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np.mean(np.exp(1.0307454049683844)-np.sin(2*np.pi*array_x/3.912281903423713)*abs(array_x+2.9937879279966553*4.528001503072417), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(5.129995151524023)-np.sin(2*np.pi*array_x/4.007160138406384)*abs(array_x+5.540235452529949*3.692037140747991), axis=1))) |
4.275270616086389+np.prod(8.413963820378992+array_x, axis=1)+np.sin(2*np.pi*4.057725892155792+np.prod(7.577261301468324+array_x, axis=1)) |
np.square(np.cos(2*np.pi*np.mean(array_x, axis=1))*7.8778215053484715) |
np.round(np.mean(np.square(8.387401380766516+np.square(array_x)+np.cos(2*np.pi*np.exp(2.870342942708054))), axis=1)) |
np.mean(np.square(np.square(np.square(5.576962502369655+array_x/np.square(6.013174015876189/array_x)-np.log(abs(9.850913741439015))))), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.square(np.square(6.71133705638421+array_x/np.square(9.066447929174846/array_x)-np.log(abs(2.0526885497282072))))), axis=1)) |
np.sum(np.square(3.698899340460617*10*(np.square(6.157576837730016+np.exp(array_x)))), axis=1) |
np.mean(np.exp(2.7210525496767937-array_x)/np.log(abs(array_x))-4.458963200947026+np.exp(array_x), axis=1)+np.sin(2*np.pi*np.mean(np.exp(8.961554978109753-array_x)/np.log(abs(array_x))-8.403659163902208+np.exp(array_x), axis=1)) |
np.mean(np.exp(abs(4.243417542342048-np.cos(2*np.pi*abs(array_x))-8.507750406864565-array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(abs(2.755280672302877-np.cos(2*np.pi*abs(array_x))-9.56024817028326-array_x)), axis=1))) |
np.mean(np.cumsum(8.11666434086455*np.exp(array_x), axis=1), axis=1) |
np.mean(np.round(7.155673067301757*array_x)-np.exp(7.693329528558066-(np.dot(array_x, np.array([[0.5333663655629076, 0.8588682037555321, 0.49919391785190503, 0.12906934361416733, 0.7064149404097396], [0.18832892235602572, 0.8872484714429256, 0.4220958467162471, 0.5661323616016762, 0.2707498285105966], [0.8548345636563492, 0.5879179266225574, 0.5767272689986787, 0.5809513737993126, 0.21981087998696458], [0.3665968467801072, 0.39265612978841435, 0.4045379555822549, 0.731605255624688, 0.8113523191709014], [0.05269881674130328, 0.3489861197755385, 0.5106718796483796, 0.9188869703314743, 0.7214184136809364]])))*9.724855926830209), axis=1) |
np.mean(np.square(9.889532588401112+np.round(np.exp(array_x*3.7598244325018033*abs(array_x)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(8.535628756646268+np.round(np.exp(array_x*9.533576399217921*abs(array_x)))), axis=1))) |
np.round(np.prod(array_x-5.676227371843045-2.5270219999878942+3.530195819922748+array_x/5.298502990832663-array_x*1.9889891633359742/3.6953065149017057, axis=1)) |
np.prod(np.square(1.331272675308137+array_x*np.sqrt(abs(2.391834702109417))), axis=1)*np.cos(2*np.pi*8.203422222387356) |
np.prod(np.square(array_x+1.8664409959721726)/2.320641837606334, axis=1) |
np.mean(abs(np.cos(2*np.pi*7.158520732175916))*9.3160745803698-np.cumsum(9.251701426462292*array_x, axis=1), axis=1) |
np.mean(10*(9.962709699789244+1/(np.cos(2*np.pi*1.656765397449897))-array_x), axis=1) |
4.860204624652958*np.square(np.sum(9.98034473100351-array_x, axis=1))*7.214761456241712+np.sin(2*np.pi*1.5204485437656292*np.square(np.sum(5.194097113557238-array_x, axis=1))*5.927675871240814) |
np.sum(3.8642968339068737+3.7046335373451624*array_x+1.1615240955097632, axis=1) |
np.mean(np.square(abs(4.684174989747365))-7.672296238294635-array_x*9.251801764580724, axis=1) |
np.square(np.amax(6.9407860368855605-array_x, axis=1)) |
