Datasets:

BasStein

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250000-randomfunctions-10d

like 0

np.mean(np.square(7.555032464463432-(np.dot(array_x, np.array([[0.31593987615542996, 0.7173337456003432, 0.9482294678722007, 0.3825265951450536, 0.9338362615252259, 0.05499002953878196, 0.057282424805086496, 0.551123890070263, 0.4932068631401185, 0.48188283246705466], [0.25875515491632517, 0.24839662763676817, 0.246927262123819, 0.8835417395072777, 0.4665477916929637, 0.4176544465231038, 0.3186652429282255, 0.3974517533227816, 0.24752311601244747, 0.14364366335987733], [0.9821548160803747, 0.7857404522984537, 0.9157472285633932, 0.8273121583965114, 0.8165646940265087, 0.5024824338081889, 0.27306745136860766, 0.5676060711710599, 0.09218558332084426, 0.009440152360147613], [0.8258973135006661, 0.2042913868699462, 0.8778467419125742, 0.03437876733871226, 0.17209332795166454, 0.7620206858846768, 0.5282884476833037, 0.5412150434780598, 0.2675630869680461, 0.10548428551791456], [0.6984318163993056, 0.499126315428723, 0.2887891540942943, 0.8226540147056728, 0.027022406188545234, 0.2675489670803658, 0.6912463872225524, 0.38706325711032974, 0.8072768375651709, 0.10384085187951941], [0.8087449483336743, 0.24444396030311977, 0.38623282197507025, 0.8235066553689475, 0.2019062589292927, 0.29248692943889776, 0.09960942818882657, 0.5496701043212779, 0.4498039858162124, 0.8389274037313417], [0.9618783947612974, 0.36854358527552766, 0.9438524951417354, 0.902415270719661, 0.2375865045216572, 0.5638055505112755, 0.857353804337197, 0.5425995131998986, 0.5962865282287049, 0.2761434281857056], [0.6901470541482114, 0.23280880226434375, 0.7343378513284141, 0.775674102238535, 0.5127653538487777, 0.556586540060098, 0.4143706822259172, 0.5410139190667985, 0.38894318117738047, 0.4677752476612005], [0.9541974324348196, 0.9766718649427579, 0.7912357436006628, 0.6914934188659048, 0.08446192843698408, 0.7355006091104799, 0.07946206557857993, 0.6163400565309269, 0.5735403211129514, 0.7437592195295587], [0.7046657156876748, 0.7887303645416459, 0.5381325321222039, 0.3433851544855848, 0.3267423126517589, 0.5930796813342308, 0.6209621822519986, 0.9118121886907734, 0.7162555464465826, 0.7424503617102689]]))))+1.8056878781424608*array_x/1.7040282996629548, axis=1)

np.mean(3.971166459663068-(np.dot(array_x, np.array([[0.018969322633842656, 0.07422336033057686, 0.017219202387312116, 0.5153493395533995, 0.10926580995215274, 0.7685103124262403, 0.5405751389105604, 0.8572199300220106, 0.6970592221019459, 0.32925835524822655], [0.7810019730217144, 0.5708466484244645, 0.8490684180737766, 0.8224464566371111, 0.3655286241896074, 0.12273499079903072, 0.9282580949892613, 0.7718471466311972, 0.6508186311212153, 0.31517691205132936], [0.2384693017101781, 0.17793476825460341, 0.736832964459638, 0.41631688625539465, 0.8257685531282382, 0.9833368891721154, 0.7990337671258548, 0.4436717313458409, 0.9771146567132416, 0.8086171768560176], [0.4399070113316119, 0.9377915620094966, 0.9618531517706432, 0.3423794553279845, 0.7995376360639952, 0.4422159261115147, 0.6707252357616312, 0.033063012481427845, 0.1505838086524759, 0.91828439167162], [0.7715320193758297, 0.4365307674903952, 0.00744184778557766, 0.8283339463333745, 0.9477860643806317, 0.43642906757422495, 0.20230889822453957, 0.33896558030448387, 0.47121431176844597, 0.04379978750604896], [0.9473034600847839, 0.8297876172550832, 0.13949230713028893, 0.7826681740042716, 0.29920719772543813, 7.821102276928116e-05, 0.9685031955859091, 0.009543482508298506, 0.49973477576801706, 0.44351340784215143], [0.009664447561519407, 0.9050370624185078, 0.7378163145332065, 0.7291703805208243, 0.49046001680534157, 0.4700040427499105, 0.8549163361360219, 0.9293053065543236, 0.9486278035331652, 0.4363159450795434], [0.1751458680723067, 0.7261136326365288, 0.4641600962076369, 0.9550116973833911, 0.471789490389783, 0.44890074463216667, 0.8097219441377365, 0.3036532412561068, 0.2787820609548909, 0.5210783718386688], [0.016745498565347905, 0.2862816055994114, 0.9101566870020996, 0.6397333825704563, 0.3386852601458492, 0.3745105320769415, 0.32361805800357646, 0.9187510812800428, 0.9845667357141306, 0.20766129395030808], [0.32402578906851387, 0.6072825087458894, 0.8032857385053708, 0.7433495556227024, 0.8398110632837849, 0.8896158710411937, 0.9830499856920012, 0.6635937627989713, 0.26112051767596167, 0.3696811734412365]])))+np.square(array_x*8.369654880029596+np.sin(2*np.pi*np.log(abs(np.round(3.5035972340116213))))), axis=1)

np.mean(np.square(10*(np.cos(2*np.pi*array_x-array_x+np.sqrt(abs(6.484837526250194))))), axis=1)

np.mean(np.cumsum(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)-9.211750414881775, axis=1)

np.sum(10*(-(np.square(np.exp(array_x))))-1.5350605603073295, axis=1)

np.mean(np.round(5.721901341135958*3.0353945003574045+array_x*3.8040104788345754-array_x-(np.array(range(1, array_x.shape[1]+1)))+array_x*7.519382687828955+3.171905457630637), axis=1)+np.sin(2*np.pi*np.mean(np.round(1.1138523846283523*9.090511985154766+array_x*8.864995029726721-array_x-(np.array(range(1, array_x.shape[1]+1)))+array_x*7.200137532479407+4.558574543711323), axis=1))

np.mean(4.799580885040516+np.square(np.square(6.88554428979657)+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(8.623042055245271+np.square(np.square(8.547081763076742)+array_x), axis=1)))

np.mean(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x*1.2870975080618605-(np.array(range(1, array_x.shape[1]+1)))*array_x-6.562050644157463)-np.exp((np.dot(array_x, np.array([[0.5507482480029202, 0.6590978602833952, 0.851634027766056, 0.5579202704173457, 0.14573937769307121, 0.1570141017340143, 0.7030215089164709, 0.9351889455377886, 0.7419170518517473, 0.25525991525865466], [0.4740809148499707, 0.2540236103151744, 0.9705171566683286, 0.3881562099297893, 0.1903183614421572, 0.871861508264476, 0.18947802212833809, 0.8213443620964709, 0.945052889650054, 0.48245398769361525], [0.13180124016747785, 0.162021399832865, 0.1303829696955292, 0.9556253681657728, 0.09598135796643026, 0.12394698124970893, 0.9766634876438375, 0.2641812984263511, 0.9958784857111724, 0.5116823368537854], [0.8477474340332694, 0.36347160635585085, 0.8577716204784902, 0.43647502812340067, 0.4238110650644905, 0.06301519499419928, 0.9284739314047454, 0.7868493536343736, 0.5207126929964255, 0.5127132411841737], [0.29664677087308955, 0.810995094591715, 0.8606925485861496, 0.8833469524424772, 0.0003377653088221244, 0.3552482931359652, 0.30385941423041574, 0.5217717874118067, 0.5787379366015994, 0.6050058282117485], [0.7465905283256852, 0.8780195208762719, 0.7908980826372888, 0.30248914892538203, 0.22282757383218554, 0.09084916177283886, 0.38476695285686957, 0.6935395026290165, 0.6552098689993133, 0.7333789768066589], [0.14824600893089346, 0.12288782010486377, 0.7920571073411664, 0.7649107338987771, 0.955538554390063, 0.8609998760843024, 0.4285505010610283, 0.8837733794098511, 0.988276780957666, 0.4405265002560731], [0.37075795297134795, 0.044185049781446994, 0.8216690321718794, 0.6613467372301525, 0.6535234293414856, 0.2987640385870407, 0.9511910870432806, 0.10640499225047084, 0.5826532514103882, 0.8576738304614528], [0.042830716172873484, 0.32433496342120316, 0.8824902086293954, 0.6278187593264383, 0.9851717088265252, 0.6662445766626575, 0.4390925195320441, 0.798517776138218, 0.7076222732653419, 0.4921981375599904], [0.915638141340465, 0.027302676770103784, 0.567204652884339, 0.45129595136242506, 0.8146920718944624, 0.9964803606060336, 0.5330993308903099, 0.616534722372925, 0.262743163097795, 0.3993799144304371]])))-5.990331664046169+2.410420697463133)-(np.array(range(1, array_x.shape[1]+1)))+1.9225501858684273, axis=1)

np.mean(8.080494510065682+5.807877006258207/np.log(abs(array_x)), axis=1)

np.mean(np.cumsum(np.exp(array_x), axis=1)*5.57694603115125+np.round(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.9124781460815364, 0.8387142654090428, 0.1833977785908658, 0.6117993636183413, 0.6660457264177683, 0.9842411074516206, 0.4142427239487644, 0.8995551041942372, 0.2624239142588668, 0.454728861815073], [0.7968840195439786, 0.8649656950588355, 0.9464771112028907, 0.11004269913026177, 0.18869267556035796, 0.5560486772462233, 0.1352013368059647, 0.10793910977527643, 0.7822589132906687, 0.1454072300199789], [0.2588310370335125, 0.7269688266636019, 0.12270788892748319, 0.7831007644101131, 0.13082284008762923, 0.8751585120572348, 0.7445945752770652, 0.7304028298108777, 0.05589348182926468, 0.5561098189410763], [0.7304130372254194, 0.8395842453589688, 0.24902075094305576, 0.5269378879619978, 0.5641319352515016, 0.7827882477458695, 0.7397173799598034, 0.6296522087431846, 0.08321109212918532, 0.4871554199393968], [0.936531602424298, 0.6237290027724927, 0.1037921185032662, 0.6787371293737648, 0.44471670794688256, 0.45097077061003676, 0.6779290443636875, 0.2106201968383451, 0.30352648576268415, 0.6213018790809637], [0.5852455310741738, 0.7520181371643979, 0.14632798737334096, 0.9087837311578582, 0.7190005957377864, 0.8858577238868331, 0.44730116674658515, 0.8878299121431003, 0.1519153475970364, 0.3296545819674289], [0.5742461473047957, 0.2572292973039969, 0.7165475666957724, 0.5837394553121761, 0.9766360688358146, 0.580880759739213, 0.0411761423906738, 0.7043920893315117, 0.9214418193989998, 0.7677203164049465], [0.7000070796378856, 0.535794480104703, 0.8291483069080028, 0.20994196577153668, 0.7348664555364113, 0.12637233755401245, 0.9915699601717362, 0.9593978157705274, 0.9796220198738564, 0.058857701405468754], [0.6730181169403553, 0.4276884323770712, 0.3929771038726213, 0.3557683166578317, 0.7804546700928846, 0.09150801880310877, 0.3389560571529061, 0.9712111492161045, 0.9812807892731435, 0.10874690412216736], [0.7561350620581817, 0.555188824416916, 0.31582308385460667, 0.7192941570771356, 0.526581954347519, 0.41998578765195393, 0.18150335345198376, 0.17091141690468492, 0.271293278639713, 0.6297978557403335]]))))+1.5352252646931794), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(np.exp(array_x), axis=1)*9.694305672447854+np.round(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.8353410411936324, 0.5828165087652868, 0.7451964900117454, 0.6034595282013102, 0.019152324889924244, 0.7652938853898957, 0.4313224661839906, 0.5031702786948524, 0.16588321000848527, 0.6864169973312106], [0.681287454190997, 0.7664979382003754, 0.2471172436361163, 0.1412874753103227, 0.6512157922597701, 0.6004973042362649, 0.14356446628776376, 0.2396720851644577, 0.49160058815243857, 0.21280197664214706], [0.8888536752089006, 0.7119079612404293, 0.4697774092072238, 0.8592158957725223, 0.7888639123810491, 0.11914336468844222, 0.6059149324976336, 0.4336778813782475, 0.06574469492438884, 0.8856538578165948], [0.19428965172582224, 0.7629752299965775, 0.8426609535441941, 0.9444792333139707, 0.2891811355335576, 0.553833624580146, 0.2835576644800072, 0.9278000399749773, 0.0013101573750755602, 0.7109830972559633], [0.39869982447742935, 0.17689464284942857, 0.5884052059304252, 0.8328717482077854, 0.9715897202242172, 0.34522676830250165, 0.5782798126315503, 0.9907102470417576, 0.46821472455987423, 0.6963964372500819], [0.6349444669918775, 0.8347819974690084, 0.25434659922617586, 0.40937477505756104, 0.12565189922910347, 0.8578736057623388, 0.31307174715762265, 0.7475187044643811, 0.7619931921771297, 0.5666300934025125], [0.31237675749243543, 0.026850197453822555, 0.8874228385184271, 0.9454008743407225, 0.8080242384807846, 0.5522948780135943, 0.6159653890309125, 0.8094193481074826, 0.335376465654476, 0.29599556644826286], [0.3301952138206907, 0.6970234249416966, 0.21584862354772139, 0.6799616313864479, 0.03211000827314647, 0.1024250141769818, 0.9941949275687336, 0.6432813302955014, 0.3440326602719834, 0.7139184650185495], [0.7940220426991637, 0.18569453369326494, 0.3238355622505086, 0.3170152715333747, 0.5543209775724778, 0.5706629503324303, 0.5756337769837783, 0.48563661024828086, 0.3347529059950013, 0.02972028743481281], [0.9288645589394207, 0.8002791773604827, 0.531380543345155, 0.39029302974394386, 0.123447531190918, 0.4166877156812516, 0.9277719836769063, 0.7222450694118926, 0.3419464325364773, 0.5026599224578483]]))))+9.170392568178809), axis=1))

