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250000-randomfunctions-10d

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np.mean(10*(np.cos(2*np.pi*np.exp(4.541950359129556)))/np.exp(np.cos(2*np.pi*array_x-array_x)), axis=1)

np.mean(7.954584416229838-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x+5.066533751182913*(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)

np.mean(4.017677408787099-array_x*7.776559632762476+np.cos(2*np.pi*9.812865164429185+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(2.077752648079717-array_x*9.399571014767288+np.cos(2*np.pi*5.43888250810388+array_x), axis=1)))

np.mean(6.625392625472263-np.sqrt(abs(array_x))/8.635881045525826-8.108292218221443*(np.dot(array_x, np.array([[0.5555902989097499, 0.9905539466463207, 0.5725655564109741, 0.935375701118968, 0.46158646489574195, 0.00243009763771318, 0.8126426469574666, 0.13942318148111543, 0.7408914589589999, 0.38674606749910945], [0.9763247690247935, 0.9060497964096509, 0.28961431573517205, 0.5481543415135844, 0.9288236675795062, 0.1407533122491974, 0.03436065119729215, 0.3818417941651546, 0.2882862452944157, 0.6116170307069192], [0.4409419071943451, 0.9966698117508904, 0.9755226207008394, 0.5432712253126051, 0.5186624646379131, 0.6077033213179186, 0.5266271327489619, 0.40151768160738743, 0.5416177784148474, 0.44922799836297167], [0.8467035691529377, 0.406656207748161, 0.15846464823596684, 0.39630202057864716, 0.25941650864406507, 0.9198612917418898, 0.8303250703529135, 0.722346698572828, 0.0032370885530049742, 0.3557000942325954], [0.1382495346030136, 0.28192568116030114, 0.8516445301201393, 0.8491316538518418, 0.6356287422033438, 0.7828767499525023, 0.03760193631819986, 0.3081353967518716, 0.2082686522657764, 0.2344230918475675], [0.3852303416599958, 0.8776924887799586, 0.7743051021106534, 0.9459151387451292, 0.4797483380851515, 0.7934290278428131, 0.7835458188670812, 0.03704917624438897, 0.6547805213045345, 0.21972557988774877], [0.2066073414068167, 0.6331566225406142, 0.9972480284730992, 0.29445715825781427, 0.5438114640993402, 0.2181669562056846, 0.5856919187166348, 0.6190912098849287, 0.5886549343601898, 0.783313092979688], [0.15386492439971566, 0.10958770805279039, 0.8755168743973921, 0.9027139199265697, 0.4185737814866285, 0.6594572355809969, 0.24782474106957442, 0.7159315108189095, 0.9396381165129574, 0.7455185328553335], [0.6894966561618171, 0.7735160443368825, 0.07318287656336508, 0.7058891620936069, 0.8619210170177757, 0.5202952109797229, 0.5221937392428931, 0.21336243267746757, 0.9037384383086053, 0.45266381096123487], [0.4075468935709282, 0.7846293032915175, 0.8633686786247698, 0.7062376650520729, 0.6680493433369336, 0.2121864958656683, 0.018905280810582292, 0.4395475394982816, 0.025801884337014358, 0.5154068990577226]]))), axis=1)

np.mean((np.dot(array_x, np.array([[0.3996342090116227, 0.5737401202584697, 0.06917619893210625, 0.6779677540809631, 0.3722269959058654, 0.7848700688006665, 0.13302655467734525, 0.6659157674340633, 0.34205103677096615, 0.6242309158502759], [0.9996475383577675, 0.7617222321845037, 0.30740185401585607, 0.8300083639200445, 0.09188391979807498, 0.6109687389906212, 0.11705998123541672, 0.5079774695694115, 0.5508109659352052, 0.059438795392623645], [0.5219040036329493, 0.30060826933636675, 0.9906700053052891, 0.41373684734537963, 0.9810804606027655, 0.022720343207661697, 0.8211839402844997, 0.29448721582454307, 0.0662415771422683, 0.1933134024241152], [0.23832504829259282, 0.4062273850236312, 0.42696227378143714, 0.06750375107781681, 0.020525573214426274, 0.07904118194988285, 0.07080441743565691, 0.6941441756331547, 0.22468660718422329, 0.9970705260016008], [0.9505977339132928, 0.6494781176426794, 0.24471411655069908, 0.7757035683389977, 0.8369538744237076, 0.992069005305027, 0.01506628681106359, 0.5957863589956259, 0.37177676995222175, 0.6936240979779632], [0.2817279068735724, 0.7208111056115589, 0.7543073425260954, 0.19693381433139578, 0.6223690705791917, 0.4442992821240914, 0.6917688263614389, 0.33917502098757035, 0.9551073262406651, 0.41321278379039295], [0.7323633069509818, 0.25909483481766793, 0.5278667092597115, 0.3976269317443931, 0.4538892814405723, 0.2998281261400272, 0.6973507405130835, 0.47296530654237434, 0.3592983840464855, 0.5562078342868884], [0.9246975148768057, 0.9522057124971922, 0.06661812378679266, 0.2529422694373127, 0.9126395986737108, 0.6787749385590357, 0.12041958348412873, 0.6285664356812007, 0.5465740290176442, 0.796414326556004], [0.47481421853236416, 0.0819080006069195, 0.6450824783350757, 0.1492227433488802, 0.7345622260770588, 0.9053069835364403, 0.012388464895601303, 0.4273637686361901, 0.9037117518647455, 0.47181601231392467], [0.4490184672725892, 0.32439865889439423, 0.6509010266104034, 0.07490269954201789, 0.6263116043618414, 0.40436991827452906, 0.15770277338273686, 0.7853275263303775, 0.8810731389957246, 0.42071616489517816]])))*5.664126569134578-(np.array(range(1, array_x.shape[1]+1)))+5.555396660993287/6.881649977584328, axis=1)

np.mean(np.square(8.898448257228825*array_x)-array_x+array_x+6.722941478867894, axis=1)

np.mean(3.370088311625467-(np.array(range(1, array_x.shape[1]+1)))+array_x-np.sqrt(abs(7.45734069142988/(np.array(range(1, array_x.shape[1]+1))))), axis=1)+10*(np.sin(2*np.pi*np.mean(2.4563152498780187-(np.array(range(1, array_x.shape[1]+1)))+array_x-np.sqrt(abs(2.571742103916244/(np.array(range(1, array_x.shape[1]+1))))), axis=1)))

np.mean(np.exp(np.square(array_x-array_x-2.14497295250912+(np.dot(array_x, np.array([[0.6570716655595593, 0.8888144084272028, 0.32858248861879324, 0.9452855432144301, 0.6826767418886678, 0.3112220573554415, 0.6541666175675273, 0.09694192115184241, 0.11729804760627849, 0.8726978702126913], [0.4111388531776965, 0.1126902969929493, 0.9818189264199827, 0.9784581668031369, 0.5826675894187534, 0.919977981905696, 0.48401926066587475, 0.9057434144852657, 0.22697625709660252, 0.3931331941082905], [0.018239458558473753, 0.654116601045004, 0.42273513388833617, 0.5647717180612062, 0.758586612280248, 0.2753332739680102, 0.21522719609636332, 0.8216555044918264, 0.17787933950270873, 0.8461475024642283], [0.5089177510559898, 0.5053551916783802, 0.6067352261599708, 0.8043666588943241, 0.46506397937481336, 0.6462695907112164, 0.24677786388062106, 0.08042509959545885, 0.16760067565996073, 0.07974139295200433], [0.3869330987724533, 0.059060058911129976, 0.020664849128752216, 0.8469177639635962, 0.8993626677612873, 0.6827998952150502, 0.09415561539731165, 0.3911861648180287, 0.8886039100914358, 0.6606620711306406], [0.6627808154656765, 0.06841936487969447, 0.2313675411989179, 0.9633963892175292, 0.9079781501280393, 0.8991950199721697, 0.05969202912196858, 0.9673232587798168, 0.6455513065585601, 0.4877305943934479], [0.545070100794844, 0.32034000559966647, 0.7135800321280742, 0.4637289528405868, 0.511201854168916, 0.09508043645876219, 0.34243695513301575, 0.9433348428252059, 0.49826892405702594, 0.9478076993054575], [0.8481119157525421, 0.7310995225204872, 0.43059832842422563, 0.819606632056181, 0.9582792500148684, 0.8048869720358345, 0.46352394235108973, 0.008470581334180327, 0.15402501913928257, 0.14372113314417356], [0.969822202292221, 0.9802197038799249, 0.9570860381656817, 0.7702025047295941, 0.077918679733739, 0.07472583535900956, 0.9270680155955675, 0.40998941647574294, 0.26268658553964475, 0.8861230209085704], [0.3512012439068406, 0.7515458448475472, 0.36241232349220986, 0.7730221871283558, 0.018734472222755705, 0.005836549388477108, 0.7172974233228566, 0.6338049889186801, 0.3631752086982646, 0.9926133011711662]]))))), axis=1)

np.mean(np.square(array_x)+np.sin(2*np.pi*(np.dot(array_x, np.array([[0.7196818001967271, 0.27884239618211404, 0.43726744265884876, 0.13522386315105817, 0.23136356400940616, 0.06979580555333709, 0.5196208936447623, 0.5049619086128931, 0.721441783494295, 0.8699954436119599], [0.6373203626356803, 0.271376370171017, 0.5116011724381878, 0.33998265380856896, 0.5593717638697129, 0.03236741432228063, 0.9400002424364745, 0.3484636930243129, 0.44406922644832014, 0.8379815453419589], [0.5591425355683814, 0.7544359669786934, 0.7356870765796081, 0.6555556264361128, 0.8651477336060699, 0.4515698484904108, 0.2719782124567135, 0.16633897206882575, 0.5656047594929127, 0.08612837168434018], [0.6048855127589569, 0.9089895284428905, 0.14391075751721838, 0.48489775783937383, 0.47305036997512206, 0.8006879119026608, 0.45837263421531194, 0.28471960414410913, 0.9658991112917702, 0.751322173276535], [0.27216884845340406, 0.12131952085994269, 0.7987734733512466, 0.7221376934865138, 0.1708444866888078, 0.4472793647146637, 0.9296248961098671, 0.09690420223703611, 0.9230315850723543, 0.43752239079078514], [0.5908872769618045, 0.005453454166788774, 0.594881800448191, 0.8016465464105109, 0.9276822874534287, 0.3476763245209289, 0.14366050501879823, 0.7358989962681483, 0.4226750344005934, 0.7239943498927119], [0.7023038498044155, 0.301533883950874, 0.027493495648476363, 0.3848431597743881, 0.6740525764441552, 0.01953677529557374, 0.19268962364887487, 0.8512890441468471, 0.4834309420087448, 0.8626088759317626], [0.642669962183388, 0.7564286603827733, 0.905409148498388, 0.8352309834396885, 0.1661419173341856, 0.3259927482975997, 0.15577518229238174, 0.7324429885482144, 0.7680634725849519, 0.19955626219176703], [0.7412564301284641, 0.8369964012312888, 0.28024355934318945, 0.9939968424936122, 0.5488154248502458, 0.19863055702700716, 0.46946062690936796, 0.4049471168671729, 0.7070018395225256, 0.4324400973806932], [0.6692486132644713, 0.39949437339384297, 0.9808599924289261, 0.03923702532554463, 0.9279723125925904, 0.79320258933226, 0.6613937786253086, 0.12535819913112156, 0.966638133718038, 0.5055208466262582]]))))*4.933599259014233+1.4772091513795509-array_x-array_x+7.297360573529416, axis=1)