np.cos(2*np.pi*np.sqrt(abs(4.626997309872929)))+np.square(np.square(np.mean((np.dot(array_x, np.array([[0.7604237285223355, 0.48653095465107143, 0.8367277575751217, 0.7010402287849948, 0.41417364932375633], [0.9628615108523563, 0.12735860155282908, 0.9372835474132222, 0.881031483104984, 0.22808558880778018], [0.9957273042504566, 0.5982244206687495, 0.0366566725166243, 0.4356067091107443, 0.8305909986054552], [0.6154182396308285, 0.369936556499311, 0.9343913863155917, 0.2591584612773774, 0.12313165270000925], [0.11428958812745327, 0.1340031308966183, 0.07062695066337654, 0.2034580454133178, 0.7191772274601196]]))), axis=1))) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*abs(array_x)-8.373791238455265+np.round(array_x*6.472418565735915)+array_x, axis=1)-5.1853693753346475 |
np.square(np.sum(np.square(array_x+5.283860799898129)*np.sqrt(abs(1.271045685285979))+array_x+8.472364236060606*5.973806174135742+(np.array(range(1, array_x.shape[1]+1))), axis=1)) |
np.mean(np.square(array_x+5.823285793138775*6.094590577099768-np.sqrt(abs(array_x/np.round(3.296704499461068))))-np.exp(np.square(array_x)+array_x*4.438694646993602)+6.70064948643025, axis=1) |
np.sum(7.711650389340733-array_x+np.cumsum(7.563868593073322-3.0374055314697515*array_x+np.exp(4.492216685555372), axis=1), axis=1) |
np.mean(6.374229019446483+1.2430973704992696-(np.array(range(1, array_x.shape[1]+1)))*array_x+np.sin(2*np.pi*np.sin(2*np.pi*5.986444466314741))-(np.array(range(1, array_x.shape[1]+1)))*array_x*6.88997536732152, axis=1) |
np.round(np.amax(array_x*9.722662783541416+6.069375827560682, axis=1)+2.450832789579901/2.9053291477803334) |
np.mean(10*(9.097822099833904*array_x)-4.544372705799836, axis=1) |
np.round(abs(7.295220953151938*array_x[:,0]+2.177328388435466+np.sqrt(abs(np.prod(-(array_x), axis=1))))) |
np.mean(np.cos(2*np.pi*np.log(abs(1.559705019485253-(np.array(range(1, array_x.shape[1]+1)))*array_x)))/np.exp(4.963551168719127)*np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(10*(np.sin(2*np.pi*9.312208858318591-(np.dot(array_x, np.array([[0.28961388036948754, 0.1404768096179434, 0.6043879586899771, 0.1748833226285823, 0.11833612009884042], [0.6642517211730303, 0.8366987879960308, 0.7913983793708922, 0.4039561289676218, 0.5445340625113895], [0.11054419840452923, 0.5026943186280367, 0.16074417646893513, 0.708971515116751, 0.8993541372909304], [0.35650427436584475, 0.05292551814002455, 0.8799019968459143, 0.24364111347526773, 0.9946520997981874], [0.500408501537908, 0.7019764896818591, 0.5471478091724411, 0.41311591174809237, 0.6039749509126064]])))-10*(9.082026753418347)))-(np.dot(array_x, np.array([[0.22695452135156058, 0.4734742592942488, 0.11105701513149024, 0.028847331183362912, 0.2589770796148487], [0.14306197247399532, 0.02103086609971727, 0.278811756097168, 0.29000543937207557, 0.1563511165323166], [0.6651811844907265, 0.7252923248131872, 0.916821343070534, 0.12662231038526006, 0.05554155264556593], [0.8945223348387874, 0.9513757151754908, 0.8345566337227253, 0.1257323865951595, 0.48558835556299207], [0.7932984604805214, 0.6562285184201092, 0.30947863297727884, 0.6572815639460602, 0.000968325630914757]])))-5.356138223139739, axis=1) |
np.sum(np.cos(2*np.pi*np.exp(np.sqrt(abs(np.sqrt(abs(array_x+np.cumsum((np.dot(array_x, np.array([[0.16388232818039228, 0.800526997360721, 0.7261387531686095, 0.24846714937540104, 0.6760756264802058], [0.2178821285864353, 0.6142544542984989, 0.24623163124245806, 0.7090470820627549, 0.1603497522367111], [0.664928248063925, 0.5646295108460508, 0.6281942129376777, 0.18162690000228665, 0.8145788918899918], [0.9754538616708152, 0.5915268301518818, 0.8774228413131457, 0.4599729777030622, 0.926801789587812], [0.052241089285181785, 0.77603360341266, 0.40503386704543287, 0.17771228307158893, 0.7268781575669481]]))), axis=1)))*np.round(3.1687103310489375))))), axis=1) |