np.mean(np.square(np.square(2.111242023737826-np.exp(2.976479133323505)-(np.dot(array_x, np.array([[0.3267338794193064, 0.7027113225247101, 0.35933020753627853, 0.22588621697072053, 0.7976910954841545, 0.7373844054104327, 0.3274714033360543, 0.12826006182972793, 0.9092301265441962, 0.8505342932376423], [0.7147414729614122, 0.9991207436885208, 0.3122281115052178, 0.5211950712132098, 0.277982165707956, 0.6727829683399187, 0.7234574696410622, 0.6398443968907991, 0.6525682666172937, 0.10234581442982893], [0.6348344922233377, 0.6761561267919377, 0.3023204425146967, 0.6694808543735553, 0.7302924406929859, 0.04016355618256928, 0.8141654901045935, 0.28524608973693255, 0.0920419795183044, 0.0471737377208038], [0.34857179681226047, 0.33253798898506604, 0.6311107465277589, 0.3756480301873696, 0.5025503959019394, 0.7687891036944201, 0.36929586881567333, 0.6680357697827074, 0.3324743411484886, 0.4190360594514323], [0.7334385742725194, 0.6796818696346011, 0.8140466324832775, 0.35266913827560764, 0.04174625086665451, 0.5701622933683181, 0.5406663305687645, 0.8280708874185266, 0.5044718865319133, 0.6904699432056783], [0.21606580940918707, 0.18812992783254778, 0.767283028848988, 0.04250558173385355, 0.030767525188305056, 0.6213251026648607, 0.475464196436602, 0.3688392307315851, 0.0660480812329971, 0.3195102446833338], [0.17185045660558995, 0.26622299885530876, 0.4381584863098239, 0.47122843368932477, 0.34939675360714295, 0.20136171945135273, 0.48522396820104485, 0.6893100651290724, 0.8111879914685808, 0.07675965560653475], [0.9601533014919174, 0.21765786855709057, 0.8126339583172251, 0.2650094914734359, 0.11503846072876034, 0.7098143775233016, 0.6487837859391351, 0.840833107931047, 0.9768046383776323, 0.4284763207424853], [0.20508920759946048, 0.5926366091568117, 0.20799306396675343, 0.9129686593482204, 0.9763683271232773, 0.2697720995638908, 0.0665945258525773, 0.8512712728111869, 0.35737892448643827, 0.12435096409520985], [0.4195145565972098, 0.9729988695050806, 0.756935503701957, 0.4847286377449239, 0.7043507265412392, 0.3395616661464116, 0.6018836599047938, 0.6525024994254621, 0.7194016304980345, 0.9740237140022076]])))))-np.square(np.cos(2*np.pi*np.log(abs(np.square(3.0849645944575563)))-4.881951748150195-(np.array(range(1, array_x.shape[1]+1)))*np.round((np.dot(array_x, np.array([[0.18859728307003676, 0.27309916272239043, 0.6084182782766628, 0.13538468052319907, 0.3687458331981758, 0.4239811716306924, 0.6440136791433374, 0.9304172615784017, 0.9493014444683382, 0.37287237822866615], [0.44518387843253293, 0.6059757693120891, 0.08000693457897878, 0.32935236324250916, 0.09682018700824302, 0.3827180288111781, 0.8784360183622874, 0.3625913685579295, 0.02464968332922024, 0.2919172988938129], [0.44758238817913665, 0.758066305028196, 0.2950703560822996, 0.5574118220597551, 0.0025106788105613287, 0.41855565938833283, 0.19753023152212812, 0.8490816174208992, 0.4120817559915473, 0.742632961473361], [0.21219806383165407, 0.9697157601936596, 0.1422641739858903, 0.023369125977119265, 0.35187017857476666, 0.686104541103595, 0.5832132631612571, 0.3693509220731307, 0.17079623170477065, 0.8054256337583956], [0.09896653026636626, 0.45557830635152685, 0.15847345433002435, 0.8614859948006447, 0.4445983795255244, 0.8021221555909207, 0.3539705473235557, 0.4143970303774195, 0.8727285418708229, 0.9284828024161975], [0.7261775317954943, 0.07119970977922585, 0.21456772695697812, 0.03600441572829893, 0.17290773439618623, 0.9775917955440004, 0.6152746177919077, 0.34263065054768793, 0.6673313472007898, 0.8806285078805625], [0.3539803889839841, 0.5423900059142825, 0.9420386577082409, 0.8118712104723035, 0.9099869308021935, 0.4738943874858066, 0.25021800825544993, 0.6946236489080616, 0.5489834238316215, 0.5933292217808714], [0.7591077354621613, 0.20495334788619313, 0.5913484658031674, 0.999878744149362, 0.9871316655510105, 0.6280749211817632, 0.40390003996495716, 0.30086540691324926, 0.4212789924390188, 0.9675223805420464], [0.2440056484347265, 0.13575232609067267, 0.6012941721523198, 0.6446084823922916, 0.17446099878675614, 0.2057082441665471, 0.5378750956700648, 0.29549530533281143, 0.5405726193150342, 0.026188397896001914], [0.6565448923301916, 0.3587476708161116, 0.9061456108520729, 0.13827959712128646, 0.3455145556704846, 0.37628152603684906, 0.9327073519968521, 0.8564278708310948, 0.8320581675382954, 0.7221411915084766]])))))), axis=1)

np.round(np.mean(np.square(10*(np.sin(2*np.pi*np.square(5.370516914765227*array_x+(np.dot(array_x, np.array([[0.06192847447139305, 0.44389340064231375, 0.1533903679772325, 0.40954757022270794, 0.985685331531003, 0.9954171974191337, 0.17304252363948258, 0.5549390529256455, 0.08000165484253763, 0.34026497250535326], [0.38224032166211486, 0.34192631223103265, 0.2688592541407103, 0.8860350988271919, 0.347314708931579, 0.7628687886837552, 0.27747039793927253, 0.008321029828286308, 0.8996577648650715, 0.9485983561248231], [0.773709845135555, 0.04898552059704653, 0.574533259557854, 0.9787393461816063, 0.9320583337607804, 0.9696566395777015, 0.4142785375260126, 0.6277363333005592, 0.9427233609776542, 0.8595979805793781], [0.335767947022259, 0.8641976267350373, 0.8155022347909712, 0.5670197587700128, 0.5842386433674172, 0.6764050830200113, 0.5103300743214639, 0.49160886204284715, 0.6268249601847066, 0.7530400257307129], [0.9611946368431123, 0.46315405904317897, 0.772715425893236, 0.5754925353642347, 0.013664041657483583, 0.43808332002024986, 0.042341904340041814, 0.991956717248357, 0.9466936472943176, 0.6364383311336783], [0.09856074804304227, 0.09405810917628332, 0.05902408390117886, 0.02476620749911007, 0.46409633815784224, 0.015010049247551338, 0.9144281620446159, 0.934356807539685, 0.27866254335580654, 0.9197831582774902], [0.796767241725383, 0.4723449679541113, 0.10736053411678648, 0.7595086984758241, 0.6949580094803882, 0.4595156838653289, 0.41580931108933183, 0.40950449293135627, 0.6211009768166191, 0.2733396900985675], [0.9539615463074588, 0.3495138810647168, 0.6666657323006275, 0.5571291549285736, 0.2753806609097562, 0.9830639988665157, 0.639667384396613, 0.3263257244194556, 0.7581308618047573, 0.6457948590078207], [0.9184267261393471, 0.9898264543953466, 0.6584021207037959, 0.23931385453747855, 0.6009024085009376, 0.23108521658285386, 0.7520413786079634, 0.5298345229679031, 0.5811404259181356, 0.5254107300593757], [0.5398496354988982, 0.25417345181746187, 0.34829832891226875, 0.05111075876954674, 0.3239834726291573, 0.7821485042503443, 0.2967158809595225, 0.9016303267480831, 0.9773869953446451, 0.9603321917681935]])))/np.square(6.639865450017813))-1.0625155028589364))), axis=1))

np.round(np.mean(np.square(abs(6.3457283971987914)/np.cos(2*np.pi*np.square(np.cos(2*np.pi*2.6452702924884157*array_x)))), axis=1))

np.sum(array_x, axis=1)+3.235242700126226+4.211578326285123+np.sin(2*np.pi*np.sum(array_x, axis=1)+7.128854139523046+1.3619404171376075)

np.mean(-(6.9273940325413195/6.345834492786404+3.0049218760610157*(np.dot(array_x, np.array([[0.7545655259620914, 0.5866013532441713, 0.8224866800495123, 0.7372845324421897, 0.1823181778149977, 0.81326151511881, 0.9656491331780833, 0.3030533231087228, 0.5984821460356843, 0.028628024553625342], [0.5741774558086098, 0.04167390087216338, 0.28924047535243436, 0.058343220128623585, 0.4666082602260875, 0.4430880673695452, 0.2856658511219121, 0.754934965305149, 0.7256833776527524, 0.017826820516455344], [0.31952142196550737, 0.34653043198908895, 0.33652648389466966, 0.3591948829433017, 0.5485318937160196, 0.5079968460423143, 0.04505302849661963, 0.07462744177130543, 0.8681108201708015, 0.2599473213237661], [0.46687718197394845, 0.9539590830810313, 0.8021521057114785, 0.9070475882761712, 0.21688505322585028, 0.7407429798006854, 0.7791278920559863, 0.2371422601360077, 0.46745350421138065, 0.6344741994776754], [0.4882536664580469, 0.6362869193695065, 0.3600814602266722, 0.9591095899769956, 0.10836718899993603, 0.1947232466038935, 0.8075193791431311, 0.18062519492638884, 0.01795572908243248, 0.7633013091455081], [0.8884535790731778, 0.09765213759343683, 0.006553014114851874, 0.5999516246351874, 0.9168799383151575, 0.21672942501747605, 0.9303745003325371, 0.735757092475142, 0.6012660491131543, 0.7071298224787789], [0.021703456488630812, 0.30554214175032, 0.8203559146243905, 0.6365699601746141, 0.8919378958164815, 0.2696624075095325, 0.7643310841554298, 0.5335248288419464, 0.1377252308783944, 0.9681780194319464], [0.9035077318014938, 0.043156595280223975, 0.9206859125510559, 0.8532785695287252, 0.951771357409073, 0.8578609331060498, 0.6916079486699065, 0.42585246898307516, 0.38347228838554415, 0.5160796163143758], [0.09198729082579049, 0.13028657032145752, 0.7968587794268346, 0.6486990065187794, 0.6099421863287934, 0.20366598310159312, 0.8730253614997308, 0.81184986025177, 0.06757361457564637, 0.7172772562785785], [0.7005198663711127, 0.608037924945844, 0.025231845220567872, 0.5736724497916006, 0.6531020695984161, 0.27265047493912986, 0.21749730530541234, 0.2577966505436968, 0.7182466940294084, 0.9264515386862321]])))-array_x), axis=1)

np.mean(10*(5.209336864350535+np.sqrt(abs(np.sqrt(abs(array_x)))))*np.sqrt(abs(np.exp(9.158278969775742)))*1.8182690897663765+array_x, axis=1)

np.mean(1/(4.628677815326477)+(np.array(range(1, array_x.shape[1]+1)))*np.sin(2*np.pi*abs(10*(6.000848732117134*array_x)))-np.cos(2*np.pi*array_x), axis=1)

np.mean(np.exp(5.371904693084928-array_x-array_x), axis=1)