np.mean(9.857730610509321*(np.array(range(1, array_x.shape[1]+1)))*array_x+np.exp(8.69412912944744)-1/(7.560323534965549)+6.4658378758134445, axis=1)

np.mean(10*(np.log(abs(10*(array_x-np.cos(2*np.pi*np.sqrt(abs(4.597797972646864)))*np.exp((np.dot(array_x, np.array([[0.33079103601108095, 0.617145583993806, 0.2217775088848336, 0.8748769372793459, 0.3906712468921323, 0.8221394880483767, 0.645096506119166, 0.734393103283423, 0.4954011408746305, 0.3280513104270337], [0.2056535036855125, 0.8736715323340839, 0.6398688691540735, 0.6723641340705249, 0.8723917910375835, 0.5943186959087434, 0.459015566492345, 0.7185581923450526, 0.6891111868212068, 0.5918650473486148], [0.7526878638382691, 0.2898029960615309, 0.4584382591331515, 0.1150948737952684, 0.4250050728768321, 0.9774268002557198, 0.771085896710291, 0.697475270054205, 0.047976718994715783, 0.5523193720446172], [0.8893936022562181, 0.5337599575762384, 0.6777806981108169, 0.05438951076211007, 0.05523541670495058, 0.22370921225536244, 0.5515048534201853, 0.8501275981882795, 0.3948048399678946, 0.718609507718101], [0.09191492974072624, 0.7316871418450381, 0.1635321609820254, 0.7890013973687751, 0.8979977446990798, 0.6487671901154833, 0.31104626215689823, 0.6644165656847835, 0.48731193480190516, 0.05668855739507361], [0.15873039634390285, 0.46164392600530013, 0.9630258328231138, 0.6044367660526849, 0.6488149789355916, 0.47294735571102153, 0.917251838591296, 0.30172227102509164, 0.2984715216788205, 0.8921563233744909], [0.9908700210193102, 0.5549262691738656, 0.7413381758549572, 0.6247424802045207, 0.36543959383352864, 0.6598657673959376, 0.3208960315439353, 0.26192256318140283, 0.7433836295661157, 0.18964390042068535], [0.6475121288814022, 0.48579655858522386, 0.5453680944084706, 0.23436911197667187, 0.9512092136929935, 0.06594775839059652, 0.9442885493478659, 0.08167679851229936, 0.21912435877866387, 0.9090947739762137], [0.2472504645784147, 0.5980042034676248, 0.07903797244049016, 0.16677639176510795, 0.2525625542994091, 0.024379814737027328, 0.3584346210590317, 0.8258955800306358, 0.9865125163550764, 0.7701537041616552], [0.47944335693876816, 0.5282291875805637, 0.09054622062455342, 0.16566055058930507, 0.4627085426635439, 0.5951775709736379, 0.6556224348399403, 0.3533509899923616, 0.34276182700765967, 0.5497386397589417]]))))+9.597093963636985-array_x)))), axis=1)

np.sqrt(abs(1/(np.sin(2*np.pi*np.round(np.exp(np.sum(np.sqrt(abs(array_x)), axis=1)))))))

np.mean(6.064509365681628*np.exp(array_x)*3.06818346935488, axis=1)

np.mean(np.exp(np.square(np.log(abs(-(2.3574399142850697)/7.274003114456507+np.exp(np.exp(np.sin(2*np.pi*array_x))))))), axis=1)

1.564101559264739+np.exp(np.mean(8.15352213495446+array_x, axis=1))

np.mean(np.square(7.342850893770347+(np.dot(array_x, np.array([[0.3707969189456285, 0.6365710892356641, 0.9811795011684139, 0.8329852104768282, 0.3478506086008164, 0.10080604558103334, 0.8615565708745976, 0.8004553669264309, 0.03371049737294918, 0.7076263624382848], [0.20226749388619558, 0.0385128936325303, 0.3765438435655326, 0.2224210066951926, 0.7878094011521892, 0.1889199611752549, 0.10901764360489619, 0.2184352685252321, 0.21045812195532598, 0.4890490692174203], [0.6300943847862929, 0.5394976774026603, 0.18538102997697592, 0.5892872431134037, 0.9879088148719931, 0.298795626620163, 0.16407600617572637, 0.442712723659331, 0.9047639371084482, 0.19671033458703635], [0.7092336141168014, 0.9525929737507421, 0.5235101690032852, 0.5259185143879527, 0.5698116941333614, 0.8551120864286478, 0.29585622838170944, 0.4160343418145881, 0.38264847976909144, 0.28172302375575065], [0.5539939734686179, 0.5568104933119916, 0.5877607966777237, 0.24037011615701231, 0.869870543834384, 0.47225330559392176, 0.954135789039366, 0.9371955769937393, 0.4917121744680174, 0.8504448161371568], [0.06888341552759114, 0.4706452957223549, 0.10229499690303046, 0.34842849549571653, 0.2445378153380784, 0.5472610715141503, 0.4381623767875149, 0.831266021351446, 0.4517438567742902, 0.8658445439054809], [0.06616997067019836, 0.3326206993023485, 0.5738509340683972, 0.7716591595379393, 0.5310987057459244, 0.4651350181723135, 0.48589310437169453, 0.39087711741338216, 0.6828380712755482, 0.23674159277841533], [0.6802722332620389, 0.2642358093435213, 0.43512138363027386, 0.058123251473632065, 0.8216727104314505, 0.03583899442130267, 0.50613523007319, 0.11193989738290266, 0.6439324372933224, 0.7058801221048399], [0.28459589502123495, 0.48599143595847083, 0.873335821258827, 0.6367016403326925, 0.6352807584010881, 0.2651031739706129, 0.898695928567232, 0.6619755617717474, 0.09898226533019117, 0.5317174241411872], [0.5096841441547978, 0.17024396988893553, 0.2519792575350521, 0.3486227944706939, 0.47158262999619105, 0.2988827909881906, 0.27608802676597444, 0.4334053903360312, 0.06596843617586234, 0.1264367246830257]])))-(np.dot(array_x, np.array([[0.7447429552445433, 0.9271796847916377, 0.6095486814621778, 0.5029258434676055, 0.8614432222801763, 0.9346911625417336, 0.5354822911789439, 0.6481201066286103, 0.24939844781676568, 0.6769099081608804], [0.9942586924897838, 0.8459326004683536, 0.42073317739857885, 0.8248051872967412, 0.3474730690638014, 0.5498739725464553, 0.008140918707436051, 0.29137564898259327, 0.28492444656276505, 0.9030821739729276], [0.09950824928691593, 0.23541006394473507, 0.4633310250633439, 0.5134366835279393, 0.258094984923366, 0.5737010662687415, 0.9158530180472945, 0.40677267497327996, 0.7036555016279817, 0.9241078873001292], [0.5279902149705066, 0.11915110782565763, 0.9771775764297574, 0.4089296063509975, 0.044462246700254004, 0.10741427867647146, 0.5626206910279993, 0.43529435504123204, 0.6534382832470471, 0.44867126498058674], [0.7437363928212885, 0.382893447094112, 0.8240749915790407, 0.442137559344259, 0.9833431815094946, 0.9330699990288646, 0.4121408547239257, 0.7621628708842689, 0.7646289721812872, 0.907023987375608], [0.7703590303755083, 0.6369403829417054, 0.7768969212928837, 0.1739200405560274, 0.013231397442528547, 0.18735952487144159, 0.1351828681051923, 0.8429682793243076, 0.47716516822155286, 0.8343885456965041], [0.4248236090659835, 0.9694549999420202, 0.6162235966138943, 0.8112386190450082, 0.34281132133053704, 0.9791163222319736, 0.2728794001098921, 0.14387738022355634, 0.5364976308430767, 0.3433002876150305], [0.9673778562678412, 0.5267079452135447, 0.3071810235591672, 0.8312619116184375, 0.14277349650323268, 0.17379681960073257, 0.5709947631325535, 0.476279599485236, 0.3724102452911854, 0.08586977393580386], [0.06206478252732084, 0.30019984245995124, 0.9407599775456528, 0.6942762221418644, 0.18236232234771566, 0.6829146078512991, 0.6177041812223487, 0.05804377437635011, 0.7069010849910471, 0.3081455104522527], [0.9662167942292887, 0.6705697426016494, 0.8012868584870085, 0.7475845193605727, 0.37583686796017723, 0.5880399132061179, 0.28182552044993525, 0.45843317857241517, 0.67085395099355, 0.2850394314393371]]))))-6.4932003416494855, axis=1)

np.sum(array_x+array_x*8.232489203288882-np.square(7.797661891860316)/np.sin(2*np.pi*9.951992580719526), axis=1)

np.mean(np.square(np.round(4.764607801352954-array_x))*np.sqrt(abs(2.274705180825732))-array_x, axis=1)