np.mean(1.49506251947048-(np.dot(array_x, np.array([[0.796901291499022, 0.038399827491298444, 0.571353529927324, 0.7519290611983589, 0.042000591126764686], [0.25649416118819557, 0.59579088241251, 0.37556434710299036, 0.41343921078529466, 0.26096676584571243], [0.7824388948388368, 0.23804440801645865, 0.9764862782939998, 0.08361630706207468, 0.4018943735580691], [0.2375963344267069, 0.06407653965011151, 0.5580318210965627, 0.20675920927769098, 0.5253131330052657], [0.4281576930211972, 0.6272179041399214, 0.05297731403091355, 0.6599002665218603, 0.4232663096393938]])))*(np.dot(array_x, np.array([[0.6974807544759226, 0.147679499616174, 0.053208783285764616, 0.19679919606164498, 0.42662504704125925], [0.4407639458297349, 0.9614553273266654, 0.9746216513519989, 0.060115862448496316, 0.6769531530697067], [0.9186579863967161, 0.7652600852190642, 0.4287406248846163, 0.9169349652430376, 0.9475555666851765], [0.4417450013704963, 0.6452365452950192, 0.9806484943193674, 0.8615078017294456, 0.22215887336687867], [0.8871596345985548, 0.6658627229944131, 0.19595612965670417, 0.7661559322804187, 0.5075261618017075]])))+abs((np.array(range(1, array_x.shape[1]+1)))-8.284282143924582/2.4716651706867956)/2.2616451479099173-(np.dot(array_x, np.array([[0.006280864104178385, 0.04733418152772606, 0.514306647370291, 0.8517966140922201, 0.5993102757631524], [0.6817165649333283, 0.680072038620467, 0.9797938018107881, 0.18126391895772664, 0.01710402417077206], [0.055422595010141684, 0.8816811318040659, 0.2687236574908993, 0.8681198510139463, 0.7786274326113044], [0.4810275572191862, 0.11413392781089549, 0.7415772338735275, 0.12046047066281684, 0.8230618705049066], [0.09853869263402892, 0.22169551339015037, 0.7187916885912612, 0.13213392202226182, 0.3438323148461977]])))/6.488736091883473-(np.dot(array_x, np.array([[0.7755357471633125, 0.25516670798010666, 0.9256186039744473, 0.18597068904337566, 0.5839400426523462], [0.25319640337118954, 0.4103005893803259, 0.8537901365084414, 0.6014814110960747, 0.7112536455223998], [0.7298255756890678, 0.6800609609629604, 0.2905906777607411, 0.6922650500440753, 0.15228270028581237], [0.13533479291860373, 0.11080320783579134, 0.4139500275464604, 0.49004593216829995, 0.2861919985922192], [0.40969177601424966, 0.24738480748610703, 0.11803763991283955, 0.9549046357509127, 0.36186000612516156]]))), axis=1) |
np.mean(np.square(np.cumsum(np.round(np.sin(2*np.pi*np.sqrt(abs((np.dot(array_x, np.array([[0.29669050347916714, 0.6681977233798375, 0.9021809460727837, 0.6484780452419623, 0.10584985747801912], [0.10822193325336571, 0.65353079642245, 0.2627942829269694, 0.5328584820080945, 0.37580873647612045], [0.8967133512911268, 0.23644734849968085, 0.11826201609795473, 0.3254930574567042, 0.19136079741080625], [0.5531460247004005, 0.11185174006817289, 0.7113697217815959, 0.18049435427201344, 0.3379961867983521], [0.05549250663912708, 0.6858720465379734, 0.527410363609678, 0.2287669213213135, 0.6792240095556984]])))))+8.448530968933245/3.8577234110403547-np.sqrt(abs((np.dot(array_x, np.array([[0.20234291103542212, 0.4374091690885631, 0.24895596314369595, 0.1693492338892364, 0.1663325926028909], [0.8498331337590062, 0.0789255294021447, 0.6602938070948129, 0.8308661125433717, 0.05260891133063872], [0.2909691558075216, 0.1673414971509185, 0.028424256418499882, 0.8495030031434551, 0.7180973938684486], [0.07791321248415639, 0.5947875042727634, 0.6834395336924667, 0.7477722573147643, 0.9412171285966467], [0.4101546229195697, 0.5795067759804184, 0.3684940764404019, 0.2434226233494845, 0.5545702110162282]])))))*5.717391852037259)), axis=1)), axis=1) |
np.exp(9.658838639246355/np.round(np.exp(np.sum(9.67021922737006*array_x, axis=1)))) |
np.mean(10*(8.991679574430504)-array_x-np.exp(abs(np.exp(np.sin(2*np.pi*array_x)))), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(7.582640911961295)-array_x-np.exp(abs(np.exp(np.sin(2*np.pi*array_x)))), axis=1))) |