np.mean(np.round(10*(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.11220049432402457, 0.1224527441580634, 0.13804199979274434, 0.05191382719842419, 0.7260260345353036, 0.3303853575675062, 0.8121027489776016, 0.7003038324303072, 0.8343149307594437, 0.02616457069347289], [0.8648971118055849, 0.45517784773218173, 0.5042436984706351, 0.2499870163819249, 0.36385870322782377, 0.49471529895615607, 0.806490616519424, 0.4522668129900619, 0.02783324567423484, 0.49990456433857255], [0.6234177980808382, 0.5116943567406136, 0.5853819987371436, 0.7986675736115924, 0.04904288655731115, 0.9625396788970748, 0.6387482859599957, 0.01649200815457874, 0.42500149568099976, 0.17767010640466074], [0.028177791130054808, 0.673321418306905, 0.883029529144891, 0.7747815803003293, 0.07579766586999326, 0.14766449178416452, 0.25243898834110934, 0.4808092354968154, 0.9818536451191051, 0.009808967165279459], [0.8956666277324014, 0.000659855405366061, 0.5248887237319461, 0.25811812468432627, 0.98132695225862, 0.7159719224791781, 0.8744640943751419, 0.22920669917578107, 0.737133739694663, 0.67989894360276], [0.43683726117148136, 0.16304492321960273, 0.02329789028620821, 0.6825998661864848, 0.6518897875228373, 0.24594596836390814, 0.5860731481067414, 0.11584002711011387, 0.9585603133973372, 0.34971184843458436], [0.17895367331302636, 0.8863775199956304, 0.3096165198684987, 0.6947227743451713, 0.1511451797315464, 0.11304081031510915, 0.6459761911952214, 0.645833428696375, 0.7190090307748789, 0.16361586685841134], [0.5386068656212976, 0.2699706015660346, 0.8550446787812235, 0.5597340466571704, 0.9915889394840433, 0.8112208563236372, 0.3266673264977492, 0.1473825678281031, 0.2995228374566503, 0.7870136495038923], [0.8270694824043335, 0.5259316004243889, 0.7372885753199523, 0.4733402062647205, 0.9356585138876983, 0.48255897623635835, 0.2281841217105759, 0.46718180222722194, 0.9011412479816421, 0.6155142744156042], [0.059141319655635405, 0.41238380997373425, 0.34749341521767696, 0.8220100368748249, 0.3097576860117749, 0.17958396206046867, 0.4273433071698227, 0.8520604438339607, 0.976735744906246, 0.4369261981100785]])))-8.038129589646758+np.sqrt(abs(10*(array_x))))*abs(9.90710364199353))), axis=1)

np.mean(np.square(np.square(np.round(np.sqrt(abs(array_x))))-np.sqrt(abs(6.702946858513587))*np.exp(np.round(np.log(abs(np.log(abs(6.1407187260163765+array_x))))))), axis=1)

np.mean(np.sqrt(abs(np.sqrt(abs(np.exp(2.732909004386605+6.724444239210211*9.305479725599302-array_x+2.0843593749920113))))), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(np.sqrt(abs(np.exp(6.365405122704017+4.133199317910162*2.796757956422684-array_x+3.4014475019993107))))), axis=1))

np.mean(abs(5.071635573628868)*array_x*8.709340154160873+np.cos(2*np.pi*3.969971272072666), axis=1)

np.mean(np.exp(2.749906230342421*array_x+5.595803701337735), axis=1)

np.mean(np.cumsum(np.square(array_x*3.3192843509062855), axis=1)+np.log(abs(7.56739797816025)), axis=1)

np.mean(1.7639830938847285*3.3531633462792083-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(9.906811633043239*9.057672408334357-array_x, axis=1)))

np.mean(5.797040827970497*(np.dot(array_x, np.array([[0.5595914316815643, 0.3233041350675082, 0.46479232132429615, 0.8746688743559781, 0.853810259484541, 0.8387173891502528, 0.5968237203774962, 0.042764198695656064, 0.17992897210796, 0.7544455555544465], [0.9609061377501534, 0.8092432308652491, 0.6645225435449259, 0.31171828962943515, 0.9310654012679455, 0.3394860568041931, 0.9752499266245169, 0.4646412004437699, 0.8926012242093486, 0.8643152078078127], [0.9965823933967204, 0.735723780144708, 0.17380717397414103, 0.8930271189479262, 0.4033894324681737, 0.6794497611571223, 0.8056037924118605, 0.6553367013647248, 0.36590790698158837, 0.6403550694900683], [0.4564797245945985, 0.40056155037498964, 0.0068938040007399115, 0.7382505871120846, 0.3548421646713331, 0.9823014320615433, 0.5523162814941699, 0.15609987682636395, 0.6253214607857738, 0.09998401751913799], [0.9276063570526367, 0.7591102979598225, 0.2851611793397726, 0.9769552731047978, 0.7525240144493762, 0.8596105588956331, 0.05958364734376831, 0.42523124306433635, 0.029767547302637865, 0.5521228241570487], [0.8799366105129238, 0.4173191614186833, 0.6457005908534261, 0.33511338199488305, 0.7620488509712341, 0.30709678021808706, 0.5346520324977714, 0.017127527437439394, 0.9397207955289648, 0.7326385752916683], [0.9744699649915552, 0.1333626441183633, 0.35146664566619124, 0.34728149493874005, 0.7813149610943717, 0.5320699547215992, 0.9226994824595618, 0.07306786567920298, 0.9383632345629807, 0.3411309260923404], [0.34368856764238775, 0.11585069345323407, 0.21264770543278477, 0.9256250586971849, 0.5139426488529854, 0.8666400548724237, 0.4516375625599215, 0.47066063216718623, 0.4975485404104266, 0.7525525322576228], [0.5484108479601576, 0.41785908676464656, 0.6015540090380427, 0.8325837149739327, 0.03741580465917649, 0.9680227834347666, 0.6798635204675415, 0.9623810403566514, 0.577867932092988, 0.8031741229477484], [0.91534582795687, 0.7373290961130492, 0.9538192404992493, 0.27207588969792507, 0.5252245840764019, 0.8349645292141378, 0.5103467295707772, 0.5479230028142423, 0.9328712590293993, 0.9281916036765449]])))*4.666595662762981+np.square(6.991672376632576)+array_x-6.452790890347591/5.3505983045667715+array_x-np.sqrt(abs(6.197863046711955)), axis=1)

np.mean(1/(np.round(array_x)*8.473518808689423*array_x-np.exp((np.dot(array_x, np.array([[0.8805923191176313, 0.6970863065569594, 0.6018395494595986, 0.09344484659361652, 0.15519653660782873, 0.10909689650848875, 0.28202081845154625, 0.00027109447852791124, 0.17090800606842094, 0.6034750303299427], [0.6839268349196851, 0.49653221861641605, 0.4366214487074934, 0.32935936274648636, 0.6019581112831973, 0.5056912183342598, 0.8853989436250422, 0.22258153745684273, 0.2737238539683915, 0.7163312236009104], [0.9193028471894383, 0.3757108879573664, 0.11106950950770611, 0.3519412333950407, 0.8992645950943299, 0.37238701937526786, 0.13517393883111606, 0.1745102160145694, 0.08483136585572326, 0.8761002694544269], [0.2911523244764297, 0.7671375079285998, 0.9618679045608858, 0.8998536896098407, 0.7739899732422216, 0.8908928725671504, 0.68230089842392, 0.11222494485474177, 0.488594393752246, 0.574251454450928], [0.26921941393663706, 0.7494120819029807, 0.2339217649206764, 0.17703169191892087, 0.25395512902794226, 0.8632431975881265, 0.9735686103252136, 0.5636380927666648, 0.9676970322754579, 0.17241123870634023], [0.2582500503757451, 0.7534724213077238, 0.8420695535666791, 0.6534951189907062, 0.8922099991382308, 0.543617889920453, 0.3261797235517354, 0.07382045074765309, 0.34562302462956107, 0.475944227683889], [0.20086797016912927, 0.5170872323336474, 0.5832148346563317, 0.6433084977440771, 0.1339919285085408, 0.2948384411079136, 0.9887847251580667, 0.6741779466141502, 0.5575655596401592, 0.14509716696280817], [0.14395179605326303, 0.19894056924236048, 0.17870524641273022, 0.7676345493514546, 0.9322995550189181, 0.12939737698751408, 0.1563053654190778, 0.05262519593270465, 0.3813854466906412, 0.972918214436196], [0.6308156471682738, 0.6624561208692162, 0.8881214554610138, 0.7139571935849345, 0.34089666836883514, 0.19681199840870933, 0.5048612750295224, 0.8441298392730909, 0.945658501707987, 0.4852329025106795], [0.37865654048385056, 0.10640991897437824, 0.9704200557853725, 0.3070738943581799, 0.012723651079044496, 0.27632627277921773, 0.001401690255169008, 0.11735433603663303, 0.8515039444648062, 0.6883575938585239]])))-6.919244149559044)/np.sqrt(abs(array_x-2.9598724521565125))), axis=1)+np.sin(2*np.pi*np.mean(1/(np.round(array_x)*9.217051930465429*array_x-np.exp((np.dot(array_x, np.array([[0.1646391477987177, 0.09871888210113045, 0.015523042306722412, 0.013639376214896193, 0.6827033608520473, 0.8829255995477713, 0.6911835818011114, 0.01905645801063449, 0.4426833759730233, 0.9857868179038479], [0.8578369022728624, 0.9842504520987398, 0.37266043323024367, 0.708777748866864, 0.9968529509188924, 0.29816306592714625, 0.45241765098883535, 0.7152257797289967, 0.4937567350151979, 0.06539813534071026], [0.0014506840743879756, 0.9928027918472363, 0.002028076231335385, 0.41680486612718237, 0.640943965882857, 0.730769913675724, 0.8917326970121262, 0.9983388218878014, 0.7711138328164813, 0.5532433657482787], [0.8959295427503187, 0.8845610537060541, 0.28006951319647844, 0.2549838056259942, 0.7177306470445691, 0.6244292663168461, 0.9068322326351136, 0.1968219484926642, 0.9631921741847473, 0.45733815798830635], [0.4541891629912135, 0.25437329618943905, 0.43364986351324464, 0.3904522071529607, 0.14458785902393745, 0.7596250929749655, 0.8491705300098857, 0.8983525039905098, 0.045906958333327696, 0.2733268136645973], [0.15765681088052497, 0.4743734210228975, 0.5091950698214099, 0.10006223643042766, 0.6706501137178852, 0.12447399341330867, 0.6169352670289461, 0.7449391084160102, 0.28655850390685855, 0.06460144261267886], [0.7848748432838737, 0.577823793968253, 0.6327138680380363, 0.7053438295779305, 0.9698095769843431, 0.35671976892931434, 0.21381854168720982, 0.36007766531362395, 0.2754102253434233, 0.44293901095841526], [0.0467522249237029, 0.7055512363553675, 0.3016255797532096, 0.3551271024930135, 0.6043524999686618, 0.11359438444826919, 0.10364751546553219, 0.6653578022704563, 0.21712275591618413, 0.611954962554599], [0.9372940319092045, 0.1545537488869989, 0.43429335634848054, 0.5311093165811397, 0.4872272806465623, 0.9158687120370927, 0.2338375474658525, 0.7795300624075502, 0.9294972617776598, 0.6293182705612029], [0.27975848032335404, 0.07304131821142534, 0.3134146053600039, 0.5551572896785646, 0.5856619548507274, 0.12139313389344186, 0.7541062696072407, 0.619162044517417, 0.3191619984061502, 0.9699392266214466]])))-8.191558776203006)/np.sqrt(abs(array_x-1.708152905169145))), axis=1))

np.mean(np.square(1.324744429838972)+array_x+8.212142654456049-np.sin(2*np.pi*array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(5.535500197510875)+array_x+6.785392290122958-np.sin(2*np.pi*array_x), axis=1)))

np.mean(7.065498993235816/2.975194680078765+array_x/9.002233035131505-np.cumsum(np.sin(2*np.pi*array_x)+np.cumsum(array_x, axis=1), axis=1)-np.cos(2*np.pi*4.867519756402476), axis=1)+np.sin(2*np.pi*np.mean(7.887556501117252/2.1415228503858796+array_x/2.680411448241501-np.cumsum(np.sin(2*np.pi*array_x)+np.cumsum(array_x, axis=1), axis=1)-np.cos(2*np.pi*2.2356162111048277), axis=1))

np.sum(-(np.sqrt(abs(np.exp(np.square(3.5619667362680887-np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x)))*10*(5.4779905048063595+(np.array(range(1, array_x.shape[1]+1)))*array_x)))), axis=1)

