6118701232539153, 0.4108100166140448]]))))))+array_x-9.396286586147408+np.square(array_x), axis=1)

np.mean(1.9262393281485575+array_x/6.440019319982335*4.683526681571703+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(2.2118249084278374+array_x/9.605895070058509*2.146836393344074+array_x, axis=1)))

np.exp(np.mean(np.sqrt(abs(np.exp(array_x))), axis=1))*8.648139387743209

9.767580661692923-np.sin(2*np.pi*array_x[:,0])*np.square(np.prod(1.2112757187655574+array_x, axis=1))

np.mean(5.6190229309519015+np.sqrt(abs(np.exp(np.round(array_x)+np.round(8.136451942673371)))), axis=1)

np.mean(6.411469166370706+np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(6.59822623930655+np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)))

10*(np.cos(2*np.pi*1.5517539587547224))+np.sum(array_x+array_x-7.689456259088109, axis=1)

np.mean(np.sin(2*np.pi*10*(array_x)/7.644878277767826)-np.sin(2*np.pi*np.sin(2*np.pi*np.cos(2*np.pi*4.460791184783079)))+(np.dot(array_x, np.array([[0.34340135388504267, 0.6165716608533478, 0.6823527928860759, 0.265751871110244, 0.900785328816501, 0.7762465103774615, 0.6269660455641977, 0.12374244642556542, 0.4324064122315241, 0.8335039440150704], [0.1992336859742304, 0.8602910108091687, 0.08722599402206865, 0.6478223049998667, 0.054988226945858476, 0.16796244286749917, 0.7899288298678413, 0.9606221924433525, 0.06044763939839881, 0.5645597572933712], [0.639783976079675, 0.20619030681178585, 0.7950191889649653, 0.6833282125095628, 0.21553384673960563, 0.5694873561962787, 0.10650841887982454, 0.2836664916759446, 0.9474346620572223, 0.3740380802810034], [0.15404949520452704, 0.6605622632909057, 0.34291048847420214, 0.39116056242666697, 0.6095114626357333, 0.6241776249046055, 0.11027737046973318, 0.1346585300772113, 0.6678942297773949, 0.6547074196415177], [0.43000664049860204, 0.05439553322847568, 0.9791922421509468, 0.14492857999793485, 0.41874314531394774, 0.2822357286490982, 0.05917280598871588, 0.5975817116218292, 0.37426104142951633, 0.5715640857525216], [0.7784775605128925, 0.8374938800697627, 0.18409325814802058, 0.7602415003886411, 0.2947693929882613, 0.7180214926862368, 0.9047573703794733, 0.457661988761051, 0.8345553974941005, 0.9255988148764621], [0.8422010079719792, 0.9190081179119822, 0.7019636011877305, 0.2232118987353784, 0.0926401974331249, 0.11708271458145447, 0.5844230181524681, 0.27596852847640196, 0.9725357229776156, 0.36908623276139496], [0.051884783707495385, 0.5595296940660761, 0.10352182810822419, 0.23240258662806468, 0.5670045815689017, 0.5442726581671024, 0.5396465982137691, 0.8660100801570225, 0.10083661466457017, 0.674488255790878], [0.842434967347895, 0.7442354969688459, 0.30808857473280093, 0.16400732227296422, 