np.mean(-(np.square(np.sin(2*np.pi*array_x)-9.957280394447494)*np.cos(2*np.pi*array_x/6.740996632717904)+np.sqrt(abs(1.5006640834336986))-(np.dot(array_x, np.array([[0.7315125378322481, 0.41416606901887776, 0.3088593714556941, 0.13129520089237312, 0.4152520020936311, 0.8260713751430938, 0.16003202393802762, 0.045269760649734536, 0.30347495818187153, 0.82634663270804], [0.02151994065423546, 0.21551900519846479, 0.9455378751776194, 0.6493513515984386, 0.7663846128079612, 0.3866117575802729, 0.5375634820480569, 0.6999916322019428, 0.2619740543134905, 0.8247378340523909], [0.8564799714300068, 0.1929581194789789, 0.6941944422044203, 0.9908770236324489, 0.3857972103781814, 0.7123807607992061, 0.3839996651552502, 0.2194042971510416, 0.19844015893118727, 0.36523553153756183], [0.6101274751536612, 0.30542243355390486, 0.3720036612022709, 0.6624040314653357, 0.9806080818120757, 0.351274565952038, 0.20684013383567013, 0.741028156189278, 0.6159640843320014, 0.7638892669591819], [0.8982387908529835, 0.07010482074027047, 0.025340296022230202, 0.6897528084292047, 0.3969504333617203, 0.49747975643144704, 0.3106019540175825, 0.9820471984548661, 0.1486302707931103, 0.23011395084694908], [0.046543701639774504, 0.8973960208183569, 0.07795739850513295, 0.01448471369444071, 0.31373870619806465, 0.2020360847922641, 0.8927923152971214, 0.2622061503976242, 0.36823868338804266, 0.42260440537636146], [0.9564138434003984, 0.8739742876304231, 0.04091442490805419, 0.9300273884867681, 0.06197988948092603, 0.8860341600367609, 0.3620478272379276, 0.8345805696854314, 0.49435498762606234, 0.8397966993770356], [0.6957227921558051, 0.3079188303810939, 0.27659906467592554, 0.9667550128529455, 0.0172019390473096, 0.0020493818350956206, 0.4544920640036042, 0.9732668645178061, 0.021536310916413948, 0.9612235769627904], [0.908147581114326, 0.06229606183382819, 0.3200753181071275, 0.08132111763700522, 0.5680399337590976, 0.6539300707798439, 0.8006897096412563, 0.7865758753932979, 0.27133247224393753, 0.610977074684784], [0.2830485344098703, 0.7511991620641059, 0.06173968436168731, 0.799563941614524, 0.23704349938439184, 0.28141688304904466, 0.6087590242558638, 0.35870907529523666, 0.9165754161139141, 0.35707201611102624]])))), axis=1)+np.sin(2*np.pi*np.mean(-(np.square(np.sin(2*np.pi*array_x)-3.2392554134079385)*np.cos(2*np.pi*array_x/4.162801527118553)+np.sqrt(abs(1.359726979067864))-(np.dot(array_x, np.array([[0.017773881579585815, 0.6337379023858959, 0.08924109865735119, 0.9094359753211213, 0.6554222074943129, 0.1900060826783191, 0.2086401886783541, 0.36657551416051104, 0.5483824320306806, 0.11060791349720689], [0.25720135025643875, 0.21974774773351946, 0.07479476603555513, 0.8913732700128918, 0.6105751094810442, 0.7481022562613088, 0.3761319166475392, 0.17589181682553368, 0.6430981327908418, 0.20908430260330402], [0.14800026961671875, 0.39503021235494185, 0.8259675378735987, 0.9112342541491353, 0.7169003908808604, 0.021639726267312254, 0.12881250026681867, 0.6016174698430667, 0.4552282547443609, 0.868248438132373], [0.5840742646000251, 0.6356487947343028, 0.6471178638134277, 0.8942030617963328, 0.11279140873063509, 0.4818411724125863, 0.999311939995892, 0.8716876191308964, 0.46580682824030173, 0.7490236547931868], [0.4224048426802832, 0.7480556410228891, 0.26914522201282887, 0.018815466104366463, 0.7335182296575817, 0.8009492735748914, 0.08447421919366371, 0.6922491459597182, 0.8135687512671185, 0.30105020934927396], [0.7881508767176258, 0.9504821507872596, 0.024510449171342263, 0.6203921657085107, 0.8049804440537592, 0.4416413169890179, 0.5632437157721527, 0.4415726457278982, 0.7861350373411238, 0.14800088358070373], [0.5626754685393921, 0.29898624257501316, 0.9963075455498493, 0.3336589790012453, 0.5777521110957589, 0.4026192645815915, 0.8421166240171868, 0.5881637462473377, 0.3487222058708488, 0.12419964817356244], [0.24207347789451228, 0.7530834950932269, 0.1917353296535198, 0.10299709835442983, 0.0031631492841002196, 0.13861928983972638, 0.47956858876287956, 0.3917153863622338, 0.970717246187715, 0.3272208874727176], [0.11195655273836203, 0.8015842357731994, 0.24081926251134533, 0.33419926203087125, 0.7149460003729384, 0.312760465673275, 0.9533966344539239, 0.7479687960701469, 0.11377494692022494, 0.7833806194067698], [0.5998712604558643, 0.6672865808250416, 0.8715815438796737, 0.06550700111391505, 0.5873042470268225, 0.47654545667017423, 0.09849788972122064, 0.47041971864076026, 0.6218229346333954, 0.038712588457499475]])))), axis=1))

np.mean(3.4772633479302693-np.cumsum(3.940014776323197-10*(np.sin(2*np.pi*array_x)), axis=1)-np.round(np.exp(np.sin(2*np.pi*4.153650682133896+np.square((np.dot(array_x, np.array([[0.9409298747549193, 0.4281571616299006, 0.10032074499516108, 0.3005457304858097, 0.2763100948439412, 0.7261465670382616, 0.4170415780218386, 0.9646108563803589, 0.18293112514709786, 0.1748248276468436], [0.4236039685875256, 0.9750002611361459, 0.7598480530811247, 0.6674874030448398, 0.43711843249785454, 0.28652960150129647, 0.8269824453095034, 0.14943177745683667, 0.09664685674827489, 0.9911980046040386], [0.23885649553169508, 0.6948773076362644, 0.5110531830589755, 0.9356104193170534, 0.27317113322846587, 0.0034893049062957315, 0.9121177309620405, 0.8683971942052249, 0.33575526934346867, 0.7304032823868972], [0.16454677016860986, 0.6414562691283184, 0.8198754974171399, 0.8070077481647913, 0.8914704487267303, 0.39628503461142683, 0.17789825826577366, 0.3032906945802185, 0.8404514932017543, 0.005462784716612323], [0.9260548108476506, 0.5188313359935746, 0.38358005820760044, 0.5316968981581158, 0.04200361199642755, 0.4443729532739146, 0.6000152530148429, 0.048380981630574804, 0.1986999515566431, 0.63786906669176], [0.3023384865415888, 0.3915676950575089, 0.47694526338461274, 0.8843068217754216, 0.5447001507057216, 0.15986142939707737, 0.1787353625708341, 0.27698876322443344, 0.9367677238780943, 0.06722171407699895], [0.9795797341205059, 0.9725392178033373, 0.8724608351615349, 0.5704814463369778, 0.26887930220028056, 0.8101571340764284, 0.39708436050664486, 0.005006915060656758, 0.9805827231955236, 0.8879031436740741], [0.13594387440278521, 0.5571179217481667, 0.733502538883459, 0.6669314385517736, 0.3312553530859914, 0.923859844193546, 0.9881051549329892, 0.9706506458235968, 0.2299568960058226, 0.6980795147595823], [0.15171524489493915, 0.9504618035311078, 0.8242152934330202, 0.8151639783522767, 0.28726266041120496, 0.09725835321048759, 0.5738744579116216, 0.20305756935034858, 0.8567853647312614, 0.3941780040073215], [0.6245976711152356, 0.9054969090724171, 0.09399686882797487, 0.7054504396855683, 0.6677222598115297, 0.16002463340251993, 0.030031090628017898, 0.8359989787604102, 0.36999633483135674, 0.4417933904484972]]))))*10*(-(array_x))))), axis=1)

np.mean(np.log(abs(9.41333395295139))+np.sin(2*np.pi*array_x)*6.495676649571297*np.square(2.798448181169944+array_x/9.996715397098528), axis=1)

np.mean(np.square(5.955824013240928+7.1286800574161315*(np.dot(array_x, np.array([[0.6133057377429154, 0.3957069151521714, 0.9659278955128304, 0.6408354948713952, 0.861497682955603, 0.8754038874683314, 0.18751246120609066, 0.08131566188091766, 0.2053782174799148, 0.5231966714894226], [0.4500318105268475, 0.6953451951938677, 0.776772594659735, 0.1953702244076001, 0.544229162098665, 0.6010603541288385, 0.41511557606050076, 0.46028599853581487, 0.4600726376320985, 0.4774600253167114], [0.8659536665688626, 0.6246593944926584, 0.7261720875941217, 0.23473246391718217, 0.05139664109713726, 0.8461171123049374, 0.6819489560966101, 0.09574762028220163, 0.043932462721485765, 0.15839953779516658], [0.40578136023028344, 0.8922199669027767, 0.7675640610695775, 0.8295414447557582, 0.3219569842900484, 0.5197812723787482, 0.3258077365898734, 0.943148099211346, 0.31746553959693047, 0.9024700813522648], [0.6968516021974461, 0.12200499300994738, 0.738823949568789, 0.02065143194402619, 0.8859857365343354, 0.6827882430321593, 0.0014575240968967007, 0.19645179592211848, 0.9628007827395538, 0.027731920605477023], [0.14651797517258214, 0.4327241452239844, 0.43407182270645406, 0.2439712271890282, 0.929399014331473, 0.8637249479991618, 0.8925339816072897, 0.45838188716778216, 0.28865596517629855, 0.29851591995183036], [0.7379566257984375, 0.14599500227686713, 0.14052993441975592, 0.5375987751524253, 0.41879937757367347, 0.4626860906504453, 0.6398090450064892, 0.11801972810298478, 0.6970556765522496, 0.3238171264713562], [0.5311257093740148, 0.47411216480682383, 0.22236998392675944, 0.8154344625713673, 0.30536190695464493, 0.08193031586511546, 0.33366272809703623, 0.29940736027043846, 0.8918567457435761, 0.30847232520605483], [0.2859158642309729, 0.6787017633272199, 0.043841877877656144, 0.9688649973373895, 0.3491216573714375, 0.7304720471112378, 0.3427202520089586, 0.22197255156080642, 0.23452379616018748, 0.8536740562278914], [0.5167711770053407, 0.4029287673822858, 0.8525178620475975, 0.7536517666478668, 0.5844868752334673, 0.49816216728954554, 0.026318631502136647, 0.16626411757685877, 0.347379768496902, 0.8019874865552369]])))), axis=1)