np.mean(np.round(10*(6.443136226397226+array_x+(np.array(range(1, array_x.shape[1]+1)))-(np.dot(array_x, np.array([[0.8685807356347643, 0.43326368949687555, 0.7372896168323212, 0.5978421308134404, 0.2712567348598037, 0.3043163892236034, 0.9213443907686308, 0.08585047504057897, 0.6908132992833368, 0.01430434219357779], [0.7007813069934984, 0.15070647357792633, 0.8209462635375501, 0.1662942284466412, 0.3513722240289602, 0.07381268195835333, 0.912624818685663, 0.7952006015355934, 0.6252981819744992, 0.5100145277617151], [0.1639591113825194, 0.1921158437693402, 0.10417639860175532, 0.6089872922791836, 0.9301355053845977, 0.692820252948114, 0.6096953279388245, 0.013933584436994106, 0.03971034733898138, 0.1581500579758821], [0.1045860236630144, 0.8972051295635365, 0.5959900541104026, 0.3352759846894048, 0.4788296947503653, 0.5412822335641023, 0.83555923651159, 0.5572114288873845, 0.24389064160820417, 0.3384511613908422], [0.7785869649384485, 0.05541477532613359, 0.40506723331472616, 0.30262029030742055, 0.5828901245073047, 0.9958549040954415, 0.06314424202136393, 0.9264218793261327, 0.14349780774857945, 0.07066910752141464], [0.8287635112035737, 0.23715284817965232, 0.35006622398071296, 0.06256034608739558, 0.6517636143228512, 0.8858149892379243, 0.47089123542048594, 0.9525485546173647, 0.8840567499120903, 0.6691245339594073], [0.42682358319426295, 0.2567297556414396, 0.7998822755054814, 0.7934043670124488, 0.362022927640819, 0.1780119012504504, 0.2308858897446585, 0.8725691922701312, 0.4475208281412594, 0.39757443290335714], [0.6157604698505305, 0.3923750527235371, 0.5094775166430184, 0.1503069990813105, 0.9519319227332128, 0.8783424389979422, 0.6285463648184014, 0.6683773572600151, 0.08123611182638169, 0.27671004274799804], [0.33620616031040407, 0.7930549697508517, 0.960302760186676, 0.3374879681681424, 0.7358117654662679, 0.4111287609207025, 0.5534477776595411, 0.8160212045037474, 0.2543234747963662, 0.901667988115057], [0.36304426811733226, 0.005226530758973946, 0.22269304546479274, 0.709224323223699, 0.19883125368364352, 0.2621653455467091, 0.7074493591083809, 0.3721626731069051, 0.7219179064201535, 0.5090169409883368]]))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(10*(6.393950585833493+array_x+(np.array(range(1, array_x.shape[1]+1)))-(np.dot(array_x, np.array([[0.36498254717139755, 0.08380953499668253, 0.5776724984209679, 0.8060131053396685, 0.6590887981024731, 0.38407876646120775, 0.09686900035516066, 0.3096444694188254, 0.6858711705041699, 0.6870620530058515], [0.9899728801266251, 0.6036797297045828, 0.9935563950769778, 0.15094279425189527, 0.6283629395817386, 0.5134888402289013, 0.06213636000963918, 0.43714304336455545, 0.10080409824091818, 0.8786185460735361], [0.4405723664389556, 0.97829993595756, 0.4454599699432733, 0.572044000675271, 0.8534666447761375, 0.24132376910924924, 0.3660659357962225, 0.7298152642585917, 0.2070801587105503, 0.245928008801217], [0.7971620256518716, 0.007116303787248923, 0.07463278808144436, 0.3690554426044237, 0.19359510344236408, 0.14551249786274711, 0.005593871436857301, 0.545061339496537, 0.06776969148749301, 0.8724692479021591], [0.27069477299359956, 0.3076906878793073, 0.5328570896555237, 0.7190238607898137, 0.4406220457502804, 0.36454039495125035, 0.6072760595599903, 0.3877056383385219, 0.0020411031463225537, 0.8453191035419021], [0.16385177244602667, 0.3715282440170169, 0.23572269440260185, 0.9520129921070147, 0.5012325844483498, 0.29033780796858155, 0.6212334557266993, 0.22472965306640713, 0.37413034634471976, 0.22813727006926277], [0.23258364810773713, 0.03488969905324213, 0.46275490792007845, 0.8189263806508665, 0.4265897118224289, 0.1925943597482097, 0.9212182478920106, 0.8084835810339203, 0.004707799609884211, 0.14222282311211798], [0.8983544447597942, 0.8918481161203418, 0.16032061511033402, 0.6177775572203967, 0.5582391658688007, 0.8645299221289492, 0.814124910613111, 0.6350885096830917, 0.47133568796842307, 0.1166615689120083], [0.4562127539593509, 0.791054531444692, 0.15119399635775388, 0.2219608129382199, 0.5705100668580849, 0.9914058505540647, 0.0033408540710005985, 0.4002075449192636, 0.34273726731679066, 0.28834512785970734], [0.3899476442976312, 0.3954116228061688, 0.387062911846349, 0.17424039194771235, 0.5902055189034987, 0.3474621407631001, 0.35992802630455367, 0.9812059719126502, 0.6875371323679629, 0.21879969254022802]]))))), axis=1)))

np.mean((np.dot(array_x, np.array([[0.8129635408238747, 0.7150075527477787, 0.5152800157922215, 0.03382827530344201, 0.3736874475878227, 0.9873008815172918, 0.6929897319569694, 0.14280205751808195, 0.7697383734406424, 0.10320263184073108], [0.7690012732706472, 0.2968230037398374, 0.9361008523238029, 0.6039206907021252, 0.5349519896236568, 0.9027341534362882, 0.23374792890048912, 0.929002967687508, 0.9993157338320061, 0.23187228320580577], [0.0325766120216614, 0.6462883875877631, 0.8119992391091511, 0.0032811785711071018, 0.5916970510591525, 0.5844364885880546, 0.013947878009272663, 0.27605836050560617, 0.15144067397761019, 0.9562631391342099], [0.7697001962857559, 0.9863302513746703, 0.06571827210851289, 0.012244422088706575, 0.4306180474713036, 0.31825881295515746, 0.1636945913372143, 0.19120466993099383, 0.49196178908963395, 0.10848761874190971], [0.08921744367092599, 0.30664838440679376, 0.2744104350965114, 0.35374201705974084, 0.27736732418652377, 0.08415083126864842, 0.8799541353038991, 0.702719518710423, 0.7249094500762552, 0.8659920094009709], [0.11942216907976955, 0.1380185184848578, 0.5078604617230222, 0.03375569112671806, 0.3103308598840169, 0.9659208093709039, 0.6478589389439852, 0.7506357764311521, 0.3633059953542711, 0.1452745427484612], [0.25117847236945035, 0.20385155527887155, 0.5896756092565651, 0.6511992268102703, 0.616600593908327, 0.13228672871175562, 0.05824326180545536, 0.28096961842478596, 0.8888501787087333, 0.21737348481576424], [0.45728696899570975, 0.03628122493716035, 0.03339031587079988, 0.7692361041836213, 0.42710362537671065, 0.7994042489070029, 0.090447167908423, 0.16120898337905198, 0.5688919502457644, 0.6839252807875847], [0.02853705139770779, 0.20410220506993093, 0.383291509469572, 0.4545923037945776, 0.7406194964594411, 0.11222284917568681, 0.0829211061871673, 0.3537884604123064, 0.4926410019625268, 0.8312524364212613], [0.8477774758132778, 0.7011568631975278, 0.11706200949610579, 0.44861514697956884, 0.6833309683484509, 0.06260660519262706, 0.01037348782290115, 0.7504818698968869, 0.3199927870407444, 0.1058844649991435]])))+2.673674211251355-np.cos(2*np.pi*2.6284683469207293)/1.4798834842392434-np.exp((np.dot(array_x, np.array([[0.1403292232415202, 0.08265917418789681, 0.24653302480051398, 0.43556492049198525, 0.4891441231652728, 0.08939143036406638, 0.5232994151819361, 0.3321675642350269, 0.2442940958269354, 0.8563753365008843], [0.23432747198778836, 0.06868939955944442, 0.7902016159552137, 0.9504959058435934, 0.3650815319811409, 0.23039866850676793, 0.10009652846518313, 0.6681078819516888, 0.6487955709522857, 0.6562388197022453], [0.540501277924884, 0.521426098907331, 0.5931154439075612, 0.44120349133701964, 0.9328454739436286, 0.11573567147965846, 0.16518743187298923, 0.6875187439338728, 0.06055268957379245, 0.633399613395803], [0.0534774941801085, 0.4043326374536158, 0.9944621015043746, 0.16938960658237878, 0.8420165262740711, 0.15196010138631444, 0.2652044806039856, 0.745476624436602, 0.09563970981732794, 0.9245652971021805], [0.35206208496961267, 0.4899279598769184, 0.6605659364227896, 0.8570513624675651, 0.16604478605034145, 0.23523451865756229, 0.644531567749556, 0.4477706131060273, 0.5969098145792782, 0.4789130825358203], [0.18261336221452285, 0.9080583539310862, 0.5154421068232748, 0.7711659107583425, 0.5748082434950066, 0.9511326761802279, 0.6146341872269343, 0.34047432404317324, 0.7792409282100573, 0.20573451758125882], [0.4441166001545285, 0.86714229434041, 0.22147181533788107, 0.23894228585683064, 0.2098440527474259, 0.057447455952637116, 0.043217718286797324, 0.2763992762878079, 0.3936580707805236, 0.18239573404467868], [0.8181476074963435, 0.5867867342710662, 0.19096227897243478, 0.5360921749805022, 0.9268071693652422, 0.40680031775719294, 0.8486453574332116, 0.11086344858232189, 0.6126007220022548, 0.9361015950387522], [0.5903595787489981, 0.5333997604378595, 0.5479191068171778, 0.4865736237724463, 0.6516041092306358, 0.45581933698079946, 0.8433625471419453, 0.10500340238004524, 0.5853588925181136, 0.9334810045008886], [0.21578246592773886, 0.9021746957536508, 0.1649373920612357, 0.8953142526652129, 0.21292853899244224, 0.026580693540656286, 0.3047884227475379, 0.028796776100078247, 0.6880378627313477, 0.956866323887611]]))))*-(8.154824146847776), axis=1)

np.mean(np.cos(2*np.pi*abs(np.exp(7.299986009849146)))+9.358739943082337*(np.dot(array_x, np.array([[0.22570637352839495, 0.822046508534393, 0.45825865446577474, 0.4136348996132915, 0.4313568084579078, 0.9135559256396913, 0.6523762131053384, 0.18344001195636617, 0.3698547520180844, 0.505999698153312], [0.6622327124651933, 0.26249021133367356, 0.1661384445213896, 0.7549117903108402, 0.8738648779345083, 0.5605759663658046, 0.7022908144071798, 0.9515286997758389, 0.8767342158409949, 0.09651007739627815], [0.5127204465525707, 0.9531023526116215, 0.7004243875431138, 0.8228829493837216, 0.39997044391489855, 0.4085959224217297, 0.6790974054738027, 0.04906240981740462, 0.16592803939222023, 0.8635583566504722], [0.04257711488497151, 0.6364205270940246, 0.5069128650687571, 0.4746514027199228, 0.23747609666901548, 0.1734761111964166, 0.2901917004900765, 0.3205151382919553, 0.9483163797715155, 0.5572594771964641], [0.025869842009693, 0.7451954527032059, 0.8169131129344047, 0.20486075506829782, 0.6697892791560912, 0.5685933241954794, 0.03107230607511391, 0.3048829158987554, 0.5555836137032463, 0.6027665180210593], [0.9265068056439597, 0.8846408298650781, 0.9670424819774271, 0.6225794651370112, 0.07972575509521318, 0.519756150758493, 0.29848875806393604, 0.9321071557811224, 0.6574956181132318, 0.9652613815578782], [0.9888299903719153, 0.19110614346783583, 0.5372042207860598, 0.910048152911513, 0.6690380549459367, 0.9120645864912121, 0.8887300880290502, 0.04888809060814814, 0.8349735922651949, 0.320635724231661], [0.47159746508513767, 0.3513133932679905, 0.9492572900090369, 0.9972558755883455, 0.7360046625574338, 0.2104710161011336, 0.02984451514057751, 0.7905950011477714, 0.9708435123703718, 0.5416504303347468], [0.9320349683821735, 0.03316713961227569, 0.6888279388786903, 0.5626904798467679, 0.8377123213336557, 0.6316577631400467, 0.3902943791574798, 0.30174972316020054, 0.12208139870448165, 0.98624128562191], [0.6329492115264084, 0.9423108237919944, 0.8471472991298625, 0.8848836784636654, 0.91952653395805, 0.6817787042603296, 0.4497395271154998, 0.1871148886020163, 0.4082462672963839, 0.27715277788688497]])))*array_x, axis=1)

np.mean(np.sqrt(abs(10*(2.6002860349158943*np.exp(array_x+np.sqrt(abs(8.186136329514246)))+np.cos(2*np.pi*np.sin(2*np.pi*np.cos(2*np.pi*9.232091525362266)))*array_x+7.218498267577768))), axis=1)

np.mean(7.992675523005837*np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x)-np.sin(2*np.pi*abs(5.0063402272341975)), axis=1)

np.mean(np.log(abs(np.cos(2*np.pi*array_x)))-np.square(2.168681961579482)+5.455708027700229, axis=1)

np.mean(4.593448038615179-np.square(array_x)*10*(np.square(5.912162003492421+array_x)), axis=1)

abs(np.sum((np.dot(array_x, np.array([[0.30646603739083866, 0.7281921204953858, 0.18516928036167934, 0.6578560114013869, 0.5997875877320944, 0.6664941096197021, 0.04627745533237149, 0.6783910148333749, 0.23043917032116057, 0.5083769236191875], [0.6281972419300198, 0.9798416559569181, 0.32778383399774846, 0.47008857623461897, 0.9282148682232367, 0.1258052299286906, 0.6597538282858126, 0.7550712444443372, 0.2499980054020865, 0.31463121227851265], [0.9320549219907764, 0.06333088390026598, 0.751995441176888, 0.7131346652858241, 0.22991272915557615, 0.7313330280315318, 0.856797975619509, 0.3109044054804533, 0.5074048898493702, 0.5789402803363095], [0.048397738151292, 0.0550300067831625, 0.07865947762711223, 0.3877250700404028, 0.41859074762886206, 0.27856032412597065, 0.3411464989493769, 0.6267807890165128, 0.6436634444777054, 0.3315565549265368], [0.5874551612712278, 0.42159124533509607, 0.23218259539765884, 0.8590185286117281, 0.5186610245819085, 0.07394756185701479, 0.48172828503373755, 0.06841812170932304, 0.9820154082503715, 0.929999414982935], [0.8623526459975556, 0.12126269373556031, 0.4861749290387877, 0.4691785006659259, 0.030743893831256308, 0.02765706841456672, 0.6218668840372082, 0.8740670705735643, 0.5167340567612152, 0.7452016035791558], [0.7595708706743672, 0.3553386669092429, 0.6224640652653625, 0.4907129145370278, 0.6976903450708778, 0.6493094076273308, 0.07564764790308065, 0.4479241753939788, 0.4457693454882905, 0.5851422836517497], [0.09429154823895358, 0.6475922861601628, 0.5529713788735569, 0.6237488564704917, 0.18586585946347434, 0.8051405005178054, 0.7783104405046402, 0.20143139072280525, 0.9812948638084417, 0.024286546347220384], [0.9371920214919971, 0.5215219034558237, 0.7919398741975445, 0.09417094999762088, 0.4522874027425242, 0.2900651631688429, 0.7496297729202509, 0.7172839623817191, 0.07442498348270421, 0.45579548243195256], [0.5560464323078748, 0.0547027748890444, 0.8853093421485133, 0.6995360455536145, 0.4153709654743497, 0.5995524013775572, 0.43626494255524606, 0.8201982438939087, 0.6054150385379021, 0.24052581544685625]]))), axis=1)+3.1486351459684716+7.009484934709053)