0.5526386826548262, 0.9700878321957087, 0.6717143960131339, 0.6717331343913615, 0.7595036520530996, 0.05982883105057524], [0.9193304721986068, 0.6323747414826182, 0.16319125725785733, 0.30236557177767465, 0.10795532886321457, 0.9789642836967182, 0.26234644692801923, 0.3546358889236535, 0.39126589636804654, 0.44371103262619105]])))/np.cos(2*np.pi*6.23257335256775)-7.243284869154646+np.round(array_x), axis=1)+np.sin(2*np.pi*np.mean(np.sin(2*np.pi*10*(array_x)/2.914272502328333)-np.sin(2*np.pi*np.sin(2*np.pi*np.cos(2*np.pi*5.093449510896646)))+(np.dot(array_x, np.array([[0.28079920688235493, 0.93679186736348, 0.9223510435045108, 0.6161085478500287, 0.5847434982498145, 0.01587314476678603, 0.7312319923450282, 0.9563592007467695, 0.5845713217895001, 0.528133178379817], [0.7746124423241169, 0.7972932503335755, 0.4732968808542384, 0.6960452946545479, 0.5501274272141913, 0.6522280709413026, 0.9742944334068284, 0.2829347574520191, 0.06857402156747072, 0.6401372736420333], [0.6578277548477284, 0.6802064393197255, 0.5192927862593079, 0.7158829764336551, 0.7026693542718189, 0.9877997834586112, 0.3627136321119514, 0.09879259907134619, 0.9524820521690892, 0.9806874278644242], [0.01229027773241509, 0.28883612359139865, 0.06484355587360746, 0.3234610725761369, 0.5040616048433884, 0.8660760177750029, 0.3599149589827223, 0.7939166862420854, 0.8565825733404797, 0.7654854781710705], [0.7779530895663935, 0.6643202318834096, 0.965734669622413, 0.47172299998323386, 0.0252429466483155, 0.9978894494871143, 0.425877815830507, 0.8874464113485828, 0.9826043269432218, 0.018050895426740543], [0.588199525489385, 0.09540523195006345, 0.7409228567251843, 0.1352367999205396, 0.6792415372000271, 0.6527677136837852, 0.5170287515705182, 0.6725017421052184, 0.6958967813738683, 0.6685607804170314], [0.7732590234098662, 0.5697410275912667, 0.9606929464711614, 0.7606844741111655, 0.011388117324806601, 0.17581221913689604, 0.7082223949989122, 0.7262079900797676, 0.6790911059781545, 0.11231543415209888], [0.6426436233607976, 0.01951407446044895, 0.510671818577793, 0.85854991442032, 0.978088249203502, 0.01775545934466627, 0.15314571465472737, 0.4679739695170114, 0.8787101050104074, 0.34255639245288383], [0.2917766710726797, 0.6031746295905712, 0.11106887874549387, 0.982726218094546, 0.40361672306762963, 0.5944997611931058, 0.3278603620573377, 0.8370254745385973, 0.43066133867201273, 0.9928727590540738], [0.06777052791920035, 0.5430102306665634, 0.6563375655417869, 0.4090622834009997, 0.12621734258044892, 0.19334142414221567, 0.8578230089712034, 0.998391538239398, 0.2604864795091927, 0.3421113808398001]])))/np.cos(2*np.pi*2.0418130465980555)-8.230489108125948+np.round(array_x), axis=1))