np.mean(9.978369853865985*np.cos(2*np.pi*array_x)+np.sin(2*np.pi*array_x)+5.578524893872864-(np.dot(array_x, np.array([[0.19545629342875892, 0.9376927505324208, 0.5384494697049584, 0.6836493955927611, 0.9535549231528194, 0.8293832425041132, 0.6230931234397212, 0.8555606711406243, 0.5041248054366003, 0.056602316975625566], [0.8256833802142565, 0.24942024832840326, 0.14830786966711673, 0.7759320760696555, 0.38219169761045524, 0.9554971280508703, 0.020137983078279076, 0.9577450636690299, 0.9771209668090658, 0.2255596454726032], [0.22869907493188601, 0.18375391006661057, 0.15538422265518037, 0.8816280498507191, 0.547138544790959, 0.4272080133284277, 0.9022695147474231, 0.6995723408966134, 0.5806722986177537, 0.7394384192387915], [0.9712960072296227, 0.22396905039387482, 0.7607324791208622, 0.6894221155146439, 0.07521237217503707, 0.5166076090017481, 0.9304187240778891, 0.43752727671587943, 0.2503065378936, 0.6622840071560105], [0.5129544402626508, 0.26710728319398624, 0.9058690953885041, 0.20436221709987623, 0.3459177140743168, 0.09172990116816893, 0.44210748393219745, 0.3192937214181082, 0.5608302847816123, 0.5674601405609074], [0.05088055385881507, 0.7177436514606297, 0.41138880312431436, 0.3374366771009696, 0.3356587752798207, 0.38166344964321375, 0.8477792786926428, 0.794802030739545, 0.22337309658903048, 0.7613773729345813], [0.6269355041456004, 0.32616898651422277, 0.019572069349410137, 0.602015869795681, 0.6607185337991418, 0.9961113428643782, 0.6875462528877111, 0.9242568290322735, 0.5358185324327408, 0.7751231287797886], [0.8922816525823773, 0.41761816836474974, 0.14862172631686488, 0.10944647556539866, 0.43373070496423627, 0.9546461618559948, 0.4403376369363089, 0.5953042739132919, 0.6623155700668217, 0.9871717266231301], [0.0420058347063077, 0.11099298066812058, 0.3010928683148417, 0.7497959142573127, 0.48579488153714045, 0.3340360986944839, 0.9031478300147117, 0.3030269919911188, 0.4755974697239328, 0.6260969850599796], [0.926625902854293, 0.613227797250732, 0.7655906870169271, 0.2956987491891532, 0.10762814594724512, 0.9732685130554731, 0.5173795883301664, 0.04646571081100803, 0.5008396629540965, 0.9625283624043952]])))*array_x-9.316033631657401/np.log(abs(array_x)), axis=1)

np.mean(5.111833170468861+np.sqrt(abs(3.835233565359096))*-(array_x/8.827264455031802)*abs(2.1059500273175282), axis=1)+10*(np.sin(2*np.pi*np.mean(4.197505393864251+np.sqrt(abs(5.8571830405597805))*-(array_x/4.806611638787052)*abs(5.886834818987596), axis=1)))

np.sum(array_x*8.974228507733802-6.995214747203574+array_x*6.394278465784035, axis=1)+np.square(-(np.sum((np.dot(array_x, np.array([[0.3280322885496392, 0.7003782488557875, 0.39750139081653924, 0.5823257147233496, 0.1143435565307036, 0.016627342043163185, 0.579706662628369, 0.61795849932872, 0.145677395323629, 0.9971154686308519], [0.13356961984147597, 0.6295825542221368, 0.9749102123113254, 0.5953981470884145, 0.5191155336740035, 0.21671333926919656, 0.818491976553163, 0.4823590703557059, 0.6474470550849213, 0.03353371769983593], [0.27089706908443445, 0.15727137953126502, 0.39383337783985095, 0.05705929999342463, 0.565024537434451, 0.3185544354768337, 0.3783495704314187, 0.5961443892353976, 0.26369368503224244, 0.47001476037361556], [0.36092756728736397, 0.03394129659070666, 0.6893149138040132, 0.8046011344105888, 0.4901914179571597, 0.38356576993507996, 0.9780843778927114, 0.4551564401280941, 0.9594437159363094, 0.4494766511964504], [0.6306138503668998, 0.08118691015887813, 0.8906722449678195, 0.547177936601244, 0.4718241312119046, 0.17968429147411058, 0.6225963095385763, 0.05805068120354562, 0.6752747440740334, 0.15384821227355838], [0.08440622181503266, 0.2662314712033681, 0.3825627978392412, 0.2629619511220327, 0.8152953256185383, 0.8968292861146225, 0.8596759608295871, 0.32252738875197917, 0.904936516765412, 0.5415340788719624], [0.18738212618208694, 0.4002242680882777, 0.18491216902661745, 0.5729014576922321, 0.7141478563667278, 0.06599462159074654, 0.17278736948696882, 0.6940974771243986, 0.7976387308208317, 0.21498260735923558], [0.11311200307532954, 0.8667647059043645, 0.29598144715796537, 0.37224994330716077, 0.8847192320547649, 0.6875005899048423, 0.4895676582190651, 0.8659484588505341, 0.6710911227343436, 0.9725121504385913], [0.5439672948740526, 0.5596490132039581, 0.1245024522274788, 0.8384747753092641, 0.2458620170312743, 0.9865527281356583, 0.7952891315121602, 0.045804636866797144, 0.496584440279227, 0.3321035784629006], [0.9027858920609955, 0.06891997845117093, 0.22798249868781884, 0.3835824634099656, 0.814803779296669, 0.35155301276654727, 0.1271288911399293, 0.589341424245253, 0.33739624369305, 0.22077712389684778]])))+1.936688120670785, axis=1)))

np.sqrt(abs(np.sin(2*np.pi*np.square(np.sum(np.sqrt(abs(6.32971178792809*array_x)), axis=1)))))/7.326140750516265+np.cos(2*np.pi*array_x[:,0])-10*(np.mean(1.694417611757645+array_x, axis=1))+np.sin(2*np.pi*np.sqrt(abs(np.sin(2*np.pi*np.square(np.sum(np.sqrt(abs(6.663900181488836*array_x)), axis=1)))))/6.883364396779417+np.cos(2*np.pi*array_x[:,0])-10*(np.mean(7.042696805957836+array_x, axis=1)))

np.mean(np.cumsum(array_x, axis=1)+array_x*5.549357719038341, axis=1)-np.cos(2*np.pi*6.524360304453987)+np.sum(array_x, axis=1)

np.sqrt(abs(np.sum(array_x+np.cos(2*np.pi*5.440535891233354)+3.103437022133473, axis=1)))+10*(np.sin(2*np.pi*np.sqrt(abs(np.sum(array_x+np.cos(2*np.pi*2.0728025280148583)+3.2939048173578955, axis=1)))))

np.mean(np.exp(np.square(np.sqrt(abs(7.010017412246151))-array_x)), axis=1)

np.mean(np.cos(2*np.pi*array_x*8.102989623725772)*3.9397458537992907, axis=1)

np.mean(3.04677431981282+10*(array_x)-7.800744584217235*(np.array(range(1, array_x.shape[1]+1)))-np.round(3.424055822377982+np.sin(2*np.pi*abs(array_x))), axis=1)+np.sin(2*np.pi*np.mean(6.408839490470773+10*(array_x)-6.742082605828247*(np.array(range(1, array_x.shape[1]+1)))-np.round(9.676930130298683+np.sin(2*np.pi*abs(array_x))), axis=1))

np.mean(5.889394920273759*np.cos(2*np.pi*array_x)-np.cos(2*np.pi*1/(4.275423575545398)), axis=1)

10*(np.mean(abs(8.022263354015443-array_x+array_x*6.166299738467901+array_x*np.sin(2*np.pi*4.432921238328559)+4.3669067251293905), axis=1))

np.mean(-(10*(8.841959293137911)*(np.array(range(1, array_x.shape[1]+1)))+np.sin(2*np.pi*array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(-(10*(2.6477623705787785)*(np.array(range(1, array_x.shape[1]+1)))+np.sin(2*np.pi*array_x)), axis=1)))

np.mean(array_x*3.8953440447381786+8.049815437964938-np.square(np.cos(2*np.pi*array_x)+abs(3.551267169405017)), axis=1)+np.sin(2*np.pi*np.mean(array_x*2.8403770326472837+7.216329446465665-np.square(np.cos(2*np.pi*array_x)+abs(2.977302865879138)), axis=1))

np.mean(array_x/1.1845988804600305-array_x/1.4974287663589672-10*(np.cos(2*np.pi*array_x)*np.square(np.log(abs(5.945444296257101)))), axis=1)

10*(np.round(np.mean(6.926330854405411*array_x, axis=1)-np.log(abs(np.sqrt(abs(3.9264254945807284))))))+np.sin(2*np.pi*10*(np.round(np.mean(3.566822055021584*array_x, axis=1)-np.log(abs(np.sqrt(abs(8.554784976435032)))))))

np.mean(10*(np.round((np.array(range(1, array_x.shape[1]+1)))*array_x*(np.array(range(1, array_x.shape[1]+1)))*array_x))+np.cumsum((np.array(range(1, array_x.shape[1]+1)))*array_x/7.76084334132169, axis=1)+10*(6.065342987959214), axis=1)

np.round(np.mean(np.square(array_x+10*(array_x)-1.3946399837644874-(np.array(range(1, array_x.shape[1]+1)))-6.693913794334955), axis=1))

np.mean(np.round(8.955317220005487)-abs(6.0062994961967915+array_x)+2.967985522906732-(np.dot(array_x, np.array([[0.5725527673703831, 0.26653481850853067, 0.6751006841755747, 0.09703253491544739, 0.31417994929602966, 0.39820191260280513, 0.056918283556249616, 0.5060002781362262, 0.5959171796652262, 0.4308920845091768], [0.2824087541012552, 0.946469531063983, 0.07772507995067346, 0.6054386178893113, 0.17421351556799025, 0.8718798545917057, 0.2551617659154486, 0.08092992460446924, 0.9872351692438461, 0.017500768574222225], [0.9993902188496667, 0.023407233062837873, 0.09377443872123237, 0.9070830894955138, 0.074322780651074, 0.30082681771648834, 0.8455707013329772, 0.9639463498977681, 0.20297117347206772, 0.8418022488118483], [0.43398860574372977, 0.7316068905680828, 0.8029765318614409, 0.6114729757163491, 0.09270233121778682, 0.672622768766195, 0.4256500900002915, 0.6480434410065855, 0.7740800103079682, 0.8180505292640821], [0.30515266232121097, 0.006077098099002698, 0.7092207814182925, 0.11201126323885346, 0.8861565437983228, 0.3823758673423735, 0.4001965109694031, 0.8214332052964537, 0.6426924226284794, 0.03120704953643383], [0.8108472657980702, 0.11832739202046494, 0.8991546003548908, 0.5670995682619601, 0.22190363040316052, 0.29012424462685427, 0.810168024048798, 0.08224408424609142, 0.2168507902491582, 0.7993499761084215], [0.6117178983355555, 0.9942538757491142, 0.7256585335606898, 0.2221033918286448, 0.5053820964340785, 0.1641026276912686, 0.9112568547608212, 0.6296423425336802, 0.7073506530866227, 0.6734139662875958], [0.825511313930903, 0.6083649281585796, 0.620167924118141, 0.8816406217636532, 0.6879541347962523, 0.5810537892506753, 0.3194304478936538, 0.06933141842223145, 0.8714426102123833, 0.44396549645381556], [0.32917280011538963, 0.891177849405739, 0.3324468351231247, 0.7736913245896803, 0.8172608802197415, 0.0007878919690305075, 0.907065410593068, 0.01459235555047711, 0.7172116151833381, 0.6211418066956264], [0.45376651615065833, 0.41314487243580933, 0.06489788249620287, 0.4241681029704416, 0.6260272689385021, 0.6658505655971464, 0.32054787203279655, 0.7898221542556527, 0.8748402604297132, 0.7440787402590412]])))*5.894945841850479, axis=1)