abs(-(np.mean(7.012018581070932+np.square(np.square(np.round(8.800572277970382-array_x))), axis=1)))

np.mean(np.square(np.square(np.cos(2*np.pi*-(2.1882481999226826))/3.1765630310071407-10*(array_x))), axis=1)

np.mean(np.exp(4.44643320874899-array_x/1.2861846777977384)-2.576278252651199-array_x-array_x, axis=1)

np.mean(np.sqrt(abs(-(np.exp(np.square(np.exp(np.round(array_x))))/7.0718203825244075))), axis=1)

np.mean(np.exp(np.sqrt(abs(9.68910738721817*np.square(array_x+1.9125674309649485)-9.800582566440573+6.21895099568263))), axis=1)

np.mean(6.138145991781862*array_x-5.401547156402367+3.9221567600255054-array_x-5.649881051810993+array_x*9.665342138036406, axis=1)+np.sin(2*np.pi*np.mean(2.3141804458481636*array_x-2.535488565386684+9.280587518071702-array_x-1.58622951524069+array_x*1.9298343579062642, axis=1))

np.mean(10*(array_x*6.351902795793714-np.cos(2*np.pi*10*(array_x))+np.round(6.33198566443686)), axis=1)

np.mean(1.9397042064602323-np.exp(np.cos(2*np.pi*8.772486047624785+(np.dot(array_x, np.array([[0.07641711118699612, 0.8722692689382797, 0.6534899806183211, 0.3675035216846294, 0.6860838918543706, 0.8541330381745901, 0.26984053569526456, 0.660796194538558, 0.6104132514360624, 0.9084694967622043], [0.596772130005246, 0.265188685281323, 0.0984261746118168, 0.3331092804369573, 0.15221471345663307, 0.5902215019975975, 0.45774094468371507, 0.4269827496684032, 0.09488239540818877, 0.31896358577406647], [0.8559173747895361, 0.6151693841292604, 0.0526650005096877, 0.65640024457894, 0.6679934468395669, 0.002522235370433501, 0.23714435130372047, 0.30963542206216765, 0.6342316222061583, 0.6557958040718703], [0.23405767487548546, 0.8968917337483122, 0.4981971005141098, 0.4923236945994408, 0.15021341587490955, 0.060355710379336625, 0.2037971022664662, 0.1186800335971524, 0.6366511484542874, 0.21559119124600357], [0.5558235305545205, 0.2543796842128224, 0.28743705837796063, 0.02398145862810619, 0.15677922557921498, 0.9493248544784204, 0.912341545610417, 0.9458987946158258, 0.9544442603464687, 0.14787445915006558], [0.8109241390322692, 0.8774417124802526, 0.6664707650464958, 0.14936157922568039, 0.0442918939105299, 0.9809940364758722, 0.23501478115774666, 0.6059172471140717, 0.5631978963142483, 0.18897236399679285], [0.4484838894368597, 0.9838509905705939, 0.7670046584849362, 0.41052036452616636, 0.1252610558002586, 0.8013795358424346, 0.5455564998617092, 0.8580528225071977, 0.7107055192243575, 0.5051538750736869], [0.7232717152274054, 0.269741222301642, 0.15741676011161043, 0.3654765992276058, 0.15430490577947753, 0.49674664921447476, 0.4593685244217881, 0.2167608317126405, 0.11282228506057901, 0.53146344215267], [0.7236825390476255, 0.3866315907445512, 0.7119955717483648, 0.15357048634963255, 0.2808337188902288, 0.11635211503108345, 0.23726023802868057, 0.0170738398026844, 0.18421890686304454, 0.46078102765519735], [0.8930597029238245, 0.6507020547110229, 0.10849143370322223, 0.4693601971521526, 0.18343897650600294, 0.4649825632701803, 0.8737479816815227, 0.8731902180341466, 0.7066824586883274, 0.24506535784241146]])))-6.75427696881888)+3.9992669605516635), axis=1)

10*(abs(np.mean(np.exp(9.765684796137597*array_x), axis=1)+np.mean((np.dot(array_x, np.array([[0.6996312897177644, 0.8995532858048432, 0.2898447336889718, 0.9070392880210919, 0.5151939533317699, 0.4535323048525436, 0.7223336484361218, 0.2692222674863949, 0.3170483006442555, 0.0917987937308603], [0.2636945021659197, 0.12535753059974553, 0.5728119181103191, 0.9246714279811346, 0.1308960093962097, 0.6505080186427232, 0.6598586947414946, 0.17789135995037453, 0.44478894255738355, 0.7295615288964927], [0.2709970505320273, 0.6621488187849803, 0.9307164819105106, 0.7588769536463068, 0.0835400619548432, 0.49583153951107106, 0.9182179532922887, 0.22904614018044955, 0.4305597240518515, 0.13405273357170067], [0.08941205777825145, 0.818566076340155, 0.514427619813141, 0.582503860841011, 0.4021831122122903, 0.6971662119333667, 0.48673557274830903, 0.504192138088155, 0.6140791674842893, 0.8554742696804767], [0.8901418919848513, 0.2556164597040058, 0.016611738425251654, 0.25345456412753087, 0.7776441944339508, 0.5875563572484032, 0.38708658644244454, 0.34840421287934553, 0.3710023932341662, 0.6394661459852065], [0.07580260112908466, 0.24238403779705187, 0.49551597398330294, 0.32906174781248954, 0.5903549348757293, 0.03166477447940519, 0.10441728127205241, 0.5399960434384755, 0.3079973851086929, 0.35152200611822304], [0.23148488563993963, 0.5469678489733253, 0.5024206472221179, 0.6832623529277059, 0.7769160455350176, 0.6183027838381362, 0.8370907628201195, 0.9113480277874186, 0.16672526862575554, 0.28069226676378145], [0.10563408061921631, 0.748927420896203, 0.8834652980245006, 0.9529532989334483, 0.0981058846356051, 0.88391184993804, 0.6965736168472358, 0.8990655282380888, 0.015856346813107658, 0.6191550038759808], [0.4850469600297095, 0.011285903862528479, 0.9185530727616049, 0.5493033142278588, 0.9606533887096216, 0.7302188857400123, 0.4232024851547559, 0.4502151261278494, 0.7723961696442849, 0.2973865656830351], [0.4853526365940616, 0.702233625772468, 0.4669138223360081, 0.3242033714352155, 0.1803778598429956, 0.6761253384967173, 0.39687880791384866, 0.23755278611993258, 0.20426265461606696, 0.1923008626035151]])))-np.exp(8.900406207173825), axis=1)))

np.mean(abs(6.867936942948202+array_x*10*(array_x*6.947208128468324)+7.067067135136757), axis=1)

np.round(np.mean(np.square(10*(np.sqrt(abs(np.exp(np.sqrt(abs(5.910511133130758+array_x/7.218239374889257+np.sqrt(abs(4.524482777417104))-np.sin(2*np.pi*9.323042894115666-array_x)))))))), axis=1))

np.mean(np.square(array_x-7.846939275664898), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(array_x-6.345989857718005), axis=1)))

np.mean(np.square(np.round(array_x)), axis=1)+10*(9.001153490184082)+10*(np.sin(2*np.pi*np.mean(np.square(np.round(array_x)), axis=1)+10*(2.9687109336859105)))

np.mean(10*(8.194272934324356)/(np.array(range(1, array_x.shape[1]+1)))*array_x+9.221038738116192-(np.array(range(1, array_x.shape[1]+1)))*array_x*np.round(7.230048870765293)/-(3.943062696775125), axis=1)

np.mean((np.array(range(1, array_x.shape[1]+1)))-6.915819705836007*np.cos(2*np.pi*array_x)+2.3562869833240794+np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))+1.6960775252546412-array_x)), axis=1)

np.mean(7.218652055264055-np.cumsum(8.984184529698634+(np.dot(array_x, np.array([[0.032300202831265334, 0.889537005216399, 0.7341389095476798, 0.466411424553681, 0.6921938821874161, 0.16516774308284765, 0.315191393655318, 0.8315105603135613, 0.28804335448782215, 0.1337041761872516], [0.4606261085059431, 0.07586124171345476, 0.16844255961305965, 0.25128942876796934, 0.327626663427526, 0.12827272319526706, 0.718570666209657, 0.5635454133268558, 0.6240240972467256, 0.07211975137502669], [0.01624284565439471, 0.8030597179140798, 0.9749334398918348, 0.5693933710383773, 0.5175252574456864, 0.5123707492692332, 0.16067529012313397, 0.8483875520467029, 0.9690799905139046, 0.8961983633092117], [0.7349580384657389, 0.003863051309345389, 0.8849610828067509, 0.07901067015648855, 0.5624364912261313, 0.7487812379655526, 0.7028843764038321, 0.3746119012470507, 0.4181913420247232, 0.14057663317594982], [0.6616951833273141, 0.19177023743012578, 0.05437489432143694, 0.05885381202501749, 0.5898978065959624, 0.6324359448347179, 0.01435492171536501, 0.3353069612363765, 0.2948112363977409, 0.6792553152750299], [0.04809058371144681, 0.41444153054476085, 0.056184348686510455, 0.3811729330447494, 0.8405654948922974, 0.33149025334262117, 0.7969894407313871, 0.1042756166082568, 0.6086301257051566, 0.5899853477720888], [0.1906665475667806, 0.3021569326349899, 0.054997618134631, 0.5612884115670396, 0.27961629415255507, 0.41972686131823156, 0.05254654633178801, 0.3324321106648618, 0.39514212426826145, 0.5654225450811126], [0.9818712486096169, 0.9277522610146453, 0.9929891875763126, 0.1081482012350472, 0.8035102325897421, 0.36119940259361694, 0.9595182067664937, 0.5139825518315719, 0.7316771390822123, 0.8948819295596617], [0.006646561048118893, 0.48498721045739523, 0.711468443408444, 0.9673306447541091, 0.25761306783313687, 0.8184788041903297, 0.5451509596996837, 0.8969835938775245, 0.8690808342165833, 0.25019572994585615], [0.48844310472636654, 0.8821580986759073, 0.10307179960674362, 0.2759359399333853, 0.7212194807257023, 0.48697777250516117, 0.4626852015689804, 0.7680094297876553, 0.8055370321503552, 0.35516520671060214]]))), axis=1)-9.299857839985608*array_x, axis=1)

5.395165087487018-np.cos(2*np.pi*np.sum(array_x-(np.array(range(1, array_x.shape[1]+1)))*-(4.248555413998992)+1/(np.sin(2*np.pi*1/(9.839681742208487))), axis=1))+10*(np.sin(2*np.pi*8.394627549958578-np.cos(2*np.pi*np.sum(array_x-(np.array(range(1, array_x.shape[1]+1)))*-(9.073344739646803)+1/(np.sin(2*np.pi*1/(6.776028897359921))), axis=1))))

np.mean(np.square(np.square(np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x)*4.276892513029502*3.8763421759882224+10*(8.118214435682082))), axis=1)

np.mean(10*(array_x)/np.sin(2*np.pi*3.6050950904805292)+abs(np.cos(2*np.pi*np.sqrt(abs(2.703798842313894)))), axis=1)+np.sin(2*np.pi*np.mean(10*(array_x)/np.sin(2*np.pi*4.715645644731682)+abs(np.cos(2*np.pi*np.sqrt(abs(1.724866284664923)))), axis=1))

np.sum(array_x/2.8937791126012717+np.round(7.446502521478922)-10*(1.631391693976068)*array_x, axis=1)