np.mean(np.square(9.988725900581427+array_x/9.551105355607133+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(8.867086452205903+array_x/7.314421787146471+array_x), axis=1)))

np.sum(np.sqrt(abs(array_x))*array_x+6.9481380689399534+np.exp(8.166634043953117), axis=1)+10*(np.sin(2*np.pi*np.sum(np.sqrt(abs(array_x))*array_x+6.874298440867504+np.exp(8.356123534247065), axis=1)))

np.square(-(7.959570653426633)-np.exp(np.mean(10*(array_x), axis=1)))

np.mean(9.68326130713635*-(8.270029884847785+np.round(array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(7.378049488756689*-(3.260363571315916+np.round(array_x)), axis=1)))

np.sum(np.sin(2*np.pi*3.343741451276963*-(3.867279801063508+array_x+array_x)), axis=1)

np.mean(np.square(np.round(8.674979473567273-1/(array_x+np.cos(2*np.pi*9.522809960444906)))), axis=1)

2.275851741450368-np.sum(np.sqrt(abs(10*(1.54825987767423*np.sin(2*np.pi*(np.dot(array_x, np.array([[0.24599962285039667, 0.8954324284433358, 0.9962946231014422, 0.5977346884057299, 0.7050164803270259, 0.136907098468957, 0.09405127760428511, 0.4660327834320318, 0.40756558668515974, 0.6502535610172493], [0.9679432275072208, 0.28794355914998093, 0.34515186574647283, 0.7170523830496526, 0.7449641539548633, 0.4094377727717564, 0.1375610096688672, 0.9599599907689449, 0.7249312963059608, 0.39632724198294744], [0.3418983483882396, 0.8286847035052991, 0.4860768621756285, 0.422093756821325, 0.6721650487630653, 0.9469156762839671, 0.5534563832773811, 0.8331007649609028, 0.7497596809994287, 0.6456658086656902], [0.9007623592312697, 0.7853298263004291, 0.13922972886971696, 0.24608625193800437, 0.4941776162700182, 0.47182904424080596, 0.2543714403796872, 0.3045381595711717, 0.6199932618490325, 0.8466841118250025], [0.992254415885623, 0.8634003061532278, 0.10285896447518805, 0.8034431104993691, 0.973000315690205, 0.46999509061166556, 0.13443200283024925, 0.09667623476529474, 0.5633502363342636, 0.5740575189655855], [0.6360166484588872, 0.5587664288417615, 0.5957151825022097, 0.41107692595971945, 0.42236790728576445, 0.9176313392013639, 0.02115257810633897, 0.6998734650313683, 0.0923244767729956, 0.9102286615054062], [0.14990941040513606, 0.33958442179009274, 0.11057702261181457, 0.42752892317428615, 0.6189408081875436, 0.14668293313539726, 0.09030189994967297, 0.43952459400338384, 0.9738382044519314, 0.5228131241504853], [0.7205073802691015, 0.20704112557392962, 0.06540420131142144, 0.14361444712975224, 0.868098400518729, 0.06555733744984138, 0.6560085416237864, 0.6162960962686519, 0.09955446071512242, 0.2052268595913903], [0.29208341735199017, 0.8964261668507922, 0.4344128572678553, 0.10077421296417177, 0.41450538395334857, 0.3115171037809039, 0.20922024528173444, 0.9260960923445524, 0.657471115698606, 0.46191019351294826], [0.9897986392076766, 0.02482712788797692, 0.5572796446625192, 0.16417398013926277, 0.522276669456124, 0.28403000405400736, 0.5786536203188725, 0.9106543646069528, 0.9510705853721093, 0.6196213579208276]])))-4.9328440364820025+(np.array(range(1, array_x.shape[1]+1)))*array_x)))), axis=1)

np.mean(np.sin(2*np.pi*array_x/4.8051262626946905)/np.log(abs(array_x))-array_x-4.03333893551682-2.074900718227146, axis=1)

np.mean(9.535646864291788+(np.array(range(1, array_x.shape[1]+1)))*array_x+4.682150252037976+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.round(np.mean(np.square(1.9831674538516908+array_x*-(np.exp(4.045747170840935)))+6.7783268626229, axis=1))