-(np.sum(np.square(3.6661263694778663)-array_x, axis=1))*np.round(5.4006186216493655)

np.mean(-(array_x)-10*(array_x)+np.sqrt(abs(10*(5.936294369237916))), axis=1)

np.mean(np.square(np.square(np.cumsum(array_x*3.4538989743072013-6.965889948247836, axis=1)))/4.310387871850174+6.831328050115912, axis=1)

np.mean(np.round(np.square(np.log(abs(3.12533505430626-array_x)))-np.exp(array_x+6.254388576168374+(np.array(range(1, array_x.shape[1]+1))))), axis=1)

np.sum(array_x+7.471298417159609, axis=1)-8.836186167123765+np.sin(2*np.pi*np.sum(array_x+9.405264054069184, axis=1)-2.0752448391528233)

np.mean(9.083884993299538/np.cos(2*np.pi*7.891473867590972-np.exp(np.sqrt(abs(array_x)))), axis=1)

10*(np.round(np.sum(10*((np.array(range(1, array_x.shape[1]+1)))*array_x/6.424339283189463-6.6688296456031955), axis=1)))

np.sqrt(abs(np.cos(2*np.pi*np.log(abs(5.348747027169871))+np.amax(array_x, axis=1))))+10*(np.sin(2*np.pi*np.sqrt(abs(np.cos(2*np.pi*np.log(abs(4.431691735602303))+np.amax(array_x, axis=1))))))

np.mean(np.square(np.exp(5.551186801598018)*(np.dot(array_x, np.array([[0.779422307022319, 0.811961300197653, 0.5396647069385746, 0.7732227078398636, 0.820405232198004, 0.5724034710204505, 0.23636466705599246, 0.35072189983617585, 0.7832213540026844, 0.5971822182303729], [0.7444083983598211, 0.4062104367024506, 0.7881330868966084, 0.8821344942225662, 0.13025971232257383, 0.28007450293126135, 0.06522914756609255, 0.5447710841429393, 0.27630920966609795, 0.9574727492607482], [0.23025262128910107, 0.9886072009280636, 0.02910148523257705, 0.12019816321929522, 0.3667930687221941, 0.9341642652619064, 0.3789960829516654, 0.22571718499102456, 0.13625901359867387, 0.7303730790674617], [0.01749899012154521, 0.8406170863300929, 0.29747763514399816, 0.2244683733979853, 0.31406869090522105, 0.6932322963367964, 0.11874969253181411, 0.5213963656507653, 0.26542494921799065, 0.11327442736996518], [0.45834943369972536, 0.05102195285426869, 0.996741803351049, 0.36293324954825734, 0.4731332858730648, 0.2803942015481934, 0.5950252527788777, 0.025353366178373138, 0.7886938374079993, 0.1287334675753229], [0.19808099837171977, 0.1367232022634468, 0.6165294270175705, 0.7525517899927096, 0.5633292576749189, 0.19048870509873506, 0.407942802569215, 0.13972256460506816, 0.8202259794245523, 0.5142290331928211], [0.8053160412814465, 0.8101253656427271, 0.07414631859584075, 0.3444226924630117, 0.22757310652936313, 0.6146883203720064, 0.6971462668067883, 0.11841800392618496, 0.7489332589621319, 0.49789201209134615], [0.6446686487124141, 0.21984774284726294, 0.5324006301378089, 0.43486856723358713, 0.43525907456912016, 0.3037295104542611, 0.6962275101579785, 0.4733333640953554, 0.08703885465998051, 0.3255343846651141], [0.821540951788382, 0.4673996647192502, 0.4902014816481449, 0.6136034717470269, 0.3381337130200961, 0.8439996355239555, 0.6656179352373948, 0.24807456170927555, 0.4113238268460987, 0.8674035309214426], [0.669017361003645, 0.2680786366757095, 0.5067832382841365, 0.0008090574929762173, 0.1041474453582919, 0.9208924836351691, 0.428579219840578, 0.6347007029561159, 0.21240324320605675, 0.26411965667119475]])))-array_x-3.732823317639202/np.sqrt(abs(np.sqrt(abs(9.029443228516447))))), axis=1)

np.mean(np.square(np.cumsum(5.097045721760253*array_x, axis=1)-4.858345429587354), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.cumsum(3.7098951423114928*array_x, axis=1)-1.8264719259494007), axis=1))

np.mean(10*(np.sin(2*np.pi*array_x)-np.square(1.934201562158026))-np.cos(2*np.pi*1/(array_x+8.546047026831555)-array_x-6.192853432966939), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.sin(2*np.pi*array_x)-np.square(5.730279429583702))-np.cos(2*np.pi*1/(array_x+3.4747669171211015)-array_x-9.109739892861295), axis=1)))

np.mean(np.square(array_x--(array_x)*6.999540457935859*array_x-np.sqrt(abs(6.017467563160041))), axis=1)

np.mean(np.square((np.dot(array_x, np.array([[0.22866749310186996, 0.5101588735734867, 0.5474275934068348, 0.4304311598630527, 0.14974751707929235, 0.8467972694332621, 0.35966584975138116, 0.285116710087593, 0.2537198012530554, 0.26516769611474966], [0.06629194154965157, 0.24859159053576696, 0.48849987126344396, 0.3354854726876635, 0.28403109712015207, 0.8671278318473575, 0.21780382952576638, 0.8225581607383373, 0.035519946963087023, 0.5319080544204022], [0.5550058588598532, 0.8043050276127135, 0.8773859696554387, 0.4457339525117525, 0.8148960973801118, 0.0906396587820355, 0.7589317670290665, 0.1023759805511324, 0.415841826900748, 0.5650038031978241], [0.11763540824974927, 0.09974582050239533, 0.15811198245343883, 0.07619173309950766, 0.38008924921756126, 0.08745710263790996, 0.823706076704327, 0.46298188098180804, 0.3280874770327532, 0.6517283021094044], [0.399704303664677, 0.18898764404238044, 0.25456104435242466, 0.4921637694016734, 0.004126767439607715, 0.9116500356370093, 0.09129269953519803, 0.08186274369261692, 0.40907927359873575, 0.3830515980625153], [0.3653311864256378, 0.0176278546422598, 0.09521552757014695, 0.6217022907918224, 0.22569772040176794, 0.6666043988647559, 0.7173686307551834, 0.19511069669120407, 0.5723328493077185, 0.966540746424928], [0.4007400078697497, 0.4232523761993868, 0.9741156302521392, 0.997996507954427, 0.044089379438387644, 0.4229564805395245, 0.7340681893706815, 0.4544883617223674, 0.3799057150188818, 0.24953805286052522], [0.6500857419703655, 0.5779882673896567, 0.6788504296128179, 0.7776789911964191, 0.7554198071876841, 0.700139943357255, 0.5279614958344792, 0.6060460141437098, 0.8555745253970936, 0.14115469451820406], [0.40877011983239286, 0.040556387050436626, 0.5386488776713693, 0.2766074572114138, 0.04243114489211153, 0.05625720500886611, 0.8256342463140532, 0.5986703892713294, 0.04387684133290859, 0.5928206320244568], [0.09582587205728077, 0.5371435536402928, 0.6951104316896588, 0.2029521973634465, 0.06234730470345429, 0.08801350604236369, 0.9990943943394849, 0.9805890322744358, 0.5150381764214373, 0.9915262857050334]]))))-array_x-3.6102690851271007+9.508570630461172+np.sqrt(abs(7.821186693136243)), axis=1)

np.mean(6.9276532084385005/abs(np.square(array_x-1.103177301877834)), axis=1)

np.mean(np.sin(2*np.pi*array_x+3.5046429915997743)+10*(1.4684721768812956+array_x*7.306662951186157-array_x*9.576022697516098*abs(8.020576504564138)+1.2966579858995297*array_x), axis=1)

np.mean(np.square(array_x)+8.331048737697461, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(array_x)+3.7573175403315733, axis=1)))

np.sum(np.sin(2*np.pi*np.sqrt(abs(np.sin(2*np.pi*6.386208181902492)/1.938013185073511-abs(array_x)))), axis=1)

np.mean(np.cumsum(np.exp(8.672877524060116+array_x)/2.7514880054946858/np.exp(np.cos(2*np.pi*array_x*2.0842220860811715)), axis=1), axis=1)

np.mean(10*(np.square(np.cos(2*np.pi*array_x-5.59412880971136*array_x/1.405228973602428-np.square(7.034866208928962)))), axis=1)

np.exp(np.sqrt(abs(np.mean(abs(np.cumsum((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)*4.673475663503337-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)+8.18915354030127)))

np.mean(np.round(np.cumsum(3.6444095189256265-np.sin(2*np.pi*np.cos(2*np.pi*np.sqrt(abs(array_x)))), axis=1)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(np.cumsum(8.366786152654631-np.sin(2*np.pi*np.cos(2*np.pi*np.sqrt(abs(array_x)))), axis=1)), axis=1)))

np.mean(np.round(array_x-3.2958450517965474*7.376184677488762*array_x+array_x/4.775802627342628-1.659209949911392+array_x), axis=1)+np.sin(2*np.pi*np.mean(np.round(array_x-1.3336572900019537*7.291742933499232*array_x+array_x/2.643907294067824-7.603075366544179+array_x), axis=1))

np.mean(abs(array_x*5.059776095131006/5.883386732948509+6.197048936097562/4.650575633501346+array_x+10*(array_x*array_x)), axis=1)