3.7392743651213203*np.sum((np.dot(array_x, np.array([[0.563734818094186, 0.07579144240223656, 0.06015495277488281, 0.9544015774224113, 0.7346643440528167, 0.11010695733196996, 0.7565246667886388, 0.2137867381154699, 0.06050932266442066, 0.6510299349283063], [0.5509701328890193, 0.618065308927867, 0.08609714265215895, 0.32043450220680114, 0.30191974183339554, 0.2129973666974354, 0.16382901691866336, 0.38181821627606805, 0.8545731057967465, 0.5952687171897441], [0.1916640809222414, 0.6166812214087339, 0.8590880588356627, 0.8783819778095978, 0.8383965355653413, 0.38035122976616864, 0.4208562149606714, 0.8478672887290621, 0.21638116899168502, 0.826708067758777], [0.5042258428816759, 0.5042008450156583, 0.18833581879447192, 0.9285715837601144, 0.9015829223691031, 0.31421566102035614, 0.30428245652176444, 0.9131179737564954, 0.7033404601977143, 0.8381833932664285], [0.6689000907259852, 0.9915760680993879, 0.3441557828845532, 0.9229907429093974, 0.13072244875880723, 0.900813993292963, 0.246918670860092, 0.6227742900116945, 0.8107798088228645, 0.3446268754173263], [0.532460540663831, 0.7221464128807408, 0.9840443277855003, 0.062451572470912264, 0.27878776047028064, 0.7912567642660063, 0.09667859795871936, 0.7905209197366008, 0.9740422517777816, 0.8889460425151303], [0.4455327213505371, 0.6613177982380738, 0.5729969810238829, 0.7104673874397325, 0.3735675987039334, 0.41919848861610143, 0.40726281736405434, 0.6970775620905427, 0.9577459705032085, 0.3254086477243011], [0.6739563279485861, 0.27049063196279455, 0.7489274184210569, 0.1116278728915483, 0.8943577956360481, 0.7925227875751992, 0.007947397670487355, 0.4258541408083032, 0.32313125145935717, 0.25162757485392373], [0.0343748673202916, 0.9003019331854067, 0.9498371099565635, 0.8656872438774538, 0.6328798195592087, 0.9757703071916072, 0.80308140611006, 0.28944819579267456, 0.8907664759178257, 0.3873714053505457], [0.9858159853257087, 0.29618861500039595, 0.10945497254396941, 0.728930878168884, 0.2814302290961822, 0.16526072965262673, 0.6766765158112622, 0.039385058077031965, 0.2741096935769596, 0.6650165631481031]]))), axis=1)-abs(7.004624641516549)

np.mean(np.round(1.0880977516996007)+np.log(abs(np.sin(2*np.pi*np.exp(array_x))))*1.5563454705893691, axis=1)

np.exp(np.mean(10*(array_x)-4.171144820348333+9.307538552242017, axis=1))

np.mean(3.9205090008347487+1/(array_x-4.1880877667673895+(np.dot(array_x, np.array([[0.37453042920846247, 0.13735386270716587, 0.5153127249462335, 0.17013342613604054, 0.9186138529016573, 0.10235793643553004, 0.1845725671954771, 0.3796754213196287, 0.9562299515007201, 0.5251392960749448], [0.690487245522982, 0.7764773615011062, 0.01695233046282618, 0.9329591072783969, 0.060381185272126436, 0.9147923722610827, 0.39002917155558126, 0.5460944601711646, 0.08805269144601935, 0.400961792260367], [0.8552778847172701, 0.518389554096105, 0.6233331744109142, 0.2699051133861836, 0.2616541939893319, 0.35324755970232224, 0.6780983924956655, 0.650784467426924, 0.27044171541679085, 0.43155197656750177], [0.45372238069524606, 0.19309571308186746, 0.2023099579556522, 0.9777129173113037, 0.29452272873275664, 0.5472089590833363, 0.45392564179066, 0.07226948888644225, 0.8193168650135073, 0.3754419164208509], [0.7454552031272269, 0.5282892391698699, 0.14322662151144783, 0.9508023020417028, 0.21764078582262691, 0.9228613232949433, 0.962279630713152, 0.5958822940894751, 0.9572202937979488, 0.1122311882761996], [0.4401450656929662, 0.8490183185304434, 0.5580075121962799, 0.23719065381155624, 0.9691219485902007, 0.6406871767568372, 0.8458918647720599, 0.992895814502284, 0.7781539373748548, 0.4650104091380479], [0.9217488932876804, 0.4597534425387738, 0.2006751807486029, 0.5964811867639704, 0.5295690277443775, 0.370570474318192, 0.5375809482385421, 0.22791148130106387, 0.34562714754427093, 0.7070437787238449], [0.5779599057700117, 0.3014925159225409, 0.3681507261782524, 0.8566031479803509, 0.4012530627420229, 0.14948556624332265, 0.053800671918650345, 0.13234558694805254, 0.42211923052688494, 0.4902347601166117], [0.41981743104113745, 0.9636730681856649, 0.4362653908644887, 0.05121958803978066, 0.5499985626564201, 0.6692458514616465, 0.6293721717189779, 0.3470095357320164, 0.4288033201683725, 0.10033574883896679], [0.024885089394030557, 0.2971836296720598, 0.5490603563342035, 0.7248132740276968, 0.25657693219315136, 0.00611186865499358, 0.4001077952978276, 0.1307684794427093, 0.26491903217442614, 0.31269388429152845]])))), axis=1)+np.sin(2*np.pi*np.mean(2.160385221948993+1/(array_x-2.6116767486241965+(np.dot(array_x, np.array([[0.9320573194518335, 0.11924933466183185, 0.28706793329097047, 0.9976119665157275, 0.6026594990354357, 0.062315922720075134, 0.40282722671681537, 0.9718983623242217, 0.019050135885047337, 0.5740683311431298], [0.7816240488396548, 0.4480323953709201, 0.9126020111465866, 0.42406177312683757, 0.09037789341437474, 0.6591613839287455, 0.3035079517506438, 0.9610388980417923, 0.6301502311200742, 0.2003140859979189], [0.5144288878291308, 0.849604612762801, 0.16265651573563455, 0.8591683895274365, 0.8218185566160794, 0.08042885845467562, 0.2187718935592734, 0.8308315089480255, 0.32424536489638245, 0.30644732793532015], [0.5208308403149141, 0.6798527368233231, 0.6922362101813015, 0.7512669358955288, 0.2589951616812094, 0.23008452242556432, 0.44626075642515195, 0.5553241005021242, 0.6674118144659865, 0.7539871451758082], [0.5441826171443873, 0.8279104775610673, 0.7227342698946312, 0.45054406961943616, 0.7182148116235884, 0.8601863307992708, 0.3132059743462915, 0.14433718499416293, 0.25512874557918164, 0.4992665159735953], [0.9477139241326245, 0.4681027015951289, 0.4299116344810615, 0.8454790182667525, 0.44310053487544865, 0.5512191193126903, 0.14025774325275642, 0.48350950858030106, 0.5349436314480561, 0.8537951785310566], [0.1818504284570912, 0.3158161239220234, 0.03841600382682253, 0.9336715607358913, 0.7406978247653063, 0.50764075318315, 0.05737917987520058, 0.41983510216194786, 0.4536406668513745, 0.16574702216491588], [0.38496276281385344, 0.3120527434504011, 0.6017872227862521, 0.4122262878109739, 0.4163978129977597, 0.5751929195160305, 0.24331562084374225, 0.4843321070184998, 0.49628116644815623, 0.7850776515159121], [0.7523265936728162, 0.7600227561359211, 0.5645484345015613, 0.8293544979501339, 0.5039699796685129, 0.95423393151909, 0.12661514560524423, 0.9368837150170006, 0.9482168754827103, 0.9592721889555788], [0.2373971643000613, 0.6170127571255885, 0.47706570474490584, 0.6324118561720318, 0.8195374271054816, 0.9424637699089657, 0.7568736257482573, 0.19411468922123964, 0.9004737549739046, 0.014616995388929688]])))), axis=1))

np.mean(abs(10*((np.array(range(1, array_x.shape[1]+1)))*array_x+5.545955862461527*np.log(abs(5.003897550852004))))--(6.986080367254011)-(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.mean(np.square(np.cos(2*np.pi*np.sin(2*np.pi*array_x/3.6323450844593004))-7.211391157682886*1.8622744364533421), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.cos(2*np.pi*np.sin(2*np.pi*array_x/6.62046758334407))-6.084005742332424*8.237616835761628), axis=1)))

np.mean(np.cos(2*np.pi*np.sqrt(abs(2.5026984837925568))/(np.array(range(1, array_x.shape[1]+1)))-6.761127680129259*(np.array(range(1, array_x.shape[1]+1))))+np.square(4.443798767295968)*array_x+7.414653904266702, axis=1)

np.mean(np.square(8.157319684483262)/np.sin(2*np.pi*array_x-6.615037617497122/8.370257256615682), axis=1)

np.sqrt(abs(np.sqrt(abs(np.prod(np.sqrt(abs(8.086461147364258+array_x/6.664761683126106)), axis=1)))))+10*(np.sin(2*np.pi*np.sqrt(abs(np.sqrt(abs(np.prod(np.sqrt(abs(6.66830615279326+array_x/2.579298287942419)), axis=1)))))))

np.mean(np.square(np.square(array_x)+2.0037858720847295/2.814316772371779)*4.572068782306445-array_x/8.723283270987858+5.498867320999486, axis=1)+np.sin(2*np.pi*np.mean(np.square(np.square(array_x)+6.638779630250654/5.293102224666007)*7.084823741683386-array_x/5.501168689558422+4.32929308719845, axis=1))

np.square(np.sum(4.296752808471179+(np.array(range(1, array_x.shape[1]+1)))*array_x-array_x/3.3719244115562366*np.sin(2*np.pi*array_x), axis=1))+np.sin(2*np.pi*np.square(np.sum(4.791225538151668+(np.array(range(1, array_x.shape[1]+1)))*array_x-array_x/9.451182346798976*np.sin(2*np.pi*array_x), axis=1)))

np.mean(abs(1.694975286625318/np.exp((np.dot(array_x, np.array([[0.6428761175401382, 0.8470833822752051, 0.2730311720265506, 0.8476903602842033, 0.1997689552002505, 0.6419917132597421, 0.6469790856770249, 0.16386606908001933, 0.577476360011212, 0.18719209430177497], [0.9148941981885087, 0.398945697321753, 0.7183701374153733, 0.9856319802807172, 0.1780332371503287, 0.8421784802758661, 0.28309251901439747, 0.9025190559699434, 0.17141500170283963, 0.46512366216205747], [0.35252005363859096, 0.47863490038507883, 0.3782838249537058, 0.3999605944617196, 0.26119418801151995, 0.8612343592680646, 0.4874463397564791, 0.1156146175629561, 0.1166284289252405, 0.2470454856694425], [0.39366101124244546, 0.5181780180475084, 0.3774467365120868, 0.4681282058929912, 0.5233612647356454, 0.32840269772584507, 0.4294193395153748, 0.7927748680914917, 0.9960061002704292, 0.013245243945634089], [0.820644887920171, 0.08031411821575818, 0.045798562722422664, 0.5118210580215667, 0.5722772340281669, 0.5570863998517958, 0.029235852847655064, 0.6774832330071281, 0.8691446596354125, 0.3396212901169321], [0.38208544407315037, 0.07284006399238507, 0.8177264963260467, 0.13137863901615354, 0.7899886689218039, 0.8011768784691777, 0.9159324672283763, 0.008299188778373878, 0.23290367613412144, 0.9868701116217901], [0.20444533054473968, 0.6499673301573546, 0.6634378258329362, 0.23745402770932988, 0.920692549087125, 0.15927343168533836, 0.2851252152552447, 0.9566864700975246, 0.8752494113874637, 0.9373272119262864], [0.7909894671398098, 0.610207435756388, 0.5544280511563406, 0.5817415016289395, 0.7669932496169164, 0.539883185115142, 0.5557408519930789, 0.2303205449131538, 0.702577109679477, 0.13596592726233103], [0.6491657868631898, 0.02652471170040105, 0.461899576037088, 0.07140423962194498, 0.45253208252642174, 0.8707936914391005, 0.9904453282362566, 0.24986617407041667, 0.3201245717689166, 0.5227789593549143], [0.25018797616020183, 0.34008642367222175, 0.21786595766328676, 0.9315114630198643, 0.22941284980116883, 0.850588312770478, 0.28198818111346546, 0.3169781804689089, 0.20174596814020418, 0.3285730206058821]])))-7.995042416920738)), axis=1)