np.square(np.mean((np.dot(array_x, np.array([[0.1309048218582184, 0.658037186732985, 0.6396841577064285, 0.23041728038958742, 0.409759827420364, 0.7007814836505265, 0.8251206298663902, 0.4322882156978881, 0.6102427946018817, 0.08231964289808436], [0.3517316151991714, 0.05924666108681864, 0.5511942059403694, 0.030224544223225736, 0.646718491350247, 0.7698108725295926, 0.249038332134491, 0.45795312161913126, 0.30718887195024713, 0.13416449346124337], [0.6835869595498852, 0.7863112951916512, 0.2433086389020468, 0.8844310747373691, 0.20910137303141652, 0.8539159509381035, 0.10262518923085906, 0.6598146929623041, 0.42566865526802855, 0.0236685429958714], [0.9405662994891044, 0.20048745265166457, 0.8261477633042558, 0.7707418397236426, 0.8934020834945413, 0.9543778339569833, 0.1981296794636631, 0.6411370258026878, 0.32961094914560685, 0.7601692165427684], [0.14600726239894046, 0.3187517484183914, 0.18181078736955836, 0.9227843445092221, 0.09676329950202256, 0.2655717175512722, 0.10344996718056887, 0.20400712302090074, 0.46743736799199564, 0.26998850750119174], [0.31610647554633897, 0.07129102454520653, 0.4227447211455446, 0.8876197029094791, 0.9181740116190048, 0.15386230960474145, 0.49098627524363025, 0.7386721431022251, 0.505459847780643, 0.1752545806211223], [0.9887661017400667, 0.7091669814638727, 0.9586861935472154, 0.7055435783297249, 0.6166825405525646, 0.657220766600913, 0.7231613636468269, 0.7472149189431834, 0.2673067548551854, 0.4549432429681025], [0.8408352686431364, 0.9680749822075743, 0.7189801831198229, 0.8198957864072342, 0.9405785012217123, 0.6794475948827813, 0.8413168713933428, 0.32216705358676645, 0.12851502498191414, 0.7570887119167531], [0.2652076184879609, 0.5781384691354807, 0.7490541566065227, 0.6632146786891708, 0.48129435142727184, 0.1553481915882654, 0.6323275078712777, 0.42262284561335395, 0.08599367788875212, 0.9170306944829691], [0.24344425929361835, 0.020129510294922603, 0.9163222191963459, 0.18912178056109163, 0.11371722223216352, 0.507597558483958, 0.011406207344190622, 0.8666859891587118, 0.6003218495424852, 0.8709448465683229]])))+np.sin(2*np.pi*(np.dot(array_x, np.array([[0.7113649411401394, 0.42241273919040245, 0.5483407714525025, 0.5864019201491459, 0.322826441865244, 0.3448560513164032, 0.7640907816971685, 0.5260588924898912, 0.5243371238709037, 0.36620804960443487], [0.46489319767135084, 0.5526785787838174, 0.9036641933197189, 0.34206572702310567, 0.86150202142713, 0.07490971734075891, 0.5495243256949789, 0.43098287668754354, 0.7844988963765476, 0.9925979655385131], [0.07070223697155364, 0.1309361230404147, 0.04970609387061242, 0.5303942299087556, 0.9462487154707896, 0.6555834377751328, 0.49558920621532154, 0.43979368020230947, 0.6611751187188171, 0.710752663277448], [0.9154594776276368, 0.8225614051004005, 0.6388233159487867, 0.012722401747082945, 0.05670648750266416, 0.5878071313964404, 0.07184502873196164, 0.20878249774608837, 0.16725535385044432, 0.5475061491183896], [0.5159712093364035, 0.08818368877805471, 0.6335474354997643, 0.860721243357158, 0.5049337114971058, 0.45581972057412434, 0.5461527157884674, 0.4363950022089307, 0.15928232314092772, 0.5161906144928231], [0.919491300792776, 0.8367559715283601, 0.09445082734477117, 0.9482599377964368, 0.1717671528056427, 0.3025379523818389, 0.5439869897227364, 0.8126305516197644, 0.042843438261960576, 0.9934718374051542], [0.11375211702595045, 0.985816485424013, 0.22001279906116544, 0.29741456027630486, 0.3198540763713337, 0.4689971279752436, 0.6003454839309221, 0.5764938263889318, 0.012555164842277367, 0.3148564260304759], [0.5055139229885931, 0.05444028681078572, 0.9245249628439491, 0.02058846751326293, 0.36764825850563354, 0.7124635698670434, 0.3956253483597596, 0.10179759084958195, 0.6746327205615125, 0.879931609389414], [0.1578748073656998, 0.030769789807627346, 0.7463253200950113, 0.24379930300593367, 0.7458706880249343, 0.9185340666386991, 0.719655797950108, 0.5913200744038679, 0.16819244399414135, 0.8376245723635002], [0.675184085843344, 0.2535791483753641, 0.9162598845199605, 0.9862823724618974, 0.00328134135196112, 0.4495431761565505, 0.27888689139305, 0.7281910655164162, 0.13222089369130796, 0.5593234419519971]])))), axis=1)*np.sum(array_x, axis=1)-1.1189049181875337)

np.mean(np.sin(2*np.pi*2.614263604834621*array_x)-np.sqrt(abs(8.230585752233646+array_x))+6.5933448752471735/np.exp(5.202484472538303*array_x)*6.337481656440123, axis=1)