1/(abs(10*(np.square(np.amax(array_x-2.5836531643046117+(np.dot(array_x, np.array([[0.27942169835519304, 0.008121796459910624, 0.43650957461637196, 0.6879829594305089, 0.08041907720947339], [0.6101192580093491, 0.3553126684557363, 0.2981955507305636, 0.21857311985202954, 0.6067622068021912], [0.1341569159092938, 0.07191370921142659, 0.4406089380470505, 0.5913680874207355, 0.9685325945362339], [0.8256109326424411, 0.5521880079320202, 0.1571171256563748, 0.864382967172267, 0.04062151224984323], [0.9401553775354083, 0.9101233297863918, 0.038131931850319045, 0.8610975657717627, 0.5350887662489944]]))), axis=1))))) |
np.mean(1/(np.cos(2*np.pi*10*(9.709255820813897*array_x))), axis=1)+np.sin(2*np.pi*np.mean(1/(np.cos(2*np.pi*10*(3.983491510877516*array_x))), axis=1)) |
np.mean(np.cos(2*np.pi*5.0749378091648305*array_x)*9.085770656145876, axis=1) |
np.mean(abs(np.square(array_x*array_x+5.233801053893021)), axis=1) |
np.mean(np.sqrt(abs(4.0742080329996755*(np.array(range(1, array_x.shape[1]+1)))+2.014201455932646+np.exp(np.log(abs((np.array(range(1, array_x.shape[1]+1)))-array_x))+7.610804845875192))), axis=1) |
10*(np.mean(np.sin(2*np.pi*np.exp(6.002259236191691-array_x)), axis=1))+10*(np.sin(2*np.pi*10*(np.mean(np.sin(2*np.pi*np.exp(2.9228894816062434-array_x)), axis=1)))) |
7.715304594308979+np.mean(10*((np.array(range(1, array_x.shape[1]+1)))-array_x-3.6417296390330103), axis=1) |
np.prod(2.9439070626990707+10*(np.square(array_x)), axis=1)-np.cos(2*np.pi*9.022895002382887)*np.sum(array_x, axis=1) |
np.mean(np.square(np.square(np.square(array_x-3.715920561177779))), axis=1) |
np.mean(np.square(8.612160985276532+array_x-1.1073470006110617), axis=1)+np.sin(2*np.pi*np.mean(np.square(8.026069329991554+array_x-2.1880490895984903), axis=1)) |
np.mean(np.square(np.cumsum(abs(array_x+5.306901986509343-array_x*7.551115968123243-2.447452125608871-array_x), axis=1)-np.square(5.349517188252907)*np.cumsum(array_x*6.008088913590695, axis=1)), axis=1) |
np.mean(-(np.sqrt(abs(3.2884964968410477)))/np.cos(2*np.pi*np.cos(2*np.pi*3.8630701958087528-array_x+7.327923119959846)), axis=1) |
np.mean(10*(np.cos(2*np.pi*np.exp(np.round(np.sin(2*np.pi*8.387160207236292))))-3.950316989391375*array_x-7.194168088496638), axis=1) |
np.mean(np.sin(2*np.pi*1.743602691583796)-(np.dot(array_x, np.array([[0.5083286185910489, 0.08231591001561123, 0.6624523572786023, 0.4281109756415884, 0.13124864255750712], [0.43400706216555895, 0.2520570009409615, 0.6776082830583788, 0.7876241589905514, 0.16842151520754622], [0.32741514695976415, 0.9424230560745722, 0.9531855205247926, 0.8635151390006053, 0.24427561480764926], [0.5414997155832431, 0.009052375383157174, 0.9224619093298759, 0.7998089944185877, 0.934424092987329], [0.8196437820092967, 0.8492038174351243, 0.6677638485936785, 0.1895135517251343, 0.11069779760771181]])))+(np.dot(array_x, np.array([[0.07851057662835448, 0.9026783561753001, 0.3541871316784314, 0.007948554719224843, 0.7284779105926841], [0.5004545187395023, 0.7338730238041175, 0.31946619717145963, 0.5944384186103296, 0.5365791830270481], [0.2956546685634399, 0.5065903971649208, 0.03510019629573358, 0.8071715776233271, 0.46826696389499056], [0.10477010931551411, 0.1744685668145578, 0.43185557589583434, 0.9907602348634676, 0.5969935469549225], [0.2961153262246444, 0.5586576704326166, 0.6079044137743332, 0.3098475023297722, 0.22972960588093871]])))/np.sin(2*np.pi*3.4727916663399387)-np.cumsum(6.521457752905106-array_x-array_x, axis=1), axis=1) |
np.sqrt(abs(np.square(np.sum(array_x, axis=1))/2.2800684513061285+3.2351017839379748))+10*(np.sin(2*np.pi*np.sqrt(abs(np.square(np.sum(array_x, axis=1))/7.240882095499185+2.4219299576615922)))) |
np.mean(np.round(np.cos(2*np.pi*8.237025480208622)-(np.array(range(1, array_x.shape[1]+1))))-3.4778191925794415+4.9063312077588055/np.cos(2*np.pi*np.cos(2*np.pi*np.cos(2*np.pi*array_x))), axis=1) |