np.mean(1/((np.dot(array_x, np.array([[0.8350581459223685, 0.0735097926537831, 0.42291578175342226, 0.11823234158021534, 0.8964463177010739, 0.4439127397142988, 0.478659679537654, 0.6410016447207733, 0.11486066944564521, 0.5765999165521144], [0.21767457843705162, 0.36636502191460807, 0.23711466502353706, 0.4143069905918656, 0.4849286611047038, 0.5062768534290241, 0.5309979880423339, 0.014843949854695393, 0.276641805854419, 0.15946678762262545], [0.37436021052374524, 0.6307339280940296, 0.7638286463533873, 0.7571945741919794, 0.832062847041868, 0.09231833062690298, 0.270545670723346, 0.5555586508272801, 0.3941095102882569, 0.15906483544890937], [0.005772013622135308, 0.44639877097366254, 0.9344330096873709, 0.8255783032125797, 0.8003249683035105, 0.46132641816347997, 0.9526950315269156, 0.8923501947699881, 0.4503016039625787, 0.5180892782999488], [0.027483189038741918, 0.5948452338274961, 0.1701026149694157, 0.6662074571346279, 0.46034898455459317, 0.3350533731020202, 0.36578489127523073, 0.8092378671956101, 0.02007770553343191, 0.6772968152567569], [0.02278742438177772, 0.0985728410963429, 0.06527301704038468, 0.869796075162742, 0.6122978251128012, 0.07440326899997252, 0.55618526768515, 0.36457123292310456, 0.4179429985640173, 0.19047951725242562], [0.21458692412760694, 0.8049568725034997, 0.2576737767992987, 0.1000301836221833, 0.132771292979451, 0.9316001533251675, 0.9315952550993929, 0.5416964746042738, 0.8637925924411888, 0.9770126719063028], [0.279476993069787, 0.3533223724836835, 0.6829859791149187, 0.2708911323287766, 0.5839970220665237, 0.21008748043986936, 0.8295007630531822, 0.1820171533463032, 0.6352645047733415, 0.9721410394107621], [0.2954507994656519, 0.7205719006555149, 0.9842107458747804, 0.6322334798766885, 0.9829424595931896, 0.2301967470042139, 0.2791312467627075, 0.9745383531261439, 0.10337442196034174, 0.02465823016439672], [0.7203495909886424, 0.04707131847507928, 0.5030540656396966, 0.7069149022286426, 0.20656563679185114, 0.5505217191845349, 0.002105535323507657, 0.1489324651428804, 0.1331553613850377, 0.12709261938816774]])))+3.2817757618886434)-np.square((np.dot(array_x, np.array([[0.7300747579150313, 0.5851985538382661, 0.9898341465155406, 0.3379290358979694, 0.04410835178697314, 0.4469764981893821, 0.31402771039677246, 0.5358397994665204, 0.5748535212120243, 0.04662981324008442], [0.44398848827525683, 0.1409825950230983, 0.4054259489145361, 0.00741887243352346, 0.20130209179939829, 0.3594083820316579, 0.11606387478516966, 0.9860538006484995, 0.4501433908306083, 0.22762396102787796], [0.0023604211131172947, 0.9343395794198852, 0.48018529385138575, 0.6412267104272499, 0.7532726909839633, 0.1318481455166144, 0.685776401992805, 0.21751883391743299, 0.483521129369197, 0.6644670522840853], [0.9583677350223307, 0.5052540566657469, 0.04850983075106552, 0.22881615829146285, 0.7720463491921223, 0.6426806942232152, 0.15825998013435716, 0.22472426878686658, 0.0019365356567422332, 0.017060149289064186], [0.8249735610635366, 0.9619551261660797, 0.45505047317261726, 0.8920386373414726, 0.33795674674461296, 0.21466275783925703, 0.0268751077442293, 0.05774625066094641, 0.7400240891542053, 0.2144911684473818], [0.7407678456338376, 0.5105615142312611, 0.5924678738687001, 0.7195941637024279, 0.6460280340898232, 0.5442195400769726, 0.8688418611188657, 0.9744937095696621, 0.48631400637381617, 0.15233445428372516], [0.41846601542161144, 0.7557717427026345, 0.8409768442352958, 0.54595162313961, 0.5360722532078714, 0.42887696727244995, 0.5941615452756573, 0.5585134068039451, 0.4636240338709261, 0.2976023905096986], [0.5995466719578121, 0.5370455893232564, 0.7520923261640331, 0.9317153617631089, 0.046385305128729626, 0.8759908756872095, 0.48963599484269493, 0.23914967413742694, 0.8302691986108396, 0.29417494300024094], [0.9863796668513091, 0.24561386581193656, 0.637105249115064, 0.3868304539840719, 0.9798988514740506, 0.044666962479348205, 0.9541087507788797, 0.25289261947186037, 0.22791055799124738, 0.4699147981177533], [0.9779830336197933, 0.21214852062069645, 0.4387136634311062, 0.6016537891149155, 0.3612902373628796, 0.6639657866909269, 0.6000721313849912, 0.7332900207446952, 0.28285486248640934, 0.9491986351280357]]))))-9.806687287616096, axis=1)

np.mean(9.12426435502483*9.479971229562212+array_x*np.cumsum(7.444596639892301-array_x+array_x-np.sqrt(abs(9.931358735784684)), axis=1), axis=1)

np.mean(10*(9.66955445685012)/np.cos(2*np.pi*np.cos(2*np.pi*-(3.701895070341221*array_x)))+2.3275601648051714, axis=1)

np.mean(np.square(1.8913901924120036)/np.square(1/(4.208166633342529-array_x-array_x)), axis=1)

np.square(np.round(np.sum(array_x, axis=1))+np.exp(3.701990181549065))

np.mean(np.square(np.cumsum(3.815164974623183-array_x, axis=1))+7.461987506580728, axis=1)+np.sin(2*np.pi*np.mean(np.square(np.cumsum(6.986150574015321-array_x, axis=1))+7.036730561295848, axis=1))

np.sum(np.sqrt(abs(7.094251216097582-array_x*9.484014152578098)), axis=1)

np.sum(4.469390176743985-array_x, axis=1)*2.301564575460476

np.mean(np.square(10*(9.630384425747463))*np.exp(np.sqrt(abs(6.9993368775674005*array_x-4.786828029379226))), axis=1)

np.sum(8.560320735266114*array_x+2.441124352260248-8.228224936115003*4.875977198963389, axis=1)

10*(np.sum(5.612770210526918-array_x, axis=1))+10*(np.sin(2*np.pi*10*(np.sum(1.8889440666384563-array_x, axis=1))))

np.mean(10*(abs(array_x+array_x+1/(9.400850644511797))), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(abs(array_x+array_x+1/(1.3965226690017922))), axis=1)))

np.sqrt(abs(np.mean(3.410438087536911+10*(8.48919410918327+(np.dot(array_x, np.array([[0.5266147588603225, 0.3777870407369772, 0.7745933433887978, 0.8389522931334337, 0.2948433451983846, 0.07447015442901783, 0.7855214549932689, 0.4890488600951147, 0.26613913578218384, 0.22437015322094955], [0.5080776085653, 0.9352490137350818, 0.6877473886348279, 0.03335568925891397, 0.32847191696813705, 0.43316347394344823, 0.02910285182381067, 0.06372810680723895, 0.17175994164078934, 0.148914678328963], [0.5492981598492603, 0.6731468966556501, 0.833354199725917, 0.9331532032360242, 0.3473027190486909, 0.07115092580320126, 0.35280559007030066, 0.9757583960687584, 0.9827217032821308, 0.5862184092103164], [0.9975070510420544, 0.8627656742167603, 0.11402466864668626, 0.39923133951758083, 0.924271520873374, 0.3971897940739956, 0.4630803485722551, 0.8371097728364331, 0.015809598597554708, 0.31636905841729623], [0.31057157808638625, 0.8932502186210804, 0.08531891750950382, 0.8458705356525699, 0.3554874682255721, 0.39375490052697193, 0.39275293664656274, 0.5133349213656366, 0.03360493316354218, 0.9409441232678202], [0.3739003122538601, 0.984806578726499, 0.16187176468272957, 0.9311259470592904, 0.5391182602153587, 0.9315032131957401, 0.19738167502502257, 0.9989621549511145, 0.976612464423982, 0.9153793479580288], [0.9538690890059041, 0.24931290424783004, 0.23563114306944366, 0.33886801089428153, 0.6123506072239553, 0.19974549112232953, 0.5950737382640019, 0.9175667290496369, 0.5029335440619174, 0.373439122257626], [0.19435926490618138, 0.14209962429766376, 0.06432214577422424, 0.9469391211104978, 0.3268919769692029, 0.233408421079914, 0.9177972745398137, 0.4914143709881672, 0.9125807730843625, 0.20513744782148569], [0.6996589119943994, 0.16772241211110428, 0.016992174200582078, 0.6920957016310545, 0.6851152260309953, 0.546983629314148, 0.14054175978487293, 0.6444373387059417, 0.63098752447357, 0.19751716724315804], [0.04571760013510062, 0.44792113601877637, 0.7534388896827895, 0.7089578671312583, 0.47680956721287393, 0.8442292846081286, 0.507966326695977, 0.41095660073311113, 0.7297157630461228, 0.8178066247115519]])))), axis=1)/abs(9.343947342146441)))+np.exp(np.mean(array_x, axis=1)+np.exp(1.9615108047886038))

np.square(-(np.sum(np.log(abs(np.square(1.0810211679646922/(np.array(range(1, array_x.shape[1]+1)))*array_x)-3.277553673281248)), axis=1)))