5.160984830243667*np.round(np.sum(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x-5.345774759999698-(np.array(range(1, array_x.shape[1]+1)))), axis=1))

np.mean(np.cumsum(2.3798565886175673-array_x/9.549754019684716-(np.dot(array_x, np.array([[0.9348865834583454, 0.5704937193939302, 0.3661790451554776, 0.9584517753756281, 0.7460477899663168, 0.706268177386075, 0.5177702562108609, 0.8992809099036246, 0.17970419553704675, 0.5146268492077788], [0.5294277613583419, 0.8533189990897474, 0.5067841379179365, 0.5995613943698215, 0.6775514395366642, 0.6333293621930826, 0.8013319818349075, 0.7992627224378683, 0.06669034801368012, 0.00877810093159248], [0.24431155378122404, 0.990086323765584, 0.7300712064483061, 0.4192194443389806, 0.2786586030987448, 0.5276003165462932, 0.5954101655508275, 0.9850713925750184, 0.5574895182378135, 0.16276146133456115], [0.7839941166368044, 0.14607285156498895, 0.525597127709462, 0.4377072732677788, 0.032242190560814676, 0.7187882790944541, 0.6831744591069883, 0.6565242877141397, 0.8956950076718089, 0.9330625881468525], [0.48697741763207836, 0.7184966371200222, 0.5684528759836281, 0.4394183412043373, 0.6275362655698279, 0.13370351073421594, 0.15764881089204807, 0.28628784117137285, 0.2396193576196557, 0.3129972046751148], [0.973676756552738, 0.8396923678209213, 0.052211404906007175, 0.865478453694918, 0.6267150274451678, 0.12715525318741816, 0.2596454395861507, 0.1750871419659713, 0.22725711860325237, 0.24980672782156332], [0.41436218333761665, 0.8594414894728314, 0.8311282584646192, 0.40564674601280226, 0.9085566358270106, 0.05046040889865633, 0.3611976766398969, 0.1957054296231282, 0.7876564854766401, 0.6131696276124902], [0.286751846114327, 0.7153300858487134, 0.9289481506604991, 0.5380692068309189, 0.30312794817990596, 0.8102654188013152, 0.04764292309801754, 0.9855040401368929, 0.4473956981279944, 0.7990817453268079], [0.69950924168632, 0.5897670991768926, 0.8620036217568557, 0.8163506079608123, 0.8108444549668012, 0.3526726482786774, 0.7121593601013375, 0.7424016072522004, 0.6742771926524124, 0.45362526469010167], [0.037343340265215574, 0.9318868473757764, 0.15584803861606844, 0.8171975015503661, 0.9512941538945922, 0.0782129760894672, 0.7258868999356058, 0.6067423058646693, 0.1809695138704822, 0.9336814816132443]])))-1.764682694963437/5.968823523027802+np.cos(2*np.pi*array_x)+7.5803287091302725, axis=1), axis=1)

np.sum((np.array(range(1, array_x.shape[1]+1)))*array_x+5.1346715401396334+np.sin(2*np.pi*1.455525459933293), axis=1)

np.sqrt(abs(6.453323086149009*np.square(6.555042934036113)-np.prod(6.6322804583661-array_x, axis=1)))

np.round(np.square(np.sum(7.89833472867593-np.cos(2*np.pi*7.274485155133505)+array_x, axis=1))-np.log(abs(np.cos(2*np.pi*1.4457949719727812))))

np.mean(np.exp(7.22797281197468)+np.log(abs(1.5719007182919964))-array_x*np.square(2.383014093999587*10*((np.array(range(1, array_x.shape[1]+1))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(1.2478985706368446)+np.log(abs(1.9033437798461867))-array_x*np.square(8.27373634769634*10*((np.array(range(1, array_x.shape[1]+1))))), axis=1)))

np.mean(5.657983096993441-array_x*4.324631291690285, axis=1)+10*(np.sin(2*np.pi*np.mean(9.334083716747145-array_x*7.4246797684754755, axis=1)))

np.mean(7.695981989602498-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)

np.mean(10*(10*((np.array(range(1, array_x.shape[1]+1)))*array_x/np.sqrt(abs(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))))))-5.055511528610112)-np.sin(2*np.pi*np.cos(2*np.pi*abs((np.array(range(1, array_x.shape[1]+1)))*array_x)/6.567021612267784))), axis=1)

np.sum(2.3949233975682893*(np.dot(array_x, np.array([[0.571499450883636, 0.02203926138542689, 0.2692893775527173, 0.7248886832773976, 0.3329891790291346, 0.11432494716343722, 0.2561931292502032, 0.27052481805070616, 0.05697156103049705, 0.5257018444623595], [0.853893114836278, 0.13453034233518313, 0.9030142196883678, 0.8453979322848383, 0.6185365750078491, 0.32432728171388026, 0.6216005561366176, 0.42419678147225637, 0.6851109893139626, 0.9613015983137954], [0.9852800917711756, 0.03430353837752642, 0.9288032761018241, 0.16234222893413985, 0.8829971300186827, 0.2633062660527954, 0.6464390164980313, 0.6968053041505752, 0.16152925759677195, 0.4741405917157119], [0.6856744851082268, 0.01929575463575195, 0.25346384101867203, 0.978756549554923, 0.1562843803882934, 0.38423595233620156, 0.7330961702732435, 0.22819712467003583, 0.632988616432341, 0.9192274168254302], [0.4810746868865111, 0.7962390050983639, 0.46239192714229915, 0.8112148249313568, 0.5871534885147756, 0.6406975494448459, 0.6620767116391213, 0.586298768202416, 0.628569952075955, 0.3860958646242415], [0.08229276788743756, 0.2214119153611691, 0.22232604606851958, 0.9877472625365701, 0.25705640813083575, 0.17516125908194935, 0.066514086048578, 0.9051771789921177, 0.6803341653652025, 0.03903442485562436], [0.22970134857448565, 0.26104241975267795, 0.8721763679615842, 0.3176200891941986, 0.3540047887587646, 0.24325751995454736, 0.21004269856595892, 0.430307650484798, 0.4655012140952757, 0.6625320363363394], [0.973472364425216, 0.7803850423648998, 0.2721222281895491, 0.40088902838113105, 0.21884930000583125, 0.9827326067132393, 0.808476146965529, 0.593733774540647, 0.3992605619166464, 0.4769620005272699], [0.07953241510231512, 0.40350092515649993, 0.6769973355783112, 0.9761748678515185, 0.4583672824098197, 0.7821949243447572, 0.26845411987455114, 0.7875826936433447, 0.742665029840238, 0.27665021195121786], [0.826576389847375, 0.8114663501051999, 0.5310518271563962, 0.2878832749372797, 0.9946422511097963, 0.6909359231926153, 0.2226606772236076, 0.09804948030752936, 0.3471021112677698, 0.8570305898816832]])))-7.92031638379015+np.log(abs(5.379359134944909))/7.899076253869168+array_x, axis=1)

np.mean(np.exp(array_x+np.sqrt(abs(array_x*2.5287761453917126))), axis=1)+np.sin(2*np.pi*np.mean(np.exp(array_x+np.sqrt(abs(array_x*3.2398048619807014))), axis=1))

np.round(np.mean(10*(8.173006936128786+np.round(np.log(abs(np.square(9.244485744002764)))*array_x)), axis=1))

np.mean(np.exp(array_x*8.585647669859483*1.2313247177272415+1.0392258157525558+array_x-array_x/1.6567271445247087), axis=1)+np.sin(2*np.pi*np.mean(np.exp(array_x*2.8313519822638393*6.524754617873418+2.5404028328396833+array_x-array_x/3.793594498365884), axis=1))

-(np.exp(np.mean(np.cumsum(array_x, axis=1)-np.round(np.sqrt(abs(1/(np.sqrt(abs(2.7610518809892968)))))), axis=1)))

np.sum(3.9454571208037232*array_x, axis=1)-5.081369352728463-np.sqrt(abs(8.311929299650178))

np.square(np.square(np.log(abs(9.636965220795407+np.sin(2*np.pi*np.sum(array_x, axis=1))))))

np.mean(abs(np.sqrt(abs(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x))))-9.10027923700104+6.96270468857854*(np.dot(array_x, np.array([[0.663327395123216, 0.2681381783232143, 0.9719454229952225, 0.6429599668464159, 0.8068448613415466, 0.7165596446000831, 0.10118735922544986, 0.5211020628260948, 0.054051028469492834, 0.7435024312837595], [0.8449269424399373, 0.8882172576399355, 0.08624839457475908, 0.2512092757999822, 0.8234263791537356, 0.8659899017791334, 0.7231806182201794, 0.47330556256712164, 0.7721928871895302, 0.9777227929139928], [0.13325544033068526, 0.1561799102222532, 0.3435736373962288, 0.05438214911148487, 0.337651877715771, 0.7769806081139088, 0.31191213875518065, 0.09567702758161623, 0.5173201187645544, 0.9692564824259418], [0.43211005092099586, 0.7788347170558905, 0.6762158351859835, 0.1506622158000075, 0.22510226760103358, 0.9268006963924917, 0.798520945751415, 0.8827123637273357, 0.8277159726715908, 0.49422797550223374], [0.12728309872092125, 0.2816208381906722, 0.05116632084463846, 0.15056123941103572, 0.03047940210184341, 0.2536786002535416, 0.4587266210041273, 0.007120112430099801, 0.27010389459967854, 0.731021403990016], [0.17001656668312015, 0.47309547134060226, 0.9011716476116115, 0.3652863053249604, 0.5517815532616466, 0.15827572590754502, 0.019562901903711905, 0.9458045377754442, 0.8246883844593798, 0.02999414191564298], [0.6772525729483775, 0.07251259191978732, 0.34415584745452144, 0.9753619132088654, 0.26346596072736683, 0.8095536226478638, 0.058017710958820135, 0.4793189797851053, 0.7859394141776681, 0.1434661280748707], [0.20727088010454253, 0.505909524996916, 0.5428307596435967, 0.06782382255312702, 0.30057977934340663, 0.6849950853376504, 0.992759441579492, 0.8138217525185775, 0.7848558371984071, 0.31459534750140217], [0.2546238143155479, 0.9254918734795695, 0.39443317698797464, 0.23684466466577792, 0.014614206525443985, 0.031537997912555515, 0.22585154780607786, 0.8526518312032165, 0.7371133217413208, 0.364330314885117], [0.5778913083431177, 0.9784175002975782, 0.13375251401350396, 0.1389786455876072, 0.5350437925655872, 0.6198961913573765, 0.04764270762731215, 0.616178962943976, 0.7698905342078532, 0.021903747869139067]])))*abs(np.exp(7.599980504904025))), axis=1)

np.mean(np.square(7.755723101538874-np.square(8.652041507098197+array_x)*2.45381068562956), axis=1)

abs(1.113449200364404+np.sum(array_x, axis=1)*8.996924472739092)

np.mean(np.square(5.887698229612344+np.round(array_x)-np.sqrt(abs(9.319528979841436))+array_x/1.1960578194929585), axis=1)

np.sum(np.square(np.square(4.515685950001465-array_x)), axis=1)*np.sin(2*np.pi*4.268803869413015)*8.875818920669793

np.mean(9.311764454780711*array_x-2.985357845273372+5.93727392509172, axis=1)+np.sin(2*np.pi*np.mean(8.183357518888052*array_x-7.757875191091789+2.612721922937838, axis=1))

10*(np.mean(abs(6.558350165134168-array_x)-array_x-9.53191827048897*9.759322855012796, axis=1))+np.sin(2*np.pi*10*(np.mean(abs(8.093080387520327-array_x)-array_x-2.80614355261138*4.255968784804991, axis=1)))

np.sin(2*np.pi*1.544971787852655)+np.sum(-(np.sin(2*np.pi*array_x)), axis=1)+np.sin(2*np.pi*np.sin(2*np.pi*5.140277426431032)+np.sum(-(np.sin(2*np.pi*array_x)), axis=1))

np.exp(np.exp(np.mean(np.sin(2*np.pi*array_x*np.round(2.5773561037595476)/6.353329715186909), axis=1)))+np.sin(2*np.pi*np.exp(np.exp(np.mean(np.sin(2*np.pi*array_x*np.round(6.852539160152784)/9.393810274046977), axis=1))))

np.mean(np.square(np.square(8.102072823909218)-array_x)-8.25492705213869, axis=1)

np.mean(10*(8.442948683287895-(np.dot(array_x, np.array([[0.23073728720253917, 0.604299612249545, 0.5677563208281252, 0.23496707879726786, 0.8244428020774968, 0.43232748480436844, 0.8502987040380063, 0.15609755611807208, 0.32171185363312205, 0.1884458673085967], [0.5956129654840255, 0.7393669572250829, 0.27180179479409305, 0.3260462029167541, 0.5606250175394465, 0.40829417619633446, 0.25331329435575267, 0.7005051162640475, 0.40291608912538346, 0.15472240206410504], [0.023982556590599446, 0.42766814363361416, 0.39768387848294273, 0.6034678358498793, 0.23639083089220514, 0.011723002550969808, 0.29403544788897784, 0.24233409880439183, 0.8919345142528089, 0.20341266229275312], [0.6982658218633209, 0.9624088250570685, 0.18463151189812232, 0.4570479302432717, 0.7266543819049766, 0.8349542966824823, 0.2425588788999833, 0.3398946642763079, 0.7790158169334557, 0.50064307640786], [0.8763548477143046, 0.6652237935569391, 0.9377792772005404, 0.8462050231584437, 0.6148357443073926, 0.04492016008358801, 0.9958596166591605, 0.9242698811009876, 0.6292902523958742, 0.3683694413683175], [0.9401777072642094, 0.46552493278297635, 0.4705018556690168, 0.9203724161586212, 0.929788113831256, 0.9913617385180568, 0.5159331280013417, 0.870952115716765, 0.62595120087967, 0.3528360499854949], [0.5510552519531893, 0.9084242140944696, 0.15381733240346507, 0.5080831133086691, 0.6114025244175684, 0.2384213660283524, 0.4063557216117494, 0.801308808265998, 0.18564480878517864, 0.24741734851006114], [0.0891212976158483, 0.4723437651580378, 0.44126521756490467, 0.03513555094063081, 0.2536424245473339, 0.22342925868970065, 0.1272341587975614, 0.6483126728957295, 0.06821671569612708, 0.39607717195131875], [0.8444287756879764, 0.8642146008358085, 0.9806031175656215, 0.20480387878569928, 0.3135786875543445, 0.43438599373529085, 0.9954597290145709, 0.3102849202410216, 0.12400900174671126, 0.4993333493699734], [0.9874485840413518, 0.4326730384510785, 0.8423125305950212, 0.27049682493732186, 0.6390929671492438, 0.09601378895331325, 0.9607788948901479, 0.7369925587323077, 0.2666987856485814, 0.17846330959654932]])))-(np.dot(array_x, np.array([[0.7827554528840426, 0.2309163062844637, 0.8111610283823382, 0.24744906802397504, 0.855036216595046, 0.10958332195893239, 0.18381488146168712, 0.1591518914519141, 0.17344728647247176, 0.7777944257729945], [0.24035187230775923, 0.09040230203954203, 0.002180548943554017, 0.9066179270051509, 0.6729852867345728, 0.8949940391993698, 0.40618381127920644, 0.7488481861846623, 0.9145939691946413, 0.003987739249765476], [0.6472100276350646, 0.20724161863692747, 0.5167598144545508, 0.971474635107362, 0.14227250918249323, 0.21682187931006958, 0.628184129729609, 0.9950098512602304, 0.021334267740306223, 0.23636145092320915], [0.3449466179043451, 0.2569344000069951, 0.43817516333892337, 0.9575711575207021, 0.28293750157052755, 0.8388288942612918, 0.21877342249093212, 0.08565135223926978, 0.5877606326384105, 0.9704704270737112], [0.41440414715587115, 0.4854606843192931, 0.9028715949932932, 0.7489823595451819, 0.029782878181641936, 0.09530152987284657, 0.1370116323122048, 0.4644619093225353, 0.32382545195416956, 0.013724843754560068], [0.6644416770932989, 0.04270422738640378, 0.5893113784853684, 0.48207213354206135, 0.961950553910469, 0.8520684650431355, 0.9776231768753283, 0.7824706657020919, 0.7073870245707016, 0.9625287874548475], [0.2508189081334581, 0.8382696598299828, 0.521635493277877, 0.8113689132295229, 0.564146747867838, 0.640479624970673, 0.4485174362911889, 0.805260308095092, 0.9950234723069877, 0.3023849206733056], [0.7291998819904811, 0.9007046349902538, 0.6069208625632156, 0.4498309474109671, 0.6712867714994473, 0.812668073111743, 0.3819579702849426, 0.04827331010324498, 0.25653423083251736, 0.25947169764472267], [0.2131235145642495, 0.24444174989816725, 0.6633599458818235, 0.7424171653164415, 0.8285722572745247, 0.718377207662157, 0.21248580596582545, 0.5253139785767965, 0.917121907297996, 0.9287460060614252], [0.5254309738160602, 0.25951435214574725, 0.19903980258782838, 0.8027845068420981, 0.74542166430781, 0.031889340739711725, 0.45553261718724614, 0.2774205054047931, 0.7845236530821337, 0.23796442375761329]])))), axis=1)