np.sum(np.exp(np.sin(2*np.pi*array_x+1.1459620162006934))-np.exp(np.square(1.505756106134171)/np.exp(7.782147495148337*array_x))+7.630385556918943, axis=1)+np.sin(2*np.pi*np.sum(np.exp(np.sin(2*np.pi*array_x+8.240790111139535))-np.exp(np.square(9.415280750147517)/np.exp(3.8714386127721276*array_x))+8.449471965123774, axis=1)) |
np.mean(array_x+4.921384584321716*10*(4.284003087484264-array_x*1.5863630648742078)/4.414865532893149, axis=1) |
np.mean(np.cos(2*np.pi*1.3716341564880064)-np.exp(array_x+5.036252974334773)+np.sin(2*np.pi*9.900618243717616)+array_x, axis=1) |
np.mean(7.659331627411674*array_x*np.exp(9.596324672898639)-5.290999072092006-abs(8.248939823911483), axis=1)+10*(np.sin(2*np.pi*np.mean(1.0744839169768263*array_x*np.exp(1.7604879218559502)-3.8401534204741274-abs(3.1802812701865166), axis=1))) |
abs(np.exp(1/(np.mean(1/(np.log(abs(np.round(1.2719691070099413-array_x)))+np.log(abs(3.1240837048970294))), axis=1)))) |
np.mean(np.sqrt(abs(array_x+np.log(abs(3.6620519342320192))+np.square(np.square(array_x-9.623328013268198-4.569537873808683)-np.square(5.111715281487791)-np.sin(2*np.pi*7.695272006017873)))), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(array_x+np.log(abs(2.3734643601366776))+np.square(np.square(array_x-6.540074211978892-8.231793762449641)-np.square(6.981464945713067)-np.sin(2*np.pi*5.407227856040366)))), axis=1)) |
np.square(np.log(abs(np.prod(np.sqrt(abs(np.sqrt(abs(1.737112262726479))))+(np.dot(array_x, np.array([[0.9557635643829959, 0.23387164014244566, 0.43407877879636325, 0.5643673693251166, 0.47777505252681773], [0.5038363458968779, 0.7100387755598097, 0.7380289409763984, 0.07692132080772174, 0.13032872858944855], [0.08349304074291519, 0.9855802384685627, 0.41140655222101663, 0.5530466284354094, 0.2806510331854808], [0.14971713082204896, 0.1048117283168315, 0.45615142211883875, 0.2765970412607608, 0.5629021414683257], [0.6625968949453477, 0.6004155916447949, 0.2057774653089809, 0.5187432553871288, 0.2995993388763195]]))), axis=1)))) |
np.mean(3.3173289781997513+(np.array(range(1, array_x.shape[1]+1)))+array_x/np.cos(2*np.pi*np.cos(2*np.pi*np.cos(2*np.pi*array_x/4.552020551372107))), axis=1) |
np.mean(np.square(5.460065912053311-np.cumsum(array_x, axis=1)-array_x/9.868987243272944), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(3.8760211665666615-np.cumsum(array_x, axis=1)-array_x/7.004871453388903), axis=1))) |
np.prod(np.sqrt(abs((np.dot(array_x, np.array([[0.6824123524508846, 0.72310174520292, 0.7725914223545303, 0.0986023507165531, 0.16981741697361707], [0.37415430979392883, 0.34760801123037055, 0.46757878867781766, 0.015332025359367507, 0.2214581320805029], [0.28296895977449077, 0.6449849716187256, 0.579082460777271, 0.2634363794652088, 0.7039808440320122], [0.4476937356565688, 0.3197856133129495, 0.46368128478658577, 0.5021520757988632, 0.9320154269101907], [0.8283051839238055, 0.2364561958357959, 0.8633395739786571, 0.9557775080940139, 0.9986684538638432]]))))), axis=1)+4.355785216161195 |
np.mean(np.sin(2*np.pi*1.0013475797761295*1.9432360278699665+array_x+array_x+5.083451246948289), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*5.5331147567422585*5.295308093960712+array_x+array_x+4.971254883500778), axis=1))) |
np.exp(np.log(abs(5.502868766966899))-np.mean(array_x-7.19218565243686, axis=1)-9.140178212729083)+10*(np.sin(2*np.pi*np.exp(np.log(abs(3.463175678347034))-np.mean(array_x-3.7628091006257582, axis=1)-5.527436664342373))) |