10*(10*(np.square(8.726275673425194+np.log(abs(1/(np.mean(np.sin(2*np.pi*np.exp(np.exp(array_x))), axis=1)))))))

np.cos(2*np.pi*5.379198557152017)/np.cos(2*np.pi*np.mean(array_x*np.exp(4.389887804318086), axis=1))+np.sin(2*np.pi*np.cos(2*np.pi*4.054313080747821)/np.cos(2*np.pi*np.mean(array_x*np.exp(8.428832050163171), axis=1)))

np.mean(np.sqrt(abs(10*(np.sqrt(abs(np.square(10*(np.sqrt(abs(7.97055699928687))+np.cos(2*np.pi*array_x)))/8.721572519468697))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(10*(np.sqrt(abs(np.square(10*(np.sqrt(abs(8.57838441551551))+np.cos(2*np.pi*array_x)))/6.391848629214435))))), axis=1)))

abs(-(np.prod(4.617221265236971+array_x/1.473592520003317, axis=1)))

np.mean(np.cumsum(1.9029497508459428-array_x*array_x+5.169009901392838, axis=1)*2.159872684456513-3.536394898112902-(np.dot(array_x, np.array([[0.8321885263595639, 0.7745299087366779, 0.36377066534385805, 0.19914008701537456, 0.5706941331365432, 0.2456007680161859, 0.5073297592239219, 0.7114109707072076, 0.831152100321899, 0.012079732223641448], [0.9323441294991038, 0.42498039015712985, 0.014106044202042423, 0.4697998471401662, 0.6529067414769587, 0.5751394204486273, 0.9467548568058783, 0.1362241231321456, 0.8005185263524451, 0.9540943458623706], [0.7287719860240125, 0.7144996584433063, 0.5119429402989548, 0.28215317643793303, 0.4562534913697587, 0.4381354913982166, 0.5585167769806791, 0.2302964152308069, 0.833177349016902, 0.021882641934382163], [0.7999661780307694, 0.2616582095842792, 0.722975643465987, 0.9457297196310777, 0.7215209973654988, 0.3238228561647333, 0.1839435116776238, 0.378795167030332, 0.14981420334251327, 0.22812251516807336], [0.9106470051694274, 0.2798189757682694, 0.5644295535319809, 0.02641573340575154, 0.827140976870273, 0.22340678298720096, 0.6202214982493879, 0.8140167902815888, 0.16199201866780322, 0.814918642138744], [0.10409810680364007, 0.13338759205317963, 0.5966777618428742, 0.8591492782483873, 0.40163197307440124, 0.25782169754113893, 0.7797592715155938, 0.2734185530956845, 0.8270771159750205, 0.008244181870821765], [0.6152778970700103, 0.6569180299572942, 0.8723533500326428, 0.519595729526472, 0.3089049100432909, 0.7660736651775201, 0.8987609827310812, 0.15948021149592195, 0.14824919836202044, 0.41926861022813766], [0.5715586992010154, 0.3487152612492971, 0.8342322134692112, 0.6538944574216428, 0.9445407846187795, 0.16787777343122467, 0.7829552487924105, 0.5458971407220488, 0.7747757006186733, 0.978264766126964], [0.2654279893427176, 0.6702837192674718, 0.4279492594773854, 0.09295052204860788, 0.4151687820794441, 0.47955583855446204, 0.5753235555488075, 0.7684669574245999, 0.24809072515139508, 0.7553427162994715], [0.7507449149143971, 0.5925977632323227, 0.21095203686290953, 0.8721990273350435, 0.5173009439560198, 0.5990699466193325, 0.5191802977512059, 0.06498244657140861, 0.20802277727281282, 0.9726387653224103]])))/1.99793798121113, axis=1)

np.mean(np.cos(2*np.pi*5.081756941905122)*np.square(8.436043171093374*(np.array(range(1, array_x.shape[1]+1)))+4.319928003670325*np.cumsum((np.dot(array_x, np.array([[0.2021616003834087, 0.2611151292525945, 0.7039882446529516, 0.8607942669146238, 0.28991505090313796, 0.8207520190009393, 0.505720510161673, 0.07364869639969074, 0.37695834959658203, 0.14329086542124925], [0.45459966074014657, 0.7139280836713883, 0.6891775580107782, 0.229762750819001, 0.4094858247440215, 0.7258736948876473, 0.4676826731779382, 0.36189647305047956, 0.1763936321747137, 0.7895156051333719], [0.18027570390566172, 0.486762814469183, 0.8294569888134511, 0.3195173566392536, 0.33028950489863385, 0.22670350198784117, 0.5748187398982513, 0.6127098608222609, 0.938206331556835, 0.6034286177661105], [0.06680761616433606, 0.5772920527258384, 0.08662676850933271, 0.9748943632276811, 0.9090331433322532, 0.919734703829433, 0.7118455547915742, 0.570535154074372, 0.5573914211995302, 0.21444175468726534], [0.20284595259595017, 0.9021707589271701, 0.4233886759612596, 0.5241122331176513, 0.9279749417169217, 0.33956416952909185, 0.2638001268122945, 0.5115122745540582, 0.3359602899343871, 0.8011399587978696], [0.7392416221302844, 0.15277334262935816, 0.49771083690280116, 0.2620162828144651, 0.5314181310514282, 0.8774222695950104, 0.7617348873039781, 0.7346479701680609, 0.6640616366225427, 0.13051164538247895], [0.5614774347500097, 0.263594933263793, 0.7525986647861336, 0.6398863426477238, 0.8005713824110329, 0.238474919240938, 0.5264102558820485, 0.9683722814313874, 0.20972661118409675, 0.10724550915931541], [0.6763065977428854, 0.19129423781709243, 0.08554412533776079, 0.9644245929498191, 0.8003810880143198, 0.6900511437927185, 0.38138613719340253, 0.45377378410502833, 0.6491284463848884, 0.5233018083049092], [0.22467711675422541, 0.7457469613664635, 0.054466818651409676, 0.3070747423645648, 0.7733372772219399, 0.1069227600477951, 0.7520245693635452, 0.5862330322425645, 0.7730789991771068, 0.6654646143815802], [0.7132953624671228, 0.005121288174227301, 0.962861423321626, 0.9106727187526216, 0.22147504727699407, 0.3234312215117848, 0.13333188132122653, 0.017743120238405408, 0.5078013031863056, 0.9847711848220079]]))), axis=1)), axis=1)

np.mean(-(8.630070010285614)-np.square(np.log(abs(-(8.68499926393451))))-7.805929946779726*(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.square(np.square(3.468748565731791-np.square(np.sum(array_x, axis=1))))

np.mean(1/(np.cos(2*np.pi*10*(np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))-array_x-5.9224800619640074-np.sin(2*np.pi*7.165722739021858))))), axis=1)

np.sum(array_x-(np.dot(array_x, np.array([[0.7927781389206864, 0.13044275134211958, 0.17679771188099658, 0.48764702912164315, 0.08013438175940202, 0.5482628630767351, 0.5487036402110246, 0.9882380461757344, 0.6880617712874553, 0.45691911683372144], [0.6475374466310421, 0.241806451642821, 0.3113953789661823, 0.38221256174958596, 0.8473977612049928, 0.539409698097185, 0.6693749428354778, 0.6173126327889399, 0.47059484819712427, 0.28838491876566374], [0.8646930358442566, 0.7865363538449289, 0.2991223062219768, 0.25771563118436447, 0.7296802942036189, 0.4170561942544536, 0.9414231735431009, 0.48958470801756804, 0.5132438468463709, 0.68891903346501], [0.5159632987488476, 0.11455419423757729, 0.6589946938671022, 0.7544799600361664, 0.7189312811155742, 0.928688948389221, 0.7962855654877105, 0.8215234622241544, 0.1859758468288848, 0.0527119630058176], [0.6362060039558992, 0.23923804556404038, 0.6635130185398145, 0.2893417613872903, 0.3854364543508376, 0.5469019796270586, 0.3040134622304488, 0.6033247958160718, 0.9929237183610329, 0.25845349903116044], [0.96644614884291, 0.8943718704042846, 0.25545321331789683, 0.8387744954002112, 0.2544060827428133, 0.5695917778715562, 0.7342644175258352, 0.4343208384850168, 0.8013803736870168, 0.2814788821876296], [0.9941863822680731, 0.5057939672763789, 0.6103775529857728, 0.4065679454323984, 0.9125229491230961, 0.35323110455413875, 0.012987500859194201, 0.7467334085535499, 0.559759802537911, 0.6947144800413645], [0.5619114247199843, 0.01788658713621849, 0.49635100981704516, 0.24522403487586597, 0.38108380258470165, 0.32791952900062304, 0.6334610838788267, 0.05561092845620663, 0.20524855361166927, 0.774204360841684], [0.2387977179452876, 0.791383908806125, 0.39248431308869913, 0.34414672580499117, 0.19370474361669388, 0.5321458306619896, 0.5764664197140256, 0.45513008637949826, 0.04076858961646579, 0.03702516561432212], [0.49269781590072614, 0.48154099984157495, 0.40988637594197186, 0.3848015867665947, 0.3751513593981154, 0.02102597403754669, 0.3927306179702085, 0.7893350706856398, 0.18533303694798164, 0.9569965674493512]])))-8.797980757776642, axis=1)-np.amax(array_x, axis=1)

np.mean(np.cumsum(np.sin(2*np.pi*7.634552224563032)-array_x*3.3836483720938992-np.square(7.783607652819484), axis=1), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(np.sin(2*np.pi*7.402721940032417)-array_x*7.4309881252205985-np.square(8.813093364590554), axis=1), axis=1))

np.mean(np.round(np.round(np.square(np.sin(2*np.pi*6.48911487648339*array_x-array_x)+(np.array(range(1, array_x.shape[1]+1)))-4.652668653059013))), axis=1)