np.mean(np.log(abs((np.dot(array_x, np.array([[0.43331757939008475, 0.8357316984439607, 0.5124222698129594, 0.6210451702711431, 0.9955250309408472, 0.7819903270109394, 0.17083157678123928, 0.9164347297044521, 0.5154205383094165, 0.442446545855107], [0.9586329292454743, 0.0783320949459202, 0.6822236864750367, 0.10433170307227957, 0.2337043980603658, 0.7309283495680285, 0.46057530296384674, 0.402056768597301, 0.7112501865282536, 0.7497343900223788], [0.3004580597896306, 0.8648049500944445, 0.5421201195760301, 0.1877660437857449, 0.8523919131949174, 0.7818950573957932, 0.2946731890128599, 0.4287822655941478, 0.5308806717559095, 0.05943714025034397], [0.015171900049455234, 0.7124856767577707, 0.28624101197712304, 0.5072365959704745, 0.2877323933366892, 0.858548703052435, 0.7436448908344627, 0.7547625495313074, 0.10257267598757192, 0.8998760519190003], [0.9138263776846954, 0.5607088958937977, 0.9629693302060559, 0.423154200941982, 0.8644086030210504, 0.21582438125167547, 0.8691881239429279, 0.5637988457923361, 0.5243502190651149, 0.8850606183293629], [0.5829769148077827, 0.10102816864401643, 0.572486049952476, 0.4556882021340558, 0.5357114803262001, 0.9652048304796863, 0.2968406365562648, 0.3356535099743966, 0.6139822702586089, 0.2001095754862895], [0.7901956009548828, 0.9441671363540585, 0.791223406535435, 0.37457584068502436, 0.7628601167626995, 0.2973789534233837, 0.09888291177659614, 0.5950885422556974, 0.9565308381886614, 0.21249571590898375], [0.04075271020294824, 0.5070306374665906, 0.9756031281009984, 0.7804087212592675, 0.3690023529604748, 0.09895914860091215, 0.9968891289684283, 0.15058490879138542, 0.040238142213318895, 0.3909798952615837], [0.3543603683037122, 0.22016075036484373, 0.8485664213274594, 0.06541195136192812, 0.6700996726360587, 0.07096692331780996, 0.9184434362420243, 0.32458410827631023, 0.13424894656404174, 0.1772815806611614], [0.012964951951409232, 0.3588029345662377, 0.3822939245757381, 0.24913491144313404, 0.9612159598028517, 0.523650460365702, 0.7910397114221183, 0.45689406942782196, 0.621545206572805, 0.35584589485164364]])))+6.2991834490119745/np.sin(2*np.pi*np.cos(2*np.pi*9.968141089091182))/np.sqrt(abs(10*(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.13380015414601687, 0.35538411773191636, 0.44788610382800764, 0.5874127647844634, 0.6250757553377917, 0.5101753938488556, 0.01720866394545073, 0.08111481005617793, 0.7161509768672826, 0.45563427515907373], [0.9925436181212471, 0.16311363330252704, 0.10965163028910485, 0.7271057707076936, 0.026813938768197154, 0.6168814523223083, 0.22417160581883544, 0.6595571166541936, 0.028885442229522384, 0.551801167860333], [0.6811650300399611, 0.9021277399254266, 0.7117546097044116, 0.5245251953682026, 0.6130702917895313, 0.310087166761713, 0.1754408328092386, 0.169304352637335, 0.4834741971278861, 0.5552868986691356], [0.4517519020025931, 0.8743082154634421, 0.6910922198491118, 0.28571096466876433, 0.4336500448314289, 0.4304457873497244, 0.6762598291914236, 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0.6366598200956021, 0.7977050118147425, 0.038655140979453995, 0.6878301760788963, 0.9157851138341068, 0.17995968608800506, 0.32176788730626826, 0.39050710463485017], [0.19721819525348738, 0.5834668990492787, 0.34915198056196084, 0.4154343179933244, 0.3683286505648241, 0.17960674095476292, 0.687818868504736, 0.014393259299244066, 0.5233004888687346, 0.9082200862626831], [0.8145407332001974, 0.7863004800508108, 0.6131463133118101, 0.5414343000437083, 0.327167144041163, 0.06070308060352769, 0.38182176632708986, 0.599216205699109, 0.10085616909658845, 0.20557965978298187], [0.4481755135923703, 0.5803233616177018, 0.31584483873417823, 0.04205358869088527, 0.504071676113324, 0.44190546372066875, 0.2902615618956761, 0.1367625634815478, 0.41802019414347436, 0.3457591189866195], [0.0568343050020238, 0.6731826366015025, 0.5747739451095701, 0.6600092051808473, 0.6441039009880531, 0.5505816183322872, 0.8519585256744653, 0.991154013985868, 0.6212305638585577, 0.5980905222194154], [0.8020089400769363, 0.560991638216528, 0.8157954052694933, 0.2584920996321597, 0.9563725865810832, 0.5961050095222363, 0.5878048787242061, 0.9035938119647003, 0.4891292917632124, 0.4867680312986328], [0.8520807287963726, 0.33489532872809824, 0.642647900078375, 0.7431013357148525, 0.00483607381712492, 0.1315189555195755, 0.9089705961130026, 0.06286474663556874, 0.8137395678268547, 0.682494112917609]])))-2.4753327880253053-9.142055956714602*(np.dot(array_x, np.array([[0.8246947389962108, 0.7163689479559113, 0.9718747246328812, 0.6825815274253042, 0.6721430126116262, 0.6731696428052357, 0.8848714725530628, 0.6510364912231665, 0.43129412210635143, 0.21610262345174092], [0.5739863129814241, 0.49853965703835956, 0.0168479434551162, 0.4889130628400189, 0.42271101716695936, 0.8584577040487485, 0.41272886985196144, 0.32034711981073005, 0.7917510703503253, 0.9091113690880036], [0.5414027068210262, 0.5647870708786791, 0.4073752642216626, 0.7039745136289275, 0.11424764789460418, 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np.sum(abs(array_x+3.1597541343148565*3.010381303822461-5.159156334325972+array_x), axis=1)

np.mean(np.cos(2*np.pi*8.803029929602925-(np.dot(array_x, np.array([[0.10316512903090558, 0.8169213449895041, 0.9260263208726175, 0.898222620914577, 0.5235180367141854, 0.03152075274093613, 0.20998505473655382, 0.41639971968429135, 0.09441002862813397, 0.16164074256666283], [0.5554681470963139, 0.30836493628879647, 0.2984394293102326, 0.4392139541782144, 0.43897922857032134, 0.2509031734572167, 0.43214798026524126, 0.48105869903467435, 0.9865210467061443, 0.11269282763746769], [0.8236619030253601, 0.4705602580258452, 0.9882665275386557, 0.8231860708375363, 0.7472216916155564, 0.11134890218095306, 0.3502015363277048, 0.4677499704092102, 0.8763095109603256, 0.2251652284071598], [0.011624043681470297, 0.161750184627374, 0.25421344264858436, 0.2051389564134325, 0.47486144290696997, 0.19567286460143452, 0.7857579552698735, 0.8180967338793147, 0.6488303573105167, 0.2879851626737683], [0.559395168095528, 0.3711369118613347, 0.11120949170383299, 0.2500729901947568, 0.16061616877508178, 0.635729958531444, 0.4206631254302894, 0.5557220660023479, 0.03509701414686883, 0.0004369058652826663], [0.03091191516255054, 0.5498765703344277, 0.8200578185427149, 0.09828767497160007, 0.878690343730417, 0.22581606939154264, 0.8013428715056393, 0.4343635511868862, 0.5288285648442747, 0.1766293495549266], [0.5008820691768201, 0.47971608428332924, 0.3610631570913908, 0.212407538257966, 0.8353782547516423, 0.8249741846387402, 0.8452978436607407, 0.2295812291585423, 0.10632150469981316, 0.3023501156920392], [0.25031772126408836, 0.41151140166462286, 0.9517858097252798, 0.15528518808254776, 0.8454115492196123, 0.13707202254273043, 0.07987413325286508, 0.46040828352361374, 0.8810014198251253, 0.3529486231939558], [0.9070130302582069, 0.7086992433067113, 0.4157324992435676, 0.4392847807621614, 0.7476182633750212, 0.8066607464726503, 0.19513643028960737, 0.6850075538698503, 0.5373300342106188, 0.7558970787463493], [0.029983964958869347, 0.4283180957973566, 0.24501547800796408, 0.9760751251697407, 0.11672140212974069, 0.21938220679816356, 0.1688153546500335, 0.5044010297806567, 0.6930998069136916, 0.47173114569772834]])))-3.1264103303143016)+np.square(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.8433030882276568, 0.2609610092949488, 0.7275280528933744, 0.9340724041139391, 0.6814710606827549, 0.014218541085579406, 0.5084172294505582, 0.6242440185280486, 0.15079077546676578, 0.8376273179270337], [0.35532149206497077, 0.8179193326545013, 0.24371429348398566, 0.9847703690197441, 0.5411134547898184, 0.5604570700015351, 0.395301040638423, 0.7682666213588448, 0.40127401656626727, 0.6319320587834253], [0.3674160948010604, 0.9000394922857646, 0.9674515841090665, 0.8613191052213572, 0.3506720113045463, 0.49599337935049115, 0.28483982936723473, 0.740131608997436, 0.5717136555035491, 0.46735314971912745], [0.9982153429125188, 0.50872253389664, 0.9369028931201696, 0.027824513954814, 0.7861853769720027, 0.6667641995867819, 0.9610218041328075, 0.25122110227895456, 0.3212885043671657, 0.23522442668127763], [0.22406570863421527, 0.7674683680897315, 0.47188272998326686, 0.12111803261495246, 0.08080851686603951, 0.9463208076201236, 0.6882384866500237, 0.4582876338221683, 0.37236658785861776, 0.3924036571452325], [0.7612701361825491, 0.8208112469265727, 0.6479043901613323, 0.06660475701656354, 0.25994684912324384, 0.5763859061839934, 0.09705557034915524, 0.6732214670690863, 0.5455064205146674, 0.7613466038878943], [0.3636587422420209, 0.6794087848353261, 0.09192751717854575, 0.9286918308619466, 0.3954692125248812, 0.6620595962279917, 0.35515779347021326, 0.9673161089909542, 0.8267353309000395, 0.1451080205402162], [0.6468694105903634, 0.05845929609575795, 0.4904027930445851, 0.18657251952892095, 0.0638805499922781, 0.8331469077384032, 0.39889696806467145, 0.5148753172415231, 0.7193859163148891, 0.5422557793152804], [0.38683058021340466, 0.7536188426571985, 0.620750242143201, 0.1934233705862678, 0.5912164298895629, 0.0514970353470976, 0.2639167805829723, 0.04771232361576394, 0.08874793840094974, 0.44006675815966945], [0.8793296881549989, 0.6754672647959877, 0.3764481600870184, 0.8644048548873549, 0.6151644083034487, 0.23783509025991534, 0.8296345273471811, 0.7320678045475696, 0.03747226356148281, 0.29351051232116443]]))))+(np.array(range(1, array_x.shape[1]+1)))+5.987782483659282-np.exp(1.6186014631341088)), axis=1)