np.mean(np.square(np.sqrt(abs(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.08759131475998116, 0.45636188044896897, 0.9292240786714042, 0.5402681816615106, 0.9840255750835835], [0.40312022061998876, 0.6717359391014396, 0.1997223434255777, 0.06847142545571805, 0.6444196743492638], [0.16615300886926976, 0.9601599655809882, 0.43570512648259363, 0.9006161190667809, 0.780997201406491], [0.09003376636790938, 0.5262002826452427, 0.5461032144180864, 0.4612190504708512, 0.5351229919278263], [0.15131057313144214, 0.10887113272716376, 0.8212512828780276, 0.931498971076634, 0.13573501024858048]]))))-np.cos(2*np.pi*(np.dot(array_x, np.array([[0.9918850828095915, 0.5153279431758245, 0.6776060623450325, 0.5960538976164498, 0.8275589130590307], [0.11271447532833301, 0.4262692007838398, 0.20111709044184067, 0.31633433502668074, 0.7232649648261416], [0.6532690507962227, 0.6579161748744943, 0.43431144525843834, 0.9877478396629845, 0.21663905150918317], [0.38879398332506765, 0.4632993997489956, 0.9107600123061621, 0.18088416706158605, 0.6037186740324759], [0.9402904494930452, 0.7581390104510302, 0.3548741776209403, 0.9532194740069907, 0.0692415905031285]]))))+8.288332800170574))+9.618400503156153+np.round(np.log(abs(8.78284576422729))+(np.dot(array_x, np.array([[0.6850054112411303, 0.09232299152794643, 0.839336269302669, 0.762640805707316, 0.17660626975868488], [0.08397711114851414, 0.11498662799208492, 0.0706016285945319, 0.15388726439774536, 0.5262032320154287], [0.2202523249893069, 0.889941206121965, 0.18834991586054417, 0.6894923461778877, 0.5845065125383301], [0.593190576635225, 0.7411782736676144, 0.17876324067929328, 0.6634913168540406, 0.6832336984918579], [0.6978126375968133, 0.07102055741799107, 0.4790561579698379, 0.4362765944381727, 0.847126165427537]]))))), axis=1) |
np.mean(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x))+(np.array(range(1, array_x.shape[1]+1)))*array_x*2.8390214119277792+2.273697136096523, axis=1) |
np.mean(np.square(np.exp(5.194592314405868)+array_x/3.6246026235780557)-np.square(np.round(array_x/3.3822653078242904)), axis=1) |
np.mean(3.9838400076569895+5.597809339885114*array_x+10*(7.907787266676604), axis=1)+np.sin(2*np.pi*np.mean(3.5864653491061977+8.435394626586357*array_x+10*(5.312752539991374), axis=1)) |
np.mean(8.87617682660042-(np.array(range(1, array_x.shape[1]+1)))*array_x+4.634763874179084*np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x)+(np.array(range(1, array_x.shape[1]+1)))*array_x+2.718284514623137/(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.mean((np.array(range(1, array_x.shape[1]+1)))-9.037402253809834*array_x+4.900908838733663/np.cos(2*np.pi*3.2839479208632807)*1.225397215400402, axis=1) |
np.mean(np.cumsum((np.array(range(1, array_x.shape[1]+1)))*array_x-np.sin(2*np.pi*4.91153445415326)*np.sqrt(abs(7.54130120402629))+(np.array(range(1, array_x.shape[1]+1)))*array_x+9.274611985141796+np.cos(2*np.pi*np.square(np.sin(2*np.pi*8.34180031002386))), axis=1)+9.574757788721836, axis=1) |
-(np.sum(abs(5.368120450600401*np.sqrt(abs(array_x))-1.6413082736944773)/np.sin(2*np.pi*np.square(abs(array_x)-8.237375177671431-5.0675933101721515)-array_x*6.659455340514471), axis=1)) |
np.square(np.sum(np.cos(2*np.pi*array_x*2.0708370820863493)+4.606199814204267, axis=1)) |
np.log(abs(3.865986999049394))+np.sum(array_x-array_x*abs(5.32629491560246), axis=1) |
np.mean(np.exp(abs(array_x)*6.495669686479393/np.exp(1/(np.sin(2*np.pi*1.7050341898574735))))-1/((np.dot(array_x, np.array([[0.6198074168109424, 0.46411779629855165, 0.37816831720323507, 0.255572848937193, 0.9729482241985302], [0.9805267704386674, 0.14826055426147766, 0.4840319449522371, 0.6453881311722464, 0.21490382462839996], [0.6540250234395806, 0.8802031053066636, 0.641302372797255, 0.029355080208636752, 0.9102150597204163], [0.38971076263088966, 0.18026732667761303, 0.9795060734499828, 0.6810739799784494, 0.40751806136683344], [0.9233948599728998, 0.7281115740552276, 0.3619608399800013, 0.8053504471838975, 0.08120892185599904]])))-9.812515504310555), axis=1) |
np.sum(5.795332736571444+np.round(np.square(array_x+9.171721060310814))/7.7805536214884485, axis=1) |