np.mean(8.516904256529433-np.exp(3.4123500811251892)*(np.dot(array_x, np.array([[0.475801529782204, 0.3065178408310949, 0.584425653965532, 0.9356191180145069, 0.08753658607962711, 0.18683029701445286, 0.15936024021756512, 0.08907149582608753, 0.5153337389414693, 0.21103672301906196], [0.23845899299607165, 0.6141816389313431, 0.5619241046807234, 0.5543356571533591, 0.9040644276082797, 0.97315586360376, 0.5111406354404509, 0.422300877702233, 0.4481204613009381, 0.07362445810995877], [0.11629723067040187, 0.9815248233095271, 0.4024318464038775, 0.20395068089668522, 0.22358170837843483, 0.6133019454412729, 0.015804537558023468, 0.6800458850525595, 0.8717857531638, 0.4320497223114129], [0.8243403052714919, 0.12025202581143357, 0.16262431531764587, 0.3522716643105297, 0.6091567445724981, 0.21516463643928563, 0.9669185627060337, 0.6738725736591982, 0.26688802490490005, 0.5979805782543989], [0.05168546343322178, 0.8003695375963509, 0.7582722534499704, 0.8853351013436255, 0.938320930102822, 0.3002153985324041, 0.2469693557107362, 0.89605267333526, 0.7382512352736454, 0.658505644920352], [0.8506235582591587, 0.951976793226307, 0.891807417520469, 0.16662698457205094, 0.54614276994009, 0.36689851323634437, 0.32370655116258895, 0.6966707811878236, 0.9431061501801126, 0.7429388259838028], [0.15487987906697853, 0.00124702141147226, 0.8505414255481346, 0.18228277773703205, 0.2207947980454139, 0.9309687476527071, 0.8631661810045632, 0.2506030522445325, 0.1720270565154881, 0.19759884295078545], [0.1730485277577184, 0.9046193526351299, 0.8712596148617836, 0.03984444463299919, 0.45286086431788863, 0.21100181734747925, 0.21889225396830558, 0.24681140793026635, 0.538428596353444, 0.9097319328350812], [0.9535317247918034, 0.8724056541662456, 0.9459457588542238, 0.35172995540068897, 0.0566774189234327, 0.33968680015104946, 0.876070068319906, 0.3580849988186088, 0.11610054624022093, 0.5770492807660238], [0.7207463601105685, 0.6178187148918894, 0.2813970115603691, 0.8914033499936709, 0.8892526819947861, 0.1985506204137587, 0.8981176418590352, 0.09750065502110217, 0.6667302326628115, 0.4608725642128991]])))-10*(5.459327510533906)-np.square(np.square(-(np.cos(2*np.pi*np.sin(2*np.pi*array_x))))), axis=1)+np.sin(2*np.pi*np.mean(6.925452813446023-np.exp(9.180134685105898)*(np.dot(array_x, np.array([[0.5819379788408696, 0.284300086276152, 0.01996735142090289, 0.9613081622898061, 0.09844324751723721, 0.405184223794377, 0.795132592877034, 0.8803231956025895, 0.5656977864736681, 0.27679531661183476], [0.6477866927769389, 0.10943257342544432, 0.6775961269662127, 0.772221419807659, 0.6895273334682239, 0.3828423335278158, 0.1984380186051079, 0.745187318192152, 0.11686817387282855, 0.3712892372018337], [0.12419753603345296, 0.5138045431238768, 0.6217437304315716, 0.7283391689035444, 0.33881857555612005, 0.08254529414231715, 0.3447706304738064, 0.7823150978101627, 0.35451853359973495, 0.7856979005878534], [0.10276778147326493, 0.0023056094257878357, 0.7855028188392766, 0.7460998442044515, 0.21939359672854075, 0.5742976394365157, 0.7089965984950262, 0.9711653643334623, 0.8160026810407384, 0.8835511273152363], [0.019123543034886614, 0.38780877507937017, 0.4700446392665524, 0.9275137516064318, 0.24019515117971701, 0.16396124732828776, 0.10241328329181676, 0.6869882572697951, 0.35171095704991606, 0.6234190022698954], [0.5081988986380117, 0.4565867229844497, 0.47640920270192544, 0.8053713304020648, 0.48797435547198775, 0.8028816811607192, 0.1321817215706591, 0.7661550521231013, 0.582271542391474, 0.7766647234717949], [0.47690186726195194, 0.8379436644938275, 0.3063481568829173, 0.5092146312324041, 0.5709185124690402, 0.008773914118546, 0.0894177283025267, 0.19424496562350357, 0.8155047623044612, 0.09194214450857174], [0.20761094263172997, 0.030313283719076045, 0.16054859482517636, 0.19367744351321292, 0.20290004835937736, 0.8044673663957016, 0.9722986811498348, 0.8739229677373778, 0.33694454397546025, 0.6163180861586174], [0.586667764771973, 0.46392369198106087, 0.22687317059414658, 0.9474091738410093, 0.6533542871886118, 0.8134598967015291, 0.7752173501413501, 0.05645287096434248, 0.9067664952952302, 0.8275856032690376], [0.6753567393777975, 0.03989233846370088, 0.720472422939533, 0.5063506472771448, 0.6114612130424238, 0.31228599556255554, 0.17656852969241077, 0.31570393405538966, 0.2574000220326711, 0.24739363478047205]])))-10*(4.408390387133647)-np.square(np.square(-(np.cos(2*np.pi*np.sin(2*np.pi*array_x))))), axis=1))

np.exp(8.054967797877659-np.square(np.mean(array_x, axis=1)))+np.sin(2*np.pi*np.exp(1.0775793457952483-np.square(np.mean(array_x, axis=1))))

np.mean(7.8620934677572665+10*(array_x*3.1165156469288813), axis=1)

np.round(np.mean(7.441668243318437*np.exp(10*(np.sin(2*np.pi*4.088766269983258))-array_x), axis=1))

np.square(np.cos(2*np.pi*np.sum(np.sqrt(abs((np.dot(array_x, np.array([[0.995234470774242, 0.4304014455062495, 0.8627529493980629, 0.3036975429264166, 0.4567501336005413, 0.8168769447956684, 0.09994453233015577, 0.37009447929779404, 0.9718028399769817, 0.17836568622031845], [0.34127564279890443, 0.9994542043766551, 0.5670990701641656, 0.04963927416705294, 0.02219424904565248, 0.91481335955378, 0.3427622491343293, 0.6888531472018289, 0.13948983690389105, 0.8901770305256792], [0.6775135430302586, 0.5217071307673794, 0.5085589848927591, 0.3885059319960332, 0.392308837639331, 0.08767026181535376, 0.9073153902824537, 0.9259695851382204, 0.42554119543619584, 0.44532795206442455], [0.6458904256668787, 0.5156808218648491, 0.7350628351829669, 0.1630889575378024, 0.5654638751884504, 0.43339192650033087, 0.3326466424194534, 0.399666380907891, 0.629029057265338, 0.2806029888588033], [0.9391772517118192, 0.31295233567845004, 0.3335958429105338, 0.46995306928511194, 0.6489154976476804, 0.21196929808763576, 0.9779754079469376, 0.5777433156306171, 0.9934790020743116, 0.5599266945012211], [0.4233975839673938, 0.6201147632615358, 0.9194827233092093, 0.41282639938109267, 0.8625842772521337, 0.5580533717323455, 0.48206529142566223, 0.5194120097328595, 0.5461149021103989, 0.4231330484281385], [0.8967970313922705, 0.4693525440560383, 0.7787752066307689, 0.4619973721972216, 0.3243172702080197, 0.19791246368259496, 0.006826705375038156, 0.8616273248953507, 0.058333828594702486, 0.8138522937045718], [0.7755706562065368, 0.03276730775942793, 0.8116756469445404, 0.25828178755174125, 0.8446680556146167, 0.08148564300295391, 0.6431740387177727, 0.7123395965014707, 0.582418051554644, 0.47023876766050454], [0.1713274708983037, 0.9278059477398625, 0.08920908536646233, 0.7676629339503009, 0.2222857684110271, 0.6988169983216727, 0.3856530135847698, 0.8377072950783924, 0.49781374045975546, 0.3175279513144452], [0.24106063302491143, 0.4966949739482468, 0.4964899337397163, 0.34335510381224266, 0.523082626225566, 0.7656899509714842, 0.33732007830629707, 0.571934438598327, 0.4928343659427642, 0.9750943645522941]])))+9.727861270131866)), axis=1))-9.212698850469144)

np.mean((np.array(range(1, array_x.shape[1]+1)))*4.650240391359606-array_x, axis=1)-8.415103666404232+10*(np.sin(2*np.pi*np.mean((np.array(range(1, array_x.shape[1]+1)))*2.5833250857308943-array_x, axis=1)-3.8462706703589298))

np.mean(10*(4.3265321701451915*(np.array(range(1, array_x.shape[1]+1)))*array_x-7.838028902930225), axis=1)

np.mean(np.sqrt(abs(np.cos(2*np.pi*array_x*4.521373036208714)/np.sin(2*np.pi*9.729301938279102)))-5.9549651137104*array_x+3.205641619038971-array_x*np.square(np.square(8.819089250386654)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(np.cos(2*np.pi*array_x*4.950644486729923)/np.sin(2*np.pi*3.935397713357025)))-5.2742430355663625*array_x+2.009255523157737-array_x*np.square(np.square(3.4713510600149053)), axis=1)))

np.mean(6.511596353429921+np.exp(array_x)-np.cumsum(np.sin(2*np.pi*abs(4.821019751275157-array_x/np.sqrt(abs(np.log(abs(5.820591205074596)))))), axis=1), axis=1)

np.mean(np.square(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.697718346094866, 0.14362381622658005, 0.02765607269462156, 0.9484490613649946, 0.7661698164184814, 0.7858034824026112, 0.9107761308428602, 0.09246346893341606, 0.07874703076094913, 0.03127788006651255], [0.9197232983282112, 0.2281515054721196, 0.7280597061116972, 0.4710029602301853, 0.4921315616745844, 0.5654100320106047, 0.7743362116576586, 0.5802473673902817, 0.682658349693216, 0.5822134548370217], [0.7711021359396255, 0.7102917748091409, 0.9791201498884704, 0.06220951035315181, 0.4430673295815888, 0.9506571758547877, 0.6982372563522383, 0.21866641201417358, 0.5049681514113948, 0.8197766589684069], [0.4482511949359632, 0.8674368418048641, 0.6638683651888482, 0.7207213037506388, 0.11225718351134406, 0.36153232111791456, 0.19752971872753855, 0.19711356248001377, 0.877308018145779, 0.816981601117065], [0.917072513453827, 0.7068825098580298, 0.4171893290295797, 0.26991207548095053, 0.31747426701522385, 0.6130508141697598, 0.042834167887900065, 0.8259659017267802, 0.6127017569459464, 0.3666077385897827], [0.015979982378153124, 0.7834980157642658, 0.11735323656517804, 0.1748014797009524, 0.4350408008386518, 0.5025015995287662, 0.2590063977541005, 0.19129608109850482, 0.09383519725807399, 0.7897285973162144], [0.783922215811375, 0.07240713723729253, 0.977215155792615, 0.05462249093614324, 0.37172015822177895, 0.08941647677249576, 0.38243718961602513, 0.37443547636687924, 0.027265809082689718, 0.9869036503527436], [0.8590402726110427, 0.3198693257329027, 0.8776073985429668, 0.2782924692364249, 0.5316492443161356, 0.6763880909991558, 0.6779291733806967, 0.3423633054386894, 0.06804449914240296, 0.636811286115696], [0.6025443600583477, 0.728487929418747, 0.14498235756219124, 0.7555889402599909, 0.9015027233359589, 0.14231046545529424, 0.8598274256180931, 0.9202721206622109, 0.6768817759037954, 0.14719962383057028], [0.6181524480563442, 0.9826438086967878, 0.5936934234273039, 0.3037398486710555, 0.7828893884333291, 0.004995634050081077, 0.2420801175403552, 0.12851076059866628, 0.682561756051776, 0.01928659218366624]])))+7.423879777425991*np.round(array_x+np.square(9.64178292059143-array_x)))-4.167080314100026), axis=1)

np.mean(np.square(np.log(abs(3.6771169919289424))/3.1333735768298836+(np.array(range(1, array_x.shape[1]+1)))*array_x+6.873693771146892)*abs(6.358029099155873+(np.array(range(1, array_x.shape[1]+1)))*array_x)/10*(1.2012766293538344), axis=1)