-(np.prod(np.sqrt(abs(6.445217765461784))-(np.dot(array_x, np.array([[0.8132027280778471, 0.12739466354190188, 0.3696202953736586, 0.6451518080431795, 0.14543982617962048], [0.4821105713862128, 0.0067221266668396895, 0.3401808076853602, 0.6497641004638739, 0.6231060068205285], [0.7242187837218771, 0.022766979125605102, 0.8377911531681641, 0.09449696048716627, 0.7791258299067427], [0.9805690128895158, 0.894087498380995, 0.6701188192056522, 0.21588992286693143, 0.16239369301459694], [0.9303496740616011, 0.4950785846403781, 0.3698646560152361, 0.2671902517568955, 0.38966051221464826]])))-array_x, axis=1)) |
np.sum(np.cumsum(9.310236178789296-array_x, axis=1), axis=1)-np.exp(np.sqrt(abs(2.4200028602931702)))+np.sin(2*np.pi*np.sum(np.cumsum(1.9794747413191862-array_x, axis=1), axis=1)-np.exp(np.sqrt(abs(7.496353273042061)))) |
np.mean(4.409756349293941*np.exp(5.3058783915126675+np.exp(array_x)), axis=1)+np.sin(2*np.pi*np.mean(6.570558433990259*np.exp(3.658150859912391+np.exp(array_x)), axis=1)) |
np.mean(np.square(7.346021362995171)+1/(np.exp(np.sin(2*np.pi*3.3153827885872795+array_x))-(np.array(range(1, array_x.shape[1]+1)))), axis=1) |
np.mean(np.sqrt(abs(np.log(abs(np.square(4.449732335686353)))))-10*(array_x+10*(6.5053973477498745)), axis=1) |
np.square(2.8674973765243084)*np.cos(2*np.pi*np.mean(array_x*3.248651800648113, axis=1)+2.0000901845023673)-np.sum(array_x-array_x, axis=1) |
np.mean(np.cos(2*np.pi*np.exp(7.901281406412673+array_x/9.709249195678654))+1.624000697966363-np.cos(2*np.pi*10*(2.3371136519710842)/10*(array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*np.exp(2.090549542001388+array_x/5.85088278051951))+1.5198513536958458-np.cos(2*np.pi*10*(9.609568206595169)/10*(array_x)), axis=1))) |
np.mean(array_x-np.sin(2*np.pi*(np.dot(array_x, np.array([[0.32109766511908655, 0.2138189282009687, 0.40087441819814595, 0.019601935418197214, 0.9064996670162518], [0.757071057547509, 0.19157319039049625, 0.8818321783194831, 0.12357105076350938, 0.2104106939487984], [0.49468756084353827, 0.33796551014076304, 0.42686477325862937, 0.003925506277069668, 0.3004594811564909], [0.804493156293901, 0.5172317169641917, 0.037734627459102166, 0.67551568777401, 0.8477298719055235], [0.7627977281080116, 0.03171998182404212, 0.10809524299725604, 0.5180613182783452, 0.4648763255964643]]))))/8.884343821144402+6.817551738162167, axis=1)+10*(np.sin(2*np.pi*np.mean(array_x-np.sin(2*np.pi*(np.dot(array_x, np.array([[0.0038004448493007548, 0.9852602083236413, 0.8566736230963777, 0.8769956019217909, 0.36051071025766035], [0.1781191637971371, 0.46787907497261083, 0.8871030757738179, 0.5407625212141506, 0.039854813646875265], [0.5015832135637547, 0.5379442694169273, 0.24262992674724249, 0.7595723197391522, 0.030337901723597183], [0.010077009091783218, 0.30887657282080117, 0.7913919153502956, 0.37790870199240634, 0.8603335915950046], [0.2363023279818146, 0.16188893833828022, 0.38403015702336774, 0.182162216448563, 0.8232940163084116]]))))/2.140383256591731+2.3373018990362473, axis=1))) |
np.round(1.1906210293564559+np.amax(array_x, axis=1)-4.000541345177906-array_x[:,0]*9.825348346785928) |
np.mean(np.sqrt(abs(9.440366089239713+9.797805519549549*np.cumsum(array_x, axis=1)+4.988188920434471)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(5.920702608476547+3.578959224569507*np.cumsum(array_x, axis=1)+2.199141474664053)), axis=1))) |
np.mean(np.square(np.square(4.547246751448659)+abs(array_x)/7.022651700461207*(np.array(range(1, array_x.shape[1]+1)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.square(4.181621640679486)+abs(array_x)/4.97140919355904*(np.array(range(1, array_x.shape[1]+1)))), axis=1))) |
np.mean(np.square(np.round(9.544382068469062)+4.774490452065356-array_x/8.179307589319482-8.034333672012329-array_x*1.4691125584540892*np.square(6.839206585743246)), axis=1) |
np.mean(np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))-array_x-6.24524478134088)+8.585572011121062/2.101063678817194/(np.array(range(1, array_x.shape[1]+1)))+3.5366268610884517/5.2436254449982, axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))-array_x-6.598916333178841)+7.865383426058186/9.342905794222023/(np.array(range(1, array_x.shape[1]+1)))+2.5312302472129335/2.030994674417175, axis=1))) |
np.mean(np.exp(7.6988169645148705+array_x/6.802111025435014), axis=1) |
np.mean(6.924296025525805*array_x+array_x+5.7547756804133705/np.exp(np.exp(array_x-7.36576099176954-(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.mean(1.644432269282619+np.sqrt(abs(9.614039603588443-array_x))/np.cos(2*np.pi*array_x/2.025495430200229), axis=1) |
np.mean(10*(7.6091389159065805)+array_x*3.550236776121534*array_x/1.1448559736272235-np.square(array_x*4.174606702619458), axis=1) |
np.exp(np.mean(np.sqrt(abs(np.exp(1.722425670449863-array_x))), axis=1))+5.355289399926854+np.sin(2*np.pi*np.exp(np.mean(np.sqrt(abs(np.exp(2.2506343149055965-array_x))), axis=1))+2.926946863103951) |
np.mean(np.exp(abs(8.519171065136518+(np.dot(array_x, np.array([[0.981807527617894, 0.7063598800156529, 0.793555720219324, 0.8295817588521527, 0.25379038964880896], [0.7352527935609482, 0.4065627854091154, 0.045178115772471106, 0.6965762398276045, 0.29477733122312477], [0.4720366554089126, 0.6488722111577148, 0.7880574634995343, 0.44565861075357116, 0.901226160617936], [0.8311289025463183, 0.18513956242494034, 0.5941555069706647, 0.28033570355687465, 0.0695711734697465], [0.9228168839436042, 0.9916408728907461, 0.6634434027553836, 0.02675170597236265, 0.7244915649833514]]))))+np.cumsum(np.sin(2*np.pi*array_x), axis=1)), axis=1) |
np.sin(2*np.pi*-(10*(abs(np.cos(2*np.pi*1/(2.001594071547777)-np.exp(np.mean(np.sin(2*np.pi*array_x), axis=1)))))))-10*(abs(np.cos(2*np.pi*1/(6.51057400863308)-np.exp(np.mean(np.sin(2*np.pi*array_x), axis=1))))) |
np.mean(np.exp(array_x*9.090556176230931+array_x)+10*(7.047038988299867)+np.sqrt(abs(9.775838591730167))+array_x+6.4495582671763305, axis=1) |
np.mean(4.229926719599346-4.952283158593315*np.exp(array_x)-array_x+np.exp(np.round(7.547892332848289))/7.932501327022978, axis=1) |
np.mean((np.dot(array_x, np.array([[0.8961655569640553, 0.34501477125165547, 0.17599881010626428, 0.3388985829704678, 0.8749946107432766], [0.8309047485191653, 0.35517626602925634, 0.4113719331301189, 0.0703144581676588, 0.4291452804477771], [0.2720021273496167, 0.782623367895368, 0.21205470939120186, 0.3892678946066671, 0.16778513686444885], [0.9964110476460021, 0.566010323494899, 0.33735794250235196, 0.9174689904949386, 0.8508553254771158], [0.05778198115213862, 0.15432663955683124, 0.471749147810934, 0.10923387860141576, 0.23001631836790426]])))+7.745264489376214-abs(array_x)+(np.array(range(1, array_x.shape[1]+1)))-2.0438894134153154, axis=1)+10*(np.sin(2*np.pi*np.mean((np.dot(array_x, np.array([[0.3342702110109572, 0.4129593456672025, 0.11963906592756712, 0.0893472888286917, 0.1649576513429456], [0.7995889016772721, 0.8269210359716376, 0.7621912005525808, 0.06203455030410454, 0.2976126328885844], [0.841241591839514, 0.9165960233510979, 0.46588506978571553, 0.2966239414184021, 0.3169852661926099], [0.9738821931672914, 0.7941788186332843, 0.8056793637334099, 0.1201638096099733, 0.25698938253403036], [0.9664915351915336, 0.6428666445765531, 0.20418166205779287, 0.9204781322005765, 0.4351544402155626]])))+2.1480799002344613-abs(array_x)+(np.array(range(1, array_x.shape[1]+1)))-3.1222730822714406, axis=1))) |
np.mean(np.cumsum(1/(array_x*5.9545767228707644+np.cos(2*np.pi*array_x-5.00598981555072))+3.058986512754724, axis=1), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(1/(array_x*3.7811156259136594+np.cos(2*np.pi*array_x-3.679236173501697))+2.2498891188616037, axis=1), axis=